Insight into CEE border-town smuggling economics by CIVITTA

Insight into CEE border-town smuggling economics Final report

31th March, 2018

The aim of the report The aim of this report is to present Phase 3 activities and final results of the PMI Impact granted project “Insights into CEE border-town smuggling economics”, performed in the period from 1st of February to 30th of March. A methodological part of Phase 3 activities is provided in Section 5 “Phase 3: Expert interviews and final recommendations”, while the results and findings are provided separately for each project country in Section 6 “Results”. Activities performed during the last stage of the project were the continuation of Phase 1 and Phase 2 activities and findings.

Insight into final results During the Phase 1 of the PMI Impact granted project “Insights into CEE border-town smuggling economics” CIVITTA has performed economic research of 8 countries (4 EU countries and 4 non-EU countries). During the research, data from different dimensions (e.g. social, business, demographic, criminal, etc.) was collected from 187 different municipalities, on average consisting of approx. 98 different statistical indicators for the period of 2012-2015. In order to successfully build MIMIC models for each country and execute economic/comparative analysis, additional 3 analyses, namely, Principal Component Analysis, Propensity Score Matching and 1Factor Causal Clustering were performed. The Phase 2 activities included quantitative research (face to face surveys) and qualitative research (focus group discussions). The scope of quantitative surveys covered face-to-face interviews in 64 municipalities of 8 countries, resulting in responses of 6400 inhabitants, while focus group discussions were implemented in 20 EU border municipalities with about 160 participants in total. In Phase 3, interviews with ~30 experts of different fields and international workshop in order to polish recommendations were executed. The MIMIC model revealed that even though there are some specific cases when indexes move to a different direction, on average, shadow economy index in all of the targeted countries is bigger in the border municipalities compared to the inland municipalities. Also, constructed models helped to understand, which of the variables positively or negatively influence the size of shadow economy of the country. The economic/comparative analysis revealed the main differences between the border and inland municipalities as groups and highlighted indicators, which fluctuate the most within each of the group. From the performed analysis the tendency is clear: border and inland municipalities mostly differ according to the amount of registered income, GDP per capita generated and gross investment - all of those indicators are lower in the border municipalities, especially, in EU member states. The analysis of the attitudinal surveys revealed that border municipalities across the region and within countries are different from each other and that attitude of inhabitants regarding different subjects (attitude towards municipality, tax evasion, smuggling, etc.) differs. This helped to group municipalities by subjects of interest and attitude and to develop their unique profiles. Also, the analysis of quantitative research revealed that in most of the cases (with 2 exceptions) the attitude of EU border municipality inhabitants’ is not statistically different from the one of corresponding municipalities on the other side of the frontier. The qualitative research confirmed 2 observations: firstly, each of the border municipalities has a specific profile regarding satisfaction about tax expenditures and tax evasion; secondly, the inhabitants of municipalities have different perception and scope of the shadow economy as well as have distinct possible tools/solutions/measurements to fight it. Most importantly, discussions within focus groups helped to perceive the reasoning behind each of the arising attitude, disclose measurable, tangible and observable objects, which can be changed. This led to notion that all these attitudes have influence on the size of the shadow economy in particular municipalities. Finally, the initially formed recommendations were verified with the experts and crystallised on the country level. The proposed recommendations encompass various forms of communication, development of local policies, changes in administration matters and altering education system. 2

Abbreviations EU MIMIC PCA PSM FGD LOI

European Union Multiple Indicator Multiple Cause Model Principal Component Analysis Propensity Score Matching Analysis Focus Group Discussion Length of Interview

Table of Contents THE AIM OF THE REPORT.................................................................................................................... 2 INSIGHT INTO FINAL RESULTS ............................................................................................................ 2 ABBREVIATIONS ................................................................................................................................. 3 TABLE OF CONTENTS ......................................................................................................................... 4 1. PROJECT DESCRIPTION AND MAIN GOALS ....................................................................................... 6 2. METHODOLOGY.............................................................................................................................. 7 Phase 1 ............................................................................................................................................................7 Phase 2 ............................................................................................................................................................8 Phase 3 ............................................................................................................................................................8 3. PHASE 1: MULTIPLE INDICATOR MULTIPLE CAUSE MODEL ............................................................ 8 3.1. Identification of border and inland municipalities ...................................................................................9 3.2. Data collection .........................................................................................................................................9 3.3. Principal Component (PCA) and Propensity Score Matching (PSM) analysis .................................... 10 3.4. 1-Factor Causal Clustering ................................................................................................................... 11 3.5. MIMIC .................................................................................................................................................. 11 3.6. Economic and comparative analysis ..................................................................................................... 14 4. PHASE 2: QUANTITATIVE SURVEY AND FOCUS GROUP DISCUSSIONS .............................................. 14 4.1. Quantitative research ............................................................................................................................ 14 4.2. Survey data analysis .............................................................................................................................. 15 4.3. Focus group discussion ......................................................................................................................... 16 5. PHASE 3: EXPERT INTERVIEWS AND FINAL RECOMMENDATIONS ................................................... 18 5.1. Expert interviews .................................................................................................................................. 18 5.2. Recommendations ................................................................................................................................. 19 6. RESULTS ....................................................................................................................................... 19 6.1. Estonia................................................................................................................................................... 19 6.2. Latvia .................................................................................................................................................... 35 6.3. Lithuania ............................................................................................................................................... 58 6.4. Romania ................................................................................................................................................ 82 6.5. Belarus ................................................................................................................................................ 108 6.6. Moldova .............................................................................................................................................. 118 6.7. Russia (incl. Kaliningrad) ................................................................................................................... 125 6.8. Ukraine................................................................................................................................................ 134 APPENDIX 1 .................................................................................................................................... 143 APPENDIX 2 ....................................................................................................................................... 160 APPENDIX 3 .................................................................................................................................... 183 4

APPENDIX 4 .................................................................................................................................... 191 APPENDIX 5 .................................................................................................................................... 196 APPENDIX 6 .................................................................................................................................... 197

1. Project description and main goals Low income level or absence of economic opportunities in borderland areas are considered to be the main drivers of illegal trade. Due to significant differences in taxes and tariffs between EU and non-EU countries, tobacco smuggling is one of the most widespread illicit activities and additional source of income among inhabitants of border municipalities. For example, in 2015 almost 20% of the cigarettes smuggled in Europe came through the municipalities which have a borderline with Ukraine, Russia, Belarus and Moldova. In order to decrease the impact of the illegal activities, 3 ultimate goals of the project were raised: 1. Explore the size of shadow economy generated, in part, by cigarette smuggling in border municipalities within Central and Eastern Europe; 2. Examine factors that motivate border municipality inhabitants to smuggle; 3. Provide clear investment guidelines and action plans for governments on how to reduce shadow economy. The project takes a detailed look at the economic situation of 48 border municipalities that are strategically situated between 8 countries: •

EU border countries: Estonia, Latvia, Lithuania, Romania;

•

Non-EU countries: Russia (incl. Kaliningrad), Belarus, Moldova, Ukraine.

In order to reach the above mentioned goals, additional 96 inland municipalities that can be used for comparison with border municipalities and lead to further development of recommendations were involved in the project (see Figure 1). Figure 1: Project scope 8 Countries

48 border municipalities

48 inland municipalities

•

4 EU countries: Estonia, Latvia, Lithuania, Romania

•

4 Non-EU countries: Belarus, Moldova, Russia (incl. Kaliningrad), Ukraine

•

Estonia: Jõhvi Parish, Narva, Põlva, Sillamäe, Valga, Võru

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Latvia: Alūksne, Daugavpils, Daugavpils municipaliy, Kraslava, Ludzas, Rezekne

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Lithuania: Alytus, Druskininkai, Marijampolė, Šalčininkai, Švenčionys, Visaginas

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Romania: Botoșani, Huși, Rădăuți, Sighetu Marmației, Satu Mare, Iasi

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Belarus: Braslav, Oshmyany, Ostrovets, Smorgon, Verhnedvinsk, Voronovo

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Moldova: Briceni, Cahul, Cantemir, Hîncești, Rîșcani, Ungheni

•

Russia (incl. Kaliningrad): Gusevskiy, Kingisepp, Ostrovsky, Pechora, Pskov,Sovetsk

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Ukraine: Hlyboka, Rahiv, Snyatyn, Storozhynets, Tyachiv, Vynohradiv

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Estonia: Haapsalu, Haku, Kohtla-Järve, Kuressaare, Pärnu, Rae, Rakvere, Rapla, Saue, Tartu, Türi, Viljandi

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Latvia: Adažu, Cesu, Jekabpils, Jelgava, Jūrmala, Kandavas, Kuldiga, Liepaja, Madonas, Ogre, Stopinu, Talsi

•

Lithuania: Biržai, Elektrėnai, Jonava, Kaišiadorys, Kaunas, Kelmė, Panevėžys, Prienai, Rietavas, Šilalė, Širvintos, Ukmėrgė

•

Romania: Baia Mure, Beiuș, Brașov, Buzău, Craiova, Fălticeni, Focșani, Onești, Pașcani, Râmnicu Sărat, Sibiu, Târgu Mureș

•

Belarus: Chashniki, Dokshytsy, Dubrovno, Dyatlovo, Korelichi, Krupki, Lepel, Nesvizh, Novogrudok, Rogachev, Senno, Stolbtsy

•

Moldova: Basarabeasca, Călărași, Cimișlia, Dondușeni, Drochia, Florești, Glodeni, Nisporeni, Orhei, Sîngerei, Strășeni, Telenești

•

Russia (incl. Kaliningrad): Chernyakhovsky, Desnogorsk, Kimry, Kirishskiy, Korolyov, Livny, Ostashkovsky, Podporozhskoe, Svetloskiy, Velikiy Novgorod, Volkhov, Vyshniy

•

Ukraine: Bobrynets, Derazhnya, Haivoron, Horodyshche, Kozyatyn, Lypovets, Monastyryshche, Olevsk, Ostroh, Talne, Terebovlia, Tul'chyn

Source: compiled by authors

Target EU countries Target non – EU countries

Other countries

2. Methodology The project methodology is divided into 3 phases and consists of 3 main stages: MIMIC, quantitative and qualitative analysis techniques (see Figure 2). Figure 2: Methodology of the project Phase II and III 3rd

Phase I 2nd

stage Qualitative research

stage Quantitative research

1st stage MIMIC

Hypothesis 1

Assumption 1

Hypothesis 2

Assumption 2

Hypothesis 3

Assumption 3

Hypothesis 4

Assumption 4

Hypothesis 5

Assumption 5

Hypothesis 6

Assumption 6

Hypothesis 7

Assumption 7

Hypothesis 8

Assumption 8

Hypothesis 9 Hypothesis n •

Focus group interviews

•

Expert interviews

X y

Assumption 9 Hypothesis raised to explain how these attitudes can be changed to reduce smuggling

Assumption n

•

Causal variables of shadow economy

Indicator variables of shadow economy

Causal variables tha t are significant Indicator variables that are significant

Y4 Attitudinal questions raised to explain causal variables that show significance

Surveys

Assumption

Shadow economy

Assumptions raised to explain causal variables that show significance

Yn •

MIMIC model

•

Comparative analysis

Hypothesis

Raised hypothesis

Hypothesis

Approved hypothesis

Source: Compiled by authors

Phase 1 The Phase 1 consists of 2 major activities: MIMIC model and economic/comparative analysis. Based on the publicly available data, the aim of the MIMIC model is to explore the size of shadow economy by calculating shadow economy indexes for each identified border and inland municipality. The model enables to rank country municipalities according to a prevailing level of shadow economy from lowest to highest. Moreover, it also enables to identify the significant causal variables which influence shadow economy as well as indicator variables, which are affected by shadow economy the most. The ability to identify causal variables enables to investigate motivation and hidden meanings behind these variables by raising attitudinal questions. Hence, surveying method was chosen in order to test the assumptions raised in particular countries and it was during Phase 2. Moreover, during the 1st Phase of the Project, comparative analysis was executed to evaluate the main differences between border and corresponding inland municipalities. The results of comparative analysis were used for hypothesis formulation in later project phases that led to further development of recommendations.

Phase 2 The main activities of Phase 2 were both formulating the questionnaire and conducting the survey. Based on the MIMIC model results, questionnaires, which consisted of attitudinal questions, were formed for each country individually. The survey was implemented in all the targeted countries and involved more than 9000 respondents. After the attitudes which required the most attention in particular municipalities were identified, the qualitative research was executed in form of a focus group discussion. During the FGD, the initial hypothesis and observations related to the specific actions, which could possibly increase the welfare of inhabitantsâ&#x20AC;&#x2122; and at the same time decrease the overall level of shadow economy, were tested.

Phase 3 During the Phase 3, the qualitative research method â&#x20AC;&#x201C; expert interviews - was implemented. The qualitative research methods conducted in Phase 2 and 3 were used not only to confirm the recommendations, but also to gain deeper insights of the experts as well as the examples of the best practices. The results of expert interviews were used to form the final recommendations for the governments to reduce shadow economy in those particular border municipalities as well as strengthen local economies.

3. Phase 1: Multiple Indicator Multiple Cause model The main objective of Phase 1 was to estimate the size of shadow economy by calculating shadow economy index of each shortlisted border and inland municipalities while adapting MIMIC model. In order to achieve this goal, the following sub-objectives had been implemented prior to it: 1. Identify border and inland municipalities in each target country; 2. Collect all relevant data for each of the identified border and inland municipalities; 3. Perform a principal component analysis to reduce a number of variables into smaller number of factors that have a sufficient explanatory power; 4. Perform a propensity score matching (PSM) to identify all the corresponding inland municipalities to each border municipality based on factors produced in principal component analysis 5. Perform 1-Factor causal clustering (incl. discriminant analysis) to form the latent variables; 6. Calculate shadow economy index while adopting the MIMIC model. 7. Calibrate calculated shadow economy indexes to size of the shadow economy as a % of economic value generated in each of the EU border and inland towns. Furthermore, the collected statistical indicators were used for economic and comparative analysis. The aim of the before-mentioned analyses was to estimate the main differences between border and inland municipalities within all project countries. The results of those 2 analyses were used to raise a final hypothesis and to provide recommendations on the measurements to shadow economy in border municipalities. More detailed explanations of Phase 1 activities are presented in the following sections.

3.1. Identification of border and inland municipalities The initial list of border and inland municipalities consisted of 6 border and about 3 times more inland municipalities in each project country. In order to have a final list of border municipalities, 61 border municipalities in EU countries (Estonia, Latvia, Lithuania, Romania) were identified. The primary list of border municipalities was shortlisted by excluding the municipalities which were located 50 km or more away from the border. In order to identify a final list of EU border municipalities which potentially were the most involved in illegal tobacco trade, desk research was carried out. Based on the publicly available data of illegal tobacco market share and already conducted researches, such as Nielsen â&#x20AC;&#x153;Empty Pack Surveyâ&#x20AC;?, the list was shortened to 6 border municipalities in each of the EU country which participates in the project. After determining the final list of border municipalities in 4 EU countries, the nearest border municipalities on the other side of the frontier in Belarus, Moldova, Russia (incl. Kaliningrad) and Ukraine were identified. After the list of border municipalities had been finalised, 3-4 times more inland municipalities in each country were identified following 2 criteria. Firstly, the population of inland municipality had be similar to the corresponding border municipalities. Secondly, the inland municipalities had to be located not less than 60 km away from the border. Based on the criteria mentioned above, the initial list consisted of 6 border municipalities and 1622 inland municipalities for each project country. The final list of border municipalities with corresponding inland municipalities was formed using a principal component and a propensity score matching analysis.

3.2. Data collection The goal of data collection stage was to gather and systemise as much relevant information as possible. Therefore, the municipality level was selected as the smallest administrative unit for which it was possible to gather a large enough amount of data. The data for the period of from 2012 to 2015 was obtained from various sources, including National Statistics Bureau, governmental institutions, municipality sources, State Tax Inspectorate, police, etc. The collected data includes: 1. General data (e.g.: average population, average earnings, employment rates and etc.); 2. Demographic data (e.g.: number of emigrants/immigrants, median age of the population, number of persons of working age, etc.); 3. Social data (e.g.: number of recipients of socials assistance benefits, rate of expenditure on social assistance benefit, etc.); 4. Educational data (e.g.: number of general school pupils, educational attainment of general school pupilsâ&#x20AC;&#x2122; and etc.); 5. Taxes (e.g.: income taxes paid and included in municipal budgets, land taxes paid and included in municipal budgets, etc.); 6. Criminal data (e.g.: number of persons suspected of criminal offences, number of group criminal offences, number of smuggling cases reported, etc.); 7. Business data (e.g.: economic entities in operation, investment in tangible fixed asset, foreign direct investment at the end of the year, etc.);

8. Municipal budget (e.g.: income tax to municipal budget, property tax included to municipal budget, grants included to municipal budget, expenditure of municipal budget on education, etc.). In order to avoid multicollinearity problem and to have standard measurements all data was adjusted to particular indexes, such as: per 1000 of population, per 1000 of working age population, per capita, per 1000 general school pupils, etc.

3.3. Principal Component (PCA) and Propensity Score Matching (PSM) analysis Before proceeding to the MIMIC model calculations a few additional analyses had been done. In order to match selected border municipalities with inland municipalities, 2 different analyses had been carried out, namely, a principal component and a propensity score matching methods were used. These statistical techniques helped to discard an issue of possible multi-correlated variables. The matching cities/municipalities were selected after evaluating not only some common criteria, but also all the possible statistical indicators. The matches were found without any possible preselection bias by involving different dimensions, such as demography, business, social benefits, taxes, etc. Propensity Score Matching (PSM) The rationale for selecting more inland municipalities than it was necessary for comparison was due to an initial assumption that border municipalities differ from each other according to various factors and profiles. Also, another assumption was that border municipalities are different from inland ones, this is the reason why matching of those had to be done based on all the possible statistical indicators. Analysing the border municipalities as separate cases and comparing them to similar inland municipalities helped to draw some valuable insights. Therefore, the technique called Propensity Score Matching1 (PSM) was selected in this case. PSM analysis was a valuable tool for the research, which helped to link the municipalities from 2 different groups by using all the collected statistical indicators for particular countries. It should be noted that matching border municipalities with 2 other corresponding inland municipalities instead of 1 were selected, in case of any discrepancies during the research appeared. Principal Component Analysis (PCA) Before using a PSM method, multicollinearity between the source variables had been addressed and the profiles of each municipality had been concluded. The multicollinearity arrised because of the following reasons. Firstly, the collected datasets for each country contained a large number of different variables that possibly express similar information. Secondly, PSM uses a logistic regression, which is very prone to multicollinearity. Thus, PCA2 was a useful dimension-reduction tool, which uses an orthogonal transformation in order to squeeze a large number of indicators \ into a smaller number of uncorrelated underlying factors. The constructed factors were based on all of the collected statistical parameters, which had an incompatible weights on them. Also, the regression coefficients of the PCA worked as an input for PSM. Using those coefficients it was possible to tell what the profile of matched municipalities for particular country was.

1 2

Detailed explanation of PSM: http://www.statisticshowto.com/propensity-score-matching/ Detailed explanation of PCA: https://klevas.mif.vu.lt/~tomukas/Knygos/principal_components.pdf

In the context of the research, the final output of PSM had matched border municipalities with 2 inland municipalities according to the selected algorithm.

3.4. 1-Factor Causal Clustering One more step before constructing a MIMIC model for different countries had been executed. Due to large datasets of all countries, a multicollinearity remained for further MIMIC calculations. Another possible issue was that while constructing MIMIC model from the source variables as an input, there was an increased possibility to get a large volume of indicators influencing shadow economy, which was going to be problematic to interpret. Thus, a 1-Factor Causal Clustering3 analysis on source variables was selected to run in order to construct the latent variables and discriminant analysis to find individual significant variables that would have influence on the model. A 1-Factor Causal Clustering held an assumption that there were 3 different variables, which had similar covariance and tried to add the other ones on it, checking whether variance explaining increases or not. If it did not explain latent variable better, variables were dismissed until the latent variable was constructed consisting of 3-6 statistical indicators, and this process was iterative. After constructing latent variables, the remaining source variables were examined through a discriminant analysis and the ones showing significance were going to the dataset for MIMIC calculations together with latent variables. Those 2 methods helped to reduce a number of variables for the MIMIC model without losing the main information and constructing the variables using method which assumes causality.

3.5. MIMIC MIMIC model is a specific formulation of a structured equation modelling4 (SEM) and is quite often used for estimating shadow economy. This method estimates those variables, which are difficult to observe directly, as well as comprises of other complex factors. MIMIC model has its own pros and cons but it was useful in calculating the shadow economy indexes for both border and inland municipalities in all of the 8 researched countries. The Model also helped to investigate the causal and indicative variables which were the most significant in determining the size of shadow economy in each country. Shadow economy in this model is equated to hidden (latent) variable, which, on the one hand, was tied with a set of observable indicators (revealed the change in the size of shadow economy), while, on the other hand, was tied with a set of causal variables, which significantly affected the activity of shadow economy. Formally, the MIMIC consists of 2 parts: the structural equation model and the measurement model. A sufficient amount of causal and indicator variables enabled to evaluate model by applying a standard econometric procedures5. The structural equation model is given by: đ?&#x153;&#x201A;đ?&#x2018;Ą = đ?&#x203A;ž â&#x20AC;˛ đ?&#x153;&#x2019;đ?&#x2018;Ą + đ?&#x153;?đ?&#x2018;Ą

Detailed explanation of 1-Factor Causal Clustering: http://www.kdd.org/kdd2016/papers/files/rpp0520kummerfeldA.pdf 4 Detailed explanation of SEM: http://www.statsoft.com/Textbook/Structural-Equation-Modeling 5 Andreas Buehn and Friedrich Shneider. â&#x20AC;&#x153;MIMIC Models, Cointegration and Error Correction: An Application to the French Shadow economy, pp. 7-8

where: đ?&#x153;&#x201A; is a scalar latent variable (the size of shadow economy); đ?&#x153;&#x2019; â&#x20AC;˛ đ?&#x2018;Ą =( đ?&#x153;&#x2019;1đ?&#x2018;Ą , đ?&#x153;&#x2019;2đ?&#x2018;Ą â&#x20AC;Ś , đ?&#x153;&#x2019;đ?&#x2018;&#x17E;đ?&#x2018;Ą ) is a (1 x q) vector of time series variables indicated by the subscript t. Each time series đ?&#x153;&#x2019;đ?&#x2018;&#x2013;đ?&#x2018;Ą , i=1,â&#x20AC;Ś,q is a potential cause of latent variable đ?&#x153;&#x201A;đ?&#x2018;Ą ; đ?&#x203A;ž â&#x20AC;˛ = (đ?&#x203A;ž1 , đ?&#x203A;ž2 , â&#x20AC;Ś , đ?&#x203A;žđ?&#x2018;&#x17E; ) is a (1 x q) vector of coefficients in the structural model describing the â&#x20AC;&#x153;causalâ&#x20AC;? relationship between the causal variable and its causes. Since the structural equation model only partially explains the latent variable đ?&#x153;&#x201A;đ?&#x2018;Ą , the error term đ?&#x153;?đ?&#x2018;Ą represents an unexplained component. The MIMIC model assumes that the variables are measured as deviations from their means and that an error term does not correlate to the causes, that is: E( đ?&#x153;&#x201A;đ?&#x2018;Ą ) =E( đ?&#x153;&#x2019;đ?&#x2018;Ą ) =E( đ?&#x153;?đ?&#x2018;Ą ) =0 and E(đ?&#x153;&#x2019;đ?&#x2018;Ą đ?&#x153;?â&#x20AC;˛đ?&#x2018;Ą )=E(đ?&#x153;?đ?&#x2018;Ą đ?&#x153;&#x2019;â&#x20AC;˛đ?&#x2018;Ą )=0. The variance of đ?&#x153;?đ?&#x2018;Ą is shortened to ď &#x2122; and ď &#x2020; is the (q x q) covariance matrix of causes đ?&#x153;&#x2019;đ?&#x2018;Ą . The measurement model represents the link between a latent variable (đ?&#x153;&#x201A;đ?&#x2018;Ą ) and its indicators, i.e. the latent unobservable variable is expressed in terms of observable variables. The measurement model equation is given by: đ?&#x203A;žđ?&#x2018;Ą = đ?&#x153;&#x2020;đ?&#x153;&#x201A;đ?&#x2018;Ą + đ?&#x153;&#x20AC;đ?&#x2018;Ą where: đ?&#x203A;žâ&#x20AC;˛đ?&#x2018;Ą =(đ?&#x203A;žđ?&#x2018;Ą1 , đ?&#x203A;žđ?&#x2018;Ą2 , â&#x20AC;Ś , đ?&#x203A;žđ?&#x2018;?đ?&#x2018;Ą ) is a (1 x p) vector of individual time series variables đ?&#x203A;žđ?&#x2018;&#x2014;đ?&#x2018;Ą , j=1,â&#x20AC;Ś,p; đ?&#x153;&#x20AC;đ?&#x2018;Ą =(đ?&#x153;&#x20AC;1đ?&#x2018;Ą , đ?&#x153;&#x20AC;2đ?&#x2018;Ą ,â&#x20AC;Ś, đ?&#x153;&#x20AC;đ?&#x2018;?đ?&#x2018;Ą ) is a (p x 1) vector of disturbances where every đ?&#x153;&#x20AC;đ?&#x2018;&#x2014;đ?&#x2018;Ą , j=1,â&#x20AC;Ś,p is a â&#x20AC;&#x2DC;white noiseâ&#x20AC;&#x2122; error term. Their (p x p) covariance matrix is given by Î&#x2DC;đ?&#x153;&#x20AC; . The single đ?&#x153;&#x2020;đ?&#x2018;&#x2014; , j=1,â&#x20AC;Ś,p in (p x 1) vector of regression coefficients đ?&#x153;&#x2020;, represents a magnitude of the expected change of respective indicator for a unit change in change in the latent variable. Like the causes of MIMIC model, the indicators are directly measurable and expressed as deviations from their means, i.e. E(đ?&#x203A;žđ?&#x2018;Ą )=E(đ?&#x153;&#x20AC;đ?&#x2018;Ą )=0. Furthermore, it is assumed that error terms and measurement model do not correlate either to the causes đ?&#x153;&#x2019;đ?&#x2018;Ą or to the latent variable đ?&#x153;&#x201A;đ?&#x2018;Ą , therefore, E(đ?&#x153;&#x2019;đ?&#x2018;Ą đ?&#x153;&#x20AC;â&#x20AC;˛đ?&#x2018;Ą )=E(đ?&#x153;&#x20AC;đ?&#x2018;Ą đ?&#x153;&#x2019;â&#x20AC;˛đ?&#x2018;Ą )=0 and E(đ?&#x153;&#x201A;đ?&#x2018;Ą đ?&#x153;&#x20AC;â&#x20AC;˛đ?&#x2018;Ą )=E(đ?&#x153;&#x20AC;đ?&#x2018;Ą đ?&#x153;&#x201A;â&#x20AC;˛đ?&#x2018;Ą )=0. The final assumption is that the đ?&#x153;&#x20AC;đ?&#x2018;Ą do not correlate with đ?&#x153;?đ?&#x2018;Ą , that is: E(đ?&#x153;&#x20AC;đ?&#x2018;Ą đ?&#x153;?â&#x20AC;˛đ?&#x2018;Ą )=E(đ?&#x153;?đ?&#x2018;Ą đ?&#x153;&#x20AC;â&#x20AC;˛đ?&#x2018;Ą )=0. A general structure of MIMIC model is shown in Figure 3. Figure 3: General structure of the model

Source: compiled by authors

From the structural and measurement equations models and the use of equations, the MIMIC model covariance matrix â&#x2C6;&#x2018; is derived. This matrix describes the relationship between the observed variables in terms of their covariances. Decomposing the matrix derives the structure between the observed variables and the unobservable, latent variable (shadow economy). The model covariance matrix is given by: đ?&#x153;&#x2020;(đ?&#x203A;ž â&#x20AC;˛ ÎŚđ?&#x203A;ž + Î¨) + Î&#x2DC;đ?&#x153;&#x20AC; â&#x2C6;&#x2018; =( ÎŚđ?&#x203A;žđ?&#x153;&#x2020;â&#x20AC;˛

đ?&#x153;&#x2020;đ?&#x203A;žâ&#x20AC;˛ÎŚ ) ÎŚ

where: â&#x2C6;&#x2018; is a function of the parameters đ?&#x153;&#x2020; and đ?&#x203A;ž and the covarianceâ&#x20AC;&#x2122;s contained in ÎŚ, Î&#x2DC;đ?&#x153;&#x20AC; and Î¨. Since, the latent variable was not observable, its size was unknown, and the parameters of the model had been estimated using the links between observed variables variances and covariances. Therefore, the goal of estimation procedure was to find the values for parameters and covariances which produce an estimate for â&#x2C6;&#x2018; that is as close as possible to the sample covariance matrix for the observed causes and indicators. So far, the MIMIC model was used to calculate indexes on a country level. However, for the first time, the size of the shadow economy using the MIMIC model was estimated for a smaller demographic area such as a municipality. For that reason, the model was calculated for each project country individually and each country had different model equations. The same equation of the model can be used to estimate shadow economy index only for the border and inland municipalities of a certain country. Furthermore, each participating country has a different data available on a municipality level. Hence, different variables were included in the model on country level. As a result, each project country had different equations with different significant causal and indicator variables. In addition, to have a better representation of the indexes, those were calibrated to a number which represents the size of the shadow economy in terms of the economic value generated in the municipality. This procedure was executed only for EU countries, as recommendations are provided only for those countries and calibration process requires high volume of additional data in order to have accurate estimations. The calibration process was executed by using the calculated municipalityâ&#x20AC;&#x2122;s shadow economy index for a particular year, the unweighted average of all the municipalities and using estimated shadow economy index as % of GDP for particular year6. The formula for calibration: đ??¸đ?&#x2018; đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;&#x161;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x2018; đ?&#x2018;&#x2020;đ??¸ đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;đ?&#x2018;Ľ đ?&#x2018;&#x201C;đ?&#x2018;&#x153;đ?&#x2018;&#x; đ?&#x2018;?đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018;˘đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;&#x; đ?&#x2018;&#x161;đ?&#x2018;˘đ?&#x2018;&#x203A;đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x2013;đ?&#x2018;Ąđ?&#x2018;Śđ?&#x2018;Ą đ?&#x2018;&#x2C6;đ?&#x2018;&#x203A;đ?&#x2018;¤đ?&#x2018;&#x2019;đ?&#x2018;&#x2013;đ?&#x2018;&#x201D;â&#x201E;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x2018; đ?&#x2018;&#x17D;đ?&#x2018;Łđ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018;&#x17D;đ?&#x2018;&#x201D;đ?&#x2018;&#x2019; đ?&#x2018;&#x153;đ?&#x2018;&#x201C; đ?&#x2018;&#x2020;đ??¸ đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;đ?&#x2018;Ľđ?&#x2018;&#x2019;đ?&#x2018; đ?&#x2018;Ą

Ă&#x2014; đ?&#x2018;&#x2026;đ?&#x2018;&#x2019;đ?&#x2018;&#x201C;đ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;?đ?&#x2018;&#x2019; đ?&#x2018;&#x2020;đ??¸ đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;đ?&#x2018;Ľ đ?&#x2018;&#x153;đ?&#x2018;&#x201C; đ?&#x2018;?đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018;˘đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;&#x; đ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018;˘đ?&#x2018;&#x203A;đ?&#x2018;Ąđ?&#x2018;&#x;đ?&#x2018;Śđ?&#x2018;Ą

Moreover, the additional municipalities, which were not included in the research but highly contribute to the economy of the country, were used for the calibration process in order to have more accurate estimates. Then, the average SE index of each municipality for the period of 20122015 was estimated. In addition, based on the causal variables which are significant to the MIMIC model, the questionnaires with attitudinal questions to investigate into motivation and hidden meanings behind these variables were built for each country individually.

The reference indexes were taken from Schneider (2015) â&#x20AC;&#x153;Size and Development of the Shadow Economy of 31 European and 5 other OECD Countries from 2003 to 2015: Different Developmentsâ&#x20AC;?. Access online: http://www.econ.jku.at/members/Schneider/files/publications/2015/ShadEcEurope31.pdf

3.6. Economic and comparative analysis The very last step in the project Phase 1 was an economic/comparative analysis. The analysis was carried out in order to estimate different economical, welfare and income parameters while applying a data gathered in the data collection stage. The aim of this analysis was to examine the differences between border and inland municipalities And to distinguish them into 2 separate groups with a detailed specifications. The examples of derived indicators: registered income, diversity of old dwellings, quality of job offers, skills relevance, inventory of vehicles, direct/indirect tax share, etc. The economic/comparative analysis revealed that some important issues should have been taken into account: whether border municipalities differed from inland ones as a group or rather as separate cases, whether registered income and welfare status were the same for both groups of municipalities, etc. The analysis also provided a possibility to notice some interesting discrepancies and dimensions in which the 2 groups differed the most. After the economic/comparative analysis was finalised and initial trends and differences were noticed, all of our indicators were accessed statistically while applying a T-Test7 technique. The TTest examined whether the differences between 2 groups were significantly distinct and occurred not by chance. To test this hypothesis 90% confidence interval was selected to be applied. After running the T-Test, conclusions whether the differences are significant were provided and results were used in the further activities of the project.

4. Phase 2: Quantitative survey and focus group discussions The main objective of Phase 2 activities was to investigate the attitudes of inhabitants of researched municipalities regarding the municipality’s overall situation and smuggling related activities by implementing a quantitative survey. Furthermore, based on the survey results, the objective was to formulate the hypothesis about possible actions to improve a current situation and reduce smuggling. The hypothesis was tested through the application of focus group method.

4.1. Quantitative research The quantitative research consisted of a development of attitudinal questionnaires specifically adopted to every project country based on the MIMIC results. The face-to-face surveys were executed in particular border and inland municipalities. For the quantitative research 3 different questionnaires were developed: •

For the respondents residing in EU border municipalities;

•

For the respondents residing in EU inland municipalities;

•

For the respondents residing in Non-EU border municipalities.

A questionnaire for the residents of EU border municipalities (Estonia, Latvia, Lithuania and Romania) consisted of attitudinal and smuggling related sections. Both of those sections were developed for each project country individually, depending on the indicators, which appeared to be the most significant ones determining shadow economy index in the Phase 1. The rationale of this procedure lies behind a need to investigate the motives that are hidden behind each of the indicator, the reasons for which those motives appear in the shadow economy determination and the patterns of behaviour it could have revealed. The attitudinal questions section included questions, such as

Detailed explanation of T-Test: https://www.investopedia.com/terms/t/t-test.asp

“how do you evaluate the municipality’s spending in various sectors?”, “what is your attitude towards the social benefit receivers?”, “what is the share of your payments in cash?” etc. The smuggling related questions section included questions, such as “do you know where to purchase smuggled goods?”, “what are the most popular routes for smuggling goods into the country?” etc. Basic questions about the demography, incl. age, education, occupation, etc., were also asked. The questionnaires for inhabitants of EU inland municipalities were different in a way that there was no section about smuggling activities. This part was dismissed due to the notion that these municipalities were used as a reference for corresponding border municipalities. Thus, it was necessary to know inhabitants’ attitudes towards different subjects in order to use the observations while developing profiles and recommendations for border municipalities. Questionnaires for non-EU member states (Belarus, Moldova, Russia and Ukraine) were constituted differently from the surveys fitted to EU member states. A generic questionnaire was developed for all of the non-EU countries, based on the indicators, which had shown a significance for determination of shadow economy, as well as for smuggling related questions, such as “do you know anyone who buys/sells goods that are later on resold across the border as smuggled goods?”,” what are the reasons for smuggling?” etc. Those questions were used in comparison to the corresponding EU border municipalities in order to check whether the answers of both municipalities are similar or different, making an assumption that if the answers were not significantly different, the inhabitants of both municipalities possibly have some linkages or at least similar attitude. Questions related to the demographics (age, education, occupation) of inland municipalities inhabitants were also included in the questionnaires. Despite the fact that there were different questions for all of the countries, based on the obtained results from previous phase of the research, the survey and sample methodology was alike in all of the questionnaires: •

Survey was executed face-to-face with the computer’s assistance (CAPI);

•

Inhabitants aged 18 – 65 and have been living in the municipality for at least 12 months were questioned;

•

Households were selected regarding a random route procedure;

•

Respondents from households were selected regarding the youngest male rule.

The sample size (n) and approximate length of the interview (LOI) for different types of municipalities are provided bellow: •

EU border municipalities: n=100; LOI=25min;

•

EU inland municipalities: n=100; LOI=15min;

•

Non-EU border municipalities: n=100, LOI=10min.

Hence, the quantitative research was performed in 64 municipalities of 8 countries by interviewing 6400 respondents. The insights from this research helped us to formulate the initial recommendations for each of the border municipalities in EU. The initial recommendations were examined during the qualitative research, which was implemented in all EU border municipalities, in order to grasp a sense of necessity and feasibility.

4.2. Survey data analysis In order to raise a hypothesis encompassing all possible actions, which would contribute to the decrease of the shadow economy, and to formulate the guidelines for the focus groups, the data from the surveys was analysed. The survey data analysis consisted of 3 main parts:

1. Attitudes and beliefs impacts on shadow economy: A. Regression analysis; B. Distinctive issues in attitudes among the residents of different municipalities attitudes within particular country. 2. Profiles of particular municipalities based on attitudes: A. Principal component analysis; B. Plotting the municipalities based on to the produced factors. 3. Hypothesis to test in focus group interviews: A. Profiling of the municipalities based on both - regression scores and significant variables from the questionnaire and appearance of the municipalities on dimensions produced via PCA. Firstly, all of the answers of the survey were recoded according to some particular index (e.g. answer “fully agree” was equal to 2, answer “fully disagree” was equal to -2, etc.). Then all of the recoded answers were used to calculate the averages for each of the questions (statements) for each of the municipalities separately. After completing all of the data cleaning and recoding steps, the data for each EU country was analysed individually. The regression analysis helped to explore the relationship between shadow economy indexes and the statements (attitudes from the survey). This analytical approach was applied to study the relationship of Latvia, Lithuania and Romania. However, a different approach which was applied to Estonia is described in Appendix 4. The statements (attitudes), which were significant factors for determining shadow economy for each of the countries, were described in more detail, providing the prevailing tendencies and conceivable hypothesis. Also, in order to understand the profiles of the municipalities better, the Principal Component Analysis was implemented. The key point is that the PCA was applied to reduce a number of variables and use those for visualizing distribution of the municipalities regarding different dimensions. All of the information gathered from the regression analysis, attitude comparison and plotted positions of the municipalities according to PCA was used to draw the particular profiles for each of the EU border municipalities. The specific profiles helped to form the initial hypothesis, which were tested during the focus group discussions.

4.3. Focus group discussion The aim of the qualitative research, which was implemented in EU border towns, was to test the initial hypothesis and observations, which were raised after analysing the results of quantitative surveys. The observations and hypothesis were related to the specific actions, which could possibly increase the welfare of the inhabitants of border municipalities and, at the same time, decrease the overall level of shadow economy. The qualitative research in the border municipalities of EU countries was executed in the form of focus group discussion (FGD). The focus group discussions were executed in twenty different border municipalities; each of the FGD comprised of approx. 10 participants and took about 2 hours. The participants for the FGD were selected by using recruitment questionnaire. First of all, the respondent, who agreed to participate in FGD by leaving their phone numbers during the face-to-

face interviews, were approached. Additional participants for FGD were found using a snowball technique. The main criteria for the FGD and selection of participants were: •

Each focus group should have had a gender ratio as close to 50/50 as possible;

•

Each focus group needed to be comprised of 2 age groups: 25-35 y.o. and 36-50 y.o. The ratio of age groups should have been as close to 50/50 as possible;

•

Preferable to have respondents with the education level equal to unfinished/finished higher education;

•

The participants of the FGD had to speak a native language of the country, where FGD took place;

•

Participant was eligible for focus group if he/she either lived in the specific area or worked there. Also, participants had to be distributed around the particular town/district to avoid geographical concentration;

•

Each focus group was constructed in a way to have 50/50 or 40/60 ratio of people with an opposite opinions. Preferable that participants selected would have had an extreme views towards smuggling – either really positive or negative;

•

Respondents had to have ability to communicate;

•

Participants or someone from their household could not have worked in such sectors: 1. Advertising; 2. Marketing consulting; 3. Psychology; 4. Market or marketing research; 5. Police, border-security, customs.

Only those inhabitants of the municipalities, who were willing to join the FGD and met all the necessary criteria, were invited to participate. The FGD itself was comprised of 5 main stages, namely, an introduction, a warm-up, a main stage, a general hypothesis testing and an ending. During the introduction stage, each of the participants shortly described themselves; the moderator explained the aim and the rules of the discussion. The warm-up stage was dedicated to adaption and preparation of participants in order for them to start talking and thinking. The warm-up stage was comprised of a few questions, which were broad and distant but somehow related to the topic (e.g. “what is a good life consisted of?”). Following the warm-up, a main section of the FGD was carried out. This section was consisted of various questions, which had derived from the survey analysis (e.g. “Does your municipality acts enough in improving and developing a local economy?s Why? How do you notice that?” etc.). The aim of the main section was to grasp more specific issues, which could be improved/changed/introduced in order to change the attitude or raise a satisfaction of the inhabitants of a particular border municipality. The 4th stage of FGD was developed in order to test the general hypothesis about the possible ways to reduce an existing shadow economy. First of all, several methods were tested (the ones from the best practices implemented by other states), then, in upcoming FGDs the newly proposed ideas were proposed and tested together with the previous ones. The ending stage was dedicated for Q&A session to remove the uncertainties. The insights from the qualitative research were used for the final phase of the project, where all the knowledge from the Phase 1 and Phase 2 was combined in order to develop specific recommendations for each of the analysed EU border municipalities.

5. Phase 3: Expert interviews and final recommendations The main objective of the Phase 3 activities was to test the preliminary recommendations during an expert interview. Furthermore, the expert interview was conducted not only to confirm the recommendations, which were formed after focus group discussion, but also to gain a valuable insights and examples of best practices how to fight with shadow economy. The results of the expert interviews were used to form the final recommendations for governments how to reduce shadow economy in particular border municipalities and strengthen local economies.

5.1. Expert interviews In order to test the preliminary recommendations, which were devised for each of the EU border municipality, as well as to systemise the examples of the best practices how to fight shadow economy, expert interviews were executed in each EU country. Since the preliminary recommendations cover many areas, experts with different profiles and backgrounds were necessary. The criteria for experts followed: •

1 municipality level expert from EU border municipality that had the highest shadow economy index compared to other border municipalities in the country. The municipality level expert have to work in a local government and be related to shadow economy or public relations;

•

1 municipality level expert from border municipality that had the lowest shadow economy index compared to other border municipalities in the country. The municipality level expert had to work in a local government and be related to shadow economy or public relations;

•

1-2 country-wide experts that specialise in shadow economy or smuggling on a country level;

•

2 communication experts from public relations or political science.

Expert interviews were executed on a country level; on average each of the interviews included 78 experts from different fields, incl.: •

State Tax Inspectorate;

•

Customs Office;

•

Border Guard Office;

•

Public relations/communication experts;

•

Police;

•

Organizations fighting illicit trade;

•

Representatives from the municipalities with the highest and the lowest shadow economy index.

The insights from the expert interviews helped to grasp a practical sense about the efficiency of the initially formed recommendations. The interviews revealed what kind of initiatives or measurements had already been tested, how communication should be targeted in the future and what was the overall attitude towards selected research approach.

5.2. Recommendations The process of development of recommendations started from the results of applying MIMIC method, which proposed a hypothesis for quantitative study items. A quantitative study revealed which attitudes on tax evasion and tax expenditure impact the size of SE. During the FGD measurable, tangible, and/or observable objects that lead to forming particular attitude were uncovered. Thus, the recommendations were formulated by keeping in mind the assumption that bringing a change in these objects would change the attitude as well as shrink the size of shadow economy in a particular municipality. After the insights from the expert interviews, the substantiated guidelines to local as well as to state governments regarding investment strategies and how shadow economy should to be mitigated, were formed. Based on the executed activities throughout all of the phases of the project, the final recommendations for researched EU border municipalities as well as for each EU country are provided. Even though, the recommendations are highly specified for individual municipalities, most of the recommendations are presented in a userfriendly form encompassing different means of communication strategy, development of policies, changes in administration matters, compliance with the law and alteration of education system.

6. Results This section represents the main findings of the activities held in Phase 1, Phase 2 and Phase 3. The results are provided for each country individually.

6.1. Estonia Identification of border and inland municipalities. First of all, the border municipalities in Estonia that could have been eligible for this research were examined. The initial list consisted of 13 border municipalities eligible for the research, which were selected based on approximation to the border and administrative unit (for the research the municipality level was selected). The initial list of border municipalities is presented below. Table 1: List of initial Estonia’s border municipalities Initial list of border municipalities (Estonia) 1. Ahtme

8. Räpina

2. Jõhvi

9. Rõuge

3. Misso

10. Sillamäe

4. Mooste

11. Valga

5. Narva

12. Värska

6. Piusa

13. Võru

7. Põlva municipality

Source: compiled by authors

After determining the list of eligible border municipalities, a desk research was conducted to find out which of the initial candidates had the highest level of shadow economy and, thus, could have been examined further in the research. The desk research included the analysis of various reports, research, and articles in various media sources. After thorough analysis, 6 border municipalities with the highest level of shadow economy were chosen. Further 20 inland municipalities, which were the closest to the border municipalities in terms of population and were not less than 60 km from the border, were selected. The list of Estonia’s border and inland municipalities is presented below. Table 2: List of Estonia’s border and inland municipalities before principal component and PSM Border municipalities

Inland municipalities

1. Jõhvi

1. Haapsalu

10. Rakvere

2. Narva

2. Harku municipality

11. Rapla municipality

3. Põlva municipality

3.Keila

12. Saku municipality

4. Sillamäe

4. Kohtla-Järve

13. Tapa

5. Valga

5. Kuressaare

14. Tapa municipality

6. Võru

6. Maardu

15. Tartu

7. Paide

16. Türi

8. Pärnu

17. Viimsi municipality

9. Rae municipality

18. Viljandi

Source: compiled by authors

Data collection. The data collection process consisted of gathering a large volume of data from various data sources as well as calibrating data to indexes in order to avoid the population influence in total numbers. In Estonia, the data was collected from 24 municipalities for the period from 2012 to 2015. The gathered dataset for Estonia consisted of 94 different statistical indicators. These indicators covered the topics related to demographics, social welfare, business affairs in the country and other areas, which provided all the relevant information to perform various analysis included in the research. Most of the collected data for Estonia came from: •

Statistics Estonia;

•

Different municipality sources.

During the data collection process in Estonia the main issues were: getting the data related to the welfare, especially data on vehicle types, their age; as well as information related to real estate. Most of this data was not specified to the scrutiny necessary for an accurate research. Nevertheless, the process of data collection in Estonia could be considered as a successful one, since the final dataset had quite a high number of indicators without any major gaps.

Principal Component (PCA) and Propensity Score Matching (PSM) analysis. The first analyses performed were PCA and PSM, which were done in order to get rid of a multicollinearity. In the case of Estonia, PCA produced 19 factors with the different hidden patterns. Furthermore, a PSM analysis was performed using the factor coefficients obtained from PCA. The factors and their respective values are presented in the table below. Table 3: Factor values of Estoniaâ&#x20AC;&#x2122;s Propensity Score Matching Factor

Value

Factor

Value

FAC1

0.556

FAC11

-0.416

FAC2

0.707

FAC12

-0.850

FAC3

-4.050

FAC13

-0.534

FAC4

0.115

FAC14

0.936

FAC5

0.643

FAC15

-5.043

FAC6

-4.616

FAC16

-2.920

FAC7

1.139

FAC17

- 3.526

FAC8

-1.025

FAC18

0.557

FAC9

-0.809

FAC19

-3.245

FAC10

2.481

Source: compiled by authors Note: Bolded factors and their values have the biggest weight on the particular matching of Estoniaâ&#x20AC;&#x2122;s municipalities.

As it can be seen from the table, 19 factors were produced and 4 factors played a major role in determining some specific matches. Thus, the profiles of the matched municipalities were based on the statistical indicators, which had the highest coefficients in loading of those particular factors. It means that matched cities are akin to each other in terms of the patterns hidden in the loading of these factors. The main statistical indicators of the factors and their description for matching Estonian cities are provided below. Table 4: Statistical indicators determining profiles of the municipality matching (Estonia) Factor FAC3

Statistical indicators with the highest weight Average state social insurance disability pension, EUR; Number of police officers per 100 000 population;

FAC6

Number of recipients of state social insurance pensions for work incapacity per 1000 working-age population;

Description Factor is described, mainly, through average insurance disability pension paid by municipalities and safety assurance via number of police officers. Factor defines pattern of social insurance, more precisely, the

Factor

Statistical indicators with the highest weight

Description

Number of recipients of State social insurance work incapacity (disability) pensions per 1000 working-age population; FAC15

Average number of days of State social insurance sickness benefit paid per 1 working person; Number of cases of State social insurance sickness benefit paid per 1000 population;

FAC17

Deals in tangible and intangible assets included to municipal budget, sales, EUR per working-age person; Number of general school pupils per 1000 population;

number of social insurance benefit receivers. Duration and cases of social insurance plays the major role making up this factor, hence, matching the municipalities for Estonia. Indicators of municipality activities regarding tangible and intangible assets and demographic situation regarding school pupils are defined through this factor.

Source: compiled by authors Note: presented statistical indicators are considered as having the biggest weight for the loadings of these particular factors, this way determining profiles of matched Estonian municipalities.

To summarise, the matches of municipalities in Estonia’s case were mainly based on similarities in the social insurance pensions, in the number of people receiving them, in the demographic data on school pupils, in the indicators of safety in the municipality and of municipalities’ activity regarding tangible and intangible assets. Table with the exact matches and their scores is presented below. Table 5: Details of Estonia’s matched observations Border municipalities

Logit (Propensity score)

JÕHVI

7.390

NARVA

PÕLVA

SILLAMÄE

VALGA

VÕRU

Inland municipalities

Logit (Propensity score)

Distances

PÄRNU

-7.109

14.499

SAKU

-7.340

14.729

RAPLA

-6.374

13.262

TÜRI

-6.884

13.772

KOHTLA-JÄRVE

-6.504

12.956

HAAPSALU

-7.244

13.696

TARTU

-6.975

14.126

RAKVERE

-7.032

14.183

VILJANDI

-7.061

14.401

KURESSAARE

-7.101

14.441

HARKU

-6.554

15.943

RAE

-7.390

16.779

6.888

6.452

7.151

7.339

9.389

Source: compiled by authors Note: Matching was done by minimizing numerical distance between the municipalities. Each border municipality is matched with 2 inland municipalities. The lower propensity score, the lower difference between border and inland municipalities is.

The table shows that each of the border municipalities has a corresponding inland municipality (marked in dark grey) and one alternative inland municipality (marked in light grey), which would had been used in case some discrepancies with the main corresponding inland municipality arise. All 18 municipalities were used in further steps of the research. 1-Factor Causal Clustering. In order to prepare the dataset for the MIMIC model 1-Factor causal clustering was adopted. After performing the 1-Factor causal clustering on variables, 17 latent variables suitable for Estonia were constructed. The exact content of respective latent variables and the beta coefficients of composed variables are shown in Appendix 1 (Table 101). All 17 latent variables as well as individual variables, which have been significant during the discriminant analysis, were used in the MIMIC model determinations for Estonia. MIMIC. After the data set of latent and source variables was prepared, MIMIC model was built. The graphical representation of MIMIC model equation is presented below.

Figure 4: Graphical representation of the model equation (Estonia)

Source: compiled by authors Model represents causal and indicator variables that show significance in determining equation of shadow economy for Estonia. The expanded version of Estoniaâ&#x20AC;&#x2122;s MIMIC model is presented in Appendix 2.

From the graphical representation of the model it can be seen that there are six causal variables, which significantly affect the size of the shadow economy. Three of them influence the shadow economy negatively (meaning that these variables decrease the shadow economy) and three of them influence it positively (meaning that they increase it). The generic formula for Estonia: đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ?&#x2018;&#x153;đ?&#x2018;¤ = â&#x2C6;&#x2019;0.34 Ă&#x2014; đ?&#x2018;&#x2020;đ?&#x2018;&#x20AC;đ??¸đ?&#x2018; đ?&#x2018;&#x2026;đ?&#x2018;&#x2019;đ?&#x2018;&#x161;đ?&#x2018;&#x153;đ?&#x2018;Łđ?&#x2018;&#x2019;đ?&#x2018;&#x2018;đ?&#x2018;&#x201C;đ?&#x2018;&#x;đ?&#x2018;&#x153;đ?&#x2018;&#x161;đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x201D;đ?&#x2018;&#x2013;đ?&#x2018; đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x; + 0.15 Ă&#x2014; đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2013;đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;Ąđ?&#x2018;&#x2021;đ?&#x2018;&#x17D;đ?&#x2018;Ľđ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;&#x2019; â&#x2C6;&#x2019; 0.10 Ă&#x2014; đ??ż2 + 0.41 Ă&#x2014; đ??ż6 â&#x2C6;&#x2019; 0.36 Ă&#x2014; đ??ż7 + 0.25 Ă&#x2014; đ??ż13 The detailed model of the MIMIC for Estonia with t-values and fit of the model indicators can be found in Appendix 2. Then, shadow economy indexes for all included municipalities in Estonia, using produced equation were calculated. The table with calculated shadow economy indexes for all border and corresponding inland municipalities is presented below.

Table 6: Averages of shadow economy indexes for Estonia’s municipalities, 2012-2015 Municipality type: Border (B) inland (I)

Municipality

Shadow economy as % of economic value in municipality

Jõhvi Parish

26.45%

Pärnu

32.32%

Saue Parish

21.54%

Narva

33.14%

Rapla Parish

24.35%

Türi Parish

29.66%

Põlva

Kohtla-Järve

31.82%

Haapsalu

30.67%

Sillamäe

32.62%

Tartu

30.79%

Rakvere

26.15%

Valga

39.99%

Viljandi

28.07%

Kuressaare

25.53%

Võru

32.85%

Harku Parish

20.63%

Rae Parish

17.92%

32.1%

Average of border municipalities

32.86%

Average of inland municipalities

26.62%

Source: compiled by authors Shadow economy index can have both positive and negative values. Higher the index – higher shadow economy in particular municipality is, lower the number – lower the shadow economy is.

The table above shows that in five out of six border municipalities the shadow economy is larger than in the main corresponding inland municipality. However, there is a case, in which inland municipality has a higher index of shadow economy than the border municipality. This means that based on the MIMIC model we cannot state that all border municipalities have a higher level of shadow economy with 100% certainty because of this unique case. Despite that on average indexes of shadow economy differ to the great extent for all included border and inland municipalities. Economic and comparative analysis. In Estonia, during the economic and comparative analysis, different indicators were constructed using a dataset gathered in the data collection stage. There were 11 different economic indicators and 7 indicators describing registered income and welfare, constructed. By analysing those indicators and comparing them between the border and inland municipalities of Estonia, as well as between the municipalities within each group, a few important insights were found. Even without testing all the insights statistically, it can be stated that there was 25

a clear tendency that GDP per capita, value-added products in service sector and value of EU grants attracted are lower for border municipalities than inland municipalities. Moreover, the registered income of border municipalities is lower by approx. 25% and the percentage of persons that are not participating in labour market is by 6.97% higher. After analysing the indicators of Estonia and different cases of each city in detail, in order to grasp the idea and better understand, what kind underlying issues there are and what information could be listed in attitudinal surveys, all of those indicators were proved to be true statistically via T-Test to find out whether those differences appeared not just by accident. After examining all indicators, 6 of them had shown significance according to selected 90% confidence level. Statistically, significant differences appeared in these indicators: registered income, employment rate, value of flat deals, GDP per capita, value added in service sector and value added in industry sector. Thus, it can be stated that there is a statistically significant difference between the border and inland municipalities in Estonia according to those 6 indicators, meaning, that border municipalities have less value in all of them. Quantitative research. Due to the high variations of Estonia’s shadow economy indexes produced by the MIMIC model, which has appeared mainly due to a low quality of public data, it was decided to continue a quantitative research only in 4 municipalities. For this reason, a quantitative survey was implemented in 2 border municipalities with the highest shadow economy index, namely, Sillamäe and Narva, and 2 corresponding inland municipalities: Tartu and Rapla. Due to a low number of municipalities, the regression analysis, which was used to analyse the rest of EU countries was not valid in this case, thus, another approach was chosen. In order to analyse the survey data, which would lead to the particular profiles of border s municipalities and hypothesis for FGD different approach, including principal component analysis and canonical analysis were executed and is explained in more detail in Appendix 4. Graphical representation of Estonia’s results is presented below. Figure 5: Graphical representation of survey analysis (Estonia) I feel safe these days in my neighborhood Satisfied about health educational and other services provided by government I would rather pay direct taxes from my legal income than pay indirect taxes via goods I purchase

0,119

I feel safe walking in my neighborhood in the night time

I would use the chance of not paying taxes for purchased goods/services if that allows me to save money

Usage of debit/credit card Satisfied about the services provided by finance and insurance sector

Shadow Economy 0,692 0,117

Ownership of credit/debit card

Source: compiled by authors Figure represents the attitudes (statements) that have shown significance in regression analysis. The expanded version of Estonia’s data analysis is presented in Appendix 4.

The analysis revealed that two different attitudes have a direct impact on shadow economy. The more secure citizens of the municipalities feel while walking in the neighbourhood during the night time, the lower shadow economy is. A similar trend is related with the ownership of credit/debit card: it also has negative impact and decreases shadow economy. 26

Furthermore, the analysis further revealed the attitudes which had an indirect impact on the shadow economy. As it can be seen from the graphical representation above, there are four attitudes that had influenced the attitude of inhabitants to feel safe in the neighbourhood during the night time and two on the ownership of the credit/debit card. The attitude that citizens feel safe these days in neighbourhood, satisfaction about health, educational and other services and attitude to pay direct rather than indirect taxes increases the attitude to feel safe walking in the neighbourhood in the night time and overall it decreases shadow economy. However, the attitude to use the chance of not paying taxes for purchased goods/services if that allowed respondents to save money had a negative impact on the attitude to feel safe walking in neighbourhood in the night time and it increased a level of shadow economy. Besides, the analysis revealed that the usage of debit/credit cards and satisfaction about the services provided by the finance and insurance sector increased the ownership of credit/debit cards and overall it decreased a shadow economy. In order to further analyse differences of the above-mentioned attitudes among Estoniaâ&#x20AC;&#x2122;s municipalities, the answers to the statements are presented as indexes. The dark brown colour represents the answers with the highest level of disagreement, while dark blue represents the answers with highest level of agreement. The main differences in attitudes among municipalities is presented in the tables below. Table 7: Satisfaction with the work of various sectors in municipality (Estonia)

Highest level of satisfaction

Highest level of dissatisfaction

Neutral answers

Source: compiled by authors Answer choices: Fully satisfied, partly satisfied, partly dissatisfied, fully dissatisfied, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

The first two statements are measuring the satisfaction with the work of the various sectors in municipalities. The citizens of Tartu and Rapla municipalities are satisfied with health, educational and other services provided by their government. While an opposite opinion can be seen in SillamĂ¤e, where citizens are dissatisfied with health, educational and other services provided by government. The neutral opinion about the education and health services is seen in Narva. Inhabitants from SillamĂ¤e and Tartu are satisfied with the services provided by financial and insurance sector, while inhabitants of Narva are dissatisfied with those services.

Table 8: Opinion about safety in neighbourhood (Estonia)

Highest level of agreement to the statement

Highest level of disagreement to the statement

Neutral answers

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 mean complete agreement with statement, while -2 mean complete disagreement.

Only the citizens of Rapla feel safe in their neighbourhood. While the disagreement to this statement reflects insecurity in Narva. Respondents of two other municipalities, namely, SillamĂ¤e and Tartu, hold a neutral position. A similar trend can be noticed for the second statement. Only the citizens of Rapla municipality feel safe walking in their neighbourhood during the night time, while the disagreement on this statement is seen in SillamĂ¤e and Narva. Inhabitants of Tartu are neutral about this statement. Table 9: Attitude towards purchasing cheaper goods without paying taxes (Estonia)

Highest level of agreement to the statement

Highest level of disagreement to the statement

Neutral answers

The next topic measured an attitude towards purchasing goods cheaper without paying taxes in Estonia. We can see that inhabitants of SillamĂ¤e would use any chance of not paying taxes for purchased goods if it allowed them to save money. Disagreement with this statement is seen in municipality of Rapla. The citizens of Narva and Tartu municipalities are neutral about this statement.

Table 10: Attitude towards importance of paying taxes (Estonia)

Highest level of agreement to the statement

Highest level of disagreement to the statement

Neutral answers

The fourth section revealed the attitude towards the importance of paying taxes. As it can be seen from the table above, the inhabitants of Tartu and Rapla would rather pay taxes from legal income than pay indirect taxes via goods they purchased. The highest level of disagreement on the statement can be seen in Sillamäe. Inhabitants in Narva are neutral about this statement. Table 11: Attitude towards non-cash payments (Estonia)

Highest share of card owners/payments in cash

Lowest share of card owners/payments in cash

NA/DK

Source: compiled by authors Answer choices: Yes, no, NA/DK (1); 0%-10%, 11%-30%, 31%-50%, >50%, NA/DK (2). Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 mean complete agreement with statement, while -2 mean complete disagreement (1), and from 1 – 7, where 1 means lowest share of cash payments and 7 means highest share of cash payments.

The last section reveals the attitude towards cash payments. We can see that inhabitants of Rapla have the highest rate of debit/credit card ownership. The opposite trend is observed in Sillamäe and Narva, where the ownership of credit/debit cards is lower. Similar trend can be seen with the use of cash as a way of payment. Only inhabitants of Rapla have a high share of payments with credit/debit cards. The opposite trend can be seen in Narva and Tartu, where people prefer payments in cash. A further step in analysing the survey of different Estonia’s municipalities was to produce several different factors using the Principal Component Analysis from the answers to the questions. It enabled to notice the patterns and profiles of the exact municipalities easier. In Estonia’s case two graphs are represented by using four different factors.

Figure 6: Graphical representation of the Estonia’s survey answers based on factor 1 and 2

Source: compiled by authors Some of the survey statements, which contribute little to the factors, are hidden. Full list of survey statements and their correlation with the factors can be found in Appendix 4.

Sillamäe. The municipality of Sillamäe possess a high level of shadow economy and by looking to the corresponding inland municipality Tartu, it stands on the completely opposite side of the dimension, based on all of the questions of survey. The inhabitants in Sillamäe have a positive attitude towards cheating on indirect taxes when purchasing various goods and receiving an envelope salary. Based on the survey, the inhabitants of Sillamäe were not willing to start using credit/debit cards more. It should be noted that compared to Tartu municipality, there were positive attitudes towards social benefits and safety in the municipality should be improved. The municipality of Sillamäe was compared to the municipality on the other side of the frontier – Kingisepp (Russia). After executing the independent samples T-test, the hypothesis that samples are different has been rejected. It means that statistically those 2 municipalities are similar and highly possible that inhabitants of both municipalities were linked with each other, have some kind of relations or at least similar mindset. The comparison of Sillamäe and Kingisepp regarding issue of smuggling is provided in the table below: Table 12: Comparison of Sillamäe answers with the municipality on the other side of border (Kingisepp) Reasons to smuggle

Places to buy smuggled goods

Desire to earn and do that fast;

Local markets (especially, from old ladies);

To get goods cheaper (34% of respondents of Sillamäe mentioned this reason).

Through friends/acquaintances.

Ways how goods are being smuggled Cars; By themselves (in panties, coats, etc.)

Routes of smuggling goods Most of the respondents from Sillamäe and Kingisepp municipalities mentioned customs, as it is easy to smuggle goods through border checkpoints.

Source: compiled by authors

Narva. Narva is characterized by inhabitant’s attitude to disapprove a tax evasion, but still possess a high level of shadow economy. Comparing Narva with Rapla (corresponding inland town) it is 30

clear that the inhabitants of Rapla municipality are more satisfied with services provided by local government and overall situation of municipality. In Narva municipality, there is an attitude towards getting social benefits and being not satisfied with the services and situation in the town, more specifically, respondents highlighted a dissatisfaction with the financial and insurance sector and a state of safety in the municipality. Moreover, Narva municipality stands out from the other municipalities, which inhabitants would rather pay direct taxes from their legal income than indirect ones via purchased goods/services and would be willing to use credit/debit card more often if they had a chance. Narva was compared to the same municipality as SillamĂ¤e â&#x20AC;&#x201C; Kingisepp. The results were very similar - as the t-value was lower than needed, the hypothesis about similarity of the municipalities was not rejected. It proves that the municipalities have similar attitudes towards various topics. The comparison of Narva and Kingisepp regarding smuggling is provided in the table below: Table 13: Comparison of Narva answers with the municipality on the other side of border (Kingisepp) Reasons to smuggle

No other choice as smuggling is the only way to survive; fast way to get additional income.

Places to buy smuggled goods Local market of Narva from sellers who operates close to shopping places.

Ways how goods are being smuggled Cars (1/5 of respondents from Narva mentioned this way to smuggle goods); luggage bags, backpacks.

Routes of smuggling goods Absolute majority of respondents from Kingisepp mentioned that smuggled goods are going to Estonia and the main route is Narva-Ivangorod.

Differently from the Kingisepp, inhabitants of Narva do not see smuggling as business or part-time job. Source: compiled by authors

Qualitative research. The FGDs in Estonia were carried out in 2 border municipalities under the scope of the project. Discussions with inhabitants helped to get deeper insights on the attitudes of inhabitants to the specific statements that have an impact on shadow economy. Furthermore, it helped to formulate the preliminary recommendations for each border municipality individually how to fight economy. The results of the interviews are provided on municipality level and presented in a scheme. The scheme represents a short summary of all project phases results for a particular border municipality.

Sillamäe Figure 7: Preliminary recommendations for Sillamäe (Estonia)

Source: compiled by authors

As it is seen in Figure 7, the quantitative survey results revealed that 4 attitudinal statements came up to be significantly different compared to other municipalities in Estonia. The highest level of the disagreement compared to the other municipalities can be seen for all four statements: safety in the night time, satisfaction with health, educational and other services provided by government, attitude towards paying direct taxes rather than indirect ones and ownership of credit or debit card. In order to understand how these attitudes can be moderated, the FGDs were conducted. As it is shown, these are the main actions (recommendations), followed from those subjects of specific importance during focus group discussions in Sillamäe: • To improve the quality of the services related with medical help, focusing on possible solutions to make queues shorter and ensuring better/faster accession of the hospital, either by car or by ambulance; • To attract and employ more teachers, who would be able to speak fluently Estonian. • To concentrate on provision of specialties with the higher demand in the market. Provide favorable conditions for unemployed specialists and post-graduates without employment to be able to re-qualify; • To improve safety situation in the city, mainly concentrating on mentally ill and homeless people in the streets. Also, communicate all of the actions taken to solve this issue; • To make people declare their living place as Sillamäe in order to get more income taxes to municipality budget;

â&#x20AC;˘

In the possible partnership with the banks, which operates in the city, publicly support payments with the debit/credit card, highlight the safety of such payments.

Narva Figure 8: Preliminary recommendations for Narva (Estonia)

Source: compiled by authors

As it presented in Figure 8, a quantitative survey analysis revealed that four attitudinal statements came up significantly different compared to the other municipalities within the country. The highest level of disagreement can be seen for all four attitudinal statements: safety in night time, satisfaction with the services provided by the finance and insurance sector and usage/ownership of credit/debit card. In order to find out how these attitudes can be improved, FGD were conducted. As it is presented in the scheme, these are the main actions (recommendations), followed from those subjects of specific importance during focus group discussions in Narva: â&#x20AC;˘ To improve security in the city by concentrating on issues regarding homeless, mentally ill people and drug addicts in the streets and by communicating actively about actions taken to solve issues regarding these social groups; â&#x20AC;˘ To pay special attention and assign higher portion of budget to security of women and children, as citizens of these 2 categories feel most unsafe in the city;

•

In possible partnership with local banks, communicate benefits of contactless payments by highlighting safety, improving convenience of banks by setting more flexible working hours and establishing more ATMs in the places, which have high flow of citizens.

Recommendations. Based on the MIMIC, quantitative and qualitative research results that were reached throughout all project phases the final recommendations how to reduce a shadow economy and strengthen local economies in Estonia were formed. Recommendations are provided in the table below: Table 14: Final recommendations (Estonia) Categories

Communication

Development

Administration

Recommendations

Subject to recommendations

Hold campaigns on individual benefits generated by payments of taxes:

•

Narva

Health insurance;

•

Sillamäe

Pension;

Paid holidays;

Sickness leave;

Loans.

Hold information campaigns on safety of credit/debit cards payments (in partnership with operating banks).

•

Sillamäe

Hold information campaigns on non-verified quality of illegal cigarettes – potential higher danger to people health (possible partnership with legal cigarettes producers).

•

Narva

•

Sillamäe

Hold campaigns on actions taken to increase public safety.

•

Narva

•

Sillamäe

Initiate medical infrastructure development (shorter queues, quicker reach of hospital).

•

Sillamäe

Initiate negotiations with local banks on more flexible working hours in banks and more ATMs in the places, which have high flow of citizens.

•

Narva

Stimulate schools to attract more teacher who speak Estonian.

•

Sillamäe

Initiate inhabitants of the city to declare their living place (e.g. offering some free public services, events for inhabitants that are official city residents).

•

Sillamäe

Promote initiatives to increase public safety – focusing on homeless, mentally ill people, young people gangs, drug addicts and alcoholics on streets.

•

Narva

•

Sillamäe

Increase the ability for inhabitants to acquire specialties that satisfied market needs.

•

Sillamäe

Initiate a municipality unit on career development – counselling and coaching (requalification of unemployed specialist or post-graduates).

•

Sillamäe

Education

Compliance with the law

Hold information campaigns on the consequences of non-compliance with the national legislation (e.g. penalties for receiving envelope salary).

•

Narva

Source: Compiled by authors

6.2. Latvia Identification of border and inland municipalities. First of all, the border municipalities that could have been eligible for this research were examined. The initial list consisted of seventeen border municipalities eligible for the research, which were selected based on approximation to the border and administrative unit (for the research the municipality level was selected). The initial list of border municipalities is presented below. Table 15: Initial list of Latvia’s border municipalities Initial list of border municipalities (Latvia) 1. Aglonas district

10. Ilūkstes district

2. Alūksne district

11. Kārsavas district

3. Baltinavas district

12 .Krāslava district

4. Balvu district

13. Ludzas district

5. Ciblas district

14. Preijlu district

6. Daglas district

15. Rēzekne

7. Daugavpils

16. Viļakas district

8. Daugavpils district

17. Zilupes district

9. Gulbenes district Source: compiled by authors

After determining the list of eligible border municipalities, a desk research was conducted in order to find out which of them had the highest level of shadow economy and, thus, could have been examined further in the research. The desk research included analysis of various reports, researches, and articles in different media sources. After thorough analysis six border municipalities were selected with the highest level of shadow economy were chosen. Furthermore, twenty inland municipalities, which were the closest to the border municipalities in terms of population and were not less than 60 km from the border, were chosen. The list of Latvia’s border and inland municipalities, which were used in further research stages, is presented below.

Table 16: List of Latvia’s border and inland municipalities before principal component analysis and PSM Border municipalities

Inland municipalities

1. Alūksne

1. Ādažu county

11. Mārupes county

2. Daugavpils

2. Cēšu county

12. Ogre county

3. Daugavpils county

3. Jēkabpils

13. Salaspils county

4. Krāslava county

4. Jelgava

14. Siguldas county

5. Ludza county

5. Jūrmala

15. Stopiņu county

6. Rēzekne

6. Kandavas county

16. Talsi county

7. Ķekavas county

17. Tukums county

8. Kuldīgas county

18. Valmiera

9. Liepāja

19. Ventspils

10. Madonas county

20. Ventspils county

Source: compiled by authors

Data collection. The data collection process consisted of gathering a large volume of data from various data sources, and calibrating data into various indexes in order to avoid the population influence in source data. In Latvia, the data was collected from 26 municipalities for the period from 2012 to 2015. The gathered dataset for Latvia consisted of 127 different statistical indicators. These indicators covered the topics related to demographics, social welfare, business affairs and other areas, which provided all the relevant information to perform various analysis included in the research. Most of the collected data for Latvia came from following sources: •

Central Statistical Bureau of Latvia;

•

Latvian State Roads;

•

Lursoft;

•

State Tax Inspectorate.

Most of the gathered data was provided free of charge, except for data from Lursoft which provided data for Latvian business entities. Overall the biggest issue with a data collection was that, in some cases, the data was not detailed enough; or it was not divided among municipalities. However, these issues were minor and did not impact the execution of research. In conclusion, the process of data collection in Latvia can be considered to be successful because all the necessary information was available and allowed us to perform all analysis without any major discrepancies and limitations. Principal Component (PCA) and Propensity Score Matching (PSM) analysis. The first analyses performed were PCA and PSM, which were done in order to get rid of a multicollinearity. In the case of Latvia the PCA produced 18 factors with the different hidden patterns. Furthermore, a PSM analysis was performed using the factor coefficients obtained from PCA. The factors and their respective values are presented in the table below.

Table 17: Factor values of Latviaâ&#x20AC;&#x2122;s Propensity Score Matching Factor

Value

Factor

Value

FAC1

1.729

FAC10

-3.860

FAC2

-8.353

FAC11

-3.641

FAC3

-7.636

FAC12

5.005

FAC4

-2.400

FAC13

-0.945

FAC5

-4.263

FAC14

-3.467

FAC6

-2.706

FAC15

-3.603

FAC7

0.283

FAC16

2.420

FAC8

1.239

FAC17

-0.036

FAC9

-1.228

FAC18

-6.276

Source: compiled by authors Note: Bolded factors and their values have the biggest weight on the particular matching of Latviaâ&#x20AC;&#x2122;s municipalities.

As the table shows,18 factors were produced and four factors played a major role in determining specific matches in Latvia. Thus, the profiles of the matched municipalities were based on statistical indicators, which had the highest coefficients in loading of those particular factors. It means that matched cities are akin to each other in terms of the patterns hidden in the loading of these factors. The main statistical indicators of factors and their description for matching Latvian cities are provided below. Table 18: Statistical indicators determining profiles of the municipality matching (Latvia) Factor FAC2

Statistical indicators with the highest weight

Description

Number of social assistance cases to reach minimal income level per 1000 population;

The hidden patter of this factor describes situation regarding social assistance provision in the municipality and number of inhabitants working in service activities sector.

Sum of social assistance given to reach minimal income level, EUR per 1000; Number of persons that received the status of at risk per 1000 population; Number of employees in other services activities per 1000 working-age population; FAC3

Number of liquidated enterprises over the year per 1000 workingage population; Number of tourists accommodated per 1000 population;

FAC12

Marriage rate per 1000 population;

The hidden pattern of this factor describes attractiveness to tourism and survival of enterprises situation in municipalities. The hidden pattern of this factor defines incentives towards family creation and turnover generated in

Factor

Statistical indicators with the highest weight

Description

Turnover in information and communication sector per person working in that sector; FAC18

Number of employees in electricity, gas, steam and air conditioning supply per 1000 working-age population; Turnover in electricity, gas, steam and air conditionings supply per person working in that sector;

information and communication sector. The main hidden pattern of this factor indicates size of electricity, gas, steam and air conditioning supply sector and number of employees.

Accordingly, it can be stated that the matches of municipalities in Latvia are mainly based on the similarities in a number of social assistance cases, the incentives to family creation, a municipality’s attractiveness to tourists, an enterprise survival rate, in electricity, gas, steam and air conditioning sector. Table with the exact matches and their scores is shown below. Table 19: Details of Latvia’s matched observations Border municipalities

Logit (Propensity score)

ALŪKSNE

6.252

DAUGAVPILS

KRASLAVA

LUDZAS

REZEKNE

Inland municipalities

Logit (Propensity score)

Distances

TALSI

-6.950

13.202

KANDAVA

-7.505

13.757

LIEPAJA

-6.296

12.461

JELGAVA

-6.866

13.030

CESU

-7.284

22.938

ADAŽU

-7.471

23.125

JEKABPILS

-6.286

14.185

KULDIGA

-6.973

14.873

JŪRMALA

-6.605

22.259

OGRE

-7.016

22.670

MADONA

-6.468

13.745

STOPINU

-6.925

14.203

6.164

15.654

7.899

15.654

7.278

Source: compiled by authors Note: Matching is done by minimizing numerical distance between the municipalities. Each border municipality is matched with 2 inland municipalities. The lower propensity score, the lower difference between border and inland municipalities is.

The table shows that each of the border municipalities has a corresponding inland municipality (marked in dark grey) and one alternative inland municipality (marked in light grey) which would

be used in case some discrepancies would arise. All 18 municipalities were used in further steps of the research. 1-Factor Causal Clustering. In order to prepare the dataset for the MIMIC model 1-Factor causal clustering was adopted. After performing the 1-Factor Causal Clustering on variables, 10 latent variables for Latvia were constructed. The exact content of respective 10 latent variables and the beta coefficients of composing variables can be found in Appendix 1 (Table 102). All 10 latent variables as well as individual variables, which have been significant during the discriminant analysis, were used in the MIMIC model determination for Latvia. MIMIC. After the data set of latent and source variables were prepared, the MIMIC model was built. The graphical representation of MIMIC model equation is presented below. Figure 9: Graphical representation of the model equation (Latvia)

Source: compiled by authors Model represents causal and indicator variables that show significance in determining equation of shadow economy for Latvia. The expanded version of Latviaâ&#x20AC;&#x2122;s MIMIC model is presented in Appendix 2.

The graphical representation of the model shows that there are 4 causal variables, which significantly affect the size of the shadow economy. 2 of them influence the shadow economy negatively (meaning that these variables decrease the shadow economy) and 2 of them influence it positively (meaning that they increase it). The generic formula for Latvia: đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ?&#x2018;&#x153;đ?&#x2018;¤ = 0.2 Ă&#x2014; đ?&#x2018;&#x2020;đ?&#x2018;&#x2019;đ?&#x2018;&#x2122;đ?&#x2018;&#x201C;đ??¸đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x2019;đ?&#x2018;&#x2018;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x;1000 + 0.37 Ă&#x2014; đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2013;đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;Ąđ?&#x2018;&#x2021;đ?&#x2018;&#x17D;đ?&#x2018;Ľđ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;&#x2019; â&#x2C6;&#x2019; 0.47 Ă&#x2014; đ??ż1 â&#x2C6;&#x2019; 0.29 Ă&#x2014; đ??ż3 After building a model, shadow economy indexes for all the cities in Latvia, using produced equation, were calculated. The table with calculated shadow economy indexes for all border and corresponding inland municipalities is shown below.

Table 20: Averages of shadow economy indexes for Latvia’s municipalities, 2012-2015 Municipality type: Border (B) or Inland (I)

Municipality

Shadow economy as % of economic value in municipality

Alūksne Municipality

26.16%

Talsi municipality

33.24%

Kandavas municipality

26.65%

Daugavpils

28.24%

Liepaja

23.76%

Jelgava

25.22%

Daugavpils municipality

31.94%

Cesu municipality

22.85%

Adažu municipality

15.16%

Kraslavas municipality

25.87%

Jekabpils

30.87%

Kuldiga municipality

27.85%

Ludzas municipality

27.21%

Jūrmala

22.78%

Ogre municipality

21.67%

Rezekne

33.46%

Madonas municipality

29.19%

Stopinu municipality

22.09%

Average of border municipalities

28.33%

Average of inland municipalities

25.11%

Analysing the table above, we can see that in 4 out of 6 border municipalities the shadow economy is larger than in corresponding inland municipalities. However, in two cases inland municipalities have a higher index for shadow economy than the border municipalities. This means that based on the MIMIC model it cannot be stated that all border municipalities have a higher level of shadow economy because of these unique cases. Despite that, it can be concluded that based on the average values for all included border and inland municipalities, the index for shadow economy is higher for border municipalities than for inland municipalities. Economic and comparative analysis. In Latvia’s case, 10 different economic indicators and 7 indicators, which describe registered income and welfare, were constructed. By analysing those indicators and comparing them between border and inland municipalities of Latvia, as well as between municipalities within each group, a few important insights were found. Even without testing all the insights statistically, it can be stated that there was a tendency that GDP per capita, gross investment per capita, value of EU grants attracted are lower for border municipalities than 40

inland municipalities. Also, the registered income of border municipalities is lower by approx. 18% as well as private cars, sales value of dwellings and economic development go bend to the same direction. After executing the T-Test to find out whether the differences appeared not just by accident, 6 indicators have shown a significance according to selected 90% confidence level. Statistically significant differences appeared in these indicators: registered income, inventory of vehicles, sales value of flat deals per capita, GDP per capita, unemployment rate and gross investment per capita. Thus, we can state that there is statistically significant difference between border and inland municipalities in Latvia according to those 4 indicators, meaning, that border municipalities have lower value in all of them, except unemployment rate, which is higher for border municipalities. Quantitative research. In order to analyse the survey data, which lead to formulation of guidelines for focus group the regression analysis was implemented. A graphical representation of Latvia’s regression analysis with a scores is presented below. Figure 10: Graphical representation of regression coefficients (Latvia) I would use chance of not paying taxes for purchased goods/services if that allows me to save money I see public services and social benefits as benefits from receiving legal income and paying taxes I believe that money paid in taxes provides useful benefits for me

-0,9 0,778

People should use every opportunity to not pay taxes

Satisfied about spending on public order, safety (availability of police, firefighters, doctors, etc.) I feel like my municipality’s local government would help me in case of trouble I would receive envelope salary if I had a chance, and that meant higher income

0,712 -0,430

Satisfaction of spending on economy to improve and develop it

-1,049

Shadow Economy

I believe that my municipality’s local government should take care of me (helping in difficult situations, etc.) Satisfied about the health, educational and other services provided by government

0,284

I feel my municipality’s local government would help me in case of trouble

Satisfied about the quality of agriculture , forestry and fishing sector

Source: compiled by authors Figure represents the attitudes (statements) that show significance in regression analysis. The expanded version of Latvia’s data analysis is presented in Appendix 3.

The regression analysis consists of two main stages. Firstly, the shadow economy index produced by the MIMIC was chosen as a dependent variable, while results of the attitudinal statements from quantitative survey were included as independent variables. The regression analysis revealed that only 3 attitudes have a significant impact on shadow economy. From the graphical representation, we can see that attitude of using every opportunity not to pay taxes increases the level of shadow economy. Also, if the citizens are satisfied about the municipality’s spending on the economy to improve and develop it, the lower the size of the shadow economy are. Furthermore, the regression analysis revealed that the attitude of citizens to feel that municipality’s local government would help in case of trouble, has a positive impact on shadow economy. In order to further analyse these attitudes and to see the hidden meanings and motivations behind it, three attitudes, which have shown significance into the first regression, were chosen as dependent variables. The further regression analysis revealed that the attitude of inhabitants to use a chance of 41

not paying taxes for purchased goods/services, if that allows to save money, as well as the attitude that money paid in taxes provide useful benefits increase the attitude that people should use every opportunity not to pay taxes, while overall these attitudes increase the shadow economy level. The more satisfied the citizens are about the municipality’s spending on public order and safety and the stronger the belief of the citizens that public services and social benefits are the benefits from receiving legal income and paying taxes, the lower attitude that people should use every opportunity not to pay taxes is, thus, overall it decreases shadow economy. In addition, the regression analysis revealed that a state being sure about municipality’s help in case of emergency or trouble has a positive impact on the satisfaction of spending on economy to improve it, and overall, decreases a level of shadow economy. While the attitude of the citizens to receive a salary under-reporting if they have a chance and that means higher income has negative impact on citizens’ satisfaction of spending on economy and it increases the shadow economy. Last but not the least, the regression analysis revealed that the attitude of inhabitants that local government should take care of them (helping in difficult situations, providing financial support in a situation of a need) and satisfaction about the health, educational and other services provided by government have positive impact on citizens’ attitude that local government’s help for them in case of trouble and overall it increases the shadow economy. While the satisfaction about the quality of agriculture, forestry and fishing sector negatively affects the notion that local government should take care of their citizens in case of a trouble; overall, it decreases shadow economy. In order to further analyse the differences of the above-mentioned attitudes of Latvia’s municipalities, the answers to the statements were transformed into indexes. The dark brown colour represents the answers with the highest level of disagreement; dark blue represents the answers with the highest level of agreement. The answers coloured in lighter shades represent an inclination for agreement (light blue) or disagreement (light brown); the neutral option is represented in white colour. The main differences of the attitudes among municipalities are presented in the tables below. Table 21: Attitude towards municipality and local government (Latvia)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

Regarding the first two statements, we can see that in Daugavpils, Krāslava, Ludzas and Stopiņi counties inhabitants strongly believe that the local government would help them in case of a trouble. Disagreement to this statement is seen in the city of Daugavpils city and in Talsi county.

7 out of 12 municipalities do not believe that their local government should take care of their resident, and only 3 municipalities (Daugavpils, Krāslava and Stopiņi counties) believe that local government should take care of them. Table 22: Evaluation of municipality's spending in different sectors (Latvia)

Large enough spending

Too low spending

Inclination for large enough spending

Inclination for too low spending

Neutral answers

Source: compiled by authors Answer choices: appropriate, too large, too small. Index can have both positive and negative values. The range of the indexes is from -1 to 2, where 2 mean spending is appropriate, 1- spending is to large, while –1 mean that spending is too small.

The second topic in the survey was measuring an attitude towards municipalities’ budget spending in different sectors in Latvia, whether it is appropriate, too high or too low. As it is shown in the table, Krāslava and Stopiņi counties inhabitants think that the proportion of spending on the economy is large enough, while inhabitants in the Daugavpils city and Talsi county think that spending on the economy is too small. People in Daugavpils city incline towards thinking that the spending on the economy is too small. A similar trend can be noticed for government spending on public order, safety. Inhabitants of Krāslava and Stopiņi counties think that the spending is large enough, while people in Talsi county think that the spending is not large enough. Although the index is not negative, it has the lowest value out of all 12 municipalities. People in Alūksne incline towards thinking that the spending on public order and safety is large enough.

Table 23: Satisfaction with the work of various sectors in municipality (Latvia)

Highest level of satisfaction

Highest level of dissatisfaction

Incline towards highest level of satisfaction

Incline towards highest level of dissatisfaction

Neutral answers

Further satisfaction with the work of various sectors in municipalities was measured. Alūksne, Daugavpils, Talsi and Stopiņi counties are satisfied with the health, educational and other services provided by the government. Inhabitants in Jūrmala incline towards being satisfied with these services provided by government. Inhabitants in Daugavpils have the highest levels of dissatisfaction with these services. Daugavpils and Krāslava counties have the highest levels of satisfaction with the quality of agriculture, forestry and fishing sector, while people in Alūksne incline towards being satisfied with this sector. People in Jūrmala are dissatisfied with the agriculture, forestry and fishing sector. Altogether, we can see that the opinion of being satisfied dominates in both questions.

Table 24: Attitude towards purchasing cheaper goods without paying taxes (Latvia)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

The next topic revealed the attitude towards purchasing the goods cheaper without paying taxes. We can see that people in Krāslava, Ludza counties and Rēzekne would use the chance of not paying the taxes, if that allowed them to save money. People in Daugavpils county incline towards using this chance as well, while people in Cēsis county disagree to this statement. The respondents of Alūksne, Daugavpils, Liepāja, Jūrmala and Stopiņi county believe that public services and social benefits are benefits from receiving legal income, while people in Talsi and Krāslava counties disagree with this statement. Only people in Talsi county agree that they would receive a salary under-reporting if that meant higher income, while people in the Daugavpils, Liepāja and Stopiņi county disagree with this.

Table 25: Attitude towards importance of paying taxes (Latvia)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

Also, attitudes towards the importance of paying taxes in Latvia were observed. We can see that people in Alūksne and Daugavpils believe that money paid in taxes provide residents some benefits for them. People in Talsi county and to smaller extent in Stopiņi county disagree with this. People in Krāslava, Daugavpils and Rēzekne counties believe that people should use every opportunity not to pay taxes, while people in Cēsis county and Jūrmala disagree with this statement. The further step in analysing the survey of different Latvia’s municipalities was to produce several different factors using the Principal Component Analysis from the answers to the questions. It enabled to notice the patterns and profiles of the exact municipalities easier. In Latvia’s case two graphs are represented by using four different factors. Figure 11: Graphical representation of the Latvia’s survey answers based on factors 1 and 2

Figure 12: Graphical representation of the Latvia’s survey answers based on factors 3 and 4

As it can be seen from the graphs above, different municipalities appear to be in the different positions according to various attitudes. Together with the indexes provided above these graphical representations help to construct the profiles of the border municipalities in the light of the corresponding inland municipalities and other border municipalities in Latvia. Further in this section, the profiles of particular border municipalities based on the attitudes, which appear from survey, are going to be described. Alūksne is the only one, which has lower shadow economy index compared to the index of the corresponding inland municipality. This observation was supported during the survey analysis, as Alūksne stands out and differs from others as a municipality, which has a positive attitude towards paying the taxes and inhabitants understand that money paid through the taxes provide some useful benefits to them. Also, inhabitants are satisfied with the security situation in the municipality, spending on various sectors and services provided by the local government. On contrary, Talsi county inhabitants would rather receive a salary under-reporting if it led them to higher income; and would not believe in gains from paid taxes. The selected municipality for Alūksne on the other side of the frontier is Russia’s municipality Pechory. The independent sample T-Test was executed and the hypothesis about similarity failed to be rejected, meaning that there can be some connection and links between Alūksne and Pechory, which has to be taken into account. The comparison of smuggling related answers between Alūksne and Pechory is provided in the table below. Table 26: Comparison of Alūksne answers with the municipality on the other side of border (Pechory) Reasons to smuggle

Desire to earn much, fast and quickly (50% of respondents from Alūksne;

Places to buy smuggled goods Most people mentioned acquaintances or wellknown vendors;

Ways how goods are being smuggled No particular pattern; Inhabitants of Pechory mostly mentioned

Routes of smuggling goods Border checkpoints (mainly Terehovo border).

Reasons to smuggle 70% of respondents from Pechory); Differently from Pechory, respondents from Alūksne mentioned chance to earn, get additional revenue.

Places to buy smuggled goods 35% of respondents in Alūksne admitted knowing where to buy smuggled goods).

Ways how goods are being smuggled

Routes of smuggling goods

smuggling using car (49% of respondents).

Source: compiled by authors

Daugavpils appears to be different from the other border municipalities as their inhabitants have a positive attitude towards paying taxes and receiving legal income as well as seeing public services as benefit from taxes paid. However, inhabitants are unsatisfied with the quality of most of the services (health care, education, etc.) provided by local government and believe that the investments to develop economy in the Daugavpils are too low. The inhabitants of Jūrmala (corresponding inland town) are neutral about the above-mentioned cases on government spending. Daugavpils was compared to Braslaw (municipality in Russia) and the T-Test rejected the hypothesis about the differences of municipalities. Thus, the possible connection between Daugavpils and Braslaw must be taken into account. The comparison of smuggling related answers between Daugavpils and Braslaw municipality is provided below. Table 27: Comparison of Daugavpils city municipality answers with the municipality on the other side of frontier (Braslaw municipality) Reasons to smuggle

Places to buy smuggled goods

Financial reasons (41% respondents;

Market and through acquaintances;

Lack of employment (35% of respondents).

66% of respondents in Daugavpils admitted knowing where to buy smuggled goods).

Ways how goods are being smuggled Inhabitants of Daugavpils mentioned car as a main way how to smuggle goods; Respondents from Braslaw municipality highlighted truck.

Routes of smuggling goods Through border checkpoints by making deals with officers (55% of respondents from Daugavpils city municipality)

Source: compiled by authors

Daugavpils county is an interesting case because the inhabitants do not have a clear attitude to evade taxes or receive a salary under-reporting. However, they do support the people, who purchase long term goods by avoiding taxes. Even though the inhabitants are satisfied with overall situation of the town, compared to Cēsis (corresponding inland municipality) and other Latvia’s municipalities, the security matter and spending on development of economy can be improved. Due to the fact that Daugavpils county is located at the same geographical location as Daugavpils, the corresponding municipality on the other side of the border as well as for Daugavpils was Braslaw municipality. The T-Test provided the observation that the municipalities have similarities. The comparison of Daugavpils county and Braslaw answers regarding smuggling is provided in the table below:

Table 28: Comparison of Daugavpils county answers with the municipality on the other side of frontier (Braslaw) Reasons to smuggle

Desire to earn much, fast and quickly (39% of inhabitants of Daugavpils county);

Places to buy smuggled goods

Ways how goods are being smuggled

24% of respondents of Daugavpils county mentioned either market or illegal selling points.

Car and train, but differently from Braslaw, citizens of Daugavpils county have not mentioned trucks (45% of respondents in Braslaw mentioned trucks as a way to smuggle goods).

Financial reasons, but differently from Braslaw not mentioned additional income, but due to survival reasons.

Routes of smuggling goods 31% of respondents from Daugavpils county mentioned that smuggled goods come Russia or Belarus by making deal with officials.

Source: compiled by authors

Krāslava county is distinct from the other municipalities in Latvia since the residents have the most positive attitudes towards not paying taxes. The inhabitants of Krāslava municipality believe that the government should help them in case of a trouble. It is important to note that the residents are extremely satisfied with the quality of services provided by local government and spending of local budget on different spheres. Moreover, the inhabitants of Krāslava county are really positive about receiving social benefits and about receivers of social benefits and the number of credit/debit card users are relatively low compared to other municipalities in Latvia. The closest municipality on the other side of the frontier of Krāslava was selected Verkhnyadzvinsk (Belarus). After executing the independent sample T-test, the hypothesis about similarity was failed to be rejected. The comparison of Krāslava and Verkhnyadzvinsk answers regarding smuggling is provided in the table below: Table 29: Comparison of Krāslava answers with the municipality on the other side of frontier (Verkhnyadzvinsk) Reasons to smuggle

Main reason for inhabitants of Kraslava is lack of work opportunities (differently from Verkhnyadzvinsk, where people mentioned lack of money and chance to earn fast).

Places to buy smuggled goods

Ways how goods are being smuggled

Through acquaintances;

By train;

Illegal sales points;

Through Dauguva;

40% of respondents admitted knowing where to buy smuggled goods).

Inhabitants of Verkhnyadzvinsk mainly mentioned all the different types of transport (cars, trucks, train).

Routes of smuggling goods Inhabitants of Kraslava have not mentioned any particular road, but absolute majority of Verkhnyadzvinsk respondents pointed out that goods go through border checkpoints to neighbouring countries.

Source: compiled by authors

Ludza. The respondents from Ludza county took a neutral position for most of the questions. Inhabitants of municipality are neither satisfied, nor disappointed with the activities of the government and overall situation in the city. Compared to the respondents from the other cities, people from Ludza county did not feel safe walking in the neighbourhood during a night time. They also believe that there are too many people who avoid labour to receive social benefits.

Pskov was selected as a corresponding non-EU municipality for Ludza county. According to the statistics these municipalities are not statistically different, meaning, that respondents have answered to the survey’s questions similarly (or not drastically different). The comparison of Ludza and Pskov answers regarding smuggling is provided in the table below: Table 30: Comparison of Ludza county answers with the municipality on the other side of frontier (Pskov) Reasons to smuggle

Mainly financial reasons and desire to earn fast and

Places to buy smuggled goods Illegal selling points; Through acquaintances;

Ways how goods are being smuggled Majority of goods is smuggled via cars and trains.

Market; From home.

Routes of smuggling goods Border checkpoints, majority through Grebnebva/Teherova (mentioned by 27% of inhabitants from Ludza).

Source: compiled by authors

Rēzekne’s profile can be understood quite well by observing the differences comparing to Stopiņi county (corresponding inland municipality). Rēzekne has an attitude towards the tax evasion and the acceptance of salary under-reporting when it leads to higher income and it is an opposite attitude compared to Stopiņi county. Also, the inhabitants of Stopiņi county, differently from those in Rēzekne, trust in government as it can and should help inhabitants and are satisfied with the quality of services provided and with the spending of budget by local government on different sectors. Rēzekne was compared with the Ostrovsky (Russia) and the trend was the same as with the other Latvian municipalities that there is no statistical difference between municipalities. The comparison of Rēzekne and Ostrovsky answers regarding smuggling is provided in the table below: Table 31: Comparison of Rēzekne answers with the municipality on the other side of frontier (Ostrovsky) Reasons to smuggle

Desire to earn much and fast;

Places to buy smuggled goods Market; Illegal selling points.

Ways how goods are being smuggled Cars

Routes of smuggling goods No particular patterns regarding routes.

Differently from Ostrovsky, inhabitants of Rezekne mentioned lack of employment possibilities (44% of respondents). Source: compiled by authors

Qualitative research. The focus group discussion in Latvia was implemented in 6 border municipalities. Discussions with inhabitants have helped to get deeper insights on the attitudes of inhabitants to the specific statements that have impact on shadow economy. Furthermore, it helped to formulate the preliminary recommendations how to fight a shadow economy for each border municipality individually. The results of the interviews are provided on municipality level and presented into the scheme. The scheme represents a short summary of results of all the project for the particular border municipality.

Alūksne Figure 13: Preliminary recommendations for Alūksne county (Latvia)

Source: compiled by authors

As it is presented in Figure 13, the quantitative survey analysis revealed 5 attitudinal statements that need further attention and through which shadow economy in Alūksne county can be adjusted the most. The highest level of agreement compared to other municipalities in Latvia is identified in such statements as: satisfaction with the health, educational and other services provided by local government; Satisfaction with the spending on public order, safety. Based on these statements, with the help of focus group discussions, the following actions (recommendations) were developed:

•

Improve the infrastructure for local business development;

•

Communicate existing improvements in infrastructure. Highlight new buildings, which have been build and EU projects that has been implemented;

•

Increase safety on the streets, focus on vandals and street gangs;

•

Increase visibility and activity of police;

•

Improve and modernize teaching process by providing modern equipment, making learning process less formalized;

•

Provide information that people might need when dealing with crisis situation;

•

Communicate the improvements of equipment and infrastructure of schools and public transport by highlighting free transport to some events, reallocation of equipment from closed schools.

Daugavpils Figure 14: Preliminary recommendations for Daugavpils (Latvia)

Source: compiled by authors

As it is shown in Figure 14, the quantitative survey analysis revealed several attitudinal statements in Daugavpils that are significantly different from other Latvian municipalities. The highest level of disagreement can be seen for all attitudinal statements: satisfaction with the spending on economy to improve and develop it; satisfaction with the health, educational and other services provided by government; Attitude that municipality’s local government would help in case of trouble; Municipality’s local government should take care of inhabitants. Based on those subjects that require the most attention, with the help of focus group discussion, the following actions/recommendations were formed: • Communicate how different people are being supported by the municipality, show the real examples and highlight the number of people that attained help; • Communicate the criteria under which people are able to receive the help from the municipality; • Show the existing improvements of infrastructure, highlight things: playgrounds that have been built, installed security cameras in the city, renovation done in schools and kindergartens; • Inform about criteria that needs to be satisfied in order for unemployed inhabitant to get help; • Communicate the existing benefits of public transportation, concentrate on showing that the prices of riding the public transport are lower than in other cities; • Design and promote the benefits for future doctors to return to municipality.

Daugavpils county Figure 15: Preliminary recommendations for Daugavpils county (Latvia)

Source: compiled by authors

As it is presented in the Figure 15, the survey analysis of Daugavpils county revealed that 2 subjects require the most attention: attitude that local government would help in case of trouble and satisfaction with spending on economy to improve and develop it. Based on these findings, actions/recommendations in order to reduce shadow economy (based on focus group discussions) are the following: • Communicate the budget split for the development of economy. Also, splits for education and public transport sectors can be promoted; • Communicate the improvements made for infrastructure, highlight new/renovated roads, development of tourism industry, usage of EU funds; • Ensure fair competition during the procurement of projects, stimulate the engagement of small companies; • Communicate all the criteria under which municipality is obliged to help, especially emphasize the ability to help in extreme/emergency situations; • Design and communicate anti-corruption policies for inhabitants.

Krāslava county Figure 16: Preliminary recommendation for Krāslava (Latvia)

Source: compiled by authors

As it is shown in the figure above, the analysis of quantitative research helped to define 3 attitudinal statements which are the most crucial and could help to significantly decrease level of shadow economy in Krāslava. The highest level of agreement compared to other municipalities within Latvia can be seen for all 3 statements: Attitude that municipality’s local government would help in case of trouble; Attitude that municipality’s local government should take care of inhabitants; Satisfaction with the spending on economy to improve and develop it. Based on those subjects that requires the most attention, with the help of focus group discussion, the following actions/recommendations were formed: • Engage with youth by increasing activity in NGO sector; • Support local entrepreneurs in development of their business (e.g. provide co-working facilities); • Listen and support community initiatives; • Support the unemployed in search of the new job; • Communicate the criteria that enables to receive support from municipality; • Communicate the assistance provided to teachers and children during the closure of the schools.

Ludza county Figure 17: Preliminary recommendations for Ludza (Latvia)

Source: compiled by authors

As it is presented in Figure 17, the quantitative survey analysis revealed three attitudinal statements that require the most attention in Ludza county. The highest level of agreement compared to the other municipalities in Latvia is identified in statements such as: Attitude that municipality’s local government would help in case of trouble; Attitude that municipality’s local government should take care of inhabitants. Based the focus group discussions as well as on the attitudinal statements, the recommendations that could potentially decrease the size of shadow economy in Ludza county were developed: • Create more workplaces that would be interesting for youth; • Support new families at the beginning of their life together; • Provide help in crisis situation, communicate the criteria upon which the help can be provided; • Communicate the good availability of public services, highlight the services related to education and health care; • Improve accessibility of the city, especially during the winter time, for families living in rural areas.

Rēzekne Figure 18: Preliminary recommendations for Rēzekne (Latvia)

Source: compiled by authors

As it is shown in the figure above, the analysis of quantitative research revealed several important attitudinal statements. The highest level of disagreement comparing to other municipalities within Latvia can be seen for 2 statements: Satisfaction with the spending on economy to improve and develop it; Attitude that local government should take care of inhabitants and that local government should help in case of trouble, require the most attention in order to decrease the shadow economy in Rēzekne. Based on these attitudes as well as on the concluded results of focus group discussions the following recommendations were developed: • Communicate the budget split for the development of economy; • Communicate the improvements of infrastructure, concentrate on new/renovated roads and schools; • Support technical innovations, focus on providing attractive conditions for young people to start their business here (e.g. development of hubs, co-working spaces); • Decrease the level of bureaucracy in the regulatory system; • Create favourable environment for medical tourism, to have more workplaces and to make municipality more recognizable; • Communicate the exact criteria to receive compensation for healthcare services; • Communicate, how different structural units support inhabitants, concentrate on social help provision to poor people.

Recommendations. Based on the MIMIC model results, the outcomes of quantitative and qualitative research the final recommendations how to reduce shadow economy and strengthen local economy in Latvia were formulated. Most of the recommendations were composed in the form of communication, infrastructure development, administration matters and education system. The final recommendations of Latvia are provided in the table below: Table 32: Final recommendations (Latvia) Categories

Recommendations •

All municipalities

Provide examples of support granted by municipality:

•

Ludza

Communicate how quality of life improved over time;

•

Daugavpils

Show real examples of supported individuals, communicate how this support was done and why;

•

Rēzekne

•

Krāslava

Hold information campaigns on existing improvements in long-term infrastructure (buildings, roads, tourism, EU projects). Highlight improvements in tangible facilities (playgrounds, security cameras).

•

Alūksne

•

Daugavpils

•

Rēzekne

•

Daugavpils county

Hold information campaigns on budget and economy splits for economy development to engage citizens more actively.

•

Daugavpils county

Initiate improvements of local business infrastructure development to support local entrepreneurs in development of their business (e.g. provide co-working facilities, hubs).

•

Alūksne

•

Krāslava

•

Rēzekne

•

Alūksne

Enhance cooperation with youth:

•

Ludza

Look for jobs that would be interesting for youth;

•

Krāslava

Increase activity of NGO sector.

Engage with inhabitants and support them:

•

Ludza

Support community initiatives, allow inhabitants to decide for which purposes the taxes should be spend;

•

Krāslava

•

Daugavpils

Support new families at the start of their life together;

Hold campaigns on individual benefits generated by taxes:

Communication

Development

Highlight the notion that paying taxers creates an image of responsible citizen, patriot;

Highlight benefits of public transport, education and healthcare which arise due to taxes;

Positively stimulate diligent taxpayers (gratitude).

Initiate safety improvements in the municipality:

Administration

Subject to recommendations

Increase visibility and activity of police;

Implement preventive measures against vandals and street gangs.

Education

Provide help in crisis situation;

Support the unemployed in search of new job.

Initiate optimization of administration procedures:

•

Rēzekne

Decrease level of bureaucracy in the regulatory system;

•

Ensure fair competition during the procurement of projects, stimulate the engagement of small companies.

Daugavpils county

Initiate improvements for better accessibility of the city (e.g. winter time).

•

Ludza

Improve and modernize teaching process by providing modern equipment, making learning process less formalized.

•

Alūksne

Initiate courses for unemployed people that are focused on the skills demanded in the local job market.

•

All municipalities

Provide tax system consultations for free for entrepreneurs.

•

All municipalities

Initiate education program for inhabitants about tax rates, charging procedures and how it returns to people.

•

All municipalities

Source: compiled by authors

6.3. Lithuania Identification of border and inland municipalities. First of all, the border municipalities that could have been included in the research were examined. The initial list of border municipalities eligible for further research consisted of 16 border towns, which were selected based on approximation to the border and administrative unit (municipality level was selected for the research). The initial list of border municipalities is presented below. Table 33: Initial list of Lithuania’s border municipalities Initial list of border municipalities (Lithuania) 1. Alytus city

9. Druskininkai

2. Ignalina

10. Jurbarkas

3. Kalvarija

11. Lazdijai

4. Marijampolė

12. Šakiai

5. Šalčininkai

13. Šilutė

6. Švenčionys

14. Tauragė

7. Varėna

15. Vilkaviškis

8. Visaginas

16. Zarasai

Source: compiled by authors

After determining all the alternatives that could possibly be examined in the research as border municipalities, a desk research was performed in order to find out which of the border municipalities has the highest level of shadow economy. The desk research included the analysis of reports, researches and various media sources. 6 border municipalities in Lithuania were identified as the most likely of having the highest level of shadow economy. After that, 17 inland municipalities, which were the closest to the border municipalities in terms of population and were not less than 60 km from the border, were chosen. The list of selected Lithuania’s border and inland municipalities is presented below. Table 34: List of Lithuania’s border and inland municipalities before principal component analysis and PSM Border municipalities

Inland municipalities

1. Alytus city

1. Anykščiai

10. Panevėžys city

2. Druskininkai

2. Birštonas

11. Prienai

3. Marijampolė

3. Elektrėnai

12. Radviliškis

4. Šalčininkai

4. Jonava

13. Raseiniai

5. Švenčionys

5. Kaišiadorys

14. Rietavas

6. Visaginas

6. Kaunas city

15. Šilalė

7. Kėdainiai

16. Širvintos

8. Kelmė

17. Ukmergė

9. Molėtai Source: compiled by authors

Data collection. The data collection process consisted of gathering a large volume of data from various data holders and various public databases. The data was calibrated to various indexes in order to avoid the population influence to source data. In Lithuania’s case, the database consisted a data with reference to 23 different municipalities for the period from 2012 to 2015. The gathered dataset for Lithuania consists of 138 different statistical indicators. These indicators covered a variety of the topics related to demographics, social welfare, business affairs in the country and other areas, which provided all the relevant information to perform various analysis included in the research. Most of the collected data in Lithuania’s case came from the following sources: •

The Lithuanian Department of Statistics;

•

State Enterprise Centre of Registers;

•

State Enterprise “Regitra”;

•

Municipalities.

Most of the gathered data was provided free of charge. Only a very specific information, such as number of dwelling deals (according to different specifics) or value of dwelling deals (according to different specifics), was available after paying a small fee.

To conclude, data collection stage in Lithuania is considered to be successful because all of the required information was available and successfully gathered. A smooth data collection process helped to ensure that all of the required analysis could be made without any major discrepancies and limitations. Principal Component (PCA) and Propensity Score Matching (PSM) analysis. After collecting all of the required data, the first two executed analysis were PCA and PSM. They were carried out in order to remove multicollinearity. In Lithuania’s case, the PCA produced 19 factors in total with different hidden patterns. Thus, by using coefficients of factors produced during the factor analysis PSM analysis was performed. The factors and their values are presented in the table below. Table 35: Factor values of Lithuania’s Propensity Score Matching Factor

Value

Factor

Value

FAC1

-2.712

FAC11

-1.619

FAC2

-2.313

FAC12

1.699

FAC3

1.710

FAC13

0.987

FAC4

1.923

FAC14

- 0.890

FAC5

1.864

FAC15

1.783

FAC6

0.572

FAC16

1.289

FAC7

-0.029

FAC17

- 1.888

FAC8

5.273

FAC18

-4.022

FAC9

1.232

FAC19

3.682

FAC10

0.355

Source: compiled by authors Note: Bolded factors and their values have the biggest weight on the particular matching of Lithuania’s municipalities.

As it can be seen from the Table 35, 19 factors were produced and 4 of them played a major role in determining some specific matches. Thus, the profiles of the matched municipalities were based on the statistical indicators, which had the highest coefficients in loading of those particular factors. It means that matched cities are akin to each other in terms of the patterns hidden in the loading of these factors. The main statistical indicators of factors and their description for matching Lithuanian cities are provided below. Table 36: Statistical indicators determining profiles of the municipality matching (Lithuania) Factor FAC1

Statistical indicators with the highest weight Birth rate per 1000 population; Median age of the population at the beginning of the year, year (women);

Description Factor FAC1_1 mainly describes the demographic composition of the society. Cities that are matched in

Factor

Statistical indicators with the highest weight

Description

Median age of the population at the beginning of the year, year (men);

Lithuania’s case have similar patterns according to birth rate, society’s age, number of young and old persons.

Median age of the population at the beginning of the year, year (men and women); Indexes of ageing at the beginning of the year; Dependency ratio at the beginning of the year, persons (0-14 y.o.); FAC8

Rate of expenditure on social assistance benefit, EUR per social assistance benefit receiver; Expenditure of municipality budget on housing and communal economy, EUR per capita; Number of families at social risk at the end of the year per 1000 population;

FAC18

Number of employees in the transport and storage sector per 1000 working-age population; Value added in the transport and storage sector, thousand EUR per person working in that sector;

FAC8_1 consists of statistical indicators defining social situation in the cities. This factor helps to match cities according to social situation and municipality’s expenditure towards it.

FAC18_1 describes statistical indicators related to composition of employees and their value added in transport and storage sector as well as administrative and service sector.

Value added in the administrative and service activities sector, thousand EUR per person working in that sector; FAC19

Value added in the forestry and fisheries sector, thousand EUR per person working in that sector; Expenditure of municipality budget on health care, EUR per capita; Number of persons who received emergency medical care per 1000 population;

Factor FAC19_1 includes health care provision and value added of forestry/fisheries sector, meaning that municipalities, which are matched combines similar patterns regarding these indicators.

Hence, matches of Lithuanian municipalities are mainly based on similarities in demographic composition, social situation, transport and storage as well as administrative/services, forestry/fisheries sectors and health-care provision. Table with the exact matches and their scores is presented below. Table 37: Details of Lithuania’s matched observations Border municipality

Logit (Propensity score)

ALYTUS

7.051

DRUSKININKAI

Inland municipality

Logit (Propensity score)

Distances

KAUNAS

-7.286

14.336

KAIŠIADORYS

-7.651

14.702

PRIENAI

-7.086

14.424

BIRŠTONAS

-7.108

14.446

7.338

Border municipality

Logit (Propensity score)

MARIJAMPOLĖ

7.098

ŠALČININKAI

ŠVENČIONYS

VISAGINAS

Inland municipality

Logit (Propensity score)

Distances

KELMĖ

-6.740

13.838

PANEVĖŽYS

-6.918

14.015

ŠILALĖ

-6.961

14.226

RIETAVAS

-7.359

14.624

UKMERGĖ

-6.405

13.217

ŠIRVINTOS

-6.573

13.385

JONAVA

-6.734

13.703

ELEKTRĖNAI

-7.002

13.971

7.265

6.812

6.969

The table represent that each border municipality has a corresponding inland municipality (marked in darker grey) and also one alternative inland municipality (marked in light grey), which is useful in case some discrepancies would arise. However, all of those 18 municipalities were used in further steps of the research. 1-Factor causal clustering. After determining the exact matches of municipalities in Lithuania and their underlying profiles, the next step was to prepare a dataset for the MIMIC model calculations by using 1-Factor causal clustering. After running the 1-Factor causal clustering on the indicators, 19 latent variables in Lithuania’s case were constructed. The exact content of those 19 latent variables and the beta coefficients of composed variables can be found in Appendix 2 (Table 103). All of those 19 latent variables as well as source variables, which have shown significance were used in the MIMIC model determination for Lithuania. MIMIC. After the dataset of latent and source variables was prepared, the MIMIC model was established. The graphical representation of the MIMIC model equation is presented below.

Figure 19: Graphical representation of the model equation (Lithuania)

Source: compiled by authors Model represents causal and indicator variables that show significance in determining equation of shadow economy for Lithuania. The expanded version of Lithuaniaâ&#x20AC;&#x2122;s MIMIC model is presented in Appendix 2.

In the graphical representation of the constructed MIMIC model for Lithuania, it is clear that there are 4 different causal variables, which significantly contribute to the shadow economy. 3 of them influence shadow economy negatively (meaning that these variables in Lithuaniaâ&#x20AC;&#x2122;s case make the level of shadow economy higher) and 1 of them positively (meaning that it contributes to lowering a level of shadow economy). The generic formula for Lithuania: đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ?&#x2018;&#x153;đ?&#x2018;¤ = â&#x2C6;&#x2019;1.1 Ă&#x2014; đ??´đ?&#x2018;Łđ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018;&#x17D;đ?&#x2018;&#x201D;đ?&#x2018;&#x2019;đ??¸đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;&#x203A;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x201D;đ?&#x2018; â&#x2C6;&#x2019; 0.38 Ă&#x2014; đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2013;đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;Ąđ?&#x2018;&#x2021;đ?&#x2018;&#x17D;đ?&#x2018;Ľđ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;&#x2019; + 0.27 Ă&#x2014; đ??ż12 â&#x2C6;&#x2019; 0.11 Ă&#x2014; đ??ż13 Furthermore, shadow economy indexes for all of the municipalities in Lithuania were calculated while applying an above produced equation. The table with calculated shadow economy indexes for all the border municipalities and their corresponding inland municipalities is presented below. Table 38: Averages of shadow economy indexes for Lithuanian municipalities, 2012-2015 Municipality type: Border (B) or Inland (I)

Municipality

Shadow economy as % of economic value in municipality

Alytus city municipality

24.94%

Kaunas city municipality

19.52%

KaiĹĄiadorys municipality

28.72%

Druskininkai municipality

29.06%

Prienai municipality

33.97%

BirĹĄtonas municipality

33.03%

MarijampolÄ&#x2014; municipality

27.91%

Municipality type: Border (B) or Inland (I)

Municipality

Shadow economy as % of economic value in municipality

Panevėžys municipality

25.81%

Kelmė municipality

34.12%

Šalčininkai municipality

38.92%

Šilalė municipality

34.36%

Rietavas municipality

29.78%

Švenčionys municipality

33.28%

Ukmergės municipality

29.99%

Širvintos municipality

35.11%

Visaginas municipality

21.77%

Jonava municipality

23.01%

Elektrėnai municipality

19.79%

Average of border municipalities

29.31%

Average of inland municipalities

28.93%

Source: compiled by authors Shadow economy index can have both positive and negative values. Higher the index – higher shadow economy in particular municipality, lower the number – lower the shadow economy.

The table clearly shows that in five out of six cases the border municipality has a higher shadow economy index than the main corresponding inland municipality. However, one main corresponding inland municipality have a higher index than their border municipality. Therefore, based on the MIMIC model we cannot state that border municipalities have a higher level of shadow economy, because there are a few unique cases. Despite that, based on the MIMIC calculation, it is clear that on average the border municipalities have a higher index for shadow economy than inland municipalities. Economic and comparative analysis. In Lithuania, during a phase of the economic and comparative analysis 14 different economic indicators and 11 indicators, which describe the registered income and welfare, were constructed. By analysing those indicators and comparing them between the border and inland municipalities of Lithuania as well as between municipalities within each group, a few important insights were obtained. Even without testing all the insights statistically, we could state that there was a clear difference between GDP per capita, value added in industry and value added in service sector. All of them had greatly lowered values for border municipalities. The welfare indicators in Lithuania did not reflect the major differences, however, the border municipalities on average had approx. 18% lower registered income. After executing the T-Test to find out whether the differences appeared by no accident, only 1 of those 25 indicators appeared to be significant according to selected 90% confidence level. Statistically significant differences appeared in grants included to municipal budgets. Thus, it can be stated that the value of grants attracted to border municipalities statistically differ and is lower than the ones attracted in inland municipalities of Lithuania. However, despite the fact that most of the indicators have failed the statistical tests, the tendency is clear and it is worth researching more into the root causes of those tendencies.

Quantitative research. In order to do the survey data analysis, which would lead to formulation of guidelines for focus group, the regression analysis was implemented. A graphical representation of Lithuania regression analysis with coefficients is presented below. Figure 20: Graphical representation of regression coefficients (Lithuania)

I feel like my municipalityâ&#x20AC;&#x2122;s local government would help me in case of trouble

-0,719

I would receive envelope salary if I had a change and that meant higher income

1,259

I would use any chance to receive some kind of social benefits

-0,633

Satisfied about government spending on education (on general, professional, higher education)

Satisfied with the health, educational and other services provided by government

0,405

-0,757

I would prefer to receive legal income over envelope salary if the amount would be the same for both options

The inhabitants of my town would be pleased with rising number of tourist

-0,715

Shadow Economy

I support people who purchased goods for long term use (e.g. vehicle) for which taxes (like VAT) are not paid

Source: compiled by authors Figure represents the attitudes (statements) that show significance in regression analysis. The expanded version of Lithuaniaâ&#x20AC;&#x2122;s data analysis is presented in Appendix 3.

The regression analysis for Lithuania was consisted of two main stages. Firstly, the shadow economy index of Lithuania produced by the MIMIC was chosen as a dependent variable, while the results of the attitudinal questions from quantitative survey were included as independent variables. The regression analysis revealed that three attitudes have a direct impact on the size of shadow economy. Figure 20 shows that the attitude to receive legal income over the envelope salary, if the amount would be the same for both options, decreases shadow economy. Furthermore, an opinion that inhabitants of the municipality would be pleased with rising number of tourist also has positive impact on reducing a shadow economy. The opposite trend can be noticed with the attitude to support people who purchase goods for a long-term use (e.g. vehicle) and do not pay taxes (such as VAT). In order to make further analysis and to discover which statements have influence on the independent variables that have shown significance and overall indirectly affect shadow economy, the further regression analysis was executed. The further analysis revealed that the belief that municipality government would help them in case of a trouble decreases the attitude to prefer receiving legal income over salary under-reporting of the amount would be the same for both options, overall it increases shadow economy. As it is show in the graphical representation above, the analysis revealed that three attitudes have had a significant impact on the belief that inhabitants of municipality would be pleased with rising number of tourist. The attitude to receive envelope salary, if there is a chance and that means higher income, and satisfaction about government spending on education increases the belief that inhabitants of municipality would be pleased with rising number of tourists and it decreases shadow economy. While opposite trend can be noticed with the attitude to use any chance to receive some kind of social benefits that have negative impact on the belief that inhabitants of municipality would be pleased with rising number of tourist, overall it increases shadow economy.

The last regression revealed that satisfaction with health-care, educational and other services provided by local government has a negative impact on inhabitants’ attitude to support people who purchased a long-term use (e.g. vehicle) for which taxes (like VAT) were not paid and it decreases shadow economy. In order to further analyse the differences of the above-mentioned attitudes among Lithuania’s municipalities the answers to the statements were transformed into indexes. The dark brown colour represents the answers with the highest level of disagreement; the dark blue represents the answers with the highest level of agreement. The answers coloured in the lighter shades represents an inclination for agreement (light blue) or disagreement (light brown), while the neutral option is represented by white colour. The main differences of attitudes among municipalities are presented in the tables below. Table 39: Attitude towards municipality and local government (Lithuania)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

Source: Compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 mean complete agreement with statement, while -2 mean complete disagreement.

The first statement revealed inhabitants’ attitude towards municipality and local government. As it is shown in the table above the highest level of agreement that municipality’s local government would help in case of trouble is seen only in Rietavas. While inhabitants of 5 out of 12 municipalities (Alytus, Marijampolė, Kaunas, Biržai and Panevėžys) express the highest level of disagreement with this statement. Inclination for disagreement to this statement can be seen in Šalčininkai and Ukmergė. The opposite trend can be seen in Druskininkai, where inhabitants incline towards agreement with the statement.

Table 40: Evaluation of municipality’s spending in different sector (Lithuania)

Large enough spending

Too low spending

Inclination for large enough spending

Inclination for too low spending

Neutral answers

Source: Compiled by authors Answer choices: Appropriate, too large, too small. Index can have both positive and negative values. The range of the indexes is from -1 to 2, where 2 mean spending is appropriate, 1- spending is to large, while –1 mean that spending is too small.

The next step in the survey was to measure the attitude towards municipality’s budget spending in various sectors, whether it is appropriate, too high or too low. As it presented in table above, the inhabitants from 5 out of 12 municipalities (Druskininkai, Visaginas, Rietavas, Ukmergė and Jonava) think that the proportion spent on education is large enough, while inhabitants of Alytus, Švenčionys and Kaunas think that the expenditure on education is too low. The inclination for a large enough spending on education is seen in Biržai. The opposite trend prevails in Šalčininkai, where inhabitants incline towards too low spending. Table 41: Attitude towards social benefit receivers (Lithuania)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

A further attitude towards social benefit receivers was observed. We can see that inhabitants of 5 municipalities (Alytus, Šalčininkai, Visaginas, Panevėžys, Rietavas) express a high level of agreement with the statement that they would use every chance to receive some kind of social benefits. The opposite pattern can be seen in Druskininkai, Marijampolė, Švenčionys, Kaunas, Biržai, Ukmergė and Jonava, where inhabitants express a high level of disagreement with this statement.

Table 42: Opinion about town's attractiveness as a tourist destination (Lithuania)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

The fourth topic revealed an opinion about the attractiveness of their municipality towns as a tourist destination. The inhabitants of 5 out of 12 municipalities (Alytus, Druskininkai, Kaunas, Biržai, Rietavas) strongly agree that the inhabitants of the municipality would appreciate a rising number of tourist. The people from the municipalities of Marijampolė, Šalčininkai, Visaginas, Švenčionys and Ukmergė highly disagree with this statement. Inclination for agreement to the statement can be seen in Jonava municipality. The opposite trend where inhabitants incline to disagreement with the statement can be seen in Panevėžys. Table 43: Attitude towards purchasing cheaper goods without paying taxes (Lithuania)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

The last topic revealed the attitude towards the purchase of cheaper but illegal goods. From the table above it is seen that inhabitants of Kaunas municipality support people who purchase goods for long term without paying taxes. The inclination for agreement with this statement could be seen in Druskininkai municipality. The opposite trend appears in Rietavas, where inhabitants do not support the people who purchase goods for long-term use without paying taxes. The inclination towards disagreement with this statement can be seen in Šalčininkai. The second statement shows that inhabitants of Kaunas municipality would prefer to receive legal income over a salary under-reporting if the amount was the same. The inhabitants of Šalčininkai

express the highest level of disagreement with this statement. The respondents in 7 out of 12 municipalities are neutral about this statement. The last statement revealed that inhabitants of Alytus express the highest level of agreement to the statement which says they would receive a salary under-reporting if they had a chance and that meant higher income. The highest level of disagreement with this statement can be seen in Marijampolė and Ukmergė municipalities. 7 out of 12 municipalities are neutral about this statement. The further step in analysing the survey of different Lithuania’s municipalities was to produce a several different factors using Principal Component Analysis from the answers to the questions. It enables to notice the patterns and profiles of the exact municipalities easier. In Lithuania’s case 2 graphs are represented by using four different factors. Figure 21: Graphical representation of the Lithuania’s survey answers based on factors 1 and 2

Figure 22: Graphical representation of the Lithuaniaâ&#x20AC;&#x2122;s survey answers based on factors 3 and 4

As it can be seen from the graphs above, different municipalities appear to be in the different positions according to various attitudes. Together with the indexes provided above these graphical representations helps to construct the profiles of the border municipalities in light of the corresponding inland municipalities and other border municipalities in Lithuania. Further in this section, profiles of particular border municipalities based on attitudes, which appear from survey, are described above. Alytus. By looking at the 2 provided graphs Alytus clearly stands out from other municipalities and in both cases is quite distant from the other municipalities. The main features of Alytus profile are that inhabitants do not see the local government as helping in case of trouble and are unsatisfied with the spending on different fields. Also, the inhabitants believe that a safety in the municipality of Alytus should be improved as well as a number of tourists should be increased. Alytus stands out as the town, where inhabitants have an attitude to accept salary under-reporting if that means an increase in personal income. An overall attitude towards cheating on taxes is distinct from a corresponding inland town Kaunas, where inhabitants have a neutral attitude towards this issue. Moreover, unlike the results of Kaunas, despite a high rate of credit/debit card ownership, the use of the cards is lower. The corresponding municipality on the other side of the frontier of Alytus is Gusevskiy (Belarus). After executing the independent sample T-Test the hypothesis about similarity of the municipalities was rejected. This similarity should be taken into account while developing the recommendations and presented to the respondents during the FGD. The results regarding smuggling which are common to both Alytus and Gusevskiy are depicted below. T

Table 44: Comparison of Alytus answers with the municipality on the other side of the frontier (Gusevskiy) Reasons to smuggle

Places to buy smuggled goods

Desire to earn money fast;

Local market;

Differently from Gusevskyi, inhabitants of Alytus (35% of respondents) identified prices of goods as a main reason.

Illegal selling points; Through acquaintances.

Ways how goods are being smuggled No clear pattern of ways how to smuggle, but river ways (Nemunas) was mentioned by 1/10 of respondents.

Routes of smuggling goods No particular route revealed by respondents of Alytus.

Source: compiled by authors

Druskininkai is a municipality with a really specific profile. Inhabitants of the municipality are highly satisfied with the budget spending on different sectors. A current safety situation of the municipality is also above the average. Moreover, the attitude towards those who receive social benefits as well as those who try to avoid getting a job in order to sustain cash flows from social benefits is negative. The ownership and the use of the credit/debit cards are higher compared to other municipalities. Even though Druskininkai is a resort town and has one of the highest tourist flows in Lithuania, the residents of Druskininkai expressed a desire to attract even more tourists. In addition, the attitude towards cheating on taxes and accepting illegal salary is not clear because most of the people exposed themselves to neutral position. Voronava was selected as a corresponding non-EU municipality for Druskininkai. During the TTest both of the municipalities were found to be statistically similar. The comparison of Druskininkai and Voronava answers regarding smuggling is provided in the table below. Table 45: Comparison of Druskininkai answers with the municipality on the other side of the frontier (Voronava) Reasons to smuggle

Desire to earn an additional revenue; Ability to get goods cheaper.

Places to buy smuggled goods

Ways how goods are being smuggled

Local market;

Cars;

Through resellers;

Trains.

Routes of smuggling goods Land roads where it is possible to go through.

Through acquaintances.

Source: compiled by authors

Marijampolė. According to the evaluated dimensions, Marijampolė stands out as a town, where inhabitants are highly satisfied with the quality of utilities, such as gas or water supply. Similar to the corresponding inland municipality Panevėžys, the impressions of safety and safety control are rated worse compared to the other Lithuanian municipalities. Even if Marijampolė cannot be positioned clearly according to the attitude towards smuggling, a negative attitude towards all kinds of social benefits and those who receive them is apparent. For Marijampolė, 2 corresponding border municipalities of Russia, namely, Gusevskiy and Sovetsk were selected. The T-Test failed to reject the hypothesis about existing significant differences between Marijampolė and both of Russian municipalities, meaning municipalities have similar attitudes towards various topics. The answers regarding smuggling related activities which are common to Marijampolė as well as Gusevskiy/Sovetsk are presented below: 71

Table 46: Comparison of Marijampolė answers with the municipality on the other side of the frontier (Gusevskiy, Sovetsk) Reasons to smuggle

Places to buy smuggled goods

Desire to earn money fast;

Local market;

To get goods cheaper (36% of respondents in Marijampolė stated this reason).

Through acquaintances; 22% of respondents in Marijampolė admitted that they know where to buy smuggled goods.

Ways how goods are being smuggled Cars (number of respondents answered accordingly: Marijampolė - 21%; Gusevskiy – 41%; Sovetsk – 32%).

Routes of smuggling goods No particular route for Marijampolė; 50% of respondents in Sovetsk mentioned that smuggling goods are being smuggled through the border checkpoints.

Source: compiled by authors

Šalčininkai appeared to be a municipality where inhabitants would be the least likely to accept legal income in case the income would be indifferent. Also, the inhabitants possess an attitude that people would cheat on taxes if they had a chance. It is also worth noticing that the safety in the municipality should be improved. Compared to Rietavas (corresponding inland municipality), the biggest difference appears in satisfaction with the government spending on various spheres and satisfaction with utilities and services provided in the municipality, as the inhabitants of Rietavas are highly satisfied with all of these. Three corresponding border municipalities on the other side of the frontier, which have a quite close geographical location to Šalčininkai, were selected, namely, Voronava, Ashmyany and Smarhon. An independent mean of the T-Test was carried out for all of the pairs. However, a statistical gap was not found among Šalčininkai and other three Belarusian municipalities. The answers which are common to Šalčininkai and other targeted municipalities are provided in the table below: Table 47: Comparison of Šalčininkai answers with the municipality on the other side of the frontiers (Voronava, Ashmyany, Smarhon) Reasons to smuggle

Cheaper goods are the main reason for smuggling (36% of respondents in Šalčininkai stated that), differently from Belarusian cities, which noted that smuggling is supported due to ability to fast increase income.

Places to buy smuggled goods Local market; Through acquaintances; Through resellers.

Ways how goods are being smuggled All the different ways to smuggle mentioned but without any particular pattern.

Routes of smuggling goods Land roads, however, a lot of people were not willing to answer.

Source: compiled by authors

Švenčionys. According to the provided dimensions, the survey revealed that Švenčionys occupies a middle ground of both dimensions. However, there are a few statements, which the respondents of Švenčionys answered unalike respondents from the other municipalities. Firstly, inhabitants are not satisfied with both spending on health-care and education sectors and the quality of provided utilities. Also, the inhabitants of Švenčionys possess an attitude towards not paying taxes on goods

if it is possible to save money. It should be noted that the inhabitants are willing to use credit/debit card more often. Švenčionys and the corresponding border municipality of Astravets (Belarus) is not an exception in Lithuania’s case: similarly to other targeted municipalities in Lithuania and their corresponding municipalities on the other side of the border, the answers to the attitudinal questionnaires have revealed no significant differences. The table, which summarises the answers regarding smuggling activities of inhabitants from Švenčionys and Astravets, is provided bellow: Table 48: Comparison of Švenčionys answers with the municipality on the other side of the frontier (Astravets) Reasons to smuggle

Places to buy smuggled goods

Desire to earn money fast;

Illegal selling points;

To get goods cheaper (46% of respondents of Švenčionys answered accordingly).

Local market.

Ways how goods are being smuggled Trains, there is a clear pattern between Švenčionys and Astravets;

Routes of smuggling goods Smuggling goods come from Belarus, mostly via forests and waterways.

Cars was mentioned by inhabitants of Švenčionys.

Source: compiled by authors

Visaginas is a similar municipality to Druskininkai in the sense of a prevailing satisfaction with the government spending and services provided within the municipality. The existing level of safety in the municipality is also positive. Despite that, the inhabitants of Visaginas has an attitude towards cheating on taxes and not necessarily accepting a legal salary if that means the same income as an envelope salary. Moreover, Visaginas stands out from the others as having the most positive attitude towards social benefits and receivers of social benefits, especially, children from families at risk and those that use social benefits as the only source of income. Braslaw was selected as a corresponding border municipality of non-EU country for Visaginas. The T-Test has demonstrated that there is no significant statistical difference between municipalities. The comparison of Visaginas and Braslaw answers regarding smuggling is provided in the table below: Table 49: Comparison of Visaginas answers with the municipality on the other side of the frontier (Braslaw) Reasons to smuggle

Desire to earn an additional revenue; Goods are cheaper (stated by 37% of respondents in Visaginas).

Places to buy smuggled goods Through acquaintances; Local market.

Ways how goods are being smuggled Trains (45% of respondents of Braslaw answered accordingly).

Routes of smuggling goods Land roads from Belarus.

Source: compiled by authors

Qualitative research. The FGDs in Lithuania were carried out in 6 border municipalities under the scope of the project. Discussions with inhabitants have helped to get deeper insights on the general attitudes as well as on more specific statements which have had an impact on shadow economy. The qualitative research also helped to formulate the preliminary recommendations on the concrete means to fight shadow and to strengthen local economies in each of the municipalities individually. The results of the interviews for each targeted municipality are presented in the schemes. The 73

scheme represents a short visualisation of all the project phases and the results of particular border municipalities. Alytus city Figure 23: Preliminary recommendations for Alytus (Lithuania)

Source: compiled by authors

As it is shown in Figure 23, the quantitative survey analysis revealed 4 attitudinal statements that are significantly different compared to other municipalities within the country as well as require the more attention in Alytus. The highest level of the disagreement compared to other cities can be seen in 2 statements: Attitude that municipalityâ&#x20AC;&#x2122;s local government would help in case of trouble; Satisfaction with government spending on education. The highest level of agreement compared to other cities can be found in the other 2 statements: Attitude that inhabitants of the municipality would be pleased with rising number of tourist; Willingness to take salary under-reporting when higher amount is offered. Also, inhabitants of Alytus were neutral about statement: Legal income is preferred over salary under-reporting if the amount is the same. Based on these findings and the

conclusions formed after focus group discussions, the actions/recommendations to reduce shadow economy in Alytus are the following: • • • •

• • •

Communicate that the compensation for sick and maternity leave as well as pensions depends on your salary; Investigate the willingness to prioritize help for acquaintances; Design and communicate anti-corruption policies for inhabitants; Show tax expenditures of the municipality not through big numbers but rather through specific tangible cases (e.g. number of families supported; number of social services provided); Support employers, who employ women with children; Make sure that project implementation process won’t be over-promised or under-delivered; Support organizational changes in the administration of the school, concentrate on teachers’ ability to make decisions independently from director and improvements of teachers’ competencies and motivation; Create more extracurricular activities for children, based on recommendations of working people or those who are hard to reach; Communicate existence of different profiles of education places and ability for parents to choose the place, which suits their child the best; Institutionalize the engagement of more passive parents through changing technical processes in schools.

Druskininkai Figure 24: Preliminary recommendations for Druskininkai (Lithuania)

Source: compiled by authors

As it is presented in Figure 24, the quantitative research analyses help to define a several subjects, which are the most crucial and could help significantly decrease shadow economy in Druskininkai. The highest level of the agreement compared to other municipalities can be seen in 4 statements, including: Attitude that municipality’s local government would help in case of trouble; Salary under-reporting with higher income is preferred over legal income; Attitude that inhabitants of municipality would be pleased with rising number of tourist; Satisfaction with government spending on education. Compared to other municipalities, the inhabitants of Druskininkai have a neutral attitude regarding these statements: Legal income is preferred over salary under-reporting if the amount is the same; I support people who purchased goods for long term use for which taxes are not paid. Based on the survey answers and focus group discussions the following actions/recommendations were formed: • Communicate that only those below poverty line will get social services and housing; • Design and communicate anti-corruption policies for inhabitants; • Communicate about people willingness to be “clean” through working for the “clean” employer;

• • • • •

Increase the variety of extracurricular activities for kids. Concentrate on activities for personal development such as leadership basics, logic; Ask schools to institutionalize feedback process about psychological environment in order to eliminate bullying and unequal treatment at schools; Inform about the academic, artistic and sport achievements of kids; Show the benefits generated by tourists to the local community; Communicate the advantages of having legal income and social benefits it provides.

Marijampolė Table 50: Preliminary recommendations for Marijampolė (Lithuania)

Source: compiled by authors

As it is presented in the figure above, the quantitative survey analysis revealed 5 attitudinal statements that require the most attention in order to decrease an existing shadow economy in Marijampolė. The highest level of disagreement comparing to other municipalities within the country can be seen in 4 statements: Attitude that inhabitants of the municipality would be pleased with rising amount of tourist; Attitude that municipality’s local government would help in case of trouble; Salary under-reporting with higher amount of money is preferred over legal income; I would use any chance to receive some kind of social benefits. Compared to the other municipalities within Lithuania, the inhabitants of Marijampolė were neutral about statement: Legal income is preferred over salary under-reporting if the amount is the same. From all of these statements, based on the focus group discussion results, the following actions/recommendations how to decrease a shadow were developed:

•

Promote that help exist for those who have reached the “bottom”. Highlight social housing and lump-sum payments for apartment acquisition; • Communicate the existing penalties for taking envelope. Furthermore, communicate that there is no possibility to use legal procedures if employer is insolvent; • Show that social system benefits disadvantaged and alcoholics; • Induce social benefits receivers to work. Šalčininkai Figure 25: Preliminary recommendations for Šalčininkai (Lithuania)

Source: compiled by authors

As it is shown in Figure 25, quantitative survey analysis revealed that 4 subjects require the most attention in Šalčininkai. The highest level of disagreement compared to the other municipalities within Lithuania can be seen to all 4 statements: Legal income is preferred over salary underreporting if the amount is the same; Attitude that the inhabitants of the municipality would be pleased with rising number of tourist; Attitude that municipality’s local government would help in case of trouble; Satisfaction with government spending on education. Based on these findings, with the help of focus group discussion, the actions/recommendation how to decrease a level of shadow economy in Šalčininkai are provided bellow: • Improve anti-corruption activities, report the results; • Communicate that tax avoidance is labelled as criminal activity; • Communicate the advantages of having legal income. Concentrate on paid holidays, sickness leave and other social benefits;

• • •

Inform about positive results of children in municipality subordinate schools; Attract young, professional and inspirational staff to work in schools; Create extracurricular activities for smart kids, concentrate on knowledge needed for certain industry. Švenčionys Figure 26: Preliminary recommendations for Švenčionys (Lithuania)

Source: compiled by authors

As it is presented in the figure above, the analysis of quantitative research helped to define several attitudinal statements, which are the most crucial and could help to significantly decrease a level of shadow economy in Švenčionys. The highest level of disagreement compared to the other municipalities within the country can be seen to 4 statements: Attitude that the inhabitants of the municipality would be pleased with rising number of tourist; Salary under-reporting with higher income is preferred over legal income; Satisfaction with the government spending on education; Attitude to use any chance to receive some kind of social benefits. While, the inhabitants of Švenčionys were neutral about the statement: Legal income is preferred over salary under-reporting if the amount is the same. Based on these findings, the action/recommendations on how to reduce a shadow (based on focus group discussion results) are the following: • Communicate the existence of higher compensation from legal income. Highlighting social security benefits such as: medical care, sickness compensation and etc.; 79

• • •

Make initiatives to support families with kids below poverty line; Concentrate on showing positive things done for schools, highlighting renovated schools; Communicate broad range of informal activities and that majority of those activities are free of charge. Highlight sport activities and children achievements in this field; • Support individual incentives for extracurricular activities for children; • Communicate about people willingness to be “clean” through working for the “clean” employer; • Institutionalize the procedure of profiling social benefit inquiries. Visaginas Figure 27: Preliminary recommendations for Visaginas (Lithuania)

Source: compiled by authors

As it is presented in Figure 27, the analysis of quantitative research helped to define several subjects that require the most attention in Visaginas. The highest level of agreement compared to the other municipalities within Lithuania is expressed in the statement about the satisfaction with government spending on economy. The highest level of disagreement can be found in 2 statements: Attitude that inhabitants of the municipality would be pleased with rising number of tourist; Legal income is preferred over salary under-reporting if the amount is the same. The inhabitants of Visaginas were neutral about the attitude, which support people who purchased goods for a long-term use, for which taxes are not paid. The main actions (recommendations) that arises during the FGD are provided bellow:

• •

• • • • •

Communicate the range of penalties for receiving envelope salary and the severeness of consequences; Show how inequality between inhabitants who pay/do not pay taxes for long term goods affects society. Highlighting that less money is collected into the budget the lower are salary and pension; Communicate about people willingness to be “clean” through working for the “clean” employer; Communicate about benefits provided by tourist to the local community, concentrate on higher income to the budget and new work places; Promote the high quality of education. Emphasize that education is highly financed and performance of the teachers are outstanding; Communicate about availability of variety of schools and their different profiles; Create inspirational and original extracurricular activities.

Recommendations. Based on the MIMIC as well as the results of quantitative and qualitative research, the final recommendations for Lithuania were formulated. Most of the recommendations composed in the form of communication, development of policies, compliance with the law, administration matters and education system. The final recommendations how to reduce a shadow economy and strengthen local economies in Lithuania are provided in the table below: Table 51: Final recommendations (Lithuania) Categories

Communication

Recommendations

Hold campaigns on individual benefit generated by legal income:

•

Alytus

Health insurance;

•

Druskininkai

Pension;

•

Marijampolė

Paid holidays;

•

Šalčininkai

Sickness compensation;

•

Švenčionys

Ability to use legal procedures if employer is insolvent.

Hold information campaigns on the use of public resources collected from taxes on education:

•

Alytus

•

Švenčionys

Different profiles of education places;

•

Visaginas

Ability to choose kindergarten and schools near home;

Broad range of unpaid extracurricular activities.

Hold information campaigns on municipality tax expenditures by showing specific tangible cases (e.g. infrastructure development: new/renovated roads, renovated school/hospitals, etc.).

•

Alytus

•

Švenčionys

Hold campaigns on benefits provided by tourist to local community:

•

Druskininkai

•

Visaginas

•

Druskininkai

•

Marijampolė

New workplaces;

Income to the budget.

Hold campaigns that social services are provided for disadvantaged people of for those who reached “the bottom” (e.g. alcoholics).

Subject to recommendations

•

Druskininkai

•

Švenčionys

•

Visaginas

Eliminate unequal treatment in schools (e.g. all children at school need to have the same opportunities to participate in activities offered by school).

•

Druskininkai

Review policies how municipality government is working with net contributors to municipality budgets, but not net receivers.

•

All municipalities

Ensure honesty in project implementation process – not to promise things that cannot be delivered.

•

Alytus

Increase transparency in request submission and assistance process.

•

Alytus

•

Švenčionys

Initiate organizational changes in the administration of the school – improvement of teachers’ competencies, motivation and attraction of young and inspirational staff to work at school.

•

Alytus

•

Šalčininkai

Create unpaid extracurricular activities for children personal development (e.g. leadership basics, logic).

•

Alytus

•

Druskininkai

•

Šalčininkai

•

Švenčionys

•

Visaginas

Initiate bullying prevention in school.

•

Druskininkai

Hold information campaigns on the consequences of noncompliance with national legislation (e.g. penalties for receiving envelope salary, tax evasion).

•

Marijampolė

•

Šalčininkai

•

Visaginas

•

Alytus

•

Druskininkai

•

Šalčininkai

Hold campaigns of the importance of morality for the development of the society – link between people willingness to be “clean” through working for “clean” employer.

Development of policies

Administration

Education

Compliance with the law Hold anti-corruption campaigns.

Source: Compiled by authors

6.4. Romania Identification of border and inland municipalities. First of all, the border municipalities in Romania that could have been eligible for the research were examined. The initial list consisted of 14 border municipalities, which were selected based on approximate distance to the border and administrative unit (for the research the municipality level was selected). The initial list of border municipalities is presented below.

Table 52: Initial list of Romania’s border towns Initial list of border municipalities (Romania) 1. Baia Mare

8. Iași

2. Bârlad

9. Rădăuți

3. Botoșani

10. Satu-Mare

4. Brăila

11. Sighetu-Marmației

5. Dorohoi

12. Suceava

6. Galați

13. Tulcea

7. Huși

14. Vaslui

Source: compiled by authors

After determining the possible alternatives for border towns, a desk research was performed in order to find out which of the 14 municipalities had the highest level of shadow economy. The desk research included the analysis of various reports, researches and articles in media. 6 border municipalities in Romania were identified as having the highest level of shadow economy. Further 18 inland municipalities, which were the closest to the border municipalities in terms of population and were not less than 60 km from the border were chosen. The list of Romania’s border and inland municipalities which were used in further research stages is presented below. Table 53: List of Romania’s border and inland municipalities before principal component analysis and PSM Border municipalities

Inland municipalities

1. Botoșani

1. Adjud

10. Focșani

2. Huși

2. Baia Mare

11. Gheorgheni

3. Iași

3. Beiuș

12. Onești

4. Rădăuți

4. Brașov

13. Pașcani

5. Satu-Mare

5. Buzău

14. Piatra Neamț

6. Sighetu-Marmației

6. Cluj Napoca

15. Râmnicu Sărat

7. Craiova

16. Sibiu

8. Dej

17. Târgu-Mureș

9. Fălticeni

18. Turda

Source: compiled by authors

Data collection. The data collection process consisted of gathering a large volume of data from various sources as well as calibrating data to different indexes in order to avoid the possible population influence in source data. In Romania, the data was collected from 24 municipalities for the period from 2012 to 2015. The gathered dataset of Romania consisted of 68 different statistical indicators. These indicators covered the topics related to demographics, social welfare, business environment in the country and other information, which provided all the necessary information to perform various analysis included in this research. Most of the data collected for Romania came from following sources: •

Databases of Statistical Institute;

•

Data from municipalities;

•

Different public institutions.

The process of data collection had some difficulties. Some of the dimensions of information were not possible to get, e.g.: most of the times the data was scattered and was not provided for targeted administration units. Thus, dataset for Romania lacks an average income indicator, criminal and more detailed social data as well as information about vehicles. Due to an inability to acquire some data, the research faced some limitations as a few important indicators, especially during economic/comparative analysis, were not possible to be calculated. Principal Component (PCA) and Propensity Score Matching (PSM) analysis. The first analyses performed were PCA and PSM. They were performed in order to get rid of a multicollinearity. In Romania’s case the PCA produced 15 factors with the different hidden patterns. Furthermore, a PSM analysis was performed using the factor coefficients obtained from PCA. The factors and their respective values are presented in the table below. Table 54: Factor values of Romania’s Propensity Score Matching Factor

Value

Factor

Value

FAC1

5.482

FAC9

0.347

FAC2

1.869

FAC10

1.443

FAC3

1.046

FAC11

-1.816

FAC4

0.195

FAC12

0.173

FAC5

2.851

FAC13

-4.271

FAC6

-3.691

FAC14

-0.558

FAC7

-1.526

FAC15

-1.415

FAC8

1.842

Source: made by authors Note: Bolded factors and their values have the biggest weight on the particular matching of Romania’s municipalities.

As the table shows, 15 factors were produced and 4 of the factors played a major role in determining some specific matches in Romania. Thus, the profiles of the matched municipalities were based on the statistical indicators, which had the highest coefficients in loading of those particular factors. It means that matched cities are akin to each other according to the patterns hidden in the loading of these factors. The main statistical indicators of the factors and their description for matching Romanian cities are provided below. Table 55: Statistical indicators determining profiles of the municipality matching (Romania) Factor FAC1

Statistical indicators with the highest weight

Description

Median age of the population at the beginning of the year, year (women);

Factor mainly defines demographic composition of the society.

Median age of the population at the beginning of the year, year (men); Birth rate per 1000 population; Rate of natural population change per 1000 population; Marriage rate per 1000 population; FAC6

Number of employees in the mining and quarrying, manufacturing sector per 1000 working-age population; Expenditure on public order and public security, EUR per capita; Turnover in real estate operations sector, thousand EUR per person working in that sector;

FAC13

Expenditure on general government services, EUR per capita; Expenditure on insurance and social assistance from local budget, EUR per capita;

Profiling is based on the size of mining and quarrying/manufacturing sector, productivity of employees in real estate and government’s expenditure for assuring public order and security. Matched cities are similar according to expenditure of municipality’s budget towards general government services and insurance and social assistance.

With reference to the table above, it can be stated that the case the matches of municipalities in Romania are mainly based on similarities in demographic composition of the municipalities, in government expenditure on general services, insurance, and public order and safety, in turnover of real estate sector and number of employees in mining and quarrying. Table 55 represent the exact matches and their scores. Table 56: Details of Romania’s matched observations

Border municipalities

Logit (Propensity score)

Inland municipalities

BOTOSANI

7.149

TARGU-MURES

Logit (Propensity score) -6.406

Distances 13.555

HUSI

RADAUTI

-6.577

13.726

SIBIU

-6.507

16.594

BEIUS

-6.810

16.897

RAMNICU SARAT

-6.434

13.190

ONESTI

-6.589

13.346

PASCANI

-6.655

13.087

FOCSANI

-6.811

13.242

BUZAU

-6.395

13.112

FALTICENI

-6.618

13.335

BRASOV

-6.199

13.448

CRAIOVA

-6.829

14.077

10.087

6.757

SIGHETUMARMAITIEI

6.432

SATU-MARE

6.717

IASI

BAIE MARE

7.248

Source: made by authors Note: Matching is done by minimizing numerical distance between the municipalities. Each border municipality is matched with 2 inland municipalities. The lower propensity score, the lower difference between border and inland municipalities is.

The table shows that each of the border municipalities has a corresponding inland municipality (marked in dark grey) and one alternative inland municipality (marked in light grey), which would be used in case some discrepancies with data on main inland municipality would arise. All the 18 municipalities were used further in the research. 1-Factor causal clustering. After each border municipality has been matched with two inland municipalities, the next step was to make data suitable for the MIMIC model calculations. By using 1-Factor Causal Clustering analysis 5 latent variables were formed; full list of latent variables is provided in Appendix 1 (Table 104). The constructed latent variables together with the source variables, which had of significant value in discriminant analysis, were used in the MIMIC model calculations for Romania. MIMIC. After the data set of latent and source variables was prepared, the MIMIC model was built. The graphical representation of the MIMIC model equation is presented below.

Figure 28: Graphical representation of the model equation (Romania)

Source: compiled by authors Model represents causal and indicator variables that show significance in determining equation of shadow economy for Romania. The expanded version of Romaniaâ&#x20AC;&#x2122;s MIMIC model is presented in Appendix 2.

From the graphical representation of the MIMIC model for Romania, it can be seen that there are 5 causal variables that significantly affect the size of shadow economy. All 5 of the variables influence the shadow economy negatively, meaning that they decrease the level of shadow economy in Romania. The generic formula for Romania: đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ?&#x2018;&#x153;đ?&#x2018;¤ = â&#x2C6;&#x2019;0.25 Ă&#x2014; đ?&#x2018;&#x2020;đ?&#x2018;&#x20AC;đ??¸đ?&#x2018; đ?&#x2018;&#x2026;đ?&#x2018;&#x2019;đ?&#x2018;&#x201D;đ?&#x2018;&#x2013;đ?&#x2018; đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x2018;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x;1000đ?&#x2018;&#x160;đ?&#x2018;&#x153;đ?&#x2018;&#x;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x201D;đ??´đ?&#x2018;&#x201D;đ?&#x2018;&#x2019;đ?&#x2018;&#x192;đ?&#x2018;&#x153;đ?&#x2018;?đ?&#x2018;˘đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;&#x153;đ?&#x2018;&#x203A; â&#x2C6;&#x2019; 0.16 Ă&#x2014; đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;˘đ?&#x2018;&#x;đ?&#x2018;&#x2013;đ?&#x2018; đ?&#x2018;Ąđ??´đ?&#x2018;?đ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x153;đ?&#x2018;&#x2018;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x2018;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x;1000đ?&#x2018;&#x160;đ?&#x2018;&#x153;đ?&#x2018;&#x;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x201D;đ??´đ?&#x2018;&#x201D;đ?&#x2018;&#x2019;đ?&#x2018;&#x192;đ?&#x2018;&#x153;đ?&#x2018;?đ?&#x2018;˘đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;&#x153;đ?&#x2018;&#x203A; â&#x2C6;&#x2019; 0.18 Ă&#x2014; đ??ż2 â&#x2C6;&#x2019; 0.39 Ă&#x2014; đ??ż3 â&#x2C6;&#x2019; 0.28 Ă&#x2014; đ??ż4 The further shadow economy indexes for all the cities in Romania included in the research while using a produced equation were calculated. The table with calculated SE indexes for all border municipalities and their corresponding inland municipalities for a period from 2012 until 2015 is presented below. Table 57: Averages of shadow economy indexes of Romaniaâ&#x20AC;&#x2122;s municipalities, 2012-2015 Municipality type: Border (B) or Inland (I)

Municipality

Shadow economy as % of economic value in municipality

BotoČ&#x2122;ani

TĂ˘rgu MureČ&#x2122;

Baia Mure

35.08%

HuČ&#x2122;i

37.68%

36.75% 29.8%

Municipality type: Border (B) or Inland (I)

Municipality

Shadow economy as % of economic value in municipality

Sibiu

31.97%

Beiuș

37.02%

Rădăuți

35.62%

Râmnicu Sărat

38.02%

Onești

36.25%

Sighetu Marmației

36.73%

Pașcani

39.09%

Focșani

36.53%

Satu Mare

34.23%

Buzău

35.98%

Fălticeni

38.18%

Iasi

Brașov

31.85%

Craiova

32.04%

Average of border municipalities

35.48%

Average of inland municipalities

35.15%

31.9%

The table above shows that there are two cases when the index for shadow economy is higher for the corresponding inland municipality than for the border municipality. However, while the average index of the border municipalities is higher than for the inland municipalities, we cannot state that generally all the border municipalities have a higher level of shadow economy than the inland ones. This discrepancy arises due to previously mentioned cases. Economic and comparative analysis. In Romania, 4 different economic indicators and 2 indicators, describing registered income and welfare, were constructed. By analysing those indicators and comparing them between the border and inland municipalities of Romania, as well as between the municipalities within each group, a few important insights were found. The major difference appears in GDP per capita indicator, it is approx. 39% lower for the border municipalities. On average, both welfare indicators for Romania had worse results for border municipalities. After executing the T-Test to find out whether the differences appeared by no accident, none of the indicators have shown significance according to selected 90% confidence level. Thus, it is highly possible that those differences between the border and inland municipalities appeared by an accident. However, those factors were taken into consideration while forming the attitudinal questionnaires and recommendations for municipalities in further stages of the research. Quantitative research. In order to do the survey data analysis that would lead to a formulation of guidelines for the focus group in Romania’s border municipalities, the regression analysis was

implemented. The graphical representation of regression analysis with the scores is presented below. Figure 29: Graphical representation of regression scores (Romania) Satisfied about spending on economy to improve and develop it Satisfied about spending on fuel and energy sector Municipality should take care of children from families at risk

0,197

I feel like my municipalityâ&#x20AC;&#x2122;s local government would help me in case of trouble

Satisfaction with the quality of transport and storage sector

Ownership of credit/debit card

Shadow Economy

I support people who use social benefits as only source of income Satisfied about spending on communal economy I would receive envelope salary if I had a chance, and that meant higher income The inhabitants of the town would be pleased with rising number of tourist Share of payments in cash Satisfied about the health, educational and other services provided by government

-1,128

I would prefer to receive legal income over envelope salary if the amount would be the same for both options

I think that too many people are avoiding labor to receive social benefits I would cheat on taxes if I had the chance Satisfied with the quality of transport and storage sector

Source: compiled by authors Figure represents the attitude (statements) that show significance in regression analysis. The expanded version of Romaniaâ&#x20AC;&#x2122;s data analysis is presented in Appendix 3.

The regression analysis consists of two main stages. First of all, the shadow economy index of Romania produced by the MIMIC was chosen as a dependent variable, while results of the attitudinal statements from the qualitative survey were included as independent variables. As it can be seen from Figure 29, the regression analysis revealed that only two attitudes have a significant impact on shadow economy. The graphical representation also reflects a prevailing satisfaction with the quality of transport and storage sector as well as an attitude to prefer legal incomes over salary under-reporting if the amount was the same. Both options have a negative impact on a shadow economy, which means that those attitudes help to decrease shadow economy. In order to understand the hidden meanings and motivation behind the independent variables which were significant in the first regression, they were chosen as dependent variables for the following regression analysis. The further analysis revealed that the satisfaction with the local government spending on economy to improve and develop it increases the satisfaction with the quality of transport and storage sector, while overall those attitudes decrease shadow economy. A satisfaction with spending on fuel and energy sector decreases the satisfaction with the quality of transport and storage sector, overall it increases a shadow economy. The belief of the citizens that municipality 89

should take care of the children of families at risk and that local government should help them in case of a trouble increases the satisfaction with the quality of transport and storage sector, which overall decreases the shadow economy. The last attitude that has a negative influence on satisfaction with the quality of transport and storage sector is the ownership of debit/credit card, overall it increases shadow economy. The last regression revealed that the satisfaction about spending on communal economy and the quality of transport and storage sector have a negative effect on the attitude that citizens of municipality would prefer to receive legal income over salary under-reporting if the amount was the same for, overall it increases a level of shadow economy. While the satisfaction with health, educational and other services provided by government have an opposite effect and overall it decreases shadow economy. The attitudes of supporting people who use social benefits as the only source of income and cheating on taxes, if there is a chance, decrease the attitude to prefer legal income over salary under-reporting if the amount was the same for both options and it increases the shadow economy. The same trend can be noticed with the share of payments in cash, the higher the proportion of payments in cash is, the lower the attitude to prefer legal income over salary underreporting is, overall it increases shadow economy. Furthermore, the attitude that too many people avoid joining a labour force to receive social benefits and prefer a salary under-reporting, if that meant higher income, increases the attitude to prefer legal income over salary under-reporting, if the amount was the same, and overall it decreases shadow economy. The same trend can be noticed after examining the attitude that inhabitants of the municipality would be pleased with rising number of tourists, which decreases shadow economy. In order to further analyse the differences of the above-mentioned attitudes among Romanian municipalities, the answers to the statements were transformed into indexes. The dark brown colour represents the answers with the highest level of disagreement; the dark blue represents the answers with the highest level of agreement. The answers, coloured in lighter shades, represent inclination towards agreement (light blue) or disagreement (light brown). A neutral option is represented in white colour. The main differences of attitudes among targeted municipalities are represented in and explained below. Table 58: Attitude towards municipality and its local government (Romania)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

The first 2 statements are measuring the attitude towards municipality and its local government. It can be seen that the inhabitants of Huși, Satu Mare, Sibiu, Râmnicu Sărat, Fălticeni and Brașov municipalities strongly believe that the local government would help them in case of trouble. The disagreement to this statement is seen in the municipalities of Sighetu Marmației and Târgu Mureș.

6 out of 12 municipalities believe that their local governments should take care of them, and only 1 municipality, namely Brașov, has an opposite belief. Table 59: Evaluation of municipality's spending in different sectors (Romania)

Large enough spending

Too low spending

Inclination for large enough spending

Inclination for too low spending

Neutral answers

Source: compiled by authors Answer choices: Appropriate, too large, too small. Index can have both positive and negative values. The range of the indexes is from -1 to 2, where 2 means spending is appropriate, 1- spending is to large, while –1 means that spending is too small.

The second topic of the survey was the evaluation of municipality’s spending on different sectors in Romania, whether it is appropriate, too large or too small. People living in Râmnicu Sărat, Fălticeni and Brașov municipalities suppose that spending on economy is large enough, while the inhabitants of 6 out of 12 municipalities (Botoșani, Sighetu Marmației, Satu Mare, Iasi, Târgu Mureș and Pașcani) think that spending on the economy is too small. 5 out of 12 inhabitants of municipalities’ (Rădăuți, Sighetu Marmației, Râmnicu Sărat, Fălticeni and Brașov) think that spending on the communal economy in Romania is large enough, while people in Botoșani, Huși, Iasi, Târgu Mureș and Pașcani think that spending on communal economy is too small. A similar trend is prevalent regarding government expenditure on fuel and energy sector. The inhabitants of Rădăuți, Râmnicu Sărat, Fălticeni and Brașov think that the spending is large enough, while the people from Botoșani, Huși, Sighetu Marmației, Satu Mare, Iasi, Târgu Mureș, Sibiu and Pașcani think that spending is too small.

Table 60: Satisfaction with the work of various sectors in municipality (Romania)

Highest level of satisfaction

Highest level of dissatisfaction

Incline towards highest level of satisfaction

Incline towards highest level of dissatisfaction

Neutral answers

The next statements measured the satisfaction with the quality of work of various sectors in municipalities. The respondents of Sibiu, Râmnicu Sărat and Fălticeni municipalities are satisfied with the heath, educational and other services provided by government. People in Huși incline towards being satisfied with the quality of this sector. The inhabitants of Botoșani, Sighetu Marmației, Satu Mare, Iasi and Târgu Mureș have the highest level of dissatisfaction with the mentioned services. Sibiu, Râmnicu Sărat and Brașov municipalities have the highest level of satisfaction with the quality of transport sector, while the inhabitants of Botoșani, Rădăuți, Sighetu Marmației, Satu Mare and Iasi remain dissatisfied. People in Huși incline towards being satisfied with this sector. Table 61: Attitude towards social benefit receivers (Romania)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

The attitude towards social benefit receivers was observed as well. The inhabitants of all 12 municipalities have a neutral approach towards supporting people who use social benefits as the only source of income.

While the inhabitants from 8 out of 12 municipalities (Botoșani, Huși, Sighetu Marmației, Satu Mare, Iasi, Târgu Mureș, Sibiu and Pașcani) think that too many people avoid labour to receive social benefits; only the residents of Râmnicu Sărat and Brașov disagree with this statement. Table 62: Opinion about town's attractiveness as a tourist destination (Romania)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

The fifth topic has revealed the opinion about the town’s attractiveness as a tourist destination. The inhabitants of 6 out of 12 municipalities (Botoșani, Huși, Rădăuți, Sighetu Marmației, Sibiu and Brașov) strongly agree that the inhabitants of their municipality would be pleased with rising number of tourist. The remaining six municipalities (Satu Mare, Iasi, Târgu Mureș, Râmnicu Sărat, Pașcani and Fălticeni) highly disagree with this statement. Table 63: Attitude towards purchasing cheaper goods without paying taxes (Romania)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

The sixth topic revealed an attitude towards purchasing cheaper goods without paying taxes. The survey reveals that the residents in Huși, Satu Mare, Târgu Mureș and Râmnicu Sărat municiaplities would prefer to receive legal income over salary under-reporting if the amount was the same for both options. The inhabitants of Sibiu incline towards choosing to receive legal income over envelope salary. While the inhabitants of 5 out of 12 municipalities (Botoșani, Rădăuți, Sighetu Marmației, Pașcani and Fălticeni) highly disagree with the statement.

According to the responses to the second statement, we can conclude that people in Sighetu Marmației, Râmnicu Sărat and Brașov would receive the salary under-reporting if that meant higher income. The disagreement with this statement is observed in 6 municipalities, namely Botoșani, Huși, Rădăuți, Târgu Mureș, Sibiu and Pașcani. The inhabitants of Iasi incline towards disagreement to this statement. Table 64: Attitude towards importance of paying taxes (Romania)

Highest level of agreement to statement

Highest levels of disagreement to statement

Inclination for agreement to statement

Inclination for disagreement to statement

Neutral answers

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

The attitude towards the importance of paying taxes in Romania was measured as well. The produced indexes are negative for all the municipalities. However, the lowest negative values are observed in Rădăuți and Brașov, implying that citizens of these municipalities would be most likely to cheat on taxes if they had a chance. A negative attitude towards cheating on taxes is observed in Botoșani, Huși, Iasi, Târgu Mureș, Sibiu and Brașov. Table 65: Attitude towards non-cash payments (Romania)

Highest share of card owners/payments in cash

Lowest share of card owners/payments in cash

Inclination towards owning a card/using more cash

Inclination towards using less cash

NA/DK

Source: compiled by authors Answer choices: Yes, no, NA/DK (1); 0%-10%, 11%-30%, 31%-50%, >50%, NA/DK (2). Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement (1), and from 1 – 7, where 1 means lowest share of cash payments and 7 means highest share of cash payments.

The last topic revealed an attitude towards a non-cash payment in Romania. From the table above, we can clearly indicate that the majority of inhabitants in Botoșani, Satu Mare, Iasi and Sibiu own the credit/debit cards. The opposite trend is observed in Rădăuți, Sighetu Marmației, Râmnicu Sărat, Pașcani and Fălticeni, where inhabitants do not own credit/debit cards.

The highest share of payments in cash belongs to inhabitants of Sighetu Marmației and Râmnicu Sărat municipalities, while the lowest share of payments in cash is observed in Rădăuți, Târgu Mureș and Pașcani municipalities. The further step in analysing the survey results was to produce several distinct factors using the Principal Component Analysis. The method enabled to notice the patterns and profiles of the exact municipalities easier. The two graphs, which include four different factors applicable to Romania, are represented bellow. Figure 30: Graphical representation of the Romania’s survey answers based on factors 1 and 2

Source: Source: compiled by authors Some of the survey statements, which contribute little to the factors, are hidden. Full list of survey statements and their correlation with the factors can be found in Appendix 6.

Figure 31: Graphical representation of the Romania’s survey answers based on factors 3 and 4

Botoșani’s uniqueness from other cities can be observed from Figure 30. The municipality is positioned for having a negative attitude towards local government spending and services provided, as well as a prevailing perception that too many people avoid labour in order to receive social benefits. Nonetheless, the inhabitants advocate for supporting children from families at risk. The inhabitants of Botoșani also do not support cheating and would not accept salary under-reporting even if that meant higher income. The Botoșani was compared with three selected municipalities from non-EU countries, namely, Rîșcani (Moldova), Briceni (Moldova) and Hlyboka (Ukraine). After testing whether there are significant differences between each of the pairs by using an independent mean of T-Test, Botoșani and Briceni appeared to be statistically different (t-value - -1.71; p-value - 0.093). It means that the inhabitants of the municipalities have completely different opinion towards tax evasion and smuggling-related activities. It should be noted that Rîșcani appeared to be closer statistically to Botoșani (p-value - 0.123). However, the T-Test for Botoșani and Hlyboka failed to reject a null hypothesis about similarity. Basically, that means that inhabitants of Botoșani have more similar mindset and attitude to inhabitants of Hlyboka, what could prompt more active smuggling activities between two municipalities. The Table summarises the answers regarding smuggling activities of inhabitants from Botoșani and Rîșcani/ Briceni/ Hlyboka municipalities. Table 66: Comparison of Botoșani answers with the municipality in the other side of frontier (Rîșcani, Briceni, Hlyboka) Reasons to smuggle

Easy and fast money but it is different from the reason of Moldavian and Ukrainian cities, where

Places to buy smuggled goods Municipality market.

Ways how goods are being smuggled Mainly by hiding smuggled goods in the car.

Routes of smuggling goods Border checkpoints with Moldova (Stefanesti, Stanca Costesti).

Reasons to smuggle

Places to buy smuggled goods

Ways how goods are being smuggled

Routes of smuggling goods

people see smuggling as a way to survive. Source: compiled by authors

Huși is positioned as a town, which has quite a high portion of payments in cash, even if the rate of ownership of the credit/debit cards is quite high. Moreover, the respondents described Husi as a municipality where the safety conditions are satisfying. Huși is characterised as having a negative attitude towards tax evasion, acceptance of salary under-reporting and government spending on various sectors. Three corresponding Moldova’s border towns on the other side of the frontier, which are closed to Huși, were selected, namely, Hîncești, Cahul and Cantemir. The T-Test for each of the paired towns has shown that municipalities are not statistically different. The comparison of Huși and Hîncești/ Cahul/ Cantemir answers regarding smuggling is provided in the Table below. Table 67: Comparison of Huși answers with the municipality on the other side of frontier (Hîncești, Cahul, Cantemir) Reasons to smuggle

For inhabitants of Husi the main reasons are easy money that could be earned fast, however, in all of the Moldavian municipalities more than a 50% of respondents noted that the reason is survival.

Places to buy smuggled goods No locations that would have particular pattern were mentioned.

Ways how goods are being smuggled

Routes of smuggling goods

Absolute majority respondents mentioned cars as a main way to hide and get smuggled goods to Husi.

Smuggled goods come from Moldova, particularly through border checkpoints (Albita was mentioned by 35% of respondents from Husi who noted any particular route).

Source: Compiled by authors

Rădăuți. According to the city’a position on the provided dimensions, Rădăuți clearly stands out as a municipality, where the rate of ownership of credit/debit cards is low. However, those who own the cards, use them often. Also, the inhabitants have not noticed some real benefits from paying taxes compared to all the other municipalities. It is worth mentioning that Rădăuți demonstrates one of the highest scores towards cheating on taxes. One more feature which appears to be distinct from the other border towns, is that the inhabitants of Rădăuți are more satisfied with the government spending on different sectors and services provided. Three Ukrainian border municipalities were selected to compare, namely, Hlyboka, Storozhynets and Snyatyn. After executing the statistical tests, it appeared that all of the tests have failed to reject a null hypothesis, which proves that cities are similar to each other. However, the t-value (t-value -1.654; p-value - 0.104) of Hlyboka was really close to the critical value and that should be kept in mind during FGD and the last phase, when the final recommendations are developed. The answers regarding smuggling related activities for Rădăuți and Hlyboka/ Storozhynets/ Snyatyn are presented below:

Table 68: Comparison of Rădăuți answers with the municipality on the other side of the frontier (Hlyboka, Storozhynets, Snyatyn) Reasons to smuggle

Places to buy smuggled goods

Desire to earn additional revenue fast.

No particular places noted, as people was not willing to answer.

Ways how goods are being smuggled No particular pattern regarding ways of smuggling.

Routes of smuggling goods Through border check points of Târgu Frumos/Lupcina/Siret.

Source: Compiled by authors

Sighetu Marmației appears to hold a similar position as Rădăuți, since the rate of owning credit/debit cards is low and the inhabitants do not see benefits from paying taxes. However, differently from Rădăuți, those who own credit/debit cards do not use them so often. Sighetu Marmației also stands out from the other municipality, as the local government seems to have the lowest level of trust in case of any trouble happened. Also, Sighetu Marmației inhabitants have the highest willingness (compared to border towns) to receive a salary under-reporting in case that would mean higher income. Two corresponding border municipalities of Ukraine, namely, Tyachiv and Rakhiv were selected for carrying out the T-Test. The test failed to reject the hypothesis about the existing significant differences between Sighetu Marmației as well as the answers of attitudinal survey. The Table below summarises the answers regarding the attitudes toward smuggling of inhabitants from Sighetu Marmației and Tyachiv/ Rakhiv. Table 69: Comparison of Sighetu Marmației answers with the municipality on the other side of the frontier (Tyachiv, Rakhiv) Reasons to smuggle

Financial reasons and ability to earn money fast; Personal consumption.

Places to buy smuggled goods Local market (38% of respondents from Sighetu Marmatiei, who agreed to answer to this question, stated that).

Ways how goods are being smuggled No particular pattern of ways how smuggling is done.

Routes of smuggling goods Mainly through the SighetUkraine border checkpoint.

Source: compiled by authors

Satu Mare. The collected answers of inhabitants of Satu Mare reflects quite a clear profile. Compared to the Fălticeni (corresponding inland town) and the other border towns, Satu Mare stands out as a town, where inhabitants are not satisfied with the local government spending on different sectors as well as discontent with the different services provided. The inhabitants of Satu Mare differ from the others because the disapprove people, who avoid taxes and understand the benefits provided through taxes. Vynohradiv was selected as a corresponding non-EU municipality for Satu Mare. With reference to the statistics these municipalities are not statistically different, what means that respondents answered to the survey questions similarly (or not drastically different). The answers regarding smuggling related activities common to both Satu Mare and Vynohradiv are presented below.

Table 70: Comparison of Satu Mare answers with the municipality on the other side of the frontier (Vynohradiv) Reasons to smuggle

Majority is smuggling due to the financial reasons, as smuggling allows to get money fast.

Places to buy smuggled goods Local market;

Ways how goods are being smuggled Cars.

Buy straight from the smugglers.

Routes of smuggling goods Mainly from Ukraine, through Helmeu customs.

Source: compiled by authors

Iasi. The overall performance of Iasi is similar to the Satu Mare and, in most of the cases, opposite to BraČ&#x2122;ov (corresponding inland town). The inhabitants of Satu Mare are not satisfied with the government spending on different sectors and the quality of such services, incl. education, health, utilities, etc. On contrary, the inhabitants of BraČ&#x2122;ov are genuinely satisfied with these things. Also, the inhabitants of Iasi possess one of the highest number of credit cards compared to the other municipalities and have quite a neutral attitude towards receiving an envelope salary. The municipality of Iasi was compared to the municipality on the other side of the frontier Ungheni. After executing the independent samples the T-Test, the hypothesis that samples are similar failed to be reject. The comparison of Iasi and Ungheni answers regarding smuggling is provided in the Table 70. Table 71: Comparison of Iasi answers with the municipality on the other side of the frontier (Ungheni) Reasons to smuggle

Financial reasons mainly, however, for Romanians it is mainly in order to get additional income and for Moldavians in order to survive.

Places to buy smuggled goods

Local market (agro-food market); Easy to bring by yourself from Moldova.

Ways how goods are being smuggled

Cars.

Routes of smuggling goods

Through the custom checkpoints with the Moldova (Sculeni, Albita).

Source: compiled by authors

Qualitative research. The focus group discussion in Romania was implemented in 6 border municipalities. Firstly, the discussions with inhabitants helped to gain deeper insights on the inhabitantsâ&#x20AC;&#x2122; attitudes towards the specific statements, have a direct impact on shadow economy. Secondly, it helped to formulate the preliminary recommendations how to fight a shadow and strengthen local economies for each of the municipalities individually. The results of the interviews are presented in the scheme for each municipality individually. The scheme represents a short summary of all the project phases and their results for a particular border municipality.

Botoșani Figure 32: Preliminary recommendations for Botoșani (Romania)

Source: compiled by authors

As it is shown in the Figure 32, the analysis of quantitative research revealed 3 attitudinal statements which require the most attention in order to decrease a level of shadow economy in Botoșani. The highest level of agreement with the statement compared to other municipalities within Romania can be found for all 3 statements: Legal income is preferred over salary under-reporting if the amount is the same; Tax evasion if opportunity arise; and Satisfaction with health, educational and other services provided by government. Based on those subjects that require the most attention as well as on the discussions of focus group, the following actions/recommendations were formed: • • • • •

100

Communicate legal income benefits, highlighting social security benefits and ability to use legal procedures if employer is insolvent. Improve trustworthiness of doctors (hotline to report about corrupted doctors could be established). Provide additional services and additional activities to children who have parents living abroad. Support schools which train teachers to use modern teaching approach. Increase the transparency of public expenditures. Highlight what kind of things has been bought, for how much and from where.

Huși Figure 33: Preliminary recommendations for Huși (Romania)

Source: compiled by authors

As is it shown in the figure above, the quantitative survey analysis of Huși revealed that 7 subjects to lower the size of shadow economy require more attention. The highest level of agreement compared to the other cities within Romania can be seen in 6 out of 7 statements: Satisfaction with the quality of transport and storage sector; Satisfaction with a health, education and other services provided by government; Attitude that municipality should take care of children from family at risk; Attitude that municipalities local government would help in case of trouble; and Attitude that too many people are avoiding labour to receive social benefits. Referring to the attitudes, the inhabitants have a positive attitude about the tax expenditure. A high level of disagreement was expressed to the statement about taking a salary under-reporting with a higher amount of money over legal income. The main recommendations that came up from the subjects of specific importance, by testing those in focus group discussions, are provided bellow: • Promote assistance provided by municipality to children. Highlight housing for families in need, child protection service and NGO’s; • Communicate fast administration response to a request made by inhabitants; • Communicate the provided social help to poor people, concentrate on housing and canteen; • Communicate the benefits of legal income, emphasize social security;

101

• • • •

Communicate about the good quality service provided by local hospital and high availability of student dorms; Establish the hotline to report about corrupted doctors; Communicate that giving bribes is not what inhabitants are obliged to do; Communicate that social services are provided for disadvantaged people or for those who reached the “bottom” (e.g. alcoholics).

Rădăuți Figure 34: Preliminary recommendations for Rădăuți (Romania)

Source: compiled by authors

As it is presented in the Figure 35, the analysis of quantitative research helped to define 6 attitudinal statements which are the most crucial and could help to significantly decrease a level of shadow economy in Rădăuți. The highest level of agreement to the statement, compared to the other municipalities in Romania, can be concluded from 3 following statements: Satisfaction with the spending on economy to improve and develop it; Attitude that inhabitants of the municipality would be pleased with rising number of tourist; and Attitude to cheat on taxes if there is a chance. Furthermore, the highest level of disagreement can be seen for: Satisfaction with the quality of transport and storage sector; Legal income is preferred over salary under-reporting if the amount is the same; Salary under-reporting with higher amount of money is preferred over legal income. Based on those subjects that require the most attention as well as on the discussions of focus group, the following actions/ recommendations were formed: • Communicate the investments that have been made to develop infrastructure, highlight school and swimming pool construction and renovation; • Make sure that the implementation of municipality’s investment projects would not be overpromised or under-delivered;

102

•

Improve transparency of public expenditures by showing specific cases and what has been done (e.g. how many families received municipality help) rather than just providing high level numbers; Promote the main tourism attraction points. Start from tourism routes design: make descriptions, provide historical background and information why specific places are interesting; Communicate benefits of paying taxes, highlight social benefits such as retirement compensation.

Sighetu Marmației Figure 35: Preliminary recommendations for Sighetu Marmației (Romania)

Source: compiled by authors

As is it presented in the Figure 35, the quantitative survey analysis of Sighetu Marmatiei revealed that 9 subjects require the most attention: Satisfaction with the quality of transport and storage sector; Satisfaction with the spending on economy to improve and develop it; Satisfaction with the health, educational and other services provided by government; Legal income is preferred over salary under-reporting if the amount is the same; Attitude that municipality’s local government would help in case of trouble (these 5 statements have a high level of disagreement); Satisfaction with the spending on communal economy; Attitude to take salary under-reporting with higher 103

amount of money over legal income; Attitude that inhabitants of the municipality would be pleased with rising number of tourist; Attitude that too many people are avoiding labour to receive social benefits (these 4 statements have high level of agreement). Based on these finding as well as on discussions of focus groups, the actions/recommendations to reduce a shadow economy in Sighetu Marmației are provided below: • • • • •

•

• •

104

To communicate the improvements made by new mayor, concentrate on the kindergarten that is planned to be build and streets/roads which are going to be paved. Increase trustworthiness in public authorities, concentrate on efficiency improvement of municipality’s administration request management procedures. Communicate the existing improvements made on infrastructure, highlight recently renovated buildings, cleanliness of the city, good conditions in hospital pediatric department. Induce more investigations into the way how salaries are paid for the employees in construction sector. Promote the main tourism attraction points, develop roads. Start from tourism routes design by making descriptions, providing historical background and information why specific places are interesting. Limit the political favouritism when choosing high rank public employees by publishing public listings of open job positions and informing about the persons who have been hired to work for municipality. Improve hospital’s infrastructure, firstly, concentrate on overall renovation of appearance of the hospital. Provide accessible consultation services for financial planning skills and career development.

Satu Mare Figure 36: Preliminary recommendations for Satu Mare (Romania)

Source: compiled by authors

As it is shown in the figure above, the analysis of quantitative survey helped to define 5 attitudinal statements which are crucial to decreasing shadow economy in Satu Mare. The highest level of agreement compared to other municipalities within Romania is identified in statements, such as: Attitude that municipality’s local government would help in case of trouble; Legal income is preferred over salary under-reporting if the amount is the same. While subjects, incl.: Satisfaction with the quality of transport and storage sector; Satisfaction with the health, educational and other services provided by government; Attitude that inhabitants of the municipality would be pleased with rising amount of tourist have shown the highest level of disagreement. The main actions (recommendations), which came up from the subjects of specific importance testing during FGD, are provided bellow: • Communicate the help provided regarding housing assistance, concentrate on provision of housing for those in need. • Improve the safety in the city, concentrate on gypsies who are begging for money. Show the solved cases. • Communicate the improvements made to tourism industry and show what kind of benefits it generates to inhabitants. • Communicate the improvements made to publics schools and hospitals, highlight renovation of schools and hospitals. • Improve quality of medical system, concentrate on providing more attention to hospitalized patients and profiling system creation for patients to decrease waiting lines and prioritize the most urgent cases.

105

Iași Figure 37: Preliminary recommendations for Iași (Romania)

Source: compiled by authors

As it is shown in Figure 37, the analysis of quantitative research revealed several attitudinal statements that needs attention and are crucial to decrease shadow economy: Satisfaction with the quality of transport and storage sector; Legal income is preferred over salary under-reporting if the amount are the same; Satisfaction with spending on economy to improve and develop it; Opinion that inhabitants of the municipality would be pleased with rising number of tourist; Satisfaction with the health, educational and other services provided by government; Attitude that too many people are avoiding labour to receive social benefits. The formulated attitudes and focus group discussions helped to develop the following recommendations: • Make sure that implementation of the project would not be over-promised or underdelivered (e.g. smart traffic lights which are not working properly); • Support local entrepreneurs in development of their businesses; • Promote the main tourism attraction points. Start from design of tourism routes: make descriptions, provide historical background and information why specific places are interesting; • Review the list of things provided for teachers that are currently financed with a help of parents. Provide some of those things for teachers; • Design voucher system for vocational education to enable private institutions to provide the education demanded by inhabitants; • Improve the quality of necessary medical equipment; • Establish hotline to report about corrupted doctors;

106

•

Recommendations. Based on the MIMIC and the results of both quantitative and qualitative research, the final recommendations on how to reduce shadow economy and strengthen local economy in Romania were formed. The final recommendations are presented in the table below. Table 72: Final recommendations (Romania) Categories

Recommendations

Hold campaigns on individual benefits generated by payments of taxes:

•

Botoșani

Quality of healthcare;

•

Huși

Pension;

•

Iași

Loans.

•

Rădăuți

•

Rădăuți

•

Huși

•

Satu Mare

•

Sighetu Marmației

•

Huși

•

Sighetu Marmației

•

Rădăuți

•

Botoșani

Support local entrepreneurs – create jobs by harnessing local opportunities.

•

Iași

Initiate tourism development infrastructure (promotion, information, connection, etc.).

•

Iași

•

Rădăuți

•

Satu Mare

•

Sighetu Marmației

Support civil society in implementing community development projects.

•

Iași

Ensure honesty in public communication – not to promise thigs that cannot be achieved.

•

Iași

•

Rădăuți

Initiate transparency in public spending.

•

Botoșani

•

Iași

•

Rădăuți

Hold information campaigns on the use of public resources collected from taxes on public investment/services: education, health, social, local/public infrastructure. Communication

Hold campaigns on the importance of ethics and morality for the development of society – the link between patriotism and duty towards society.

Development

Administration

107

Subject to recommendations

Education

Compliance with law

Initiate fast/prompt response to citizens’ demands for public expenditure.

•

Huși

Initiate participatory budget/public debates related to opportunity and the need for public spending.

•

All municipalities

Provide financial education initiatives.

•

Sighetu Marmației

Provide financial planning initiatives.

•

Sighetu Marmației

Provide counselling and coaching initiatives for inhabitants’ career development (transition to a better paid job).

•

Sighetu Marmației

Hold information campaigns on national and local legislation (e.g. What is the excise duty on cigarettes).

•

All municipalities

Hold information campaigns on the consequences of non-compliance.

•

All municipalities

Promote campaigns done by law enforcement/ inspection/ control authorities (border police, local police, financial department, labour inspection, etc.).

•

All municipalities

Hold general information campaigns on the magnitude of the smuggling phenomenon and law violations, as well as the consequences for the wellbeing society.

•

All municipalities

Hold anti - corruption campaigns.

•

Botoșani

•

Iași

•

Huși

•

Iași

•

Huși

Initiate remuneration of the whistle – blowing practice (offering a percentage reward to those who provide information about corruption acts). Source: Compiled by authors

6.5. Belarus Identification of border and inland municipalities. Firstly, border municipalities that could have been included in our research were selected. Because Belarus is a non-European country, the goal of the identification was to find the closest municipality for the selected ones across the EU border (Lithuania and Latvia). Referring to this criterion, 6 municipalities in Belarus were selected. After determining the border municipalities, which were eligible for conducting the research, 18 inland municipalities, which were the closest to the border municipalities in terms of population and were not less than 60 km from the border, were chosen. The list of selected Belarusian border and inland municipalities presented in the table below.

108

Table 73: List of Belarus’ border and inland municipalities before principal component analysis and PSM Border municipalities

Inland municipalities

1. Ashmyany

1. Asipovichi

10. Kirovsk

2. Astrovets

2. Beshenkovichi

11. Krupki

3. Braslaw

3. Chashniki

12. Lepiel

4. Smarhon

4. Chervyen

13. Lyakhavichy

5. Verhnedvinsk

5. Dokshytsy

14. Navahrudak

6. Voranava

6. Dubrowno

15. Nesvizh

7. Dzyatlava

16. Rahachow

8. Kapyl

17. Stolbtsy

9. Karelichy

18. Syanno

Source: compiled by authors

Data collection. Process of data collection in Belarus consisted of gathering a large volume of data from public databases as well as from different private data holders in the country. In order to avoid influence of population on total numbers, the data was calibrated to different indexes. The full dataset of Belarus consisted of 87 different statistical indicators for the period from 2012 to 2015. The data covered demographic, business, social welfare and other topics, which provided all the necessary information for further analysis. Data was collected for 24 municipalities. The major sources of collected data: •

National Statistical Committee of the Republic of Belarus

•

Regional Statistic Committees (for Grodno, Brest, Vitebsk and Minsk regions)

•

Ministry of Internal Affairs of the Republic of Belarus

•

Ministry of Health of the Republic of Belarus

•

Regional Executive Committees (each town)

A few main issues arose during the stage of data gathering, such as lack of the information on a number of cars and value added/turnover from various business sectors. The main reason was that this kind of information has not been collected and provided before. Also, the information about the municipality budget expenditure required to put a lot of effort to collect and systemise it. This issue happened because this kind of data has not collected in one integrated source; therefore, the reports of each municipality were analysed manually. All things considered, the gathered dataset did not have any major discrepancies or gaps, hence, each municipality had quite a homogenous dataset. Principal Component (PCA) and Propensity Score Matching (PSM) analysis. After collecting all of the required data, PCA and PSM analyses were executed in order to eliminate multicollinearity. Firstly, the PCA was performed, after which a PSM analysis was performed using the factor coefficients obtained from PCA. The factors and their respective values are presented in the table below. 109

Table 74: Factor values of Belarus’ Propensity Score Matching Factor

Value

Factor

Value

FAC1

0.909

FAC10

8.873

FAC2

3.787

FAC11

6.151

FAC3

13.244

FAC12

4.710

FAC4

-4.275

FAC13

-8.994

FAC5

5.272

FAC14

-2.876

FAC6

0.144

FAC15

-2.265

FAC7

5.634

FAC16

-11.406

FAC8

6.382

FAC17

3.797

FAC9

0.208

Source: compiled by authors Note: Bolded factors and their values have the biggest weight on the particular matching of Belarus’ municipalities.

As the table shows, 17 factors were produced and 4 factors played a major role in determining some specific matches in Belarus. Thus, the profiles of the matched municipalities are based on statistical indicators, which has the highest coefficients in loading of those particular factors. It means that matched cities are akin to each other in terms of the patterns hidden in the loading of these factors. The main statistical indicators of factors and their description for matching Belarusian cities are provided below. Table 75: Statistical indicators determining profiles of the municipality matching (Belarus) Factor FAC3

Statistical indicators with the highest weight Dependency ratio at the beginning of the year, persons (0-14 y.o.); Number of general school pupils per 1000 population;

FAC10

Share of unprofitable companies (% of all companies); Profitability of sales by regions, cities and districts, %; Agricultural production index, % to the previous year;

FAC13

Area, m2; Transportation of goods by road, tons per working age population;

110

Description Factor has a hidden pattern of society’s age. Helps to match municipalities based on age structure of particular municipalities. Factor describes profitability status of the cities, including overall profitability of different industries, especially, agricultural, as well as share of unprofitable companies. Size of the municipality and road transportation capacities play a major role loading this factor.

Factor FAC16

Statistical indicators with the highest weight

Description

Registered unemployed persons per 1000 population; Tolls paid and included in municipal budgets per 1 working-age person, EUR;

Cities are matched based on municipalitiesâ&#x20AC;&#x2122; unemployment rate and the amount of tolls person pays for municipality.

Therefore, after evaluating all of these factors it can be stated that the matches of municipalities are mainly based on similarities in age structure of the society, registered unemployment, profitability of businesses and agricultural entities, size of the municipalities, transportation capacities and various tolls paid to municipal budgets. The exact matches based on the listed similarities are presented in the table below. Table 76: Table 7: Details of Belarusâ&#x20AC;&#x2122; matched observations Border municipality

Logit (Propensity score)

BRASLAW

7.065

ASHMYANY

ASTROVETS

SMARHON

VERHNEDVINSK

VORANAVA

Inland municipality

Logit (Propensity score)

Distances

NESVIZH

-7.778

14.844

DZYATLAVA

-7.940

15.005

RAHACHOW

-6.952

13.843

SYANNO

-7.813

14.703

KRUPKI

-7.640

18.503

LEPIEL

-9.446

20.309

CHASHNIKI

-6.567

13.212

STOLBSTY

-6.598

13.242

NAVAHRUDAK

-6.369

12.669

DUBROWNO

-6.409

12.709

DOKSHYTSY

-7.120

22.773

KARELICHI

-7.287

22.941

6.890

10.863

6.644

6.300

15.654

The table shows that each of the border municipality has a corresponding inland municipality (marked in darker grey) and also one alternative inland municipality (marked in light grey) which would be used in case some discrepancies with data from main inland municipality would arise. However, all of the 18 municipalities were used for the MIMIC index calculations in the later stages. 111

1-Factor Causal Clustering. After determining the exact matches of municipalities in Belarus and their underlying profiles, the next step was to prepare a dataset for the MIMIC model calculations by using the 1-Factor Causal Clustering. After executing the 1-Factor Causal Clustering on the source variables, 11 latent variables, which have a significant impact on the causality of shadow economy in Belarus, was constructed. A deeper explanation of those 11 latent variables and the coefficients of underlying statistical indicators can be found in Appendix 1 (Table 52). All of those latent variables as well as the source variables, which have shown significance during discriminant analysis, were used in the MIMIC model determination for Belarus. MIMIC. After municipalities of Belarus were matched and latent variables constructed, the MIMIC model for Belarus was produced. The graphical representation of MIMIC model equation is presented below.

112

Figure 38: Graphical representation of the MIMIC model equation (Belarus)

Source: compiled by authors Model represents causal and indicator variables that show significance in determining equation of shadow economy for Belarus. The expanded version of Belarusâ&#x20AC;&#x2122; MIMIC model is presented in Appendix 2.

In Belarusâ&#x20AC;&#x2122; case there are 8 different causal variables, which significantly contribute to the shadow economy. 3 of them influence shadow economy negatively (meaning that these variables in Belarusâ&#x20AC;&#x2122; case make the level of shadow economy lower) and 5 of them have a positive impact (meaning that it contributes to the higher level of shadow economy). The generic formula for Belarus: đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ?&#x2018;&#x153;đ?&#x2018;¤ = 8.684 Ă&#x2014; đ?&#x2018; đ?&#x2018;˘đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018;&#x201A;đ?&#x2018;&#x201C;đ?&#x2018;&#x2020;đ?&#x2018;&#x2019;đ?&#x2018;&#x2122;đ?&#x2018;&#x201C;đ??¸đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x2019;đ?&#x2018;&#x2018; â&#x2C6;&#x2019; 0.476 Ă&#x2014; đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x2021;đ?&#x2018;&#x17D;đ?&#x2018;Ľđ?&#x2018;&#x192;đ?&#x2018;&#x17D;đ?&#x2018;&#x2013;đ?&#x2018;&#x2018; + 90.088 Ă&#x2014; đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x2021;đ?&#x2018;&#x17D;đ?&#x2018;Ľđ?&#x2018;&#x192;đ?&#x2018;&#x17D;đ?&#x2018;&#x2013;đ?&#x2018;&#x2018; â&#x2C6;&#x2019; 2.303 Ă&#x2014; đ??ż3 + 12.602 Ă&#x2014; đ??ż5 + 8.371 Ă&#x2014; đ??ż6 â&#x2C6;&#x2019; 5.018 Ă&#x2014; đ??ż9 + 4.408 Ă&#x2014; đ??ż10

113

Then, indexes for all included cities in Belarus, using produced equation were calculated. The table with calculated average indexes for all border municipalities and their corresponding inland municipalities is presented below. Table 77: Averages of shadow economy indexes for Belarus municipalities, 2012-2015 Municipality type: Border (B) or Inland (I)

Municipality

Shadow economy index

Braslav

Dyatlovo

-28,18

Nesvizh

-105,49

Oshmyany

-32,82

Rogachev

-19,79

Senno

Ostrovets

-52,49

Krupki

-28,83

Lepel

-30,94

Smorgon

-59,65

Chashniki

-32,5

Stolbtsy

-64,11

Verhnedvinsk

-27,92

Dubrovno

-21,58

Novogrudok

-40,28

Voronovo

10,96

Dokshytsy

-9,02

Korelichi

-37,92

15,72

-6,37

Average of border towns

-24,37

Average of inland towns

-35,42

Source: compiled by authors Shadow economy index can have both positive and negative values. Higher the index â&#x20AC;&#x201C; higher shadow economy in particular municipality, lower the number â&#x20AC;&#x201C; lower the shadow economy is.

As it is presented in the table above, there are only 1 border municipality in which the index for shadow economy is higher than for the corresponding inland municipalities, while there are 5 cases in which the index for shadow economy in border municipality is higher than in inland municipalities. While from the average values we can see that the index for shadow economy for border municipalities is higher than for the inland municipalities, we cannot state that in generally all border municipalities have a higher level of shadow economy than the inland municipalities, because of the cases where results indicate opposite conclusion about inland municipalities having higher index for shadow economy than border municipalities.

114

Economic and comparative analysis. In Belarusâ&#x20AC;&#x2122; case, 7 different economic indicators and 4 indicators, which describe registered income and welfare, were constructed. By analysing those indicators and comparing them between border and inland municipalities of Belarus, as well as between municipalities within each group, a few important insights were found. Even without testing all the insights statistically, it can be stated that there was a clear difference between government spending and employment rate indicators. On the average, the border municipalities government spending is approx. 10.3% lower and the employment rate is 5% lower. Welfare indicators for Belarus revealed that the border municipalities have 5.15% more working age people, which do not take a part in labour market; the registered income is lower by 8.8%. After executing the T-Test to find out whether differences appeared by no accident, three indicators appeared to be significant, corresponding to 90% of the confidence level. The statistical significance appeared for government spending, rule breaking per capita and rate of unemployment indicators. Thus, it means that the lower values in Belarusâ&#x20AC;&#x2122; border municipalities according to those indicators are significant and the root causes are worth further analysis. Quantitative research. The results of Belarus quantitative survey were used for comparison with other EU countries in order to perceive whether the attitude towards tax evasion and smuggling are similar between border municipalities of Belarus and corresponding EU border municipalities. Furthermore, the results of the survey were used to compare whether the border municipalities of Belarus have distinct attitudes or the answers have particular the patterns inherent to the whole group. In order to see the differences among Belarus municipalities, the answers to the statements were transformed into indexes. The dark brown colour represents the answers with the highest level of disagreement, while the dark blue represents the answers with the highest level of agreement. The answers coloured in the lighter shades represent inclination towards agreement (light blue) or disagreement (light brown). A neutral option is represented by white colour. The main differences of the attitudes among municipalities are presented in the tables bellow.

115

Table 78: Attitude towards importance of paying taxes (Belarus)

Source: Compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

The first topic revealed a prevailing attitude towards the importance of paying taxes in Belarus. The first statement reveals that the inhabitants of Oshmyany and Verhedvinsk express the highest level of agreement to the statement that they would rather pay direct taxes from their legal income than pay indirect taxes via goods they purchase. The inhabitants of Ostrovets incline towards the agreement to this statement. The highest level of disagreement to the first statement can be seen in Braslav. The second statement shows that the inhabitants of all the border municipalities disapprove with the taxpayers who evade taxes. Despite that, comparing to other municipalities, the highest level of disagreement to this statement is noticed in Oshmyany municipality. Additionally, the survey shows that only people in Verhedvinsk believe that money paid in taxes provide them some useful benefits. People in Voronovo and Oshmyany disagree with this statement, and people in Ostrovets and Smorgon show the inclination towards disagreement. The forth statement revealed that inhabitants of 5 municipalities (Braslav, Ostrovets, Smorgon, Verhedvinsk and Voronovo) believe that money paid in taxes provide some useful benefits for society, while the highest level of disagreement to this statement is seen in Oshmyany municipality. The fifth statement shows that inhabitants of Oshmyany, Ostrovets and Smorgon agree that they would cheat on taxes if they had a chance. People in Verhedvinsk disagree with this statement. The last statement shows that on the average inhabitants of all the border municipalities disagree that people should use every opportunity to not pay taxes. Compared to other municipalities, the inhabitants of Ostrovets, Verhedvinsk and Voronovo show the highest level of disagreement to the 116

statement, while the inhabitants of Braslav show the inclination towards disagreement with this statement. The inhabitants of Oshmyany show the highest level of agreement that people should use every opportunity not to pay taxes. Table 79: Attitude towards smuggling of goods (Belarus)

Source: Compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement (1). The range of index if rom -1 to 1, where 1 means yes and -1 means no (2-6).

The second topic revealed the attitude towards smuggling of goods in Belarus. The table above shows that people in Oshmyany, Ostrovets and Smorgon believe that smuggling cigarettes, alcohol products and fuel across the border and selling them there for a higher price is justifiable, while the highest level of disagreement to this statement is seen in Braslav and Voronovo municipalities. The second statement revealed that on the average inhabitants of all the border municipalities have not heard about someone who buys goods in country, resell them and gain profit form neighbourhood countries. Compared to the other municipalities, the highest share of people, who have heard about someone can be found in Oshmyany municipality. The third, fourth, fifth and sixth statements show that inhabitants of all the border municipalities deny that they know anyone, who smuggles or sells/buys goods, which are later on resold across the border as smuggled goods. Despite that, the highest share of people who have brought cigarettes across border in the past 6 months; who know someone who has smuggled cigarettes; or who know someone who sells and buys goods that are later resold across the border as smuggled goods are in Oshmyany municipality. People in Ostrovets, Smorgon and Voronovo show a strong disagreement to all of these statements. People in Braslav express a disagreement to the third statement, but shows an inclination towards disagreement to fourth and fifth statement; they provide neutral answers for last statement. The inhabitants of Verhedvinsk provide some neutral answers to all six statements. To sum up, it is clear that municipalities of Belarus are different from each other, especially, regarding the questions related to tax evasion. Each of those municipalities had to be taken into 117

account separately when the linkages with EU municipalities were analysed. However, the municipalities are more similar to each other in corresponding to the smuggling questions, only Oshymany and Verhedvinsk are clearly different.

6.6. Moldova Identification of border and inland municipalities. The border municipalities in Moldova were selected based on the closest municipality on the other side of the frontier criteria for the already selected EU border municipalities, in this case for Romania. According to this criterion 6 border municipalities were selected. After the final list of Moldova border municipalities was concluded, 12 inland municipalities, which were the closest to the border municipalities in terms of population and were not less than 60 km from the border, were chosen. The list of selected Moldavian border and inland municipalities is presented below. Table 80: List of Moldova’s border and inland municipalities before principal component analysis and PSM Border municipalities

Inland municipalities

1. Briceni

1. Basarabeasca

7. Glodeni

2. Cahul

2. Călărași

8. Nisporeni

3. Cantemir

3. Cimișlia

9. Orhei

4. Hâncești

4. Dondușeni

10. Sângerei

5. Râșcani

5. Drochia

11. Strășeni

6. Ungheni

6. Florești

12. Telenești

Source: compiled by authors

Data collection. Data collection process consisted of collecting data from the open sources databases. After that, when all possible relevant data was gathered from open source databases, the missing information was requested from the different data holders. In order to avoid influence of population on total numbers, the data was calibrated to different indexes. The full dataset encompassed the information about 18 municipalities of Moldova and consisted of 61 statistical indicators for the period from 2012 to 2015. The collected dataset comprised general, social, demographic, business and other data. The main sources of the data: •

National Bureau of Statistics

•

Agency for Public Services

•

Local Transparency Sources

•

Different municipality sources

During the stage of gathering Moldavian data the main issue was to collect data related to transport and real estate. The information about a number and value of cars and real estate was not collected and accessible publicly. Compared to the other countries, Moldova has one of the smallest datasets collected. All things considered, the gathered dataset did not have any major discrepancies or gaps.

118

Principal Component (PCA) and Propensity Score Matching (PSM) analysis. After collecting all of the required data, PCA and PSM analyses were executed in order to eliminate multicollinearity. The PCA analysis produced 9 factors, after which a PSM analysis was performed using the factor coefficients obtained from PCA. The factors and their respective values are presented in the table below. Table 81: Factor values of Moldovaâ&#x20AC;&#x2122;s Propensity Score Matching

Factor

Value

Factor

Value

FAC1

3.623

FAC7

14.760

FAC2

-8.273

FAC8

4.266

FAC3

-6.824

FAC9

7.325

FAC4

-2.386

FAC10

8.808

FAC5

3.230

FAC11

-0.448

FAC6

-16.638

FAC12

14.048

Source: made by authors Note: Bolded factors and their values have the biggest weight on the particular matching of Moldovaâ&#x20AC;&#x2122;s municipalities.

As the table shows, 9 factors were produced and 4 factors played a major role in determining specific matches in Moldova. Thus, the profiles of the matched municipalities are based on statistical indicators, which have the highest coefficients in loading of those particular factors. It means that matched cities are akin to each other in terms of the patterns hidden in the loading of these factors. The main statistical indicators of factors and their description for matching Moldavian cities are provided below. Table 82: Statistical indicators determining profiles of the municipality matching (Moldova) Factor FAC6

Statistical indicators with the highest weight

Description

Municipal budgets income, (other income) per 1 working-age population;

Matches of the municipalities are based on income from other activities and number of persons who receives social benefits within those municipalities.

Average number of persons receiving state social insurance disability pensions per 1000 population; FAC7

Investment in tangible fixed assets per capita, EUR; Number of employees in the information and communication sector per 1000 population;

FAC12

119

Municipal budgets income, EUR (tax transfers from state budgets) per 1 working-age population;

Factor is made up of patterns describing investments in fixed assets and size of information/communication sector expressed by number of employees. Different transfers to municipal budgets, as well as, size of administrative and service activities

Factor

Statistical indicators with the highest weight

Description

Municipal budgets income, EUR (transfers) per 1 working-age population; Number of employees in the administrative and service activities sector per 1000 working-age population;

sector and number of school pupils are the main significant indicators producing FAC12_1, hence, the matches of Moldova’s municipalities.

Number of general school pupils per 1000 population; Source: compiled by authors Note: presented statistical indicators are considered as having the biggest weight for the loadings of these particular factors, this way determining profiles of matched Moldavian cities.

Based on the factors which have largest values, the matches of municipalities in Moldova are based on generated income by municipalities from other activities, a number of social benefit receivers, an investment in tangible fixed asset and a number of employees in various sectors. The list of exact matches of Moldova’s municipalities is provided in the table below. Table 83: Details of Moldova’s matched observations Border municipalities

Logit (Propensity score)

BRICENI

6.716

CAHUL

CANTEMIR

HANCESTI

RASCANI

UNGHENI

Inland municipalities

Logit (Propensity score)

Distances

STRASENI

-6.441

13.157

CIMISLIA

-6.949

13.665

FLORESTI

-6.576

22.229

CALARASI

-15.654

31.307

GLODENI

-6.416

22.070

TELENESTI

-7.015

22.668

DROCHIA

-6.903

13.119

SANGEREI

-12.767

18.983

ORHEI

-6.247

12.672

DONDUSENI

-6.826

13.251

NISPORENI

-6.546

12.882

BASARABEASCA

-7.124

13.460

15.654

6.217

6.425

6.336

As it is shown in the table above, each of the border municipalities has the main corresponding inland municipality (marked in dark grey) and an alternative inland municipality (marked in light grey) which would be used in case some discrepancies with main corresponding municipality arose. All municipalities were used further in the research.

120

1-Factor Causal Clustering. After determining the exact matches of municipalities in Moldova, the next step was to prepare a dataset for the MIMIC model calculations. By using 1-Factor Causal Clustering analysis 9 latent variables were formed; a full list of latent variables is provided in Appendix 1 (Table 106). The constructed latent variables together with the source variables which have shown a significance during the discriminant analysis were used in the MIMIC model calculation for Moldova. MIMIC. After the dataset of latent and source variables was prepared, the MIMIC model was built. The graphical representation of the MIMIC model equation is presented below. Figure 39: Graphical representation of the model equation (Moldova)

Source: compiled by authors Model represents causal and indicator variables that show significance in determining equation of shadow economy for Moldova. The expanded version of Moldovaâ&#x20AC;&#x2122;s MIMIC model is presented in Appendix 2.

The MIMIC model for Moldova revealed that there are 4 significant causal variables which have an influence on shadow economy. The majority of significant causal variables have a positive influence on shadow economy, what means that the variables increase a level of shadow economy in Moldova. There is only 1 significant causal variable which influences shadow economy negatively, meaning that it decreases the level of shadow economy. The generic formula for Moldova: đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ?&#x2018;&#x153;đ?&#x2018;¤ = 1,76 Ă&#x2014; đ??ˇđ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;?đ?&#x2018;Śđ?&#x2018;&#x2026;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2019; + 0,09 Ă&#x2014; đ?&#x2018; đ?&#x2018;˘đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018;&#x201A;đ?&#x2018;&#x201C;đ?&#x2018;&#x160;đ?&#x2018;&#x153;đ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;&#x192;đ?&#x2018;&#x2019;đ?&#x2018;&#x;1000đ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A; + 1,79 Ă&#x2014; đ??ż3 â&#x2C6;&#x2019; 3,19 Ă&#x2014; đ??ż5 Based on this equation the shadow economy indexes for each of the Moldovaâ&#x20AC;&#x2122;s border and inland municipalities were calculated. The table with the average indexes of shadow economy for the period from 2012 to 2015 is provided below.

121

Table 84: Averages of shadow economy indexes of Moldova’s municipalities, 2012-2015 Municipality type: Border (B) or Inland (I)

Municipality

Shadow economy index

Briceni

18,02

Strășeni

18,87

Cimișlia

18,98

Cahul

17,64

Florești

18,79

Călărași

18,95

Cantemir

22,66

Glodeni

17,43

Telenești

21,71

Hâncești

19,48

Drochia

17,02

Sângerei

21,06

Râșcani

19,13

Orhei

17,58

Dondușeni

15,95

Ungheni

17,79

Nisporeni

21,78

Basarabeasca

16,5

Average of border towns

19,12

Average of inland towns

18,72

As it is presented in the table above, there are only 2 of 6 cases when the border municipality has a higher shadow economy index than both corresponding inland municipalities. The border municipalities usually have a higher shadow economy index compared to corresponding inland towns. However, based on the MIMIC calculations for Moldova, it cannot be stated that generally all the border municipalities have a higher level of shadow economy compared to inland municipalities. Economic and comparative analysis. After the economic and comparative analysis, different indicators were constructed using dataset gathered in data collection stage. 7 different economic indicators and 5 indicators, which describe registered income and welfare, were constructed. By analysing economic indicators, no major differences, just a slight difference in budget collected from the income taxes and the gross investment per capita, were found. Welfare indicators of Moldova also revealed a similar situation when comparing border and inland municipalities: the registered income of border municipalities on average is lower by approx. 13%.

122

After running the T-Test to find out whether some of those slight differences are significant, as it could be guessed, none of the indicators showed up to be significantly different between the inland and border municipalities in Moldova. One of assumptions what are the reasons for this issue to happen was s small size of the country. Quantitative research. Differently from the EU countries, the quantitative survey in non-EU countries including Moldova’s border municipalities was implemented in order to find some relationship with EU border municipalities. For this reason, the results of the quantitative survey did lead to the formulation of FGD guidelines and further qualitative research was not implemented in Moldova. The results of Moldova quantitative survey were used for the comparison with EU countries in order to see whether the attitudes towards tax evasion and smuggling are similar between non-EU border municipalities and corresponding EU borders municipalities. Furthermore, the results of the survey were used to compare whether the border municipalities of Moldova have distinct attitudes or the answers have particular the patterns inherent to the whole group. In order to see the differences among Moldavian border municipalities, the answers to the statements were transformed into indexes. The dark brown colour represents the answers with the highest level of disagreement, while the dark blue represents the answers with the highest level of agreement. The answers coloured in lighter shades represent an inclination for agreement (light blue) or disagreement (light brown). A neutral option is represented by white colour. The main differences of the attitudes among municipalities is presented in the tables below. Table 85: Attitude towards importance of paying taxes (Moldova)

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

The first topic has revealed the attitudes towards the importance of paying taxes in Moldova. As the table above shows, the inhabitants of Briceni, Cantemir, Riscanți, Cahul and Ungheni express the highest level of agreements to rather pay direct taxes from legal income than pay indirect taxes via goods they purchase, while disagreement to this statement can be seen in Hâncești municipality. The inhabitants of Riscanți incline towards the agreement to the statement. 123

The second statement shows that inhabitants in Briceni, Cantemir, Riscanți and Ungheni disapprove the taxpayers who evade taxes, while the inhabitants of Hâncești disagree with this statement. The inclination towards the agreement with this statement could be seen in Cahul. As the table above shows, the attitudes towards the third, the fourth and the fifth statements are similar among all the border municipalities of Moldova. The inhabitants of all the 6 border municipalities believe that money paid in taxes provide useful benefits for themselves as well as for the society as a whole. Furthermore, they express the agreement that they would cheat on taxes if they had the chance. However, the answers to the statements are not different in all Moldavian municipalities. The last statement shows that inhabitants of Briceni, Cantemir, Hâncești and Cahul believe that people should use every opportunity not to pay taxes, the highest level of disagreement to this statement is found only in Riscanți municipality. Table 86: Attitude towards smuggling of goods (Moldova)

Source: Compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement (1). The range of index is from -1 to 1, where 1 means yes and -1 means no(2-6).

The second topic revealed the attitude towards smuggling goods in Moldova. As the table above shows, on the average the respondents from all the border municipalities do not believe that smuggling cigarettes, alcohol products and fuel across the border and selling them for a higher price is justifiable. Compared to other municipalities, the highest share of inhabitants who express highest level of agreement to this statement can be found in Briceni, Riscanți and Cahul municipalities. While the highest level of disagreement to this statement is seen in Cantemir, Hâncești and Ungheni municipalities. The second statement revealed that inhabitants of 4 out of 6 municipalities have shown a higher level of disagreement to the statement about knowing someone who buys goods and sells them in other country to gain profit. 124

The third statement revealed that inhabitants of all the 6 border municipalities of Moldova have not brought cigarettes across the border in neighbouring country in the past 6 months. The next statement revealed that inhabitants of all municipalities do not know anyone who has been smuggling goods. Comparing to the other municipalities, the highest share of people who know someone who smuggle are found in Cahul and Ungheni, while the inhabitants of Briceni show highest level of disagreement to this statement. The fifth statement revealed that inhabitants of all the municipalities do not know anyone who sells goods that are later on being sold across the border as smuggled goods. Despite that, the highest number of inhabitants who know anyone who sells goods that are later resold across border as smuggled goods belong to Cahul municipality. The last statement revealed that inhabitants of all the border municipalities do not know anyone who buys goods that are later resold across the border as smuggled goods. To sum up, there is a clear tendency that most of the statements of the survey have no significant agreement or disagreement in municipalities, meaning that in most cases the municipalities contain the same attitude (mind-set). Since Moldova is a relatively small country, this tendency can be explained with the reference to the size of country.

6.7. Russia (incl. Kaliningrad) Identification of border and inland municipalities. Since Russia is a non-EU country, the border municipalities in this country were selected based on the closest municipality on the other side of the frontier criteria to the already selected EU countries (in this case Estonia, Latvia and Lithuania). According to this criterion, 6 municipalities in Russia (incl. Kaliningrad) were selected. After identifying border the municipalities for Russia, 18 inland municipalities, which are the most similar to border municipalities in terms of population and are not less than 60 km from the border, were selected. The list of the elected Russian border and inland municipalities is provided in the table below. Table 87: List of Russiaâ&#x20AC;&#x2122;s border and inland municipalities before principal component analysis and PSM Border municipalities

Inland municipalities

1. Gusevskyi city district

1. Chernyakhovsky city district

10. Luzhsky municipal district

2. Kingisepp municipal district

2. Chudovsky municipal district

11. Nelidovo municipal district

3. Ostrovsky municipal district

3. Desnogorsk city district

12. Ostashkovsky municipal district

4. Pechora municipal district

4. Himki city district

13. Podporozhskoe municipal district

5. Pskov city district

5. Kimry city district

14. Porkhovsky municipal district

6. Sovetsk city district

6. Kirishskiy municipal district

15. Svetlovskiy city district

7. Korolyov city district

16. Velikiy Novgorod city district

8. Livny city district

17. Volkhov municipal district

9. Lodeynopolsk municipal district

18. Vyshniy volochok city district

125

Source: compiled by authors

Data collection. Data collection process consisted of gathering available data from the open source databases. Furthermore, the missing relevant data was requested from different data holders. After conducting all the relevant information, the data was calibrated to various indexes in order to avoid population influence on total numbers. The full Russia’s dataset consisted of 84 different statistical indicators for the period of from 2012 to 2015. The data covered demographic, business, social welfare and other topics, which provided all the necessary information for further analysis. The main sources of the data were: •

Official statistics of municipalities (by request)

•

Regional statistical reports

During the stage of data collection, the main issue was a passive attitude of different ministries and agencies because the inquiries were ignored. In addition, because of the confidentiality issues, another obstacle was related with the data regarding criminal activities and security provision. Furthermore, it was impossible to find statistics on social support provided by governments, value added in different sectors, a value and number of vehicles. A dataset of Russia had some gaps; one of the most extreme cases was a lack of data for the municipality of Kingies. Since not all of the statistical indicators were equally available, there were some limitations for the further analysis of Russian municipalities as. Principal Component (PCA) and Propensity Score Matching (PSM) analysis. After the dataset for Russian municipalities was conducted, the PCA and the PSM analysis were carried out in order to eliminate multicollinearity. The PCA produced 15 factors. Furthermore, the PSM analysis was performed using factor coefficients obtained from the PCA. The factors and their respective values are presented in the table below. Table 88: Factor values of Russia’s Propensity Score Matching Factor

Value

Factor

Value

FAC1

-1.242

FAC9

-0.846

FAC2

-4.803

FAC10

7.118

FAC3

-2.869

FAC11

0.677

FAC4

-1.603

FAC12

-0.080

FAC5

-5.352

FAC13

5.981

FAC6

3.136

FAC14

0.135

FAC7

-3.116

FAC15

-1.603

FAC8

-4.456

Source: compiled by authors Note: Bolded factors and their values have the biggest weight on the particular matching of Russia’s municipalities.

As the table depicts, 15 factors were produced and 4 of those factors played a major role in determining some specific matches in Russia. Thus, the profiles of the matched municipalities were 126

based on statistical indicators, which had the highest coefficients in loading of those particular factors. It means that matched cities are akin to each other according to the patterns hidden in the loading of these factors. The main statistical indicators of factors and their description for matching Russian cities are provided below. Table 89: Statistical indicators determining profiles of the municipality matching (Russia) Factor FAC2

Statistical indicators with the highest weight

Description

Indexes of ageing at the beginning of the year; Dependency ratio at the beginning of the year, persons (65+ y.o.);

FAC5

Social support for dwellings and utility payments, EUR per capita; Number of business enterprises and economic entities in operation at the beginning of the year per 1000 working-age population;

FAC8

Number of emigrants per 1000 population;

FAC2_1 separates municipalities according to age composition. Matches of the cities are based on similarities regarding the number of business and economic enterprises and amounts of money flowing to social support for dwellings and utilities. Factor describes migration activities within the municipalities and amount of collected excises.

Number of immigrants per 1000 population; Excises on excisable products and goods produced on a territory of Russian Federation per working-age person, thousand EUR; FAC10

Investment in tangible fixed assets per capita, EUR;

Investments in tangible assets and number of booths/kiosk have the biggest weight on making up FAC10_1.

Number of booths and kiosks per 1000 population;

FAC13

Income tax included to municipal budget, EUR per working-age person; Expenditure of municipality budget on education, EUR per capita;

Factor is mainly based on the amount of collected income taxes and expenditures on education from municipal budgets.

As the table above shows, the matches of municipalities in Russia are based on composition of age structure within society, number of business and economic enterprises, migration activities, amount of collected excises and investment in tangible assets. Based on the method of a matching profile the pairs of municipalities are presented in the table below. Table 90: Details of Russiaâ&#x20AC;&#x2122;s matched observations Border municipality

Logit (Propensity score)

GUSEVSKYI

11.361

KINGISEPP

127

7.296

Inland municipality

Logit (Propensity score)

Distances

DESNOGORSK

-7.103

18.464

KIRISHSKIY

-7.198

18.559

OSTASHKOVSKY

-6.903

14.199

OSTROVSKY

PECHORA

PSKOV

SOVETSK

PODPOROZHSKOE

-6.930

14.227

VYSHNIY

-6.519

13.182

CHERNYAKHOVSKY

-6.750

13.413

KIMRY

-7.014

22.667

SVETLOVSKY

-7.064

22.718

VELIKIY

-6.322

12.088

KOROLYOV

-6.867

12.633

LIVNY

-6.627

12.879

VOLKHOV

-6.721

12.972

6.663

15.654

5.766

6.251

As it is presented in table, each of the border municipalities has two corresponding municipalities: the main one (marked in dark grey) and an alternative one (marked in light grey). The alternative corresponding inland municipality would be used in case some discrepancies with the main corresponding municipality arose. All the data from 18 municipalities is used further in the research. 1-Factor Causal Clustering. After determining the exact matches of municipalities in Russia and their underlying profiles, the next step was to prepare a dataset for the MIMIC model calculations. Using the 1-Factor Causal Clustering 12 latent variables were formed; a full list of Russiaâ&#x20AC;&#x2122;s latent variables and their regression coefficients is provided in Appendix 1 (Table 107). The final dataset which was used for the MIMIC model calculations for Russia consisted of constructed latent variables and source variables which show significance in the discriminant analysis. MIMIC. After municipalities of Russia were matched and latent variables constructed, the MIMIC model for Russia was produced. The graphical representation of the final MIMIC model is provided below.

128

Figure 40: Graphical representation of the MIMIC model equation (Russia)

Source: compiled by authors Model represents causal and indicator variables that show significance in determining equation of shadow economy for Russia. The expanded version of Russiaâ&#x20AC;&#x2122;s MIMIC model is presented in Appendix 2.

The MIMIC model results show that there are 11 significant causal variables that have influenced shadow economy. A majority of significant causal variables have had a negative effect on shadow economy (decrease the level of shadow economy in Russia). Only one significant causal variable L4 has had a positive effect on shadow economy (this variable has increased the size of shadow economy). The generic formula for Russia: đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ?&#x2018;&#x153;đ?&#x2018;¤ = â&#x2C6;&#x2019;0,509 Ă&#x2014; đ??źđ?&#x2018;&#x203A;đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x;đ??ľđ?&#x2018;˘đ?&#x2018;&#x2018;đ?&#x2018;&#x201D;đ?&#x2018;&#x2019;đ?&#x2018;Ąđ??şđ?&#x2018;&#x;đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;Ąđ?&#x2018; â&#x2C6;&#x2019; 9,469 Ă&#x2014; đ?&#x2018; đ?&#x2018;˘đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018;&#x201A;đ?&#x2018;&#x201C;đ??ľđ?&#x2018;&#x153;đ?&#x2018;&#x153;đ?&#x2018;Ąâ&#x201E;&#x17D;đ?&#x2018; đ??´đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ??žđ?&#x2018;&#x2013;đ?&#x2018;&#x153;đ?&#x2018; đ?&#x2018;&#x2DC;đ?&#x2018; â&#x2C6;&#x2019; 1,432 Ă&#x2014; đ??´đ?&#x2018;&#x161;đ?&#x2018;&#x153;đ?&#x2018;˘đ?&#x2018;&#x203A;đ?&#x2018;Ąđ?&#x2018;&#x201A;đ?&#x2018;&#x201C;đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x2021;đ?&#x2018;&#x17D;đ?&#x2018;Ľđ?&#x2018;&#x2019;đ?&#x2018; đ?&#x2018;&#x192;đ?&#x2018;&#x17D;đ?&#x2018;&#x2013;đ?&#x2018;&#x2018; â&#x2C6;&#x2019; 291,459 Ă&#x2014; đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2013;đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;Ąđ?&#x2018;&#x2021;đ?&#x2018;&#x17D;đ?&#x2018;Ľđ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;&#x2019; â&#x2C6;&#x2019; 16,020 Ă&#x2014; đ??ż2 + 101,040 Ă&#x2014; đ??ż4 â&#x2C6;&#x2019; 51,225 Ă&#x2014; đ??ż5 â&#x2C6;&#x2019; 159,515 Ă&#x2014; đ??ż6 â&#x2C6;&#x2019; 47,754 Ă&#x2014; đ??ż7 â&#x2C6;&#x2019; 33,748 Ă&#x2014; đ??ż9 â&#x2C6;&#x2019; 15,065 Ă&#x2014; đ??ż10

129

Based on this equation the shadow economy indexes for each of the Russian border and inland municipalities were calculated. The table with the average indexes of shadow economy for the period from 2012 to 2015 is provided bellow. Table 91: Averages of shadow economy indexes of Russia’s municipalities, 2012-2015 Municipality type: Border (B) or Inland (I)

Municipality

Shadow economy index

Gusevskiy city

2,11

Desnogorsk

7,18

Kirishskiy

1,59

Kingisepp

1,39

Ostashkovsky

3,70

Podporozhskoe

2,26

Ostrovsky

18,3

Vyshniy Volochok

Chernyakhovsky city

2,47

Pechora

4,05

Kimry city

7,52

Svetloskiy city

0,99

Pskov city

6,01

Velikiy Novgorod city

2,77

Korolyov city

5,04

Sovetsk city

3,46

Livny city

7,52

Volkhov

6,06

10,96

Average of border towns

5,89

Average of inland towns

4,84

After the analysis of the output produced over the MIMIC model calculations, it can be observed that not all of the border municipalities have a higher shadow index compared to the corresponding inland municipalities. Even though the average of shadow economy index in the border municipalities is higher, it cannot be stated that a shadow economy index in selected Russian border municipalities is generally higher. Therefore, there are a few cases worth noticing and investigating in those a bit deeper. Economic and comparative analysis. In Russia’s case, there were 9 different economic indicators and 2 indicators, which describe registered income and welfare, were constructed. By analysing those indicators and comparing them between the border and inland municipalities of Latvia as well as between municipalities within each group a few important insights were gained. Even without 130

testing all the insights statistically, it could be stated that there was a tendency that GDP per capita, gross investment per capita and number of social support receivers on average were different for border and inland municipalities. However, no major differences by comparing those two welfare indicators were found. After executing the T-Test, GDP per capita appeared as a statistically distinct indicator between the border and inland municipalities. Quantitative research. The results of the survey conducted in Russia (incl. Kaliningrad) were compared with other EU countries in order to understand whether the attitude towards tax evasion and smuggling is similar between the border municipalities of Russia and the corresponding EU border municipalities. In addition, the results of the quantitative survey were analysed to compare the Russian border municipalities with each other and to explore whether the municipalities within Russia have very distinct attitudes or contrary, the tendencies are inherent to the whole group. In order to see the differences among the border municipalities, the answers to the statements were transformed into indexes. The dark brown colour represents the answers with the highest level of disagreement; the dark blue represents the answers with the highest level of agreement. The answers coloured in lighter shades represent inclination towards either agreement (light blue) or disagreement (light brown). A neutral option is represented by white colour. The main differences of the attitudes among municipalities are presented in the tables bellow.

131

Table 92: Attitude towards importance of paying taxes (Russia (incl. Kaliningrad))

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

The first statement revealed the attitude towards the importance of paying taxes in Russia. The first statement provided in the table above shows that the inhabitants of all the border municipalities would rather pay direct taxes from legal income than pay indirect taxes via goods they purchase. Despite that, the largest number of inhabitants who have an opposite opinion can be found in Sovetsk municipality. The second statement revealed that inhabitants of all the municipalities disapprove taxpayers who evade taxes. Comparing to the other municipalities, the highest level of agreement to this statement prevails in Ostrov, Pskov and Pechory municipalities. The inhabitants of Gusev show the highest level of disagreement with this statement. The next statement revealed that inhabitants of all the border municipalities do not believe that money paid in taxes provide some useful benefits for them. Compared to other municipalities, the highest share of inhabitants, who believe that money paid in taxes provide useful benefits to them, are found in Kingisepp. The fourth statement explained that inhabitants of Gusev and Ostrov believe that money paid in taxes provides useful benefits for society, while the inhabitants of Pskov municipality do not believe in such a statement. The inclination towards agreement to this statement can be seen in Kingisepp; people in Sovetsk and Pechory municipalities were neutral about this statement.

132

The fifth statement provided in the table above showed that inhabitants of all the municipalities would cheat on taxes if they had a chance. Compared to other municipalities, the highest level of disagreement to this statement can be seen in Sovetsk and Gusev municipalities. From the last statement, we can see people from 3 out of 6 municipalities (Sovetsk, Gusev and Kingisepp) do not agree with the statement that people should use every opportunity not to pay taxes, while the inhabitants from Pskov and Pechory municipalities think that people should use such an opportunity. The inclination towards the agreement of this statement can be seen in Ostrov municipality. Table 93: Attitude towards smuggling of goods (Russia (incl. Kaliningrad))

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement (1). The range of index if rom -1 to 1, where 1 means yes and -1 means no (2-6).

The second topic revealed the attitude towards smuggling of goods in Russia. The first statement shows that the inhabitants of Sovetsk, Kingisepp, Pskov, Pechory compared to the inhabitants of other municipalities express the highest level of agreement to the statement which says that smuggling cigarettes, alcohol products and fuel across the border and selling them for a higher price is justifiable. The disagreement to this statement can be seen only in 2 out of 6 municipalities (Gusev and Ostrov). The next statement revealed that inhabitants of Sovetsk, Gusev, Pskov and Pechory have heard about someone buying goods in country in order to sell them and gain profit in neighbourhood countries. The inhabitants of Kingisepp have not heard about this earlier.

133

The third statement revealed respondentsâ&#x20AC;&#x2122; involvement in smuggling. As it is shown in the table above, the respondents from all the 6 municipalities have not brought cigarettes across the border from their country to neighbouring country in the past 6 months and the scores of their answers are not significantly different. The fourth question shows that inhabitants of all the municipalities do not know anyone who smuggle. Comparing to other municipalities, the highest share of inhabitants, who know someone smuggling, is found in Gusev municipality. While the highest disagreement to this statement can be seen in Kingisepp and Ostrov municipalities. The last two statements revealed that the highest share of people, who know someone selling or buying goods which are later resold across the border as smuggled goods, are the inhabitants of Gusev municipality. Furthermore, the opinion of Sovetsk, Kingisepp, Ostrov, Pskov and Pechory inhabitants do not differ significantly as they deny knowing someone related to smuggling. To sum up, there are few statements on which the results of the municipalities of Russia do not differ significantly. However, in majority of the cases the attitudes of the respondents of the municipalities are distinct from each other. As a result, the municipalities have to be observed and analysed separately.

6.8. Ukraine Identification of border and inland municipalities. Border municipalities in Ukraine were selected based on the closest municipality on the other side of the frontier to already selected EU border municipalities (in this case selected municipalities of Romania) criteria. According to this criterion 6 municipalities were selected. After 6 Ukrainian border municipalities were identified, 18 inland municipalities, which were the closest to the border municipalities in terms of population and were not less than 60 km from the border, were selected. The list of Ukraine border and inland municipalities is provided in table below. Table 94: List of Ukraineâ&#x20AC;&#x2122;s border and inland municipalities before principal component analysis and PSM

Border municipalities

Inland municipalities

1. Hlyboka

1. Bobrynets`

10. Kozyatyn

2. Rahiv

2. Chyhyryn

11. Lypovets`

3. Snyatyn

3. Derazhnya

12. Monastyryshche

4. Storozhynets

4. Hadyach

13. Olevsk

5. Tyachiv

5. Haisyn

14. Ostroh

6. Vynohradiv

6. Haivoron

15. Pohrebyshche

7. Hlobyne

16. Talne

8. Horodyshche

17. Terebovlia

9. Kobelyaky

18. Tul'chyn

134

Source: compiled by authors

Data collection. The process of data collection consisted of collecting available data from open source databases. Later on, the missing relevant data was requested from different data holders. After a full dataset was gathered, the information was adjusted to various indexes in order to avoid the population influence in total numbers. The full Ukraine’s dataset consisted of 97 different statistical indicators for the period from 2012 to 2015. The data covered demographic, business, social welfare and other topics, which provided all the necessary information for further analysis. The main sources of the data: •

State Statistic Service of Ukraine

•

Pension Fund of Ukraine

•

Administrations of different municipalities

Data collection for Ukraine was quite successful; most of the necessary statistical parameters and dimensions were accessible. However, the information about vehicles was inaccessible because it was stored in a closed information system. A few indicators were also unavailable to get for certain cities. Overall, the data collection for Ukraine can be described as a successful one. Principal Component (PCA) and Propensity Score Matching (PSM) analysis. After the data set of Ukraine was conducted, the PCA and PSM analysis were executed in order to eliminate multicollinearity issues and construct factors, which would be helpful in matching the border and inland municipalities. In Ukraine’s case, the PCA was performed, after which a PSM analysis was performed using 15 factor coefficients obtained from PCA. The factors and their respective values are presented in the table below. Table 95: Factor values of Ukraine’s Propensity Score Matching Factor

Value

Factor

Value

FAC1

0.159

FAC9

1.979

FAC2

1.069

FAC10

-2.236

FAC3

-1.234

FAC11

-4.928

FAC4

-2.222

FAC12

-0.224

FAC5

-4.128

FAC13

-0.473

FAC6

-3.614

FAC14

-0.194

FAC7

-2.775

FAC15

-0.805

FAC8

-2.534

Source: compiled by authors Note: Bolded factors and their values have the biggest weight on the particular matching of Ukraine’s municipalities.

As the table shows, 15 factors were produced and four of those factors played a major role in determining specific matches in Ukraine. Thus, the profiles of the matched municipalities were based on the statistical indicators, which had the highest coefficients in loading of those particular 135

factors. It means that matched cities are akin to each other according to the patterns hidden in the loading of these factors. T main statistical indicators of factors and their description for matching Ukrainian municipalities are provided below. Table 96: Statistical indicators determining profiles of the municipality matching (Ukraine) Factor FAC5

Statistical indicators with the highest weight

Description

Population density, persons per 1km2;

Share of equipped total space in housing, % (drainage);

FAC5_1 is mainly made up of population density and welfare of the housing within the municipalities.

Dynamics of labour movement (accepted), per 1000 working-age persons;

Matches are based on the labour movement indicators.

Share of equipped total space in housing, % (water supply);

FAC6

Dynamics of labour movement (retired), per 1000 working-age persons; FAC11

Proportion of population in working age; City housing stock, m2 per 1000 population;

Factor shows the patterns of working age population (demographic structure) and housing stock of the municipalities.

From the information provided above, the matches of municipalities in Ukraine are based on the similarities in demographic structure of working age population, in dynamics of labour movement, in population density and in welfare state of the housing. Table 97: Details of Ukraine’s matched observations Border municipalities

Logit (Propensity score)

HLYBOKA

7.005

RAHIV

SNYATYN

STOROZHYNETS

TYACHIV VYNOHRADIV

136

Inland municipalities

Logit (Propensity score)

MONASTYRYSHCHE

-6.731

13.735

OLEVSK

-6.850

13.855

DERAZHNYA

-6.094

12.822

HAISYN

-6.645

13.372

KOZYATYN

-6.583

12.708

TALNE

-6.591

12.716

HORODYSHCHE

-6.318

13.043

TUL’CHYN

-6.546

13.270

TEREBOVLIA

-6.321

12.802

OSTROH

-6.487

12.968

BOBRYNETS’

-6.716

13.948

Distances

6.727

6.125

6.725

6.481 7.231

LYPOVETSâ&#x20AC;&#x2122;

-7.047

14.278

As it is shown in the table above each of the border municipality has a main corresponding inland municipality (marked in dark grey) and an alternative inland municipality (marked in light grey) which would be used in case some discrepancies with the data for main inland municipality arose. All of the 18 municipalities were used for the MIMIC index calculations in the later stages. 1-Factor Causal Clustering. After determining the exact matches of municipalities in Ukraine the next step was to prepare data to be suitable for the MIMIC model calculations. By using the 1Factor Causal Clustering analysis 9 latent variables were formed; a full list of latent variables is provided in Appendix 1 (Table 108). The constructed latent variables together with the source variables that were of significant value in discriminant analysis were used in the MIMIC model calculations for Ukraine. MIMIC. After the dataset of latent and source variables was prepared, the MIMIC model was constructed. The graphical representation of the MIMIC model is presented below. Figure 41: Graphical representation of the MIMIC model equation (Ukraine)

Source: compiled by authors Model represents causal and indicator variables that show significance in determining equation of shadow economy for Ukraine. The expanded version of Ukraineâ&#x20AC;&#x2122;s MIMIC model is presented in Appendix 2.

The graphical representation of the model shows that there are 5 causal variables that significantly impact the level of shadow economy in Ukraine. 3 of them impact the shadow economy negatively (decrease the level of shadow economy in Ukraine), and 2 of them affect the shadow economy positively (increase the size of shadow economy). The generic formula for Ukraine:

137

đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ?&#x2018;&#x153;đ?&#x2018;¤ = â&#x2C6;&#x2019;0.148 Ă&#x2014; đ??¸đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;&#x203A;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x201D;đ?&#x2018; + 0.152 Ă&#x2014; đ?&#x2018;&#x2C6;đ?&#x2018;&#x203A;đ?&#x2018;&#x2019;đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x2019;đ?&#x2018;&#x2018;đ?&#x2018;&#x192;đ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018; đ?&#x2018;&#x153;đ?&#x2018;&#x203A;đ?&#x2018; + 2.283 Ă&#x2014; đ??ˇđ?&#x2018;˘đ?&#x2018;&#x;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;&#x153;đ?&#x2018;&#x203A;đ?&#x2018;&#x153;đ?&#x2018;&#x201C;đ?&#x2018;&#x2C6;đ?&#x2018;&#x203A;đ?&#x2018;&#x2019;đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;Ą â&#x2C6;&#x2019; 0.0140 Ă&#x2014; đ?&#x2018; đ?&#x2018;˘đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018;&#x153;đ?&#x2018;&#x201C;đ?&#x2018;&#x201A;đ?&#x2018;&#x2122;đ?&#x2018;&#x2018;đ??´đ?&#x2018;&#x201D;đ?&#x2018;&#x2019;đ?&#x2018;&#x192;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x153;đ?&#x2018;&#x203A;đ?&#x2018;&#x2026;đ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x2013;đ?&#x2018;Łđ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018; â&#x2C6;&#x2019; 0.202 Ă&#x2014; đ?&#x2018;&#x192;đ?&#x2018;&#x2019;đ?&#x2018;&#x;đ?&#x2018; đ?&#x2018;&#x153;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2020;đ?&#x2018;˘đ?&#x2018; đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x2018;đ?&#x2018;&#x160;đ?&#x2018;&#x2013;đ?&#x2018;Ąâ&#x201E;&#x17D;đ??śđ?&#x2018;&#x;đ?&#x2018;&#x2013;đ?&#x2018;&#x161;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x201A;đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2019;đ?&#x2018; Based on this equation, the shadow economy indexes for each of the Ukraineâ&#x20AC;&#x2122;s border and inland municipality included in research were calculated. The table with the average indexes of shadow economy for the period from 2012 to 2015 is presented below. Table 98: Averages of shadow economy indexes of Ukraineâ&#x20AC;&#x2122;s municipalities, 2012-2015 Municipality type: Border (B) or Inland (I)

Municipality

Shadow economy index

Hlyboka

4,67

Monastyryshche

4,19

Olevsk

5,13

Rahiv

3,21

Derazhnya

4,11

Haivoron

2,67

Snyatyn

5,38

Kozyatyn

3,07

Talne

4,45

Storozhynets

6,02

Horodyshche

4,68

Tul'chyn

3,17

Tyachiv

3,53

Terebovlia

Ostroh

5,56

Vynohradiv

2,85

Bobrynets

3,80

Lypovets

3,42

4,3

Average of border towns

4,28

Average of inland towns

4,05

As we can see from the table above, there are four cases, in which the index of shadow economy is higher in the main corresponding inland municipalities than in the border municipality. Even though, on the average, the border municipalities have a higher index for shadow economy than the inland ones, it cannot be concluded that in generally speaking all the border municipalities have a higher level of shadow economy than the inland municipalities. 138

Economic and comparative analysis. In Ukraine’s case, 8 different economic indicators and 7 indicators, which describe registered income and welfare, were constructed. By analysing those indicators and comparing them between the border and inland municipalities of Ukraine, as well as between municipalities within each group, a few important insights were gained. Even without testing all the insights statistically, it can be stated that there is a tendency that on the average the administrations of border municipalities tend to spend two times more money and receive majorly higher grants from the EU. A rate of direct and indirect taxes differs as border municipalities tend to have a higher share of direct taxes. When analysing the welfare indicators, a major difference appeared exposed in the percentage of people who do not take a part in a labour market: the border municipalities have higher rate by 7.9%. The registered income of the border municipalities is also lower by approx. 19% in Ukraine. After executing the T-Test to find out whether differences appeared by no accident, in Ukraine’s case a big part of indicators showed the significance. From initial 15 indicators, 11 of them appeared to be significant. The significant differences between border and inland municipalities lies in: •

% not in labour market;

•

rule breaking per capita;

•

skill relevance;

•

quality of households;

•

% of labour movement;

•

unemployment rate;

•

employment rate;

•

total social insurance receivers;

•

EC grants received;

•

share of indirect tax;

•

share of direct tax.

Thus, based on the executed T-Test it is clear that the border and inland municipalities differ significantly according to economic, welfare and registered income indicators. Quantitative research. The results of Ukraine’s quantitative survey were used for comparison with EU countries in order to see whether the attitudes towards tax evasion and smuggling are similar between border municipalities of Ukraine and corresponding EU border municipalities. Furthermore, the results of the survey were used to compare whether the border municipalities of Ukraine have distinct attitudes or the answers have particular the patterns inherent to the whole group. In order to see the differences among Ukraine municipalities, the answers to the statements were transformed into indexes. The dark brown colour represents the answers with the highest level of disagreement, while the dark blue represents the answers with the highest level of agreement. The answers coloured in lighter shades represent inclination toward either agreement (light blue) or disagreement (light brown. A neutral option is represented by white colour. The main differences of the attitudes among municipalities are presented in the tables below.

139

Table 99: Attitude towards importance of paying taxes (Ukraine) Statement

Municipality Border Sniatyn Storozhynets

Hlyboka

Rakhiv

.33

.91

.51

-.52

1.28

3. I believe that money paid in taxes provides useful benefits for me.

-.63

4. I believe that money paid in taxes provides useful benefits for society.

1. I would rather pay direct taxes from my legal income than pay indirect taxes via goods I purchase (VAT tax, sales tax, etc.)

Tiachiv

Vynohradiv

.31

1.26

1.05

.50

-.58

1.25

1.49

.07

-.50

-.69

.30

-.52

.68

.22

-.26

.74

.86

.70

-.49

.08

.70

.01

-.17

.42

-1.05

-.63

.19

-.80

-.88

2. I disapprove of taxpayers who evade taxes.

5. I would cheat on taxes if I had the chance.

6. People should use every opportunity to not pay taxes.

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means complete agreement with statement, while -2 means complete disagreement.

The first topic reveals the attitude towards the importance of paying taxes in Ukraine. As the table above shows, the inhabitants of all the border municipalities show the agreement to rather pay direct taxes form legal income than pay indirect taxes via goods they purchase. Compared to the other municipalities, the lowest level of agreement with this statement is found in Hyboka, Sniatyn and Strorozhynets municipalities. The second statement shows that inhabitants of Rakhiv, Tiachiv and Vynohradiv disapprove those taxpayers who evade taxes, while the inhabitants of Hlyboka and Storozhynets disagree with this statement. As the table above shows, the inhabitants of Rakhiv, Tiachiv, Vynohradiv believe that money paid in taxes provide some useful benefits for them individually as well as for the society as a whole. The disagreement with both of the statements is seen in Hlyboka and Storozhynets. The people from Sniatyn do not believe that money paid in taxes provide useful benefits for them individually but remain neutral when it comes to benefits for society. The fifth statement shows that the inhabitants of Hlyboka and Storozhynets express the highest level of agreement to the statement that they would cheat on taxes if they had a chance. The inhabitants of Rakhiv and Vynohradiv show the highest level of disagreement to this statement. The last statement shows that the inhabitants of Hlyboka and Storozhynets express the highest level of agreement to the statement that people should use every opportunity not to pay taxes; the disagreement to these statements can be seen in Rakhiv, Sniatyn, Tiachiv and Vynohradiv municipalities.

140

Table 100: Attitude towards smuggling of goods (Ukraine) Statement

Municipality Border Sniatyn Storozhynets

Hlyboka

Rakhiv

1. I believe that smuggling cigarettes, alcohol products and fuel across the border and selling them there for a higher price is justifiable.

Tiachiv

Vynohradiv

.46

-.95

-.63

-.30

-1.13

-1.30

2. Have you heard about someone buying goods in your country to sell them and gain profit in neighbouring country?

.87

.23

.05

.64

.21

.47

3. Have you brought cigarettes across border from your country to neighbouring countries in the past 6 months?

-.85

-.97

-.98

-.83

-.86

-.78

.31

-.53

-.45

.09

-.52

-.36

5. Do you know anyone who sells goods that are later resold across border as smuggled goods?

.25

-.75

-.60

-.07

-.71

-.53

6. Do you know anyone who buys goods that are later resold across border as smuggled goods?

.17

-.73

-.54

-.10

-.69

-.53

4. Do you know anyone who has done that?

Source: compiled by authors Answer choices: Fully agree, partly agree, partly disagree, fully disagree, NA/DK. Index can have both positive and negative values. The range of the indexes is from -2 to 2, where 2 means completely agreement with statement, while -2 means completely disagreement (1). The range of index if rom -1 to 1, where 1 means yes and -1 means no (2-6).

The second topic reveals the attitude towards smuggling of goods in Ukraine. As the table above suggests, the only people who believe that smuggling cigarettes, alcohol products and fuel across the border and selling them there for a higher price is justifiable are in Hlyboka municipality. The highest level of disagreement to this statement is found in Rakhiv, Tiachiv and Vynohradiv municipalities, while people in Sniatyn incline towards disagreement. The second statement demonstrates that the inhabitants of Hlyboka and Storozhynets have heard about someone buying goods in Ukraine in order to sell them and gain profit in neighbouring country. Inhabitants of Vynohradiv incline towards agreeing to this statement. Although, the indexes are positive for all the municipalities, the highest level of disagreement to the second statement is seen in Rakhiv, Sniatyn and Tiachiv. An opposite situation is seen in the third statement, where all the indexes are negative meaning that people in all municipalities disagree to statement that they have brought cigarettes across border from Ukraine to neighbouring country in the past 6 months. Even so, the highest level of agreement to the statement was observed in Hlyboka, Storozhynets, Tiachiv, Vynohradiv, while strong disagreement is seen in Rakhiv and Sniatyn municipalities. From the results of the fourth statement it is seen that people in Hlyboka and Storozhynets know someone who has brought cigarettes from Ukraine to a neighbourhood country, while people in Rakhiv, Sniatyn, Tiachiv and Vynohradiv do not know anyone who has done that. The two last statements reveal that people in Hlyboka know someone who sells and buys goods that are later resold across border as smuggled goods, while people in Rakhiv, Sniatyn and Tiachiv do not know anyone proceeding such an activity. The respondents from Vynohradiv do not know anyone who buys goods that are later resold, and incline towards not knowing anyone who sells the goods like that. 141

To sum up, from the results of Ukraine survey analysis, it is clear that municipalities are not entirely identical to each other in terms to the attitude towards tax evasion and smuggling. However, the municipalities can be grouped into two categories, since the attitude of Hlyboka and Storozhynets are quite similar and the answers of remaining four municipalities have quite different patterns

142

Appendix 1 Table 101: Latent variables (Estonia) Latent variable L1

Short name

Full name

Beta

ExpGeneralServices

Expenditure of municipality budget on general government services, EUR per capita

-0,557

PopDensity

Population density at the beginning of the year, kmÂ˛

0,54

PurchasesOfTangibleAndIntangibleAssets

0,464

ExpPublicOrder

Deals in tangible and intangible assets included to municipal budget, purchases, EUR per working-age person Expenditure of municipality budget on public order and public security, EUR per capita

NumEducation

Number of employees in the education sector per 1000 working-age population

0,57

ValueAgricultureForestryFishing

Value added in the agriculture, forestry and fishing sector, thousand EUR per employee

0,501

ExpHousingCommunalEconomy

Expenditure of municipality budget on housing and communal economy, EUR per capita

-0,423

LandTax

Land taxes paid and included in municipal budgets per kmÂ˛, EUR

0,458

NumTransportationStorage

Number of employees in the transportation and storage sector per 1000 working-age population

0,375

NumberOfPoliceOfficers

Number of police officers per 100 000 population

0,476

SalesTangibleAndIntangibleAssets

0,281

Size

Deals in tangible and intangible assets included to municipal budget, sales, EUR per workingage person Size (km2)

ValueWholesaleRetail

0,726

ExpRecreationCultureReligion

Value added in the wholesale and retail trade; repair of motor vehicles and motorcycles sector, thousand EUR per employee Expenditure of municipality budget on recreation, culture and religion, EUR per capita

NumAgricultureForestryFishing

0,509

ValueEducation

Number of employees in the agriculture, forestry and fishing sector per 1000 working-age population Value added in the education sector, thousand EUR per person working in that sector

ExpEconomy

Expenditure of municipality budget on economy, EUR per capita

-0,279

ExpHealthCare

Expenditure of municipality budget on health care, EUR per capita

-0,336

NumHealthCareSocialWork

Number of employees in the human health and social work activities activities sector per 1000 working-age population

0,562

0,502

0,521

0,522

0,559

Latent variable L6

Short name

Full name

Beta

NumberOfDisabilityPensionReceivers

0,554

NumConstruction

Number of recipients of State social insurance work incapacity (disability) pensions per 1000 working-age population Number of employees in the construction sector per 1000 working-age population

Population

Average annual population

-0,46

SicknessBenefitPaidCases

Number of cases of State social insurance sickness benefit paid per 1000 population

0,575

OtherIncomeIncludedToMunicipalBudget

Other income included to municipal budget, EUR per working-age person

0,573

ValueArtsEntertainmentAndRecreation

Value added in the arts, entertainment and recreation sector, thousand EUR per employee

0,481

ValueInOtherServices

Value added in the in other service activities sector, thousand EUR per employee

0,505

InsuranceDisabilityPension

Average state social insurance disability pension, EUR

-0,668

NumInformationCommunication

0,668

L10

Earnings

Number of employees in the information and communication sector per 1000 working-age population Average earnings (monthly), EUR

L10

NumAccomodationFoodService

0,565

L10

NumWaterSupplyWasteRemediation

L11

NumElectricityGasSteam

L11

NumManufacturing

Number of employees in accommodation and food service activities sector per 1000 workingage population Number of employees in the water supply; sewerage, waste management and remediation activities sector per 1000 working-age population Number of employees in the electricity, gas, steam and air conditioning supply sector per 1000 working-age population Number of employees in the manufacturing sector per 1000 working-age population

L11

RecordedCriminalOffences

0,376

L11

ValuePublicAdministration

L12

Marriage Rate

Number of recorded criminal offenses per 1000 population, there was a change in the law (2015) that affects the numbers Value added in the public administration and defense; compulsory social security sector, thousand EUR per person working in that sector Marriage rate per 1000 population

L12

Value Construction

Value added in the construction sector, thousand EUR per employee

0,45

L12

ValueWaterSupplyWasteRemediation

0,571

L13

NumAdministrativeSupportService

L13

NumPublicAdministration

Value added in the water supply; sewerage, waste management and remediation activities sector, thousand EUR per employee Number of employees in the administrative and support service activities sector per 1000 working-age population Number of employees in the public administration and defense; compulsory social security sector per 1000 working-age population

144

0,532

-0,488

0,54 0,539 -0,327

0,5 0,563

-0,419 0,464

Latent variable L13

Short name

Full name

Beta

PersonsSuspectedOfCriminalOffences

Number of persons suspected of (charged with) criminal offences per 1000 population

0,449

L13

ValueFinancialInsuranceActivities

0,409

L14

EnvironmentalPollutionTaxes

L14

ExpEnvironmentalProtection

Value added in the financial and insurance activities sector, thousand EUR per person working in that sector Environmental pollution taxes paid and included in municipal budgets per 1 working-age person, EUR Expenditure of municipality budget on environmental protection, EUR per capita

L15

SmugglingCases

Number of smuggling cases recorded per 1000 population

0,292

L15

ValueElectricityGasSteam

-0,746

L15

ValueInformationCommunication

L16

BusinessEntities

L16

ValueHealthCareSocialWork

Value added in the electricity, gas, steam and air conditioning supply sector sector, thousand EUR per per person working in that sector Value added in the information and communication sector, thousand EUR per person working in that sector Number of business entities in operation at the beginning of the year per 1000 working-age population Value added in the human health and social work activities sector, thousand EUR per employee

L16

ValueTransportationStorage

Value added in the transportation and storage sector , thousand EUR per employee

-0,4

L17

Employed

Employed persons per 1000 population

0,397

L17

PopulationInWorkingAge

Proportion of population in working age (%)

0,439

L17

ValueRealEstate

Value added in the real estate activities sector services sector, per person working in that sector

-0,715

Source: compiled by authors

145

0,681 0,681

0,512 0,523 0,567

Table 102: Latent variables (Latvia) Latent variable L1

Short name

Full name

Beta

ExpEducationCapita

Expenditure on education, EUR per capita

ExpPublicOrderAndSecurityCapita

Expenditure on public order and public security, EUR per capita

TinElectricityGasPerPersonW

Turnover in Electricity, gas, steam and air conditioning supply per employee

BusinessEntInOperationPer1000work

EUFundsERAFesfKFCapita

Number of business entities in operation at the beginning of the year per 1000 working-age population EU funds (ERAF, ESF, KF), EUR per capita

0,523 0,529 -0,506 -0,519

TinAccommodationAndFoodServicePerPersonW

Turnover in Accommodation and food service activities per employee

EarningsGross

Average earnings (monthly) gross

ExpEconomyCapita

Expenditure on economy, EUR per capita

Population

Average annual population

TinHumanHealthPersonAndSocialWorkPerPersonW

Turnover in Human health and social work activities per employee

ExpRecreationCultureAndReligionCapita

Expenditure on recreation, culture and religion, EUR per capita

MarriagePer1000

Marriage rate per 1000 populaton

UnemployedPer1000

Registered unemployed persons per 1000 population

ExpHealthCareCapita

Expenditure on health care, EUR per capita

TinConstructionPerPersonW

Turnover in Construction per employee

TinFinancialAndInsuranceActivitiesPerPersonW

Turnover in Financial and insurance activities per employee

DisabilityPension

Average state social insurance disability pension, EUR

ExpEducationPerStudent

Expenditure on education per 1 student, EUR

EconomicEntitiesInOperationPer1000work

Economic entities in operation at the beginning of the year per 1000 working-age population

TinAgricultureForestryAndFishingPerPersonW

Turnover in Agriculture, forestry and fishing per employee

TinEducationPerPersonW

Turnover in Education per employee

IncomeTaxAdressEmployeePer1000

Income tax according to address of employer per 1000 working-age population

PopulationDensity

Population density at the beginning of the year, kmÂ˛

FamiliesReceiveApartBenefitPer1000

Number of families that receive apartment benefit per 1000 population

146

0,500 0,547 0,565 0,300 -0,276 0,562 0,43 0,387 -0,737 -0,545 0,553 0,482 0,667 -0,667 0,521 0,521 -0,518 0,606 0,606 -0,555

Latent variable L9

Short name

Full name

Beta

PopulationWorkingAge

Proportion of population in working age

TinAdministrativeAndSupportPerPersonW

Turnover in Administrative and support service activities per employee

L10

EmployeesInEducationPer1000

Number of employees in Education per 1000 working-age population

L10

EmployeesInElectricityGasSteamAndAirConditioni ngSupplyPer1000 EmployeesInProfessionalScientificAndTechnicalAct ivitiesPer1000

Number of employees in Electricity, gas, steam and air conditioning supply per 1000 workingage population Number of employees in Professional, scientific and technical activities per 1000 working-age population

0,540 0,502 0,531 0,475

L10

Source: compiled by authors

147

0,571

Table 103: Latent variables (Lithuania) Latent variable L1

Short name

Full name

Beta

IndustryVal

Value of industry sectors

0,416

LTUvsAP

0,287

NumFLangPup

Long-term unemployment per 1000 working-age person mean vs Available job positions per 1000 working-age person mean Proportion of general school pupils studying foreign languages, % mean

TaxEvasions

Tax evasion cases recorded during the audit per 1000 population mean

0,537

L10

AgeingIndex

Indexes of ageing at the beginning of the year mean

0,525

L10

PrivateCars

Number of private cars at the end of year per 1000 population mean

0,573

L10

ServicesEmpl

Number of employees in services sectors

0,474

L11

FourWheel

Number of four-wheel vehicles per 1000 population mean

-0,344

L11

Mopeds

Number of mopeds per 1000 population mean

0,465

L11

MvsD

Marriage rate vs Divorce rate

0,325

L11

OtherIncBudg

Other income included to municipal budget, EUR per working-age person mean

0,244

L11

RealEstTaxes

Real estate taxes paid and included in municipal budgets per 1 working-age person, EUR mean

0,475

L12

AvgDaysSocIns

0,564

L12

ExpenditureGen

Average number of days of State social insurance sickness benefit paid per 1 working person mean Expenditure of municipality budget on general government services, EUR per capita mean

L12

InvestmentFA

Investment in tangible fixed assets per capita, EUR mean

0,563

L13

Crimes

Number of recorded criminal offenses per 1000 population mean

0,574

L13

NumSickBen

Number of cases of State social insurance sickness benefit paid per 1000 population mean

-0,484

L13

PoliceOfficers

Number of police officers per 100 000 population mean

0,435

L13

Unemployment

Registered unemployed persons per 1000 population mean

0,263

L14

GrowthExpenditure

Expenditure of municipality budget on growth services

-0,498

L14

PopChange

Proportion of natural population change compared to whole population mean

0,578

L14

RedWorkCapacity

0,486

L15

FamSocRisk

Number of persons of working age with a reduced level of capacity for work established for the first time per 1000 population mean Number of families at social risk at the end of the year per 1000 population mean

148

0,524

0,456

-0,745

Latent variable L15

Short name

Full name

Beta

GenSchGrads

Educational attainment of general school pupils graduates per 1000 general school pupils mean

0,541

L15

VocationalGrads

Graduates of vocational training institutions per 1000 working-age population mean

0,249

L16

AvgOldPens

Average state social insurance old age pension, EUR mean

0,487

L16

Grants

Grants included to municipal budget, EUR per working-age person mean

0,5

L16

MenAge

Median age of the population at the beginning of the year, year (men) mean

0,565

L17

DeclarationTax

Tax arrears from declarations, EUR per person mean

0,568

L17

Dependency14

Dependency ratio at the beginning of the year, persons (0-14 y.o.) mean

0,544

L17

WorkingAgePerc

Proportion of population in working age mean

0,457

L18

GoodsServTax

Taxes on goods and services included to municipal budget, EUR per working-age person mean

-0,521

L18

ServicesVal

value of services sectors

0,502

L18

ValueForFish

0,529

L19

NewToOldSMEs

Value added in the forestry and fisheries sector, thousand EUR per per person working in that sector mean Number of new to disbanded SMEs

L19

SocServChildren

0,526

L19

TotalTaxPaid

Number of children from families at social risk who received social services in day care centers per 1000 population mean Total tax paid per inhabitant

Area

Average area of , mÂ˛ mean

0,493

Motorcycles

Number of motorcycle per 1000 population mean

-0,45

Smugglers

Number of smuggling cases recorded per 1000 population mean

0,365

WomenToMen

Number of women per 1000 men at the beginning of the year mean

0,438

Age

Median age of the population at the beginning of the year, year (men and women) mean

-0,472

DisRecipients

0,755

ValueInfComm

Criminals

Number of recipients of State social insurance work incapacity (disability) pensions per 1000 working-age population mean Value added in the information and communication sector, thousand EUR per person working in that sector mean Number of persons suspected of (charged with) criminal offences per 1000 population mean

SocRiskChildren

Number of children in families at social risk at the end of the year per 1000 population mean

0,483

Tourists

Number of tourists accommodated per 1000 population mean

-0,547

149

0,556

0,486

0,333 0,542

Latent variable L5

Short name

Full name

Beta

DomViolence

Cases of domestic violence reported to the police per 1000 population mean

0,446

EvsI

Emigrants/Immigrants

-0,351

NetIntMigration

Net internal migration per 1000 population mean

0,579

Trucks

Number of trucks per 1000 population mean

0,378

AvgIncPens

Average state social insurance indemnity for work incapacity, EUR mean

-0,352

ExpenditureHousing

Expenditure of municipality budget on housing and communal economy, EUR per capita mean

0,334

IncRecipients

0,489

RecSickBen

Number of recipients of state social insurance pensions for work incapacity per 1000 workingage population mean Number of recipients of social assistance benefit per 1000 population mean

SocialExp

0,192

AccEstablPop

Rate of expenditure on lump-sum allowances for persons eligible for social support paid from the municipal budget, EUR per capita mean Number of accommodation establishments per 1000 population mean

TotalVal

Total value of economy

-0,546

Trailers

Number of trailers per 1000 population mean

0,5

DealsBudget

0,478

EmergencyCare

Deals in tangible and intangible assets included to municipal budget, EUR per working-age person mean Number of persons who received emergency medical care per 1000 population mean

NewDwellings

Number of dwellings completed per 1000 population mean

0,508

TempResidents

Number of persons staying in temporary residence institutions per 1000 population mean

0,522

Dependency65

Dependency ratio at the beginning of the year, persons (65+ y.o.) mean

-0,051

ForInvestment

Foreign direct investment per capita at the end of the year, EUR mean

0,516

GroupCriminals

Number of group criminal offences per 1000 population mean

0,057

PassengerVehichles

Number of passenger vehicles per 1000 population mean

0,146

Pop Density

Population density at the beginning of the year, kmÂ˛ mean

0,533

150

0,427

0,557

0,236

Source: compiled by authors

Table 104: Latent variables (Romania) Latent variable

Short name

Full name

Beta

TurnForestryAndFishing

Turnover in the forestry and fisheries sector, thousand EUR per employee

-0,5

TurnMiningAndQuarrying

Turnover in mining and quarrying, manufacturing sector, thousand EUR per employee

0,29

TurnRealEstate

Turnover in real estate operations sector, thousand EUR per employee

0,747

ExpFuelAndEnergy

Expenditure on Fuel and energy, from local budget, EUR per capita

0,539

TurnProfessioanlScientificTechnicalSector

Turnover in the professional, scientific and technical sector, thousand EUR per employee

0,518

TurnTransportAndStorage

Turnover in the transport and storage sector, thousand EUR per employee

-0,521

ExpHousingAndCommunalEconomy

Expenditure on housing and communal economy, EUR per capita

0,466

TurnArtsEntertainmentRecreation

0,564

WomenPerMen

Turnover in the arts, entertainment and recreation activities, computer, personal and household goods repair and other service activities sector, thousand EUR per employee Number of women per 1000 men at the beginning of the year

PopDensity

Population density at the beginning of the year, kmÂ˛

0,586

PopInWorkingAge

Proportion of population in working age

-0,574

TurnElectricityGasWaterSupply

0,405

EmpForestryAndFishing

Turnover in electricity, gas and water supply, waste management sector, thousand EUR per person Number of employees in the forestry and fishing sectors per 1000 working-age population

MarriageRate

Marriage rate per 1000 population

0,527

TurnEducationHealthcare

Turnover in the education, health care and other utilities and social services sector, thousand EUR per employee

0,527

Source: compiled by authors

151

0,524

-0,489

Table 105: Latent variables (Belarus) Latent variable L1

Full name

Short name

Beta

Earnings

Average earnings (monthly), EUR

-0,474

NonTaxRevenuesIncludedToMunicipalPer1w

Non-tax revenues included to municipal budget, EUR per working-age person

0,553

NumOfBusinessEnterprisesInOperationPer1000w

0,543

DependencyRatioYoung

Number of business enterprises in operation at the beginning of the year per 1000 working-age population Dependency ratio at the beginning of the year, persons (0-14 y.o.)

ExpOnDefensePerCapita

Expenditure of municipality budget on defense, EUR per capita

-0,562

WomenPer1000Men

Number of women per 1000 men at the beginning of the year

0,569

AverageNumberEmployeesInBusinessEntity

Average number of employees in a business entity

-0,422

CrudeMarriagePer1000

Crude marriage rate per 1000 populaton

0,457

ExpOnHealthCarePerCapita

Expenditure of municipality budget on health care, EUR per capita

0,407

ExpOnPhysicalEducationPerCapita

0,438

EmigrantsPer1000

Expenditure of municipality budget on physical education, sport, culture and media, EUR per capita Number of emigrants per 1000 population

OtherTaxPer1w

0,485

VATper1w

Other taxes, charges and revenues from taxes included to municipal budget, EUR per workingage person VAT, EUR per working-age person

ExpOnSocialPolicyPerCapita

Expenditure of municipality budget on social policy, EUR per capita

0,423

NetInternationalMigrationPer1000

Net international migration per 1000 population

-0,555

RealEstateTaxesPaidToMunicipalPer1w

Real estate taxes paid and included in municipal budgets per 1 working-age person, EUR

0,577

NumberOfJuvenileCrimesPer1000

Number of juvenile crimes per 1000 population

0,517

OtherNonTaxRevenuesIncludedToMunicipalPer1w

Other non-tax revenues included to municipal budget, EUR per working-age person

0,233

PopDensity

Population density at the beginning of the year, people per kmÂ˛

-0,515

RevenuesFromIncomeGeneratingActivitiesPer1w

0,458

FDIperCapita

Revenues from income-generating activities included to municipal budget, EUR per working-age person Foreign direct investment per capita at the end of the year, EUR

IndexOfAgriculturalProduction

Agricultural production index, % to the previous year

0,53

152

0,422

0,507

0,575

0,565

Latent variable L7

Full name

Short name

Beta

RetailUnitsPer1000

Retail units per 1000 population

0,5

ExpOnEducationPerCapita

Expenditure of municipality budget on education, EUR per capita

0,57

ExpOnEnvironmentalProtectionPerCapita

Expenditure of municipality budget on environmental protection, EUR per capita

0,533

IndexOfIndustrialProduction

Index of industrial production, % to the previous year

0,369

UnemployedPer1000

Registered unemployed persons per 1000 population

-0,24

ExpOnEconomyPerCapita

Expenditure of municipality budget on economy, EUR per capita

0,59

ExpOnGeneralGovernmentServicesPerCapita

Expenditure of municipality budget on general government services, EUR per capita

-0,718

TollsPaidToMunicipalPer1w

Tolls paid and included in municipal budgets per 1 working-age person, EUR

0,157

L10

IndustrialOutputPerWorkingPop

Industrial output, EUR per working population

0,556

L10

NumOfGroupCriminalOffensesPer1000

Number of group criminal offences per 1000 population

-0,497

L10

RetailTurnoverPerCapita

Retail turnover per capita, EUR

0,537

L11

EmployedPer1000

Employed persons per 1000 population

0,485

L11

ImportTurnoverServicesThousandPer1000

Import turnover of goods, thousand EUR per 1000 population

0,547

L11

Population

Population at the beginning of the year (district)

-0,515

Source: compiled by authors

153

Table 106: Latent variables (Moldova) Latent variable L1

Short name

Full name

Beta

BirthRatePer1000

Crude birth rate per 1000 population

0,492

Number of employees in the professional, scientific and technical sector per 1000 population

0,667

EmployeesInProfessionalScientificTechnicalPer100 0 InvestmentInAssetsPerCapitaEur

Investment in tangible fixed assets per capita, EUR

-0,407

AccomodationPer1000

Number of accommodation establishments per 1000 population

0,697

ProportionInPreschool

Proportion of children in preschool education, compared to all children aged 1â&#x20AC;&#x201C;6*

0,293

SocialInsuranceOldPer1000WorkAge

Average state social insurance old age pension, EUR

-0,56

CriminalOffencesPer1000

Number of recorded criminal offenses per 1000 population

-0,56

EmployeesInConstructionPer1000

Number of employees in the construction sector per 1000 population

0,555

NaturalPopChangePer1000

Natural population change compared to whole population

0,452

ChargedCriminalOffencesPer1000

Number of persons suspected of (charged with) criminal offences per 1000 population

0,504

EmployeesInElectricityGasWaterWastePer1000

0,519

SocialInsuranceDisabilityPer1000

EmployeesInAccomodationCateringPer1000

Number of employees in the electricity, gas and water supply, waste management sector per 1000 population Average number of persons receiving state social insurance disability pensions per 1000 population Number of employees in the accommodation and catering sector per 1000 population

GenSchoolPupilsPer1000

Number of general school pupils per 1000 population

0,533

SocialInsuranceWorkIncapicityPer1000

-0,51

AgeingIndex

Average number of persons receiving state social insurance pensions for work incapacity per 1000 population Indexes of ageing at the beginning of the year

DivorceRatePer1000

Crude divorce rate per 1000 population

0,11

Population

Average annual population

0,62

BudgetIncomePer1WorkingAgeTransfers

Municipal budgets income, EUR (own income) per 1 working-age population

0,69

SMEsRegisteredPer1000

0,616

SocialAssistanceBenefitsPer1000

Number of small and medium enterprises registered over the year per 1000 working-age population Number of recipients of social assistance benefit per 1000 population

BudgetIncomePer1WorkingAgeSpecialMeans

Municipal budgets income, EUR (transfers) per 1 working-age population

0,532

154

0,52 0,561

-0,708

0,109

Latent variable L8

Short name

Full name

Beta

EmployeesInInformationCommunicationsPer1000

Number of employees in the information and communication sector per 1000 population

0,56

GroupCriminalOffencesPer1000

Number of group criminal offences per 1000 population

0,464

BudgetIncomePer1WorkingAgeOwn

Municipal budgets income, EUR (own income) per 1 working-age population

-0,598

EconomicEntitiesPer1000

0,167

EmployeesInEducationHealthSocialServicesPer1000

Number of economic entities in operation at the beginning of the year per 1000 working-age population Number of employees in the education, health care and other utilities and social services sector per 1000 population

Source: compiled by authors

155

0,718

Table 107: Latent variables (Russia (incl. Kaliningrad)) Latent variable L1

Short name

Full name

Beta

DependencyOld

Dependency ratio at the beginning of the year, persons (65+ y.o.)

0,517

DivorceRatePer1000

Divorce rate per 1000 population

0,499

GratuitousReceiptsPerWorkingAgeEur

Gratuitous receipts per working-age person, thousand EUR

-0,526

EmmigrantsPer1000

Number of emigrants per 1000 population

0,676

ValueGoodsAndServicesEurElectricityGasWater

0,676

L3 L3

EmployeesInTransportCommunicationsPer1000Wor kingAge SoldAssetsPerWorkingAgeEur

Own goods produced and shipped, own works and services performed (without small businesses) per employed people, EUR Number of employees in the transport and communications sector per 1000 working-age population Incomes of sold tangible and intangible assets per working-age person, thousand EUR

WomenPer1000Men

Number of women per 1000 men at the beginning of the year

0,506

DwellingsActivatingPer1000

Number of dwellings activating on a territory of municipal district per 1000 population, mÂ˛

0,184

ExpenditureOnEducationGovermentalPerCapitaEur

Expenditure of municipality budget on education, EUR per capita

-0,57

SMEsRegisteredPer1000WorkingAge

0,732

EmployeesInHealthSocialServicesPer1000Working Age ExpenditureOnHousingCommunalGovermentalPerC apitaEur PavilionsPer1000

Number of small and medium enterprises registered over the year per 1000 working-age population Number of employees in the health care and social services sector per 1000 working-age population Expenditure of municipality budget on housing and communal economy, EUR per capita Number of pavilions per 1000 population

0,529

AgeWomen

Median age of the population at the beginning of the year, year (women)

-0,14

InvestmentFromOrganizationsInFixedCapitalEurPer Capita PopDensity

Investments in fixed capital made by organizations in the territory of municipal district (excluding small businesses), EUR per capita Population density at the beginning of the year, kmÂ˛

0,517

MarriageRatePer1000

Own goods produced and shipped, own works and services performed (without small businesses) per employed people, EUR Environmental pollution taxes paid and included in municipal budgets per 1 working-age person, EUR Marriage rate per 1000 populaton

0,508

ValueGoodsAndServicesEurQuarryingManufacturin g EnviromentalPollutionsTaxPer1WorkingAge

L6 L6

156

0,507 -0,536

-0,517 0,51

0,123

0,518 0,526

Latent variable L7

Short name

Full name

Beta

ValueGoodsAndServicesEur

0,552

BirthRatePer1000

Own goods produced and shipped, own works and services performed (without small businesses) per employed people, EUR Birth rate per 1000 population

DependecyYoung

Dependency ratio at the beginning of the year, persons (0-14 y.o.)

-0,368

Number of employees in the electricity, gas and water supply, waste management sector per 1000 working-age population Number of settlements supplied not with gas per 1000 population

0,49

EmployeesInElectricityGasWaterWastePer1000Wor kingAge SettlementsWithoutGasPer1000

DeathRatePer1000

Death rate per 1000 population

-0,181

Number of employees in the quarrying and manufacturing sector per 1000 working-age population Income tax included to municipal budget, EUR per working-age person

0,533

EmployeesInQuarryingManufacturingPer1000Worki ngAge IncomeTaxPerWorkingAgeEur

SMEsRemovedPer1000WorkingAge

0,463

L10

EmployeesInFinancialPer1000WorkingAge

Number of small and medium enterprises removed from the register over the year per 1000 working-age population Number of employees in the financial sector per 1000 working-age population

L10

EmployeesInRealestatePer1000WorkingAge

Number of employees in real estate operations sector per 1000 working-age population

0,445

L10

ExpenditureOnEconomyGovermentalPerCapitaEur

Expenditure of municipality budget on economy, EUR per capita

0,181

L10

Expenditure of municipality budget on social security, EUR per capita

-0,423

L10

ExpenditureOnSocialSecurityGovermentalPerCapita Eur NaturalPopChangePer1000

Rate of natural population change per 1000 population

0,352

L11

AgeingIndex

Indexes of ageing at the beginning of the year

0,534

L11

Expenditure of municipality budget on environmental protection, EUR per capita

-0,241

L11

ExpenditureOnEnviromentalGovermentalPerCapita Eur PeopleInHazardousDwellingsPer1000

Number of people living in dilapidated and hazardous dwellings per 1000 population

0,543

L12

AlcoholDecalitersPerCapita

Retail trade turnover of alcohol, decaliters per capita

-0,507

L12

DwellingsSize

Average size of dwellings activating during a year on a territory of municipal district, mÂ˛

0,735

L12

ExpenditureOnGeneralGovermentalPerCapitaEur

Expenditure of municipality budget on general government services, EUR per capita

0,296

157

0,409

-0,455

-0,515

0,439

Source: compiled by authors

Table 108: Latent variables: Ukraine Latent variable L1

Short name

Full name

Beta

OperatingAgricultureEntitiesPer1000

Number of operating economic entities in agriculture, per 1000 natural population

0,653

TransportRoadCommTelcoITExpensesPer1000w

-0,67

EducationExpensesPer1000w

Transport, road transport, communications, telecommunications and IT expenses, EUR per 1000 working-age population Education expenses, EUR per 1000 working-age population

The number of children who were in the summer in children's health and recreation facilities, per 1000 natural population Official transfers, EUR per 1000 working-age population

0,488

NumberOfChildrenInSummerHealthAndRecreation Per1000 OfficialTransfersPer1000w

ProfitabilityOfAgriculuralOperatingActivities

Profitability of agricultural enterprises from operating activities, %

0,385

AdminChargesForNonProfitBusinessPer1000w

0,455

MarriagePer1000

Administrative charges and payments, income from non-profit business, EUR per 1000 working-age population Marriage rate per 1000 population

RetailShopsPer1000

Number of retail shops, per 1000 natural population

-0,556

ProportionInWorkingAge

Proportion of population in working age

0,49

RetiredLabor

Dynamics of labor movement (retired), per 1000 working-age persons

-0,542

SubventionsPer1000w

Subventions, EUR per 1000 working-age population

0,525

CultureAndArtExpensesPer1000w

Culture and art expenses, EUR per 1000 working-age population

0,377

FeeForAdministrativeServicesPer1000w

Fee for providing administrative services, EUR per 1000 working-age population

-0,495

FeeForSubsoilPer1000w

Fee for using subsoil, EUR per 1000 working-age population

0,305

RegisteredSMES

0,53

InvestmentInTangibleAssets

Number of small and medium enterprises registered over the year per 1000 working-age population Investment in tangible fixed assets per capita, EUR

SocialPension

The average size of a social pension, EUR

-0,572

TaxRevenuesIncomePer1000w

Tax revenues income, EUR per 1000 working-age population

0,457

DependencyYoung

Dependency ratio at the beginning of the year, persons (0-14 y.o.)

0,511

HealthProtectionExpensesPer1000w

Health protection expenses, EUR per 1000 working-age population

0,564

158

0,386

0,456

0,547

Latent variable L7

Short name

Full name

Beta

Tax revenues income, EUR per 1000 working-age population

0,499

TaxRevenuesIncomeIncreasingMarketValuePer1000 w AcceptedLabor

Dynamics of labor movement (accepted), per 1000 working-age persons

0,515

GovernmentExpensesPer1000w

Governance expenses, EUR per 1000 working-age population

0,498

UtilityExpensesPer1000w

Utilities expenses, EUR per 1000 working-age population

0,547

ChargeSpecialUseOfForestResourcesPer1000w

Charge for special use of forest resources, EUR per 1000 working-age population

-0,507

L9 L9

DivorcePer1000 EconomicEntitiesPer1000w

Divorce rate per 1000 population Economic entities in operation at the beginning of the year per 1000 working-age population

0,515 0,553

Source: compiled by authors

159

Appendix 2 Estonia Causal variables: 1. Total tax paid 2. Indirect - indirect tax share 3. L3 4. L14 5. L6 6. L15 7. L5 8. SMEsR - SMEs removed from register over year 9. L9 10. L11 11. L10 12. L12 13. L13 14. L8 15. L7 16. L1 17. L2 18. L4 19. Value added in professional services, scientific and technical services 20. Value added in manufacturing sector 21. Number of women per 1000 men 22. Ageing index 23. Dependency index (old age) Indicator variables: 1. Income tax collected 2. IncomePe - income per working age person (base indicator) 3. L17 4. L16 5. Divorce rate

Figure 42 T-values of the model (Estonia)

161

Figure 43: Graphical representation of the model (Estonia)

Shadow = - 0.338*SMEsR + 0.147*Indirect - 0.104*L2 + 0.407*L6 - 0.359*L7 + 0.251*L13 Fit of the model: AIC (Akaike, 1974)* Maximum Likelihood Ratio Chi-Square (C1) Root Mean Square Error of Approximation (RMSEA) P-Value for Test of Close Fit (RMSEA < 0.05) Normed Fit Index (NFI) Goodness of Fit Index (GFI)

Latvia Causal variables: 1. Total tax paid 2. Number of criminal offences 3. Indirect - indirect tax share (Property Tax) 162

503.873 7.573 (P = 0.0557) 0.130 0.101 0.972 0.980

4. Registered enterprises over year 5. L2 6. L7 7. Selfe â&#x20AC;&#x201C; number of self-employed persons 8. Turnover in real estate sector 9. L8 10. L4 11. L3 12. L1 13. L5 14. L9 15. Insurance old age pension 16. Benefit paid in case of no bread winner 17. L6 Indicator variables: 1. Income p - Income per working age (main variable, which are being affected negatively by SE) 2. Number of persons employed 3. L10 4. Employee - number of employees in construction sector 5. Number of employees in wholesale and retail, trade, repair of motor vehicles and motorcycles sectors 6. Accomod - number of employees in accommodation and food service activities sector

163

Figure 44: T-values of the model (Latvia)

Figure 45: Graphical representation of the model (Latvia)

Shadow = 0.205*Selfe + 0.374*Indirect - 0.467*L1 - 0.291*L3 Fit of the model: 164

AIC (Akaike, 1974)* Maximum Likelihood Ratio Chi-Square (C1) Root Mean Square Error of Approximation (RMSEA) P-Value for Test of Close Fit (RMSEA < 0.05) Normed Fit Index (NFI) Goodness of Fit Index (GFI)

165

335.655 12.005 (P = 0.2130) 0.0681 0.337 0.961 0.962

Lithuania Causal: 1. L1 2. Indirect - indirect tax share 3. Earni - average earnings 4. L13 5. L2 6. L4 7. L12 8. L15 9. L11 10. L10 11. Net migration 12. Number of employees in industry sectors 13. Old recipients 14. L16 15. L17 16. L8 17. L3 Indicator: 1. Income per working age person â&#x20AC;&#x201C; the base indicator 2. L19 3. Number of business enterprises, SMEs and economic entities 4. L5 5. L7 6. L14 7. L6

166

Figure 46: T-values of the model (Lithuania)

Figure 47: Graphical representation of the model (Lithuania)

Shadow = - 1.099*Earni + 0.267*L12 - 0.377*Indirect - 0.109*L13 Fit of the model: AIC (Akaike, 1974)* Maximum Likelihood Ratio Chi-Square (C1)

167

351.592 0.210 (P = 0.6470)

Root Mean Square Error of Approximation (RMSEA) P-Value for Test of Close Fit (RMSEA < 0.05) Normed Fit Index (NFI) Goodness of Fit Index (GFI)

168

0.0 0.680 0.999 0.999

Romania Causal variables that are included in calculations: 1. Number of unemployed persons 2. Number of economic entities 3. SMEsR- number of SMEs removed 4. Number of SMEs registered 5. Number of immigrants 6. Rate of international migration 7. L1 8. L2 9. L3 10. L4 11. Tourists - number of tourists accommodated 12. Rate of internal migration Indicator variables that are included in calculations: 1. ValueAdd- value added per all business sectors per capita (base indicator) 2. Number of employees in real estate sector 3. Number of employees in electricity, gas and water supply sector 4. Number of employees in transport and storage sector 5. EmpInfor - number of employees in information and communication sector Figure 48: T-values of the model (Romania)

169

Figure 49: Graphical representation of the model (Romania)

Shadow = - 0.245*SMEsR - 0.157*Tourists - 0.181*L2 - 0.390*L3 - 0.276*L4 Fit of the model: AIC (Akaike, 1974)* Maximum Likelihood Ratio Chi-Square (C1) Root Mean Square Error of Approximation (RMSEA) P-Value for Test of Close Fit (RMSEA < 0.05) Normed Fit Index (NFI) Goodness of Fit Index (GFI)

170

325.865 7.519 (P = 0.1109) 0.111 0.172 0.967 0.972

Belarus Initial causal variables: 1. L1 2. L7 3. Recorded criminal offences 4. Total tax paid per capita 5. Indirect tax share 6. L9 7. L10 8. L8 9. Fines and deductions included to municipal per 1 working age person 10. Collected profit tax per business enterprise 11. L3 12. L4 13. L5 14. L6 15. Investment in tangible fixed assets per capita 16. Land tax paid to municipal 17. Receivables organizations per business enterprise 18. Number of immigrants 19. L2 20. Dependency ratio (old) Initial indicator variables: 1. Earnings per employed person â&#x20AC;&#x201C; base indicator 2. L11 3. Share of employed persons in active population 4. Persons suspected with criminal offences 5. Revenue from sales per business enterprise 6. Import turnover from goods 7. Import turnover of services 8. Export turnover of goods 9. Export turnover services

171

Figure 50: T-values of the model (Belarus)

172

Figure 51: Graphical representation of the model (Belarus)

SHADOW = 8.684*X18 - 0.476*X4 + 90.088*X5 - 2.303*X11 + 12.602*X13 + 8.371*X14 5.018*X6 + 4.408*X7 Fit of the model: AIC (Akaike, 1974)* Maximum Likelihood Ratio Chi-Square (C1) Root Mean Square Error of Approximation (RMSEA) P-Value for Test of Close Fit (RMSEA < 0.05) Normed Fit Index (NFI) Goodness of Fit Index (GFI)

173

66.306 (P = 0.0000) 0.545 0.000 0.788 0.972

Moldova Initial causal variables:

1. 2. 3. 4. 5. 6. 7. 8.

L3 L4 L5 L6 L1 Number of women per 1000 men Dependency rate (young) Dependency rate (old)

Initial indicator variables:

1. 2. 3. 4. 5. 6. 7. 8. 9.

Earnings per working age persons (base indicator) Number of persons employed per 1000 Number of employees in agriculture sector Number of employees in wholesale, retail, vehicle repair sectors Number of employees in real estate sector Number of employees in administrative sector L7 L8 L9

174

Figure 52: T-values of the model (Moldova)

175

Figure 53: Graphical representation of the model (Moldova)

SHADOW = 1.762*X7 - 0.0892*X6 + 1.790*X1 - 3.188*X3 The model is ideal: AIC (Akaike, 1974)* Maximum Likelihood Ratio Chi-Square (C1) Root Mean Square Error of Approximation (RMSEA) P-Value for Test of Close Fit (RMSEA < 0.05) Normed Fit Index (NFI) Goodness of Fit Index (GFI)

176

1305.991 0.000457 (P = 0.9829) 0.000

0.950 0.950

Russia (incl. Kaliningrad) Initial Causal variables: 1. Total tax paid 2. Indirect tax share 3. L4 4. L5 5. Number of kiosks and booths 6. L9 7. L10 8. Municipal expenditure on culture 9. L8 10. Grants received 11. L7 12. L6 13. L2 14. L3 15. L1 Initial indicator variables: 1. Government expenditure (base indicator) 2. L12 3. L11

177

Figure 54: T-values of the model (Russia (incl. Kaliningrad))

178

Figure 55: Graphical representation of the model (Russia (incl. Kaliningrad))

SHADOW = - 0.509*X10 - 9.469*X5 - 1.432*X1 - 291.459*X2 - 16.020*X13 + 101.040*X3 51.225*X4 - 159.515*X12 - 47.754*X11 - 33.748*X6 - 15.065*X7 Fit of the model: AIC (Akaike, 1974)* Maximum Likelihood Ratio Chi-Square (C1) Root Mean Square Error of Approximation (RMSEA) P-Value for Test of Close Fit (RMSEA < 0.05) Normed Fit Index (NFI) Goodness of Fit Index (GFI)

179

3099.241 47.569 (P = 0.0000) 0.205 0.000 0.897 0.945

Ukraine Initial causal variables: 1. L6 2. Average earnings 3. Duration of unemployment 4. Number of unemployed persons 5. Persons suspected with criminal offences 6. Local taxes collected 7. Number of removed SME's 8. L2 9. L4 10. L7 11. L8 12. L5 13. L1 14. Number of old age pension receivers 15. L3 16. Revenues from lease payment 17. Number of social insurance receivers 18. Number of women per 1000 men Initial indicator variables: 1. 2. 3. 4. 5. 6.

180

Earnings per employed (base indicator) Number of employed persons Number of social assistance benefit receivers Provision of housing L9 Fees for special use of natural resources per

Figure 56: T-values of the model (Ukraine)

181

Figure 57: Graphical representation of the model (Ukraine)

SHADOW = - 0.148*X2 + 0.152*X4 + 2.283*X3 - 0.0140*X14 - 0.202*X5 Fit of the model: AIC (Akaike, 1974)* Maximum Likelihood Ratio Chi-Square (C1) Root Mean Square Error of Approximation (RMSEA) P-Value for Test of Close Fit (RMSEA < 0.05) Normed Fit Index (NFI) Goodness of Fit Index (GFI)

182

3790.280 13.673 (P = 0.0034) 0.199 0.00976 0.966 0.982

Appendix 3 Latvia Table 109: Model summary of survey data (Latvia)

Model 1 2 3

R .618a .790b .934c

R Square

Adjusted R Square

0.381829608 0.624693759 0.872928358

0.320012569 0.541292372 0.825276492

Std. Error of the Estimate 150.5399607 123.6429439 76.30922202

Source: compiled by authors

Table 110: Coefficients for dependent variable determination (Latvia)

Model 1 2

Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 696.095 109.267 6.371 0.000 s8r_6 258.644 104.069 0.618 2.485 0.032 (Constant) 741.805 91.721 8.088 0.000 s8r_6 238.010 85.902 0.569 2.771 0.022 s2r_1 -154.627 64.073 -0.495 -2.413 0.039 (Constant) 671.504 59.335 11.317 0.000 s8r_6 149.545 57.546 0.357 2.599 0.032 s2r_1 -327.532 58.964 -1.049 -5.555 0.001 s1r_1 300.876 76.109 0.759 3.953 0.004

Source: compiled by authors

Table 111: Model summary for the dependent variable S8r_6 (Latvia)

Model 1 2 3 4

R .689a .831b .917c .964d

R Square

Adjusted R Square

0.475406249 0.691065099 0.84030416 0.930132144

0.422946874 0.622412899 0.78041822 0.890207654

Std. Error of the Estimate 0.331314796 0.268004142 0.204376559 0.144517009

Source: compiled by authors

Table 112: Coefficients for the dependent variable S8r_6 (Latvia)

Model 1 2

183

Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) -1.467 0.193 -7.609 0.000 s7r_2 0.732 0.243 0.689 3.010 0.013 (Constant) -0.718 0.337 -2.130 0.062 s7r_2 0.661 0.199 0.622 3.325 0.009

Model

Unstandardized Coefficients Standardized Coefficients B Std. Error Beta s7r_4 -0.591 0.236 -0.469 (Constant) -0.166 0.327 s7r_2 0.448 0.170 0.422 s7r_4 -1.080 0.254 -0.858 s8r_3 0.388 0.142 0.564 (Constant) 0.089 0.246 s7r_2 0.509 0.122 0.479 s7r_4 -1.133 0.180 -0.900 s8r_3 0.535 0.112 0.778 s2r_6 -0.295 0.098 -0.369

Sig.

-2.507 -0.509 2.631 -4.258 2.734 0.362 4.167 -6.286 4.791 -3.000

0.034 0.625 0.030 0.003 0.026 0.728 0.004 0.000 0.002 0.020

Source: compiled by authors

Table 113: Model summary for the dependent variable S2r_1 (Latvia)

Model

1 2

R Square

Adjusted R Square

0.492533999 0.677003786

0.441787399 0.605226849

Std. Error of the Estimate 0.436878261 0.367395997

Source: compiled by authors

Table 114: Coefficients for the dependent variable S2r_1 (Latvia)

Model 1 2

Unstandardized Coefficients Standardized Coefficients t B Std. Error Beta (Constant) 0.251 0.138 1.823 s1r_1 0.891 0.286 0.702 3.115 (Constant) 0.242 0.116 2.090 s1r_1 0.903 0.241 0.712 3.755 s7r_5 -0.776 0.342 -0.430 -2.267

Sig. 0.098 0.011 0.066 0.005 0.050

Source: compiled by authors

Table 115: Model summary for the dependent variable S1r_1 (Latvia)

Model

1 2 3 Source: compiled by authors

184

R Square

Adjusted R Square

0.818616144 0.885304988 0.944473125

0.800477758 0.859817208 0.923650547

Std. Error of the Estimate 0.205749914 0.172461182 0.127276132

Table 116: Coefficients for the dependent variable S1r_1 (Latvia)

Model 1 2

Unstandardized Coefficients Standardized Coefficients t B Std. Error Beta (Constant) 0.146 0.060 2.436 s1r_2 0.894 0.133 0.905 6.718 (Constant) 0.023 0.073 0.320 s1r_2 0.808 0.118 0.818 6.867 s3r_2 0.267 0.117 0.272 2.288 (Constant) 0.102 0.060 1.693 s1r_2 0.979 0.105 0.990 9.353 s3r_2 0.278 0.086 0.284 3.227 s3r_3 -0.378 0.129 -0.300 -2.920

Source: compiled by authors

185

Sig. 0.035 0.000 0.756 0.000 0.048 0.129 0.000 0.012 0.019

Lithuania Table 117: Model summary of survey data (Lithuania) Model

1 2 3

R Square .728a .877b .950c

0.530251463 0.768778885 0.902575501

Adjusted R Square

Std. Error of the Estimate

0.483276609 0.717396415 0.866041314

0.094545879 0.069920154 0.048139213

Source: compiled by authors

Table 118: Coefficients for dependent variable determination (Lithuania) Model

Unstandartized Coefficients Standardized Coefficients B

1.311 -0.674 S7r_3 1.990 (Constant) -0.587 S7r_3 -0.469 S6r_2 2.486 (Constant) -0.745 S7r_3 -0.674 S6r_2 0.443 S7r_1 (Constant)

Std. Error

Sig.

Beta

0.137 0.201 0.245 0.151 0.154 0.225 0.114 0.123 0.134

9.553 -3.360 8.128 -0.634 -3.880 -0.497 -3.047 11.031 -0.804 -6.504 -0.715 -5.494 0.474 3.315 -0.728

0.000 0.007 0.000 0.004 0.014 0.000 0.000 0.001 0.011

Source: compiled by authors

Table 119: Model summary for the dependent variable S7r_3 (Lithuania) Model

R Square .719a

0.516968768

Adjusted R Square

Std. Error of the Estimate

0.468665645

0.10350668

Source: compiled by authors

Table 120: Coefficients for the dependent variable S7r_3 (Lithuania) Model

Unstandartized Coeefficients Standardized Coefficients B

(Constant) S1r_1

0.774 -0.380

Std. Error

0.044 0.116

Sig.

Beta

17.770 0.000 -0.719 -3.271 0.008

Source: compiled by authors

Table 121: Model summary for the dependent variable S6r_2 (Lithuania) Model

1 2 3

186

R Square .725a .862b .936c

0.525361571 0.743488413 0.876750386

Adjusted R Square

0.477897728 0.686485838 0.83053178

Std. Error of the Estimate

0.100898953 0.078187522 0.057484797

Source: compiled by authors

Table 122: Coefficients for the dependent variable S6r_2 (Lithuania) Model

Unstandartized Coefficients Standardized Coefficients B

2.105 0.657 2.177 (Constant) 0.956 S7r_5 -0.323 S4r_3 2.228 (Constant) 1.141 S7r_5 -0.357 S4r_3 0.196 S2r_3 (Constant) S7r_5

Std. Error

Sig.

Beta

0.162 0.197 0.128 0.187 0.117 0.096 0.151 0.087 0.067

12.980 3.327 16.962 1.055 5.103 -0.572 -2.766 23.224 1.259 7.535 -0.633 -4.128 0.405 2.941 0.725

0.000 0.008 0.000 0.001 0.022 0.000 0.000 0.003 0.019

Source: compiled by authors

Table 123: Model summary for the dependent variable S7r_1 (Lithuania) Model

R Square .757a

0.573163996

Adjusted R Square

Std. Error of the Estimate

0.530480396

0.096438586

Source: compiled by authors

Table 124: Coefficients for the dependent variable S7r_1 (Lithuania) Model

Unstandardized Coefficients Standardized Coefficients B

(Constant) S3r_2

0.186 -0.838

Source: compiled by authors

187

Std. Error

0.096 0.229

Sig.

Beta

1.935 0.082 -0.757 -3.664 0.004

Romania Table 125: Model summary of survey data (Romania)

Model 1 2

R .639a .786b

R Square

Adjusted R Square

0.408823171 0.617997438

0.349705488 0.53310798

Std. Error of the Estimate 0.478617401 0.405547441

Source: compiled by authors

Table 126: Coefficients for dependent variable determination (Romania)

Model 1 2

Unstandardized Coefficients Standardized Coefficients B Std. Error Beta (Constant) -0.010 0.138 S3r_2 -1.042 0.396 -0.639 (Constant) 0.975 0.459 S3r_2 -1.282 0.353 -0.787 S7r_3 -0.897 0.404 -0.480

Sig.

-0.075 -2.630 2.124 -3.635 -2.220

0.942 0.025 0.063 0.005 0.054

Source: compiled by authors

Table 127: Model summary for the dependent variable S7r_3 (Romania)

Model 1 2 3 4 5 6 7 8 9 10 11

R .575a .712b .874c .930d .963e .985f .995g 1.000h 1.000i 1.000j 1.000k

R Square 0.330459022 0.507193257 0.764054669 0.864280386 0.927606557 0.969603914 0.989367013 0.999207433 0.999861288 0.999995972 1 .

Adjusted R Square

Std. Error of the Estimate

0.263504924 0.397680648 0.67557517 0.78672632 0.867278688 0.93312861 0.970759286 0.997093922 0.999237085 0.999955692

0.272700328 0.246611966 0.180991334 0.146747046 0.115763346 0.082171443 0.054336883 0.017129881 0.008776852 0.00211515 .

Source: compiled by authors

Table 128: Coefficients for the dependent variable S7r_3 (Romania)

Model 1 2

188

Unstandardized Coefficients Standardized Coefficients B Std. Error Beta (Constant) 0.354 0.345 S4r_1 -1.277 0.575 -0.575 (Constant) 0.523 0.326 S4r_1 -1.186 0.522 -0.534 S2r_4 -0.370 0.206 -0.422

Sig.

1.025 -2.222 1.605 -2.271 -1.797

0.330 0.051 0.143 0.049 0.106

Model 3

189

Unstandardized Coefficients Standardized Coefficients B Std. Error Beta (Constant) 0.931 0.276 S4r_1 -1.352 0.387 -0.609 S2r_4 -0.852 0.222 -0.972 S7r_5 0.504 0.171 0.757 (Constant) -0.286 0.580 S4r_1 -1.515 0.322 -0.682 S2r_4 -0.886 0.181 -1.012 S7r_5 0.504 0.138 0.757 S6r_2 0.724 0.319 0.328 (Constant) -0.016 0.473 S4r_1 -1.690 0.265 -0.761 S2r_4 -0.986 0.149 -1.126 S7r_5 0.743 0.151 1.116 S6r_2 1.090 0.298 0.494 S9r_4 -0.147 0.064 -0.418 (Constant) 0.077 0.337 S4r_1 -1.810 0.194 -0.815 S2r_4 -1.205 0.135 -1.376 S7r_5 0.921 0.127 1.384 S6r_2 1.375 0.237 0.623 S9r_4 -0.229 0.055 -0.650 S3r_2 0.250 0.095 0.287 (Constant) -0.368 0.276 S4r_1 -1.916 0.134 -0.863 S2r_4 -1.316 0.098 -1.502 S7r_5 1.338 0.174 2.010 S6r_2 1.682 0.193 0.762 S9r_4 -0.328 0.052 -0.931 S3r_2 0.433 0.092 0.496 S4r_2 0.549 0.201 0.458 (Constant) -0.438 0.088 S4r_1 -1.736 0.052 -0.782 S2r_4 -1.318 0.031 -1.504 S7r_5 1.460 0.059 2.194 S6r_2 1.920 0.072 0.870 S9r_4 -0.381 0.018 -1.081 S3r_2 0.490 0.030 0.562 S4r_2 0.506 0.064 0.423 S8r_5 -0.269 0.044 -0.246 (Constant) -0.498 0.049 S4r_1 -1.771 0.029 -0.797 S2r_4 -1.395 0.030 -1.592 S7r_5 1.530 0.038 2.300 S6r_2 2.057 0.058 0.932 S9r_4 -0.397 0.011 -1.128

Sig.

3.370 -3.489 -3.829 2.951 -0.492 -4.703 -4.895 3.639 2.274 -0.033 -6.368 -6.604 4.917 3.661 -2.291 0.229 -9.338 -8.940 7.260 5.789 -4.147 2.628 -1.331 -14.305 -13.430 7.673 8.704 -6.366 4.708 2.727 -4.981 -33.707 -42.654 24.957 26.538 -20.692 16.088 7.924 -6.103 -10.139 -61.672 -47.022 40.538 35.465 -36.648

0.010 0.008 0.005 0.018 0.637 0.002 0.002 0.008 0.057 0.974 0.001 0.001 0.003 0.011 0.062 0.828 0.000 0.000 0.001 0.002 0.009 0.047 0.254 0.000 0.000 0.002 0.001 0.003 0.009 0.053 0.016 0.000 0.000 0.000 0.000 0.000 0.001 0.004 0.009 0.010 0.000 0.000 0.001 0.001 0.001

Model

Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta S3r_2 0.579 0.033 0.664 17.605 0.003 S4r_2 0.478 0.034 0.400 14.102 0.005 S8r_5 -0.322 0.028 -0.294 -11.352 0.008 S3r_3 -0.075 0.025 -0.092 -3.070 0.092

Source: compiled by authors

Table 129: Model summary for the dependent variable S3r_2 (Romania)

Model

1 2 3 4 5

R Square

Adjusted R Square

0.616250724 0.838596183 0.899805328 0.948667495 0.986648637

0.577875797 0.802728668 0.862232326 0.919334635 0.975522501

Std. Error of the Estimate 0.23656849 0.161721872 0.135148253 0.103414235 0.056966599

Source: compiled by authors

Table 130: Coefficients for the dependent variable S3r_2 (Romania)

Model 1 2

Unstandartized coefficients Standardized Coefficients B Std. Error Beta (Constant) -0.287 0.098 S2r_1 0.773 0.193 0.785 (Constant) 0.169 0.146 S2r_1 2.132 0.408 2.167 S2r_5 -1.761 0.500 -1.460 (Constant) -0.567 0.354 S2r_1 1.897 0.357 1.927 S2r_5 -1.408 0.447 -1.167 S1r_3 0.412 0.186 0.267 (Constant) -0.795 0.285 S2r_1 1.415 0.331 1.437 S2r_5 -0.910 0.393 -0.754 S1r_3 0.461 0.144 0.299 S1r_1 0.316 0.122 0.274 (Constant) -0.197 0.214 S2r_1 1.414 0.182 1.437 S2r_5 -1.224 0.229 -1.015 S1r_3 0.304 0.088 0.197 S1r_1 0.574 0.092 0.499 S9r_1 -0.564 0.137 -0.358

Source: compiled by authors

190

Sig.

-2.932 4.007 1.160 5.226 -3.521 -1.599 5.310 -3.148 2.211 -2.787 4.273 -2.316 3.205 2.581 -0.925 7.754 -5.335 3.462 6.245 -4.131

0.015 0.002 0.276 0.001 0.007 0.149 0.001 0.014 0.058 0.027 0.004 0.054 0.015 0.036 0.391 0.000 0.002 0.013 0.001 0.006

Appendix 4 Approach used to analyse survey results in Estonia differed from other EU countries due to the fact that Estoniaâ&#x20AC;&#x2122;s survey was executed only in 4 cities, that is why regression analysis produced invalid results. In order to find out which attitudes have influence on shadow economy 3 step approach was taken. First of all, Principal Component Analysis was executed by including the shadow economy index and this way understanding which of the statements from the survey are in line with SE index. The PCA produced the according correlation between statements and factors: Table 131: Correlation between variables and factors (Estonia)

SMEs_Removed Indirect_Tax_Share L2 L6 L7 L12 L13 SE_index s1r_1 s1r_2 s1r_3 s2r_1 s2r_2 s2r_3 s2r_4 s2r_5 s2r_6 s3r_1 s3r_2 s3r_3 s3r_4 s3r_5 s4r_1 s4r_2 s4r_3 s5r_1 s5r_2 s6r_2 s7r_1 s7r_2 s7r_3 s7r_4 s7r_5 s8r_1 s8r_2 s8r_3 s8r_4 s8r_5 191

F1 -0.973 0.022 -0.620 -0.111 -0.385 0.271 0.669 0.940 -0.819 -0.882 -0.969 0.544 -0.594 -0.904 -0.760 0.376 -0.503 -0.790 -0.888 -0.751 -0.787 0.005 0.998 -0.663 0.800 -0.708 -0.947 0.597 0.733 0.834 -0.942 -0.266 -0.227 -0.903 -0.032 -0.311 -0.609 0.276

F2 -0.214 0.998 -0.395 0.989 0.769 -0.812 -0.730 -0.038 -0.537 0.140 -0.134 0.042 -0.804 0.396 -0.645 0.498 0.742 -0.591 -0.460 0.121 -0.171 0.411 -0.045 -0.258 0.262 0.683 0.322 -0.793 -0.412 0.315 0.302 0.700 0.907 -0.390 -0.952 -0.098 -0.484 0.955

F3 0.081 0.061 0.678 -0.097 -0.511 0.516 -0.141 -0.338 -0.202 -0.449 -0.207 0.838 0.001 0.161 0.079 0.781 0.443 -0.162 0.015 0.649 0.593 0.912 0.034 -0.703 -0.540 0.178 -0.016 0.122 0.541 0.453 -0.150 0.663 -0.355 -0.182 -0.305 0.945 0.628 -0.105

s8r_6 s9r_1 s9r_2 s9r_3 s9r_4

F1 0.403 -0.986 -0.736 -0.160 0.947

F2 0.887 0.165 0.677 -0.867 -0.321

F3 0.225 0.003 0.007 0.473 -0.012

Source: compiled by authors

As the table provided above shows, in Estoniaâ&#x20AC;&#x2122;s case 3 factors were produced and the 1st factor was highly determined by SE_index. It means that the statements, which have high correlation with the 1st factor, also are in line with SE_index (either positively or negatively). From all of the different variables, which were included in PCA, those that have correlated with the 1st factor by coefficient higher than |0.9| were selected. Thus, we have 8 different variables (statements) that were used in order to find the real correlation and linkages with the shadow economy. The second step was to use those 8 highlighted variables and carry out canonical correlation analysis. Canonical correlation analysis determined a set of canonical variates, orthogonal linear combinations of the variables within each set that best explain the variability both within and between sets8. In this case the canonical analysis produced 8 factors and based on their correlation first 2 were selected to use, as they explain most of the variation. By looking at those 2 factors and their standardized coefficients it is clear that for the first 2 factors the variables, namely, s5r_2 and s9r_1 have the most influence. Meaning that these 2 variables (attitudes) have the most influence for the shadow economy in Estonia. Bellow the table with the detailed canonical correlations and standardized coefficients is shown. Table 132: Canonical correlations and standardized coefficients (Estonia)

1 Canonical Correlations s1r_3 s2r_3 s4r_1 s5r_2 s7r_3 s8r_1 s9r_1 s9r_4

0.788 0.055 0.138 0.29 0.807 0.061 0.017 0.091 0.017

0.737

0.611

0.601

0.488

0.406

0.341

0.278

0.11

0.147

0.029

0.323

0.806

-0.103

0.506

-0.159 0.07

-0.636 0.326

0.612 0.735

-0.402 0.506

0.052 -0.215

0.228 0.19

0.169 -0.243

-0.239

0.517

0.257

0.139

-0.164

-0.172

-0.229

0.032 0.081

-0.206 -0.541

-0.199 -0.108

0.398 0.351

-0.063 0.177

0.929 -0.499

-0.099 -0.612

0.955 -0.151

0.098 0.117

0.165 0.021

-0.242 -0.417

-0.045 0.65

0.077 0.22

-0.136 -0.624

Explanation is taken from https://stats.idre.ucla.edu/r/dae/canonical-correlation-analysis/

192

Source: compiled by authors

After determining that 2 of those variables have direct influence on shadow economy in Estonia, the last step was to run the classical regression analysis and to find whether there are variables (attitudes) that have influence and determine s5r_2 and s9r_1. The regression was executed taking both s5r_2 and s9r_1 as dependent variables. The output of the regression analysis is provided in the tables bellow. Table 133: Model summary for the dependent variable s5r_2 (Estonia)

Model 1 2 3 4

R Square

.693a .715b .723c .732d

0.481 0.511 0.523 0.535

Adjusted R Square 0.479 0.509 0.52 0.531

Std. Error of the Estimate 0.97487 0.94706 0.93652 0.92563

Source: compiled by authors

Table 134: Coefficients for the dependent variable S5r_2 (Estonia)

Model B 1

(Constant) s5r_1 (Constant) s5r_1 s3r_2 (Constant) s5r_1 s3r_2 s8r_1 (Constant) s5r_1 s3r_2 s8r_1 s7r_2

193

0.466 0.892 0.437 0.85 0.206 0.497 0.851 0.185 0.124 0.435 0.857 0.151 0.134 0.122

Std. Error 0.075 0.046 0.073 0.046 0.041

Standardized Coefficients Beta

Sig.

0.693

-6.239 19.216

0 0

0.661 0.177

-5.995 18.532 4.977

0 0 0

0.074 0.045 0.041 0.039

0.662 0.16 0.111

-6.668 18.772 4.468 3.164

0 0 0 0.002

0.076 0.045 0.042 0.039

0.666 0.13 0.119

-5.72 19.109 3.567 3.433

0 0 0 0.001

0.038

-0.114

-3.224

0.001

Source: compiled by authors

Table 135: Model summary for the dependent variable s9r_1 (Estonia)

Model 1 2 3 4 5 6

R Square

.691a .700b .705c .709d .713e .717f

0.477 0.491 0.497 0.503 0.508 0.514

Adjusted R Square 0.476 0.488 0.493 0.498 0.502 0.506

Std. Error of the Estimate 0.30876 0.30507 0.3035 0.30218 0.30088 0.29959

Source: compiled by authors

Table 136: Coefficients for the dependent variable S9r_1 (Estonia)

Std. Error

Standardized Coefficients Beta

(Constant) s9r_2

0.423 0.559

0.029 0.029

(Constant) s9r_2

0.431 0.56 0.049 0.432 0.56 0.051 0.026

Model 1 2

s3r_5 3

(Constant) s9r_2 s3r_5 s1r_1

(Constant) s9r_2 s3r_5 s1r_1 s3r_3

(Constant) s9r_2 s3r_5 s1r_1 s3r_3 s4r_1

194

Sig.

0.691

14.711 19.075

0 0

0.029 0.029

0.692

15.126 19.339

0 0

0.015

-0.117

-3.274

0.001

0.028 0.029

0.692

15.227 19.437

0 0

0.015 0.012

-0.122 0.081

-3.416 2.265

0.001 0.024

0.424 0.562 0.037 0.032 0.033

0.028 0.029

0.695

14.902 19.599

0 0

0.016 0.012

-0.089 0.099

-2.306 2.71

0.022 0.007

0.016

-0.084

-2.114

0.035

0.427 0.556 0.035 0.028 0.038 0.023

0.028 0.029

0.688

15.056 19.379

0 0

0.016 0.012

-0.083 0.086

-2.149 2.347

0.032 0.019

0.016

-0.095

-2.369

0.018

0.011

-0.077

-2.105

0.036

Model 6

(Constant) s9r_2 s3r_5 s1r_1 s3r_3 s4r_1 s9r_3

Source: compiled by authors

195

Standardized Coefficients Beta

Sig.

0.674

14.969 18.773

0 0

0.016 0.012

-0.07 0.078

-1.795 2.1

0.073 0.036

0.016

-0.104

-2.604

0.01

0.011 0.012

-0.089 0.077

-2.429 2.097

0.016 0.037

Std. Error

0.424 0.546 0.029 0.025 0.041 0.026 0.024

0.028 0.029

Appendix 5 Table 137: Independent sample T-Test results between EU and non-EU municipalities

EU Country Latvia

Municipality Alūksne Daugavpils Latvia municipality Latvia Daugavpils Latvia Krāslava Latvia Ludza Latvia Rēzekne Estonia Sillamäe Estonia Narva Lithuania Šverčionys Lithuania Šalčininkai

Lithuania Marijampolė Lithuania Lithuania Lithuania Romania Romania

Druskininkai Visaginas Alytus Satu Mare Sighetu Marmației

Romania

Rădăuți

Romania

Botoșani

Romania Romania

Iași Huși

Source: compiled by authors

196

Non-EU Country Russia

Municipality Pechory

Belarus Belarus Belarus Russia Russia Russia Russia Belarus Belarus Belarus Belarus Russia Russia Belarus Belarus Russia Ukraine Ukraine Ukraine Ukraine Ukraine Ukraine Moldova Ukraine Moldova Moldova Moldova Moldova Moldova

Braslaw Braslaw Verkhnyadzvinsk Pskov Ostrovsky Kingisepp Kingisepp Astravets Voronava Ashmyany Smarhon Gusevskiy Sovetsk Voronava Braslaw Gusevskiy Vynohradiv Tyachiv Rakhiv Hlyboka Storozhynets Snyatyn Riscani Hlyboka Briceni Ungheni Hîncești Cahul Cantemir

T-Test (10% significance level) t-value -0.572

p-value 0.569

0.889 1.188 -0.636 -0.655 -0.658 -0.528 -1.02 -0.644 -0.349 -0.684 -0.753 -0.4 -0.252 -0.269 -0.201 -0.438 -0.622 -0.889 -0.55 -1.654 -1.013 -1.059 -1.567 -1.264 -1.71 -1.476 -1.192 -1.536 -1.397

0.379 0.24 0.527 0.515 0.513 0.599 0.313 0.523 0.728 0.497 0.455 0.691 0.802 0.789 0.841 0.663 0.537 0.378 0.585 0.104 0.317 0.295 0.123 0.212 0.093 0.147 0.239 0.131 0.168

Appendix 6 Latvia Table 138: Correlations between variables and factors (Latvia)

S1_1 S1_2 S1_3 S2_1 S2_2 S2_3 S2_4 S2_5 S2_6

S3_1 S3_2 S3_3

I feel like my municipalityâ&#x20AC;&#x2122;s local government would help me in case of trouble I believe that my municipalityâ&#x20AC;&#x2122;s local government should take care of me. Municipality should take care of children from families at risk. Spending on economy to improve and develop it Spending on medical and health care services Spending on education (on general, professional, higher education) Spending on social benefits (monetary benefits) Spending on social security (including state provided services, consultations, etc.) Spending on public order, safety (the availability of police, firefighters, doctors, etc.) With the quality of utilities (electricity, gas, water supply and waste management) provided to me. With the health, educational and other services provided by the government. With the quality of agriculture, forestry and fishing sector.

F10

F11

-0.736

-0.197

-0.012

0.539

-0.183

0.139

0.037

0.262

0.033

-0.064

-0.024

-0.776

-0.321

-0.030

0.397

-0.126

0.126

0.291

-0.013

0.120

-0.078

0.014

-0.408

0.535

-0.373

-0.298

-0.010

0.334

-0.234

0.210

0.292

-0.155

0.001

-0.475

-0.340

-0.247

0.455

-0.529

-0.199 -0.201

-0.070

0.039

0.141

-0.071

-0.390

0.417

-0.393

0.564

0.086

-0.326 -0.235

-0.062

0.039

-0.142

0.082

-0.387

0.232

-0.458

-0.128

-0.217

-0.550

0.245

0.306

-0.247

-0.063

0.045

0.054

0.763

0.101

0.472

-0.091

-0.277

0.174

-0.223

0.065

0.113

0.012

-0.744

0.213

-0.259

-0.180

-0.063

-0.380

-0.109

-0.337

0.102

-0.125

0.038

-0.395

-0.296

-0.744

-0.063

-0.150

-0.293

-0.090

-0.124

-0.218

-0.133

0.040

0.032

0.335

0.513

0.719

0.118

-0.081

0.246

0.109

-0.029

-0.013

-0.109

-0.321

0.500

0.011

0.655

0.243

0.026

-0.037

0.332

0.100

0.020

0.190

-0.670

0.191

-0.260

-0.050

0.282

0.178

0.462

-0.333

0.071

-0.067

-0.030

S3_4 S4_1 S4_2 S4_3 S5_1 S5_2 S6_2 S7_1

S7_2

S7_3

S7_4 S7_5 S8_1 S8_2

198

With the quality of social sector (education, non-profit organization, etc.) I support people who use social benefits as only source of income I think that too many people are avoiding labour to receive social benefits I would use every chance to receive some kind of social benefit I feel safe these days in my neighbourhood I feel â&#x20AC;&#x201C; would feel â&#x20AC;&#x201C; safe walking in my neighbourhood in the night time The inhabitants of my municipality would be pleased with rising number of tourists. I support people who purchase goods for long term use, for example a vehicle, for which taxes are not paid I would use chance of not paying taxes for purchased goods/services if that allows me to save money I would prefer to receive legal income over salary under-reporting if the amount would be the same for both options I see public services and social benefits as benefits from receiving legal income and paying taxes I would receive salary under-reporting if I had the chance and that meant higher income I would rather pay direct taxes from my legal income than pay indirect taxes via goods I purchase. I disapprove of taxpayers who evade taxes

F10

F11

-0.638

0.484

-0.234

-0.071

0.121

0.096

0.138

-0.227

0.375

0.252

0.027

-0.472

-0.753

-0.039

-0.176

-0.390

-0.093

0.067

0.020

0.103

0.019

0.026

-0.498

0.321

-0.236

-0.303

0.587

0.204

0.175

0.149

-0.207

0.109

0.084

-0.505

-0.708

-0.049

-0.328

-0.168

-0.033

-0.124

0.093

-0.042

0.280

-0.004

-0.491

0.654

-0.360

-0.210

-0.160

0.199

0.007

-0.181

0.075

-0.170

-0.528

0.730

-0.331

0.019

0.040

-0.187

-0.065

0.060

-0.117

0.137

-0.031

-0.019

0.559

-0.275

-0.692

0.012

0.174

0.091

0.022

-0.294

-0.034

0.076

-0.449

-0.657

-0.070

-0.215

0.208

0.058

0.258

0.360

0.181

-0.200

0.014

-0.367

-0.646

0.044

-0.059

0.389

-0.412

-0.023

0.332

0.030

0.003

-0.096

0.068

0.411

-0.612

-0.429

-0.305

0.009

-0.055

0.237

0.329

0.082

0.015

0.689

-0.288

-0.611

0.183

-0.125

0.042

0.077

0.093

0.021

0.045

0.027

-0.169

0.487

0.811

0.041

0.247

0.090

-0.031

0.060

-0.039

0.000

-0.014

-0.404

-0.424

-0.340

0.223

-0.349

0.479

0.261

-0.210

-0.137

-0.088

0.037

0.433

-0.650

-0.308

0.333

0.161

-0.126

0.215

0.019

-0.084

0.232

0.184

S8_3 S8_4 S8_5 S8_6 S8_7 s9r_1 s9r_2 s9r_3 s9r_4

I believe that money paid in taxes provides useful benefits for me I believe that money paid in taxes provides useful benefits for society I would cheat on taxes if I had the chance People should use every opportunity to not pay taxes I support people who are self – employed Do you have a credit/debit card? Do you use your credit/debit card for payments? I would use a credit/debit card for payments more often if I had the chance. What is the share of your payments in cash?

F10

F11

0.164

-0.598

-0.637

0.136

0.274

0.088

0.022

-0.324

0.022

-0.033

-0.038

0.514

-0.055

-0.601

0.020

0.529

-0.208

0.046

0.004

0.156

-0.103

-0.102

-0.293

-0.104

0.864

-0.123

0.083

-0.313

0.054

-0.117

-0.069

-0.106

0.065

-0.517

-0.420

0.346

-0.194

0.600

-0.034

0.118

-0.132

0.003

0.074

0.001

-0.107 0.804

0.230 0.184

-0.515 -0.295

0.481 0.123

-0.084 -0.090

0.561 -0.165 -0.132 0.416

0.097 0.113

-0.279 -0.001

-0.018 -0.048

-0.048 -0.057

0.794

0.182

-0.440

-0.052

-0.044

-0.105

0.308

0.070

0.159

0.037

0.011

-0.436

-0.281

-0.464

0.247

0.605

0.098

-0.167

0.126

-0.071

0.137

-0.109

-0.378

0.038

0.583

-0.155

-0.587

0.103

0.311

0.181

0.025

0.073

-0.039

F10

F11

0.882

-0.208

-0.077

-0.178

0.189

-0.212

0.206

-0.082

0.068

0.084

0.010

0.119

-0.795

-0.221

-0.025

0.298

-0.134

0.340

0.141

-0.244

-0.012

0.050

-0.157

-0.839

0.233

0.108

0.068

-0.086

-0.099

-0.279

-0.015

0.325

0.000

0.702

-0.100

0.419

0.058

-0.101

-0.201

0.205

0.237

0.398

-0.098

-0.030

0.820

-0.184

0.029

0.060

-0.199

0.306

0.109

-0.024

0.282

0.179

0.178

Source: compiled by authors

Lithuania Table 139: Correlations between variables and factors (Lithuania)

S1r_1 S1r_2 S1r_3 S2r_1 S2r_2

199

I feel like my municipality’s local government would help me in case of trouble I believe that my municipality’s local government should take care of me. Municipality should take care of children from families at risk. Spending on economy to improve and develop it Spending on medical and health care services

S2r_3 S2r_4 S2r_5 S3r_1 S3r_2 S4r_1 S4r_2 S4r_3 S5r_1 S5r_2 S5r_3 S5r_4 S6r_2 S7r_1

S7r_2

200

Spending on education (on general, professional, higher education) Spending on social benefits (monetary benefits) Spending on social security (including state provided services, consultations, etc.) With the quality of utilities (electricity, gas, water supply and waste management) provided to me. With the health, educational and other services provided by the government. I support people who use social benefits as only source of income I think that too many people are avoiding labour to receive social benefits I would use every chance to receive some kind of social benefit I feel safe these days in my neighbourhood I feel â&#x20AC;&#x201C; would feel â&#x20AC;&#x201C; safe walking in my neighbourhood in the night time I feel that the criminal activity in my municipality is under control I think that the number of police officers in my municipality is sufficient The inhabitants of my municipality would be pleased with rising number of tourists. I support people who purchase goods for long term use, for example a vehicle, for which taxes (like VAT) are not paid I would use chance of not paying taxes for purchased goods/services if that allows me to save money

F10

F11

0.922

-0.002

-0.143

-0.185

-0.063

0.219

-0.012

0.025

0.148

0.113

-0.084

0.895

0.078

-0.102

-0.328

-0.027

-0.235

-0.061

0.073

0.095

0.025

-0.010

0.797

-0.021

0.078

-0.441

0.090

-0.233

-0.273

0.059

0.143

0.004

-0.062

0.073

-0.158

0.363

0.678

-0.266

0.054

0.492

-0.020

0.154

-0.105

0.168

0.692

-0.288

-0.260

0.415

0.297

-0.204

-0.169

-0.193

-0.036

-0.003

0.039

-0.209

-0.860

-0.053

-0.252

0.170

-0.067

-0.154

-0.131

0.129

-0.211

-0.124

0.131

0.861

0.135

0.028

0.269

-0.084

0.113

-0.122

-0.288

-0.104

-0.148

-0.200

-0.728

-0.350

0.143

-0.442

0.043

0.221

-0.078

-0.121

-0.049

-0.129

0.594

-0.559

0.340

-0.104

-0.180

0.020

0.197

0.085

-0.261

-0.127

-0.210

0.758

-0.091

0.314

-0.188

0.081

0.174

-0.311

0.257

-0.264

0.114

0.028

0.607

0.031

0.455

-0.478

-0.044

0.067

0.013

-0.380

-0.064

-0.135

-0.143

0.436

0.069

0.139

-0.310

-0.280

0.631

-0.093

-0.419

-0.100

-0.039

0.134

-0.136

-0.092

0.396

-0.649

-0.162

-0.064

0.515

0.090

-0.013

0.290

-0.086

-0.480

0.279

0.665

-0.395

-0.166

-0.120

-0.067

-0.048

0.190

-0.059

0.072

-0.082

0.433

0.260

0.212

-0.406

-0.609

0.057

-0.151

-0.315

-0.096

S7r_3

S7r_4 S7r_5 S7r_6

S8r_1 S8r_2 S8r_3 S8r_4 S8r_5 S8r_6 S9r_1 S9r_2 S9r_3 S9r_4

I would prefer to receive legal income over salary under-reporting if the amount would be the same for both options I see public services and social benefits as benefits from receiving legal income and paying taxes I would receive salary under-reporting if I had the chance and that meant higher income I support people staying unemployed for long time and using unemployment benefits as their source of income I would rather pay direct taxes from my legal income than pay indirect taxes via goods I purchase (VAT tax, sales tax, etc.). I disapprove of taxpayers who evade taxes I believe that money paid in taxes provides useful benefits for me I believe that money paid in taxes provides useful benefits for society I would cheat on taxes if I had the chance People should use every opportunity to not pay taxes Do you have a credit/debit card? Do you use your credit/debit card for payments? I would use a credit/debit card for payments more often if I had the chance. What is the share of your payments in cash?

Source: compiled by authors

201

F10

F11

-0.760

0.415

0.081

0.176

0.036

0.051

0.218

0.057

0.206

0.312

-0.134

0.117

0.063

0.233

0.196

-0.486

-0.168

-0.481

0.356

-0.414

0.295

-0.097

-0.485

-0.420

0.151

-0.411

-0.443

-0.140

0.407

0.053

-0.108

-0.001

0.025

-0.531

-0.458

-0.398

-0.229

-0.132

0.072

-0.388

0.015

0.122

-0.319

-0.083

-0.092

0.427

-0.511

-0.298

-0.529

-0.138

-0.054

-0.218

0.039

0.328

-0.038

0.711

0.349

0.314

0.184

-0.302

-0.076

0.166

0.239

0.045

-0.233

0.046

0.219

-0.238

-0.493

0.416

-0.522

-0.275

-0.029

0.063

0.293

0.109

-0.170

0.390

-0.109

-0.198

0.045

-0.546

-0.531

-0.257

-0.150

-0.191

-0.017

0.300

-0.562

0.024

-0.170

-0.658

-0.057

-0.376

0.129

-0.148

-0.026

-0.031

0.191

-0.144

0.009

-0.525

-0.477

0.074

0.080

0.124

0.625

-0.155

-0.114

0.143

-0.522

-0.346

0.661

0.039

-0.244

0.160

-0.284

0.031

-0.033

0.024

-0.376

-0.381

0.713

0.177

-0.129

0.240

-0.267

0.149

0.018

-0.023

0.065

-0.332

-0.138

0.359

-0.208

0.286

-0.534

-0.348

0.126

0.438

0.045

0.019

0.047

-0.202

0.391

0.238

0.572

-0.516

0.147

-0.126

-0.274

0.193

0.067

Romania Table 140: Correlations between variables and factors (Romania)

s1r_1 s1r_2 s1r_3 s2r_1 s2r_2 s2r_3 s2r_4 s2r_5 s3r_1 s3r_2 s3r_3 s4r_1 s4r_2 s4r_3 s5r_1 s5r_2 202

F1 F2 F3 F4 F5 F6 F7 F8 I feel like my municipality’s local government 0.480 0.685 0.043 0.263 0.277 0.058 -0.269 -0.152 would help me in case of trouble I believe that my municipality’s local -0.258 -0.239 0.805 0.183 0.030 -0.323 -0.030 0.091 government should take care of me. Municipality should take care of children -0.272 -0.065 -0.004 -0.138 0.911 -0.020 0.130 0.124 from families at risk. Spending on economy to improve and develop 0.928 -0.114 0.283 0.010 0.123 0.095 0.071 0.073 it 0.677 -0.326 0.311 0.240 0.314 0.290 -0.202 0.030 Spending on medical and health care services Spending on housing (building new houses, 0.859 -0.258 -0.191 0.289 0.029 0.206 0.134 0.049 renovating old buildings, etc.). 0.818 -0.398 -0.104 0.174 0.027 -0.101 0.303 0.057 Spending on communal economy 0.906 -0.281 0.240 0.054 -0.028 -0.040 0.006 0.150 Spending on fuel and energy sector With the quality of utilities (electricity, gas, water supply and waste management) 0.459 0.182 0.724 -0.156 0.002 0.256 0.261 0.238 provided to me. With the health, educational and other 0.705 0.146 0.215 -0.062 0.584 0.185 -0.050 -0.136 services provided by the government. With the quality of transport and storage 0.556 0.519 0.501 -0.319 0.058 0.040 -0.026 0.015 sector I support people who use social benefits as 0.214 -0.072 -0.744 -0.228 -0.027 0.100 -0.486 0.108 only source of income I think that too many people are avoiding -0.853 0.106 -0.058 0.190 0.135 0.407 0.031 -0.139 labour to receive social benefits I would use every chance to receive some 0.874 -0.091 0.155 0.309 -0.145 -0.177 -0.022 -0.221 kind of social benefit 0.316 0.397 0.335 -0.469 -0.174 0.448 -0.004 -0.249 I feel safe these days in my neighbourhood I feel – would feel – safe walking in my 0.158 0.584 0.157 -0.634 0.131 0.282 -0.251 -0.095 neighbourhood in the night time

F10

F11

0.063

0.061

-0.215

0.193

-0.187

-0.092

0.202

-0.004

-0.007

-0.100 -0.016

0.020

-0.102

0.154

0.152

-0.024

0.054

-0.088

0.085 -0.090 -0.003 0.077

-0.097 0.094

-0.077 -0.058

-0.088

-0.135

0.013

-0.127

-0.183 -0.099

-0.136

-0.183 -0.226

-0.024

-0.050 -0.021

-0.120

-0.040

0.073

0.016

0.239

-0.025

0.244

0.182

-0.074

0.007

s6r_2 s7r_1

s7r_2

s7r_3

s7r_4 s7r_5 s8r_1 s8r_2 s8r_3 s8r_4 s8r_5 s8r_6 s9r_1 s9r_2 s9r_3

203

The inhabitants of my municipality would be pleased with rising number of tourists. I support people who purchase goods for long term use, for example a vehicle, for which taxes (like VAT) are not paid I would use chance of not paying taxes for purchased goods/services if that allows me to save money I would prefer to receive legal income over salary under-reporting if the amount would be the same for both options I see public services and social benefits as benefits from receiving legal income and paying taxes I would receive salary under-reporting if I had the chance and that meant higher income I would rather pay direct taxes from my legal income than pay indirect taxes via goods I purchase (VAT tax, sales tax, etc.). I disapprove of taxpayers who evade taxes I believe that money paid in taxes provides useful benefits for me I believe that money paid in taxes provides useful benefits for society I would cheat on taxes if I had the chance People should use every opportunity to not pay taxes Do you have a credit/debit card? Do you use your credit/debit card for payments? I would use a credit/debit card for payments more often if I had the chance.

0.245

0.280

0.607

F10

F11

-0.270 -0.258 -0.395

0.480

0.294

0.485

0.105

0.012

-0.049

0.308

-0.405 -0.113

0.395

-0.308

0.049

0.179

0.259

0.055

-0.064

0.131

0.238

-0.720 -0.380

0.348

-0.290

0.192

-0.071 -0.124 -0.004

0.020

-0.285

0.442

0.518

-0.206 -0.459 -0.189

0.121

0.033

0.008

0.366

-0.131

0.141

0.707

0.506

-0.013

-0.329 -0.080

0.039

-0.168

0.080

0.159

0.734

-0.123 -0.135 -0.202 -0.304 -0.471

0.221

-0.135 -0.002 -0.066

-0.028

0.274

-0.128 -0.525

-0.504

0.217

0.335

-0.066

0.423

0.285

-0.484

0.052

0.120

-0.078

0.464

-0.418 -0.420

0.274

-0.189

0.166

-0.083 -0.036

0.179

0.014

0.318

0.741

-0.095

0.325

-0.370 -0.188 -0.148 -0.169

0.048

-0.096

-0.006

0.049

0.433

0.286

0.745

0.234

0.170

-0.162

0.177

-0.133

0.106

0.751

-0.035 -0.462 -0.031 -0.163 -0.076 -0.260

0.134

0.304

0.096

-0.006

0.819

0.137

-0.190 -0.157 -0.218 -0.064 -0.440 -0.054 -0.012

0.031

-0.021

-0.321

0.847

-0.108

0.352

-0.110

0.079

0.027

0.132

-0.038 -0.048

-0.052

-0.115

0.715

-0.277

0.579

-0.175

0.014

0.031

0.156

-0.016 -0.078

0.024

0.336

0.511

-0.468

0.462

0.167

0.077

0.176

0.289

-0.118

0.166

-0.059

0.062

s9r_4 What is the share of your payments in cash? Source: compiled by authors

204

F1 0.518

F2 0.295

F3 F4 F5 F6 -0.098 -0.464 -0.125 -0.053

F7 0.558

F8 F9 F10 -0.221 -0.075 -0.183

F11 0.052