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Factors Influencing Migrants’ Engagement with Transnational Economic Activities in Post-Conflict Countries? Work Package 4 Report Linking Motives for Remittances and Investment from the Supply and the Demand Side

Nienke Regts, Marieke van Houte & Ruerd Ruben

Radboud University Nijmegen Centre for International Development Issues (cidin) PO Box 9104, Nijmegen, The Netherlands

2010


Grant Agreement number: 210615 Project acronym:  INFOCON Project title:  International Civil Society Forum on Conflicts Funding Scheme:  Research for the Benefit of Specific Groups   Research for Civil Society Organisations Name, title and organisation of the scientific representative of the project’s coordinator: Stephan KAMPELMANN Secretary-General / Project Manager Stichting Internationalist Review po box 75 brussels 1040 belgium + 32-(0)‒26–08–24–11 stephan@internationalistfoundation.com www.infocon-project.org

Disclaimer 1 The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7 / 2007–2011) under grant agreement Nr. 210615. Disclaimer 2 The views expressed in this document are purely those of the authors and may not in any circumstance be regarded as stating the official position of all partners in the INFOCON consortium.


Table of Contents List of tables  ·  5 Abstract · 7 1. Introduction  · 9 2.  Transnational Activities and Economic Networks · 10 3.  Motives for Transnational Economic Activities: Some Key Hypotheses  ·  11 3.1. Supply Side Motives · 11 3.2. Demand Side Motives · 13 4. Research Design  · 14 5. Results · 17 5.1.  Determinants at the Supply Side   ·  17 5.2.  Determinants for the Demand Side  ·  20 6.  Conclusions and Implications   ·  21 Acknowledgements · 22 References · 22 Annex A · 25 A.1. Profiles · 27 A.1.1.  What Can Be Concluded from the Profiles above? · 28 A.1.2. Overall Picture · 28 A.2. CROSS-TABS · 29 A.2.1. City of Settlement · 29 A.2.2.  City of Settlement   ·  30 A.2.3. City of Settlement · 31 A.2.4.  City of Settlement   ·  32 A.2.5. City of Settlement · 32 A.2.6. City of Settlement · 33 A.3. TESTING HYPOTHESES · 34 A.3.1. General Information  · 34 A.3.3.  Testing Hypothesis 1, 4 and 5  ·  37

3  |  Factors Influencing Migrants’ Engagement


A.3.4.  Comparing Outcomes of Different Dependent Variables · 40 Annex B · 41 B.1. PROFILES · 43 Overall · 45 B.2. CROSS-TABS · 46 B.2.1. Country of Origin · 46 B.2.2. Country of Origin · 46 B.2.3. Country of Origin · 47 B.2.4. Country of Origin · 48 B.2.5. Country of Origin · 48 B.2.6. Country of Origin · 49 B.2.7. Country of Origin · 50 B.3. Testing Hypotheses   ·  50 B.3.1. General Information · 50 B.3.2.  Testing Hypotheses 1–5 with Dependent Variable “Received Assistance” · 51 Option 1: Outcomes Logistic Regression Using All Independent Variables · 51 Option 2: Outcomes Regression Leaving Out Political Stability (Removed Outlier 31)  ·  52 B.3.3. Testing Hypotheses 1–5 · 53 Dependent Variable “Received Remittances”  ·  53 Option 3: Outcomes Logistic Regression Using All Independent Variables   ·  53 Option 4: Outcomes Regression Leaving Out Political Stability (Removed Outlier 31)  ·  54 Option 5: Outcomes Regression Single Analyses  ·  54 B.4. MULTIPLE IMPUTATION  · 56 B.4.1. Method · 56 B.4.2.  Step 1: Pattern of Missing Values  ·  57 B.4.3.  Step 2: Multiple Imputation   ·  57 B.4.4.  Step 3: The Analyses   ·  57 Codes Used from spss Files for Analyses   ·  59 1.  City of Settlement   ·  59 2. Country of Origin · 59

Factors Influencing Migrants’ Engagement  |  4


List of Tables Table 1.  Sample Frame (Valid interviews)  ·  15 Table 2.  Descriptives of Economic Diaspora Networks at Supply Side (n = 139)  ·  15 Table 3.  Descriptives of Economic Diaspora Networks at the Receiving Side (n = 70)  ·  16 Table 4.  Factors influencing Funding of Transnational Economic Activities (Supply Side)   ·  18 Table 5. Factors influencing Receipt of Assistance and Remittances (Demand Side)  ·  20 Perceiving of Conflict  ·  29 Chi-Square Tests · 29 View on integration  ·  30 Chi-Square Tests · 30 Reason Migration · 31 Chi-Square Tests · 31 Date of Arrival  ·  32 Chi-Square Tests · 32 Frequent Travel · 32 Chi-Square Tests · 33 CSO Linkages   ·  33 Chi-Square Tests · 33 Table A.3.1. Significant determinants for involvement in activities toward economic development or poverty alleviation in country of origin (general economical involvement) · 35 Table A.3.2.  Significant determinants for attracting investment / economic assistance   ·  37 Table A.3.3.  Significant determinants for sending remittances · 38 Received Assistance · 46 Chi-Square Tests · 46 Received remittances · 46

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Chi-Square Tests · 47 Perceiving of Conflict  ·  47 Chi-Square Tests · 47 Tensions Felt in Europe  ·  48 Chi-Square Tests · 48 Role eu community in country  ·  48 Chi-Square Tests · 49 Cooperation with eu Community · 49 Chi-Square Tests · 49 Political Stability · 50 Chi-Square Tests · 50 Table B.3.1.  Significant Determinants for Receiving Assistance   ·  51 Table B.3.2.  Significant determinants for receiving assistance   ·  52 Table B.3.3.  Significant Determinants for Receiving Remittances  ·  53 Table B.3.4.  Significant Determinants for Receiving Assistance   ·  54 Table B.3.5.  Significant Determinants for Receiving Assistance   ·  54 Table B.3.6.  Significant Determinants for Receiving Assistance   ·  55 Table B.3.7.  Significant Determinants for Receiving Assistance  ·  55 Table B.3.8.  Significant Determinants for Receiving Assistance   ·  55 Table B.3.9.  Significant Determinants for Receiving Assistance   ·  55 Imputation Models · 57 Table 4.1.  Average Outcomes with Dependent Variable Received Remittances · 58


Abstract It is increasingly acknowledged that poverty reduction should be considered part of conflict mitigation and post‒conflict reconstruction processes. Whereas transnational communities play a critical role in channeling remittances and investments resources towards their home countries, it is not fully understood what is their actual impact on the regions of origin and to what extent their involvement contributes to socio‒economic development. Since there are several (economical, political, social and cultural) activities undertaken by diaspora networks, this article focuses mainly on transnational economic networks. We discuss both the determinants of migrants organizations’ economic involvement towards their home country (supply side) and the factors for receiving and attracting economic assistance (demand side). Main attention is given to economic involvement in the form of sending / receiving remittances and engagement in co‒investment. Our analysis reveals that factors like the degree of integration, engagement with civil society organizations and European linkages, perception of conflict, travel frequency and arrival date play a significant role in shaping economic diaspora networks.

Keywords: transnational networks; remittances; investment; economic integration; post‒conflict reconstruction; european capital cities.

7  |  Factors Influencing Migrants’ Engagement


1. Introduction

I

n an era of rapid developments in transport, communications and new technologies, people get more and more interlinked with each other. When borders seem to fade, international migration becomes much easier and cheaper. Contacts between migrants and their home country are shaped within Transnational Communities (TCs) that operate throughout the world. It also becomes increasingly apparent that Civil Society Organizations (CSOs) representing these TCs of migrants play a crucial role in the domain of poverty reduction, especially in processes of conflict prevention and post‒conflict reconstruction. However, it is not clear what actual impact these TCs exercise on the regions of origin and to what extent they are effectively linked to economic reconstruction activities and political reconciliation processes. Transnational Communities are generally characterized by transnational practices that transcend national borders (Levitt, 2001). They can be classified in a variety of ways, both from the source as well as from the destination. This article explores the driving forces for engagement in transnational economic activities at the supply side and the likely implications for receiving assistance and remittances by individuals at the demand side. Transnational economic activities as defined in this study include the activities that CSOs undertake which are directed at economic development or poverty alleviation in the country of origin. These includes monetary remittances to the home country as well as their involvement in activities that are aimed at attracting investment or economic assistance from others to the country of origin. The study focuses on different TCs that are active in three distinct post‒conflict areas (Great Lakes region, Turkey and Kosovo) and related migrant communities located in major Western European cities (Berlin, Brussels, London and the Dutch Randstad).1 We examine these transnational economic activities in two directions. First, we focus on supply side factors, which include TC linkages with Civil Society Organizations (CSOs) and the degree of integration within the host country. We specifically explore factors and motives that determine participation in economic activities, such as the intrinsic characteristics of the migrants (e.g. arrival date, travel frequency) and the incentives derived from the situation in the home country (reasons for migration). Second, we explore the factors that determine the reception of economic assistance from the demand side. Here, individuals who receive assistance are addressed, looking at factors like the perceived conflict and political stability in European host countries and the role of the diaspora community in conflict resolution in the home country. In both cases, we include the perception of the conflict in the home country as a key control variable. The structure of this article is as follows. Section two provides some background theories about transnationalism and transnational activities and discusses the determining factors for involvement and engagement in transnational economic activities. In §  3 we elaborate the hypotheses that are central to this study. In §  4, the research design and the profiles of the respondents are described. Hereafter, §  5 analyzes the results for the determinants of transnational economic activities at the supply side and at the demand side. The final section presents conclusions and implications for research and policy. 1. The Dutch Randstad (Dutch: rim city, i.e. a city at the edge of a circle, with empty space in the centre) is a conurbation in the Netherlands. It consists of the four largest Dutch cities (Amsterdam, Rotterdam, The Hague and Utrecht), and the surrounding areas. With its 7.5 million inhabitants, the Randstad hosts almost half of the population of the Netherlands.

9  |  Factors Influencing Migrants’ Engagement


2.  Transnational Activities and Economic Networks Different disciplines and approaches use the term ‘transnationalism’ for analyzing international networks within the context of migration. Transnationalism is described in a variety of ways, but most social scientists agree that in a broad sense it contains the notion of multiple ties and interactions that link people or institutions across the borders of nation‒states (Vertovec 1999). Wayland (2004) uses transnationalism as identities and intra‒ethnic relations that transcend state borders. She states that a ‘diaspora’ is a form of transnational community that has been dispersed from its home country and whose members permanently live in a another host country. Others depict transnationalism as the activities or practices that are undertaken by migrants which contribute to the development of transnational communities (Al‒Ali et al. 2001). According to Brettell (2000), the idea of transnationalism emerged from the realization that immigrants maintain ties with their countries of origin, making home and host society a single arena for social action by moving back and forth across international borders and between different cultures and social systems, and by exploiting transnational relations as a form of social capital for their living strategies” (cited in: Dahinden 2005: 2). In this article we refer to transnationalism as a process of interactions between individuals, groups and institutions, where communities emerge and activities and interactions take place that transcend the borders of the nation‒state. Transnational communities do not emerge in a vacuum. Development in transport and communication technology – like cheaper airline travel and internet – make it easier for migrants to maintain relations that transcend national borders (Levitt 2001). Transnational communities can thus emerge where countries of origin and settlement and migrants in different destinations are linked together (Bloch 2008). These communities are often characterized by transnational practices that transcend borders. These practices or activities can be classified in a variety of ways. The usual distinction is between activities that are (a) political, such as lobbying; (b) economic, like remittances and investments; (c) social, like promotion of the human rights, and (d) cultural, such as delivering articles to newspapers. In addition, activities may take place at the individual level or through institutional channels (Al‒Ali et al. 2001). This article focuses on the economic practices that are undertaken by individuals supported through Civil Society Organizations (CSOs) that represent different transnational communities (TCs). Transnationalism often appears related to migration (Levitt & Nyberg‒Sorensen 2004). Especially when economic activities of migrant communities are subject of study, most current analyses focus on the role of remittances and their possible contributions to local poverty alleviation (Adams & Page 2005). It is commonly acknowledged that migrants are involved in sending money to their home country, and recent studies show that these remittances have become one of the most important factors contributing to economic development in post‒conflict countries (Ratha & Mohapatra 2007, Haas, 2007), representing up to 15–20% of GDP. International migration and related remittances streams can thus generate significant welfare benefits to the countries of origin. Many families and individuals in developing countries depend on these money inflows and it helps them to move forward economically and to reinforce access to social services (Weiss Fagen and Bump 2006). Remittances are currently regarded as one of the most stable form of income and foreign exchange for developing countries, particularly in comparison with international aid that is often volatile and withdrawn before needs are fully met (Black et al. 2007; Weiss‒Fagen and Bump 2001). Remittances as a form of transnational economic activities can thus be considered as an important factor contributing to the development of the home country. In addition, transna-

Factors Influencing Migrants’ Engagement  |  10


tional linkages can be shaped through other types of economic involvement, particularly based on direct co‒investment in activities from migrants in their home country. While the importance of remittances to the home country is fairly clear, questions can be raised regarding the underlying motives or incentives of migrants who are sending money home. In general, the remittances literature classifies three types of motivations for sending remittances: (a) altruism, (b) self‒interest and (c) mutually beneficial arrangements (Blue 2004, 64). While these motives reflect different behavioral intentions of migrants, they do not inform us about other contextual conditions that go beyond individual behavior, such as their perceptions regarding the political situation in the home and / or host country and the facilitating role of social networks. This article therefore focuses on some of these contextual factors that may influence engagement in economic assistance in general, focusing on motives and driving forces for sending and receiving remittances and for attracting co‒investment.

3.  Motives for Transnational Economic Activities: Some Key Hypotheses The engagement of migrants with economic activities in their home countries can be analyzed both from the supply side and from the demand side. At the supply side, attention should be given to the motives for engaging in sending remittances or economic support. Specific factors at the supply side include the migrants’ perceptions of the domestic conflict (in the home country), their current degree of integration in the host country, the original motivation for migration, existing network linkages through Civil Society Organizations, travel frequency and date of arrival. At the receiving (demand) side, attention is given to the factors that influence the attraction of economic support. Key mediating factors include the perception of the conflict, the linkages with the sending community (living in European cities), the degree of cooperation with the migrant communities living in Europe, and the political stability in the country of origin. 3.1.  Supply Side Motives Many people living in (post)‒conflict countries are highly dependent on transnational economic activities, especially in the form of remittances (Weiss Fagen and Bump 2001). This raises the question whether people living in (post‒)conflict countries receive more assistance than people living in other developing countries and whether migrants send more money to (post‒)conflict countries than to other countries. Hansen (2008) calculates that remittances inflows to post‒conflict countries are globally equal to other poor countries. Ratha and Mohapatra (2007) find, however, that migrants tend to send more money to their home country during hard times to help their families and friends. They even register that remittances increased following financial crisis and natural disasters in several countries. Within this line of reasoning, one might expect to find the same kind of outcome when countries are recovering from internal conflict. In this perspective, Mohamoud (2006) indeed acknowledges the importance of the situation in the country of origin. He found that if there is more stability in the home country, the diaspora tends to invest more in activities that contribute to socio‒economic development, such as community welfare projects and business investments. However, when the situation of the conflict in the home country is not yet stable, migrants tend to invest more in politically‒related activities. Based on a comparison of different situations in Ethiopia and the Democratic Republic of Congo (DRC), it appears that re11  |  Factors Influencing Migrants’ Engagement


turned migrants from Ethiopia were more likely to invest economically in the country because the political situation was considered rather stable. However, migrants from DRC did not yet engage in such economic activities as the situation in the home country was still considered to be too risky. As this study shows, the perceptions regarding the socio‒political situation in the home country affects the economic behavior of the senders. Related to the study of Mohamoud, we expect to find a positive relation between the perception of the senders and their economic involvement. We therefore test the hypothesis that the more one perceives the local conflict to be improving, the more this person is likely to become involved in economic activities towards the home country. The second hypothesis that we are testing refers to the migrants’ attachment to the home country. One of the most common assumptions that has been made regarding remittances is that when migrants establish themselves in the host country, remittances will decrease over time. The reason for this is that it is assumed that migrants, who are away for a longer time, eventually will get married, settle in the host country and become better integrated. For this reason their orientation will become more inclined towards the host country and consequently the amount of remittances to the home country will decrease over time (Blue 2004). Recent arrivals, on the other hand, are to be expected to maintain close family ties and are more knowledgeable about the actual condition in their home country. Therefore, the probability that they will send remittances is higher compared to migrants who have stayed for a longer period in the host country (ibid.). Bloch (2008: 296–297) found indeed that the category of migrants with least time in the host country was most involved in sending remittances. Within the same line of reasoning, one can assume that frequent traveling towards the home country is likely to result in more economic activities. We hypothesize that more attachment to ones home country will lead to stronger economic involvement with home country activities.2 The third dimension we highlight refers to the social networks to which migrants belong and that may influence their likelihood for economic involvement with the home country. Migration can be seen as a form of linkage that transcends national boundaries and where social networks and social relations play a significant role (Levitt and Schiller 2004). A typical example of this is that migrants maintain their social ties in the country of origin to safeguard social support networks in case they need to return to their home country (Levitt and Jaworsky 2007). Social networks thus link migrants with their families, but it also connect migrants together and with other individuals and organizations. It is acknowledged that social relations and networks are one of the key determinants for all transnational activities (Levitt and Nyberg Sorensen 2004). For example, the analysis made by Blue (2004) on family remittances to Cuba ascertained that, among others, social capital motives are a major driver for sending remittances, In a similar vein, the likelihood for sending remittances substantially increased in line with the social capital of Mexican migrants, considering the ties with relatives and organizations in the United States as key aspects of social capital. Finally, Bloch (2008) also found a strong positive relationship between (kinship) networks and transnational activities. In this article, we address migration as part of a social decision‒making process, considering the motivation to migrate to be induced by the social network linkages. Even though the act of migration may be an individual decision, the motivation belongs within the realm of social networks as it may influence people who stay at home to make the decision to migrate (Menjivar et al. 1998). Migrants who decide to move away to another country because of better economic prospects, still may share responsibilities for their parents or children at home. Otherwise, migrants that leave their home country because of political reasons tend to have less 2. Attachment is supposed to be influenced by the time one is away from the home country; frequent traveling back could enhance the attachment to the home country.

Factors Influencing Migrants’ Engagement  |  12


opportunities to notice or involve family members or relatives. We therefore expect that when political motives for migration prevailed, migrants may feel morally more obligated to take care of the ones that stay behind and therefore are more likely to send remittances. Finally, another aspect of the social network of migrants – representing an important pathway for economic involvement regarded to the home country – consists of the intensity of institutional linkages with Civil Society Organizations (CSOs). Even while sending remittances is an individual decision, the CSO network can be helpful to identify suitable investment opportunities. Moreover, CSO linkages contribute to reducer transaction and information costs and thus can enhance the effectiveness of transnational economic networks (Deans et al 2007). We therefore expect that stronger CSO linkages could enhance the economic network with home countries, but might also be helpful to deliver resources towards more community‒oriented programs. 3.2.  Demand Side Motives At the demand side we review several hypotheses regarding important determinants for receiving and attracting economic assistance. Some of these hypotheses are related to the social network dimensions outlined in the previous section. The first hypothesis to be tested contains the social networks of the receivers. As explained before, the social network of migrants is expected to be a major determinant for becoming economically involved in the home country. In this perspective one also expects to find a positive relation between social networks and economic assistance at the receiving side. De Janvry and Sadoulet (2000) find that membership of migration networks is regarded as a key factor for receiving remittances incomes. These migration networks are helpful in providing information about how to invest or how to find employment in the home country. This results in an increase in remittances inflows and therefore the social network is acknowledged to be a major determinant for household income of the ones that stay behind. The social network at the receiving side is defined as the cooperation with migrant communities or organizations that are established in European cities. We expect to find a positive relation between the social networks of the receivers and the delivery of economic assistance. In the second place, transnational communities are recognized to play a significant role in the conflict of the home country. They may sustain (ethno‒political) conflicts as they provide resources that can influence the existing balance of economic, political and military power in the homeland. Group identities are no longer spatially or territorially bounded and the diaspora can become actively involved in the conflict in the home country, even though they live at another place (Demmers 2002). In a study on ethno‒nationalist networks and transnational opportunities, Wayland (2004) describes the role of ethnic networks in the Tamil diaspora. She argues that the Liberation Tigers of Tamil Eelam set up offices abroad and were engaged in several fundraising campaigns. This enabled the Tamil insurgents to sustain their quest for an independent homeland. Transnational communities can also influence the conflict in the home country by sending money, arms and equipment, or by influencing public opinion (Demmers 2002). This implies that the involvement of transnational communities in the conflict of their home country is apparent and important. In this study, we try to find an answer to the question whether and how the position of transnational communities in the local conflict influences the delivery and receipt of economic assistance. We hypothesize a positive relation between these variables: the more apparent the role of the migrant community living in Europe in the conflict, the more assistance is likely to be received. The third key variable influencing the receipt of economic support from migrant communities refers to the role played by the European‒based migrant community in the conflict of the home country. Even while migrants do not physically live in the home country anymore, they still can in13  |  Factors Influencing Migrants’ Engagement


terference in the conflict of their homeland. Demmers (2002) calls this as sort of “virtual‒conflict”: migrants live their conflicts through communication and connections like internet and telephone without direct and physical risks or suffering. Migrants are separated from the direct (results of the) conflict and might therefore experience different emotions and behavior toward the conflict. Since the people who still live in the homeland might feel emotions like fear, stress and pain, the diaspora group might feel anger, frustration or alienation (Demmers 2002). This strongly influences the perceptions of migrants regarding the conflict in their home country. Migrants might have different views on the conflict compared to the ones that stayed behind. In order to obtain a more in‒depth insight into this dimension, we included questions on the perception on the conflict to both the migrants, but also to the receivers. We expect to find a positive relation between the perception on the conflict and the receipt of economic assistance. Finally, the position of the migrant community in Europe is likely to influence the receipt of economic assistance. Given the current international economic crisis, it may be expected that migrants have less resources available to share with their family end relatives in the home country. Otherwise, upcoming tensions with (and between) diaspora communities in European cities are likely to enhance the feelings of inhospitality and could eventually reinforce the willingness to consider return. We expect a larger involvement in remittances in migrant networks that envisage future return prospects, relying on transnational economic activities to prepare their way back to the home country (Hagen‒Zanker, 2010). In a similar vein, engagement in economic assistance activities might be driven by efforts to enhance the role of the diaspora community in local conflict settlement and / or reconstruction processes. This factor is likely to increase if the perceived linkages between home and host country communities are stronger.

4.  Research Design Several CSOs representing different TCs located in the four capital cities of European countries and related to three post‒conflict areas have been selected for this study. In total 74 interviews were conducted in the home countries with people from the Great Lakes region (Burundi, Democratic Republic of Congo and Rwanda), Turkey and the Balkans (Kosovo) and 139 interviews were collected in the capital cities (Brussels, Berlin, London and the Randstad). These cities were selected to ensure general comparability of the field work and because they share major characteristic feature. A typical example of such characteristics is that big European cities often attract a multiplicity of TCs because of their economic opportunities.3 In addition, the choice for the three geographical areas was based on criteria of diversity and geographical scope and – more importantly – inclusion was based on different dimensions of (post‒)conflicts setting.4 The sampling for data collection iss based on whether respondents were involved in any CSO representing one of the selected communities. The method of random sampling was used to select the CSOs. However, in some cases there was a limited number of CSOs available and snowball

3. Other reasons include: (a) clustering of TCs in different neighborhoods is often experienced in big cities; (b) political movement and lobbying is often most effective in the big cities; (c) organization, mobilization and density of TC networks become the most visible in big cities; and (d) confrontations between and within TCs become more visible in big cities. 4. The categories of the stage of the conflicts are (a) peaceful stable situations; (b) political tension situations; (c) violent political conflict; (d) low intensity conflicts; (e) high intensity conflicts. The dimensions of the conflicts were identified by (a) cultural dimensions; (b) socio‒economic and geographical dimensions; (c) political dimensions; (d) external dimensions.

Factors Influencing Migrants’ Engagement  |  14


sampling was applied. In these cases we decided to include all the CSOs that were present. Table 1 gives an overview of the collected data in the four host cities and three home countries. Table 1.  Sample Frame (Valid interviews) Host city Randstad Berlin London Brussels Home country Great Lakes Kosovo Turkey Total

Great Lakes

Kosovo

Turkey

Total

20 – 11 20

13 5 11 –

19 20 – 20

52 25 22 40

34 – – 85

– 17 – 46

– – 23 82

34 17 23 213

Data is collected through a method of semi‒structured, in‒depth interviews. These interviews also enabled to get an in‒depth understanding of the motives of respondents. It also helped to elucidate the complex details of people’s lived experiences. Some interviews were conducted with the assistance of interpreters and took between 30 minutes to several hours to complete. Table 2.  Descriptives of Economic Diaspora Networks at Supply Side (n = 139) Dependent variables Economic involvement a Remittances sending a Attract investment a Independent variables b Perception of conflict CSO linkages Degree of integration Date of arrival Travel frequency Reason for migration

Mean

SD

Min

Max

N

0.31 0.15 0.08

0.464 0.359 0.271

0 0 0

1 1 1

139 139 114

1.34 1.25 1.85 1.85 0.67 2.42

0.477 0.432 0.800 0.597 0.470 1.047

1 1 0 1 0 1

2 2 3 3 1 4

139 130 123 133 135 135

Notes: (a) 1 = yes. (b) Perception in conflict (1 = improved, 2 = worsened); CSO linkages (1 = low / average, 2 = high); Degree of integration (0 = no integration, 1 = language, 2 = adaptation and / or participation, 3 = accept and / or respect); date of arrival (1  = > 30 years ago, 2 = 10–30 years ago, 3 = <10 years ago); frequent travel (0 = no, 1 = yes); reason for migration (1 = family / born, 2 = socio‒economic perspective, 3 = political, 4 = violence / armed conflict).

We distinguish three different economic activities on the supply side (i.e. general economic involvement, sending remittances, attracting investment) and two on the demand side (i.e. receiving assistance, receiving remittances). Data about the involvement of the respondents, importation of conflicts, conflict policies, socio‒economic issues, organization strategies and other perceptions and migration history was collected on the supply side. On the demand side similar data was gath-

15  |  Factors Influencing Migrants’ Engagement


ered including some additional questions regarding the political stability in the country and the involvement with the community living in Europe (see Table 2). Regarding the characteristics at the supply side, it becomes readily clear that almost one third of the respondents’ CSOs (31%) are involved in activities directed toward economic development in their country of origin. The CSOs of respondents from the Great Lakes seem to be the most active (44%), followed by CSOs from Kosovo (35%), while only 21% of CSOs by the Turkish / Kurds respondents are involved in activities towards economic development in their country of origin. In addition, results show that only 15% of all respondents are involved in sending remittances and an even smaller number (8%) is involved in attracting investment from others to their country of origin. Remittances are most frequent amongst respondents that origin from the Great Lakes region (43%) and more reduced for people from Kosovo and Turkey (29%). On the other hand, Kosovars are most active in attracting investment (75%), while only 25% of respondents from the Great Lakes and a negligible share of respondents from Turkey appeared to be involved in these activities. Even though only one third of the migrants’ CSOs are involved in any economic activities toward their home country, more than two third (70%) of the respondents on the demand side stated to have received any form of economic and non‒profit assistance from members of their community living abroad. Respondents from the Great Lakes received most assistance (almost 65%), while respectively 24.3% and 10.8% of the Kosovar and Turkish respondents received assistance. Globally 50% of the respondents report to have received remittances in the past year. More than half of all remittances (58%) was received by respondents from the Great Lakes. 30% was received by Kosovar respondents and 15% by the Turkish respondents. Table 3 provides the key descriptives for the respondents on the demand side. Table 3.  Descriptives of Economic Diaspora Networks at the Receiving Side (n = 70) Dependent variables Received assistance a Received remittances a Independent variables Perception of conflict (0–3) b Tensions in Europe a Role diaspora community a Cooperation migrant community a Political stability (0–3) c

Mean

sd

Min

Max

n

0.70 0.50

0.463 0.505

0 0

1 1

3 52

0.71 0.52 0.59 0.66 1.91

0.456 0.505 0.495 0.478 0.805

0 0 0 0 1

3 1 1 1 3

48 69 70 65 45

Notes: (a) 1 = yes. (b) Perception in conflict (0 = no conflict, 1 = improved, 2 = no change, 3 = worsened). (c) Political stability (1 = not stable, 2 = average, 3 = stable).

Comparing the data from demand and supply (Tables 1 and 2) it becomes apparent that respondents at the receiving side are more positive on the development of the conflict in their home country compared to the respondents at the supply side. More than half of the receivers (55.4%) perceive the conflict as improved (even though one third of the respondents feels that the political situation in their country is not stable) while only 47.1% of the latter states the same. This might indicate already important differences in perceptions amongst both groups. Finally, it should be noted that the survey design was based on half‒open questions and topics that have be coded to enable comparison. Since the main focus of the research is related to the role Factors Influencing Migrants’ Engagement  |  16


of migrant communities and CSOs in transnational economic activities, due attention is given to network dimensions. At the supply side, this is envisaged through variables that address the linkages of migrants with local CSOs, while at the demand side some variables depicting the linkages with migrants communities in Europe are included. The latter variables reflect the information and perceptions regarding prevailing tensions that migrants experience in Europe, the role played by migrant communities in addressing the problems in their home countries, and the cooperative arrangements that exist between migrants in Europe and in the home country.

5. Results Data analysis is based on statistical regression analysis for each of the economic linkages within diaspora networks, both from the demand and the supply-side persoective. 5.1.  Determinants at the Supply Side We first examine the determinants for general economic involvement from the supply side. As was stated before, these activities are undertaken partly through CSOs representing TCs. In the previous section we explained that the dependent variable was divided in one general variable (i.e. economic involvement) and two more specific variables (i.e. sending remittances and attracting investment). Table 4 shows the results obtained in the analyses for all three dependent variables. The determinants for general economic involvement will be examined first, following by an examination of the two specific dependent variables. To examine the determinants Binary Logistic Regression analyses is used, applying a dummy for involvement for each of the dependent variables (i.e. economic involvement, sending remittances and attracting investment). For the dependent variable general economic involvement, two specific hypotheses are considered: (a) more intensive CSO linkages results in more economic involvement, and (b) the more the conflict in the home country is perceived as conflictive, the less involvement in economic activities. We will first discuss the outcomes of these hypotheses following with the results for the other variables that are included. The results in Table 4 show that the variable CSO linkages is positively related to general economic involvement. This implies that an increase in the intensity of CSO linkages will result in an increased probability of economic involvement. Also significant is the position in conflict. This variable shows to have a negative impact on general economic involvement, which means that if the local conflict situation is perceived as deteriorating, migrants will be less likely to become economically involved toward their home country. Next to these main variables, the views on integration (in the host country), date of arrival and frequent travel appear to be significant. More recent arrival in the home country and frequent travel to the host country positively influence the economic involvement of respondents. For the variable ‘view on integration’ the outcomes are not very robust. The results show a trend toward more integration in the host country as a positive determinant for economic involvement in the home country. Finally, the reason for migration does not show any significant results. Before we give a more in‒depth explanation for these outcomes we discuss the determinants for sending remittances. We start with the outcomes for the variables which are related to the hypotheses: (a) better integrated members of TCs are less involved in sending remittances; (b) less sending of remittances if 17  |  Factors Influencing Migrants’ Engagement


conflict is perceived as more conflictive and (c) more remittances sending with recent arrival and frequent travel. The independent variable ‘view on integration’ again shows a significant influence on the sending of remittances. However, without analyzing the separate categories we cannot conclude anything definite about the nature or direction of this influence. Analyzing the separate categories results in the following outcomes. Respondents who define integration as ‘language’ have the highest chance to be involved in sending remittances (compared to the other categories). Respondents who have the lowest chance of being involved in sending remittances are the ones who define integration as ‘accept and respect’. Thus, the outcome that ‘view on integration’ influences the sending of remittances mainly relies on the fact that the difference between the categories ‘no integration’ and ‘accept / respect’ is significant. Yet, no sequent order of B‒coefficients is found (the coefficient order of categories is 1,0,2,3). However, given the fact that the difference between category 1 and 0 is rather small, a major trend can be identified; better integration in the host country tends to lead to less sending of remittances to the home country. Table 4.  Factors influencing Funding of Transnational Economic Activities (Supply Side) Indicator Perception of conflict in home country CSO linkages View on integration No integration (0) a Language (1) a Adapt / participate (2) a Accept / respect (3) a Date of arrival < 10 years ago (3) a > 30 years ago (1) a 10–30 years ago (2) Frequent travel Reason for migration Family / born (0) a Socio‒economic (1) a Political situation (2) a Violence / war (3) a Constant Cox and Snell R² Nagelkerke R²

Econ. Involvement (n = 93) Coeff. se sign. b

Remittances (n = 95) Coeff. se sign. b

Attract Investment (n = 86) Coeff. se sign. b

‒1.341 1.557 *** ** (ref) 2.939 ‒0.626 ‒2.602 * (ref) ‒2.014 ‒0.488 1.145 ** (ref) 0.722 ‒0.228 0.233 ‒0.830

‒1.620 0.965

0.943 ** 0.729 *

‒0.319 1.208

0.956 0.849 *

0.386 ‒1.456 ‒3.250

1.367 1.232 1.724 **

1.632 ** (ref) 2.776 ‒1.453

1.269 ** 0.813 **

1.023

1.173

‒0.257 (ref) ‒2.885 ‒1.572 0.201

1.011 ‒0.890 1.047 *** 0.871 ** 2.102

‒0.692 0.999 ‒0.340 (ref) ‒3.843

1.305

0.745 ** 0.732 ** (ref) 1.579 ** 1.119 1.538 ** ** 3.113 1.319 * 1.050 0.811 *

0.902 1.028 1.039 1.912

0.363 0.499

0.280 0.443

1.041 2.207 **

0.056 0.120

Notes: (a) reference category. (b) * = significant at 5%; ** = significant at 10%; *** = significant at 1%.

Also significant is the variable ‘position in conflict’. This variable shows a negative relation with remittances sending; respondents who define the conflict in their home country as deteriorating will be less likely to be involved in the sending of remittances. Furthermore, the date of arrival and frequent traveling now show significant influences. For the date of arrival, the same outcome Factors Influencing Migrants’ Engagement  |  18


as for general economic involvement is found; more recent arrival results in a higher probability of outflow of remittances. Remarkable is the outcome for frequent traveling. While this variable is positively related to economic involvement, it is negatively related to remittances sending. This may be related to the fact that more direct control can be exercised on the use of remittances if this is combined with frequent travel. Next to the main variables some other determinant factors were found. CSO linkages are positively related to remittances sending. Also the variable ‘reason for migration’ shows a significant influence. But, due to the fact that the outcomes of B‒values are not fully sequential (i.e. the answer categories follow not logical order), no robust conclusions can be given. A global trend toward a difference between pull and push factors can be identified. Respondents who have migrated because of their family or because of a better socio‒economic perspective in the host country (pull factors) have the highest chance of being involved in remittances sending. When migrants are being pushed out of their country (forced migration due to the political situation for example) they will be less likely to be involved in remittances sending to their home country. Finally, the results for the determinant factors for attracting investment are discussed. Because of the limited number of responses from the questionnaire (only 5.8% of the respondents stated to be involved in attracting investment from others to home country), we are not able to include exactly the same variables that were used in the previous analyses. Therefore, the matrix is reduced to four independent variables. The hypotheses to be tested on this matter are: (a) more investment by TCs with more intensive CSO linkages, and (b) less investment by TCs if more conflict is perceived. As one can find out from the table above, there is just one variable which has a significant influence on attracting investment. The CSO linkages of migrants shows to be consistently positively related to attracting investment from others to home country. This is in line with the outcomes that were given before. As stated before, social networks are one of the most important determinants for trans­national activities. As the results from this study show, the variable CSO linkages is the only factor that influences all three dependent variables at the supply side. We can therefore state that social networks is indeed an important factor for engagement in economic transnational activities; the stronger the CSO linkages, the more migrants are likely to be involved in economic activities in their home countries. In addition, it appears that the hypotheses on attachment to the home country are partly confirmed. As expected, the outcomes for recent arrival are positively related to general economic involvement and remittances sending. However, the level of integration in the host country is not fully confirmed to be positively related with transnational economic activities. Even though a general trend was identified, the outcomes are not robust. The reason for this might lie in the fact that we do not have enough observations. It is expected that additional observations will result in a more valid statement regarding this trend. Furthermore, the factor frequent traveling to the home country did not show a steady result within the three dependent variables. While more frequent traveling results in higher general economic involvement, the exact opposite outcome was found for remittances sending. This can be attributed to the importance of supervising investments made out of remittances. Finally, we found that the position in the conflict appeared to be very important for all economic activities. When people perceive the local conflict as more conflictive, this will result in less engagement in economic activities toward the home country. This outcome was to be expected as we believed that the behavior of the migrants is very much influenced by the domestic prospects in their host country. It might also indicate that economic activities of migrant communities are most likely to follow upon the domestic political conditions, and are less influential in changing or modifying prevailing local conflicts. 19  |  Factors Influencing Migrants’ Engagement


5.2.  Determinants for the Demand Side This section contains an examination of the determinant factors for receiving economic assistance in general and for receiving remittances specifically. In order to examine this relationship, the method of Binary Logistic regression is used. Table 5 shows the determinant factors influencing the receipt of assistance. Due to a small amount of responses for the dependent variable receiving remittances, we were initially not able to identify a solid matrix. Therefore, we relied on the method of Multiple Imputation before applying the logistic analyses. Multiple imputation is often used to deal with datasets with missing values. It is a technique that replaces each missing value with two or more acceptable values that represent a distribution of possibilities (Rubin, 2004: 1). In this study we imputed ten datasets which were further analyzed by using Binary Logistic regression. The outcomes for receiving remittances in Table 5 are the combined (average outcomes for the ten datasets) outcomes from the analyses on the multiple imputation. Table 5. Factors influencing Receipt of Assistance and Remittances (Demand Side) Indicator Perception of conflict Worsened (3) a Improved (1) a No change (2) a Tensions in Europe Role Diaspora Community Cooperation of migrant Community Political stability Stable (3) a Unstable (1) a Middle (2) a Constant Cox and Snell R² Nagelkerke R²

Receiving assistance (n = 40) Coeff. se Sign. b ** (ref) 2.319 1.028 ** 2.817 1.453 ** ‒2.068 1.013 ** 1.642 0.980 **

(ref) 0,089 0,244 ‒0,454 0,865

0,771 0,758 0,593 0,605

‒0.162

‒0,123

0,626

1.062

Receiving remittances (n = 72) Coeff. se Sign. b

(ref)

‒0.362

1.073

0.319 0.446

0,070 0,117

* *

‒1,111 0,687 0,932

0,761

0.142 0.190

Notes: (a) reference category. (b) * = significant at 5%; ** = significant at 10%.

We start with a description of the outcomes of the analyses on receiving assistance, followed by the outcomes for receiving remittances. For both dependent variables the following hypotheses are tested: (a) deteriorating conflict perception induces less receipt of economic assistance; (b) tensions felt in Europe lead to less receipt of assistance /  remittances; (c) stronger involvement in diaspora community leads to more receipt of assistance /  remittances; (d) stronger cooperation within migrant community leads to more delivery of economic assistance. For receiving of assistance we included a fifth hypothesis: (e) less political stability in country of origin leads to lower receipt of remittances. As one can conclude from the results in Table 5, the perception on the conflict in the home country has a significant influence on the delivery of assistance. It is difficult to make hard assumptions on the direction of causality, since the following order of the coefficients of the separate Factors Influencing Migrants’ Engagement  |  20


categories is not fully sequential. However, one can state that respondents who perceive the conflict in their home country as deteriorating have the lowest change of receiving assistance compared to respondents who perceive the conflict as improving or not changed. This is in line with the outcomes from the supply side where less economic activities are undertaken if the conflict is perceived as more conflictive. Also significant is the factor ‘tensions in Europe’. When people in the home country know about tensions that are felt in Europe related to the conflict, it is less likely that they will receive any assistance from relatives outside the country. Finally, the role of the diaspora community in Europe (i.e. whether the community living in European cities played a significant role in the situation or the conflict in the respondents’ country) is positively related to the assistance one receives. The results for the dependent variable receiving remittances also show a positive relation with the role of the diaspora community living in Europe. Moreover, the political stability in the home country influences the receipt of remittances. Respondents who perceive the conflict as unstable have the lowest chance of receiving remittances compared to respondents who perceive the conflict as stable or somewhere in between (middle). For the receiving side of economic activities there appears to be a very clear relation between the conflict in the home country and the receipt of any economic assistance, as the variables that show a significant relation are all somehow related to the conflict in the home country. As we expected before, tensions between communities in Europe results in a decrease of economic activities at the receiving side. Additionally, respondents who perceive the conflict as deteriorating are less likely to receive any assistance compared to respondents who are more positive about the conflict. This fact is in line with the outcomes on the sending side. More apparent even, are the outcomes for the role that the diaspora community living in Europe plays in the conflict; the more they are involved in the conflict, the more people are likely to receive assistance or remittances. This outcomes confirms the important role played by TCs in addressing the conflict of the home country and their contributions to economic recovery and development.

6.  Conclusions and Implications This article examined the engagement of migrants in transnational economic activities in their home countries, and tried to identify key factors influencing the receipt of economic assistance and remittances by individuals. We found that at the supply side, social networks (CSO linkages) represent the single most important factor influencing all kinds of economic activities in which migrants engage. In a similar vein, at the demand side linkages with diaspora communities represent an important factor for attracting remittances and investments. Even while sending remittances remains essentially an individual decision, it takes place within a social‒cultural network. Looking at the overall picture we can conclude that at the supply side people who consider the conflict in the home country as more conflictive will be less likely to be involved in economic activities and remittances sending. The date of arrival is also found to be important determinants for both activities: more recent arrival results in a higher probability of economic involvement and remittances. The views on integration (in Europe) and the reason for migration do not appeared to be strongly related to engagement in economic activities. At the demand side, a strong relationship between variables concerning the conflict in the home country and the economic activities was identified, pointing to an inverse impact of the conflict on the economic engagement of migrant communities. 21  |  Factors Influencing Migrants’ Engagement


As stated in the introduction, it is commonly believed that poverty reduction should be part of conflict prevention and post‒conflict reconstruction processes. Transnational communities might play a crucial role in this domain. Even though the focus of the survey did not contain specific questions regarding the way or the extend to which these communities can contribute to this issue, we discern some interesting outcomes. As was consistently found in § 4 while discussing the determinants at the supply side, variables related to the conflict (i.e. position in conflict) appear to be strongly, albeit negatively related to the likelihood of engagement in economic activities in the home countries. We cannot ascertain, however, whether the opposite relationship holds. CSO linkages and frequent travel to the home country can partly mitigate this relationship and tend to favour economic engagement. Similarly, all variables that were significantly related to the receipt of economic support are strongly related to the conflict perception in the home country: respondents who perceive the local conflict as deteriorating are less likely to receive economic assistance. Wherever the diaspora community is considered to play a role in local conflict mitigation, the likelihood of receiving remittances clearly increases. Consequently, migrant’ networks can act as an important linking pin between sending and receiving economic support.

Acknowledgements The research for this study is funded through the International Civil Society Forum on Conflicts (infocon). infocon is a joint endeavour of several research institutes, including the Université Catholique de Louvain (research direction), University of Kent, Universität Duisburg‒Essen, Institut d’Études Politiques de Lille, cidin‒Radboud University Nijmegen, Université de Liège, Université Laval (Québec) and CSOs based in the Netherlands, Kosovo, Belgium, United Kingdom and Germany.

References Adams, R.H. and J. Page (2005). Do International Migration and Remittances Reduce Poverty in Developing Countries? World Development, 33(10): (1645)–(1669). Al‒Ali, N., R. Black and K. Koser. (2001). The Limits To “Transnationalism”: Bosnian and Eritrean Refugees in Europe As Emerging Transnational Communities. Ethnic and Racial Studies, 23(4): 578–600. Black, R., L. Jones, M.C. Pantiru, R. Sabates‒Wheeler, R. Skeldon and Z. Vathi. (2007). Understanding Migration As A Driver of Poverty Reduction in Europe and Central Asia. Working Paper. Sussex: Development Research Centre on Migration, Globalisation and Povery. Bloch, A. (2008). Zimbabweans in Britain: Transnational Activities and Capabilities. Journal of Ethnic and Migration Studies, 34(2): 287–305. Blue, S.A. (2004). State Policy, Economic Crisis, Gender, and Family Ties: Determinants of Family Remittances To Cuba. Economic Geography, 80(1): 63–82. Bretell, C.B. (2000). Theorizing Migration in Anthropology. The Social Construction of Networks, Identities, Communities, and Globalscapes. in: C.B. Bretell and J. F. Hollifeld. Migration Theory. New York: Routledge, pp. 97–136.

Factors Influencing Migrants’ Engagement  |  22


Dahinden, J. (2005). Contesting Transnationalism? Lessons From the Study of Albanian Migration Networks From Former Yugoslavia. Global Networks, 5(2): 191–208. Deans, F., L. Lönnqvist and K. Sen (2007). Remittances and Migration: Some Policy Considerations for Ngos: RemittaNces, Development and the Role of Ngos. International Oxford: Intrac Ngo Training and Research Centre. Demmer, J. (2002). Diaspora and Conflict: Locality, Long‒Distance Nationalism, and Delocalisation of Conflict Dynamics. The Public, 9(1): 85–96. Haas, H. De. (2007). Remittances, Migration and Social Development: A Conceptual Review of the Literature: The Linkages Between Migration, Remittances and Social Development. Geneva: United Nations Research Institute for Social Development (unrisd). Hagen‒Zanker, J.S. (2010). Modest Expectations: Causes and Effects of Migration on Migrant Households in Source Countries. PhD Thesis. Maastricht:: Graduate School of Governance. Hansen, P. (2008). Diasporas and Fragile States. Diis Policy Brief. Copenhagen: Danish Institue for International Studies. Janvry, A., and E. Sadoulet. (2000). Rural Poverty in Latin America: Determinants and Exit Paths. Food Policy, 25(4): 389–409. Levitt, P. (2001). Transnational Villagers. Berkeley: University of California Press. Levitt, P. and N. Nyberg‒Sorensen. (2004). The Transnational Turn in Migration Studies. Global Migration Perspectives. Geneva: Global Commission on International Migration. Menjívar, C., J. Davanzo, L. Greenwell, R. Burciaga Valdez. (1998). Remittance Behavior Among Salvadoran and Filipino Immigrants in Los Angeles, International Migration Review, 31(1): 97–126. Mohamoud, A.A. (2006). African Diaspora and Post-Conflict Reconstruction in Africa. Copenhagen: Danish Institute for International Studies (diis Brief). Ratha, D. and S. Mohapatra. (2007). Increasing the Macroeconomic Impact of Remittances on Development. Development Prospects Group. Washington dc: The World Bank. Rodriguez, E.R. (1996). International Migrants’ Remittances in the Philippines. Canadian Journal of Economics, 29: 427–432. Rubin, D.B. (2004). Multiple Imputation for Non-Response in Surveys. New Jersey: Wiley. Vertovec, S. (1999). Conceiving and Researching Transnationalism. Ethnic and Racial Studies, 22(2): 447– 462. Wayland, S. (2004). Ethnonationalist Networks and Transnational Opportunities: The Sri Lankan Tamil Diaspora. Review of International Studies. 30: 405–426. Weiss Fagen, P., and M.N. Bump. (2006). Remittances in Conflict and Crises: How Remittances Sustain Livelihoods in War, Crises, and Transitions To Peace. Policy Paper. New York: International Peace Academy.

23  |  Factors Influencing Migrants’ Engagement


Annex A Data Analysis Cities of Settlement (Brussels, Randstad, Berlin and London)

Nienke Regts

December 2009


A.1. Profiles Variables Economic Involved City of Settlement Amsterdam 58.1% Brussels 14% London 16.3% Berlin 11.6% Country origin Great Lakes 44.2% Kosovo 34.9% Turkey 20.9% Most important public role Leading 53.5% Member 34.9% Other 9.3% Type organization ngo 88.4% Political 9.3% Business 2.3% View on conflict Improved 69.8% Worsened 23.3% No conflict 7% Type of economic involvement Passive 65.1% Active 20.9% Not last year 14% Type of economic involvement specific Remittances 48.8% Goods 18.6% Knowledge 16.3% Motivation economic involvement Emotional attachment 23.3% Survival 32.6% Development 25.6% Future home country 14.00%

Remittances

Attract Investment a

57.1% 19% 9.5% 14.3%

55.6% 11.1% 22.2% 11.1%

42.9% 28.6% 28.6%

22.2% 77.8% –

47.6% 42.9% 9.5%

55.6% 22.2% 22.2%

95.2% – 4.8%

88.9% – 11.1%

76.2% 19% 4.8%

77.8% 22.2% –

ni b ni ni

ni ni ni

ni ni ni

ni ni ni

(a) Profiles are based on the three dependent variables that are used for the logistic regressions: (i) Economic involved  =  respondent involved in activities toward economic development or poverty alleviation. (ii) Remittances  =  respondent sends remittances. (iii) Attract investment  =  respondent involved in activities aimed at attracting investment or economic assistance from others to country of origin. (b) ni  =  not involved.

27  |  Factors Influencing Migrants’ Engagement


A.1.1.  What Can Be Concluded from the Profiles above? More than half of the respondents who are involved in any economic activities in general (meaning combining the three dependent variables together) are settled in Amsterdam. For the respondents who declared their organization is involved in any activities towards the economic development / poverty alleviation in the country of origin and the respondents who declared to send remittances stem for more than 40% from the Great Lakes. Respondents who declared to be involved in activities aimed at attracting investment or economic assistance from others to country of origin mostly come from Kosovo (77.8%). It becomes clear that almost everyone who is economically involved is a member of an NGO or private organization. In addition, more than 50% have an average intense CSO linkage, meaning they are involved on an average level in the CSO and have an average position into the CSO. Moreover, other NGOs (national and international) are for a great deal also the most important actors they interact with and with whom they defend their interest regarding the conflict in the country of origin. When looking at how the respondents who are involved in any economic activity towards their country of origin look at the evolution of the conflict in the last 20 years than more than three third feels that is has improved – so most respondents who feel the conflict has improved over the last 20 years are also economical involved towards they country or origin. The reason for leaving the country of origin is mostly when there was a poor socio-economical perspective for the respondents. And in all three cases far more than half of them arrived between 1990 and 2000 and did spent some time in the country of origin after they were migrated. Looking at the identity card of these respondents, the ones who are involved in activities directed towards economic development / poverty alleviation and the ones who send remittances, more than half of them state they have the identity of their own community or country of origin. Of the respondents who are involved in activities aimed at attracting investment or economic assistance from others to country of origin, one third states to have the identity of their community or country of origin. A.1.2.  Overall Picture Of the interviewees who stated to be economical active for their country of origin they seem to feel like they are still rather attached to their home country (look at identity and time spent in country). In addition they are very much involved into their CSO, they have a rather high position into their own organization and they have an average CSO linkage. They also stand positive towards the evolution of the conflict, feeling that it has improved over the years. Apparent is also the date of arrival in the country of settlement. A large part of them are rather newcomers (comparing this to migrants who came around 1970–1980) (which might also explain their rather strong attachment to their home country).

Factors Influencing Migrants’ Engagement  |  28


A.2. CROSS-TABS A.2.1.  City of Settlement Perceiving of Conflict

Brussels Amsterdam City of Settlement

Berlin London

Count Expected Count Count Expected Count

Total 28 28,0 49 49,0

Count

12

8

20

Expected Count

13,1

6,9

20,0

13

9

22

14,4

7,6

22,0

78

41

119 119,0

Count Expected Count Count

Total

Perceiving Conflict New Improved Worsened 22 6 18,4 9,6 31 18 32,1 16,9

Expected Count

78,0

41,0

Chi-Square Tests Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases

Value 2,895 a 3,050 2,025 119

df 3 3 1

Asymp. sig. (2-sided) ,408 ,384 ,155

Note: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 6,89.

29  |  Factors Influencing Migrants’ Engagement


A.2.2.  City of Settlement View on integration Meaning of Term ‘Integration’ No Language Integration

Brussels

City of Settlement

Count Expected Count Adjusted Residual

Count Expected Amsterdam Count Adjusted Residual

Berlin

London

Total

Count Expected Count Adjusted Residual Count Expected Count Adjusted Residual Count Expected Count

Adaptation and / or Participation

Accept and / or Respect

Total

8

0

17

6

31

2,8

4,3

18,9

5,0

31,0

3,8

‒2,6

‒,8

,5

0

13

30

8

51

4,6

7,0

31,1

8,3

51,0

‒2,9

3,2

‒,4

‒,1

3

4

11

5

23

2,1

3,2

14,0

3,7

23,0

,8

,6

‒1,4

,8

0

0

17

1

18

1,6

2,5

11,0

2,9

18,0

‒1,4

‒1,8

3,2

‒1,3

11

17

75

20

123

11,0

17,0

75,0

20,0

123,0

Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Fisher’s Exact Test Linear-by-Linear Association N of Valid Cases

9 9

Asymp. sig. (2-sided) ,000 ,000

Exact sig. (2-sided) . b ,000 ,000

Exact sig. (1-sided)

Point Probability

1

,196

,210

,108

,020

Value

df

34,545 a 43,015 33,345 1,676 c 123

Notes: (a) 9 cells (56,3%) have expected count less than 5. The minimum expected count is 1,61. (b) Cannot be computed because there is insufficient memory. (c) The standardized statistic is 1,294.

Factors Influencing Migrants’ Engagement  |  30


A.2.3.  City of Settlement Reason Migration Main Reason for Leaving Country of Origin

Brussels

City of Settlement

Count Expected Count Adjusted Residual

Count Expected Amsterdam Count Adjusted Residual

Berlin

London

Total

Count Expected Count Adjusted Residual Count Expected Count Adjusted Residual Count Expected Count

Family / Born

(Poor) Socio‒ Economical Perspectives

Political Situation

10

24

5

1

40

8,6

14,2

8,9

8,3

40,0

,6

3,9

‒1,8

‒3,4

8

10

21

13

52

11,2

18,5

11,6

10,8

52,0

‒1,4

‒3,1

4,0

1,0

10

7

3

3

23

4,9

8,2

5,1

4,8

23,0

2,8

‒,6

‒1,2

‒1,0

1

7

1

11

20

4,3

7,1

4,4

4,1

20,0

‒1,9

‒,1

‒2,0

4,1

29

48

30

28

135

29,0

48,0

30,0

28,0

135,0

Violence / Armed Total Conflicts /War

Chi-Square Tests Pearson Chi‒Square Likelihood Ratio Fisher’s Exact Test Linear‒by‒Linear Association N of Valid Cases

Value 51,439 a 51,757 . b

df 9 9

Asymp. sig. (2‒sided) ,000 ,000

8,635

1

,003

Exact sig. (2‒sided) Exact sig. (1‒sided) . b . b . b . b

. b

135

Notes: (a) 5 cells (31,3%) have expected count less than 5. The minimum expected count is 4,15. (b) Cannot be computed because there is insufficient memory.

31  |  Factors Influencing Migrants’ Engagement


A.2.4.  City of Settlement Date of Arrival

City of Settlement

Brussels

Count Expected Count

Amsterdam

Count Expected Count

Berlin

Count Expected Count

London

Count Expected Count Count Expected Count

Total

Date of arrival new More than Between 10 Less than 30 Years in and 30 Years 10 Years in Country in Country Country 13 23 4 10,5 25,0 4,5 8 38 6 13,7 32,5 5,9 13 9 0 5,8 13,7 2,5 1 13 5 5,0 11,9 2,1 35 83 15 35,0 83,0 15,0

Total 40 40,0 52 52,0 22 22,0 19 19,0 133 133,0

Chi-Square Tests Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases

Value 24,317 a 25,517 1,022 133

df 6 6 1

Asymp. sig. (2-sided) ,000 ,000 ,312

Note: (a) 3 cells (25,0%) have expected count less than 5. The minimum expected count is 2,14.

A.2.5.  City of Settlement Frequent Travel

Brussels

Count Expected Count Adjusted Residual

Amsterdam

Count Expected Count Adjusted Residual

Berlin

Count Expected Count Adjusted Residual

London

Count Expected Count Adjusted Residual

City of Settlement

Total

Count Expected Count

Factors Influencing Migrants’ Engagement  |  32

Times Spent in Country of Origin No Yes 16 23 12,7 26,3 1,3 ‒1,3 19 32 16,6 34,4 ,9 ‒,9 4 20 7,8 16,2 ‒1,8 1,8 5 16 6,8 14,2 ‒,9 ,9 44 91 44,0 91,0

Total 39 39,0 51 51,0 24 24,0 21 21,0 135 135,0


Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Fisher’s Exact Test Linear-by-Linear Association N of Valid Cases

3 3

Asymp. sig. (2-sided) ,153 ,132

Exact sig. (2-sided) ,154 ,141 ,152

Exact sig. (1-sided)

Point Probability

1

,054

,060

,032

,011

Value

df

5,275 a 5,607 5,214 3,722 b 135

Notes: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 6,84. (b) The standardized statistic is 1,929.

A.2.6.  City of Settlement CSO Linkages

City of Settlement

Brussels

Count Expected Count

Amsterdam

Count Expected Count

Berlin

Count Expected Count

London Total

Count Expected Count Count Expected Count

CSO Linkage New Low / Average Intensity High Intensity 28 7 26,4

8,6

30

19

36,9

12,1

23

1

18,1

5,9

17

5

16,6

5,4

98

32

98,0

32,0

Total 35

35,0 49

49,0 24

24,0 22

22,0 130

130,0

Chi-Square Tests Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases

Value 11,147 a 12,736 ,656 130

df 3 3 1

Asymp. sig. (2-sided) ,011 ,005 ,418

Note: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 5,42.

33  |  Factors Influencing Migrants’ Engagement


A.3.  TESTING HYPOTHESES A.3.1.  General Information Three different dependent variables were used for testing the different hypotheses. The most general dependent variable is 1. “involvement in activities toward economic development or poverty alleviation in country of origin (economic involvement)”. 2. Next to this variable, two more specific dependent variables were used: “sending remittances”, 3. “attract investment or economic assistance”. 4. The hypotheses to be tested are the following: 1. 2. 3. 4. 5.

Better integrated members of TCs are more / less involved in sending remittances. More investment by TCs with more / less intensive CSO linkages. Less investment by TCs if more conflict is perceived. More / less sending of remittances if conflict is perceived as more conflictive. More remittances sending with recent arrival and frequent travel.

Deriving from the hypotheses above the following variables will be used in the regression: Dependent variables: • Involvement in activities toward economic development or poverty alleviation (0 = no / 1 = yes) • Sending remittances (0 = no / 1 = yes) • Attract investment or economic assistance (0 = no / 1 = yes) Independent variables: • Perceiving of conflict (1 = improved / 2 = worsened) • CSO linkages (1 = low or average intensity / 2 = high intensity) • View on integration (0 = no integration / 1 = language / communication / 2 = adapt and participate / 3 = accept and respect) • Date of arrival (1 = more than 30 years ago / 2 = between 10 and 30 years ago / 3 = less than 10 years ago) • Frequent travel (0 = no / 2 = yes) • Reason for migration (0 = family or born / 1 = socio-economic / 2 = political situation / 3 = violence or war A.3.2.  Testing Hypotheses 2 and 3 Dependent variable: • Involvement in activities toward economic development or poverty alleviation (0 = no / 1 = yes) Independent variables: • Perceiving of conflict (1 = improved /  2 = worsened) • CSO linkages (1 = low or average intensity /  2 = high intensity)

Factors Influencing Migrants’ Engagement  |  34


Table A.3.1. Significant determinants for involvement in activities toward economic development or poverty alleviation in country of origin (general economical involvement) Variables (n = 93) c Perceiving of conflict (total) CSO linkages (total) View on integration (total) No integration (0) b Language (1) b Adapt / participate (2) b Accept / respect (3) b Date of arrival (total) Less than 10 years in country (3) b More than 30 years in country(1) b Between 10 and 30 years in country (2) b Frequent travel (total) Reason for migration (total) Family / born (0) b Socio-economic (1) b Political situation (2) b Violence / war (3) b Cox and Snell R² Nagelkerke R²

b ‒1.465 1.491

se 0.694 0.716

Wald 4.452 4.335 12.746

Sign. (2-sided) c Sign. (1-sided) c 0.035** 0.018** 0.037** 0.019** 0.005** 0.003***

2.769 ‒0.839 ‒2.793

1.531 0.993 1.456

3.269 0.713 3.678 4.901

0.071* 0.398 0.055* 0.086*

0.036** 0.199 0.028** 0.043**

‒2.311 ‒0.718 1.074

1.125 0.896 0.796

4.216 0.643 1.823 1.582

0.040** 0.423 0.177 0.663

0.020** 0.212 0.089* 0.335

0.586 ‒0.364 0.140 0.414 0.552

0.843 0.974 1.011

0.484 0.139 0.019

0.487 0.709 0.890

0.244 0.355 0.445

Notes: (a) * = significant at 5%; ** = significant at 10%.  (b) Reference category. (c) Removed outliers 115, 115, 54; constant excluded.

Interpretation of the Outcomes for Hypothesis 2: “More economic involvement by TCs with more / less intensive CSO linkages”. The variable CSO linkages has a significant influence on activities toward economic development or poverty alleviation. Because the variable CSO linkages is a dichotome variable, a positive B indicates that an increase in the independents variable score will result in an increased probability of the cases recording a score of 1 in the dependent variable. For this case it thus means that the higher the intensity of respondents’ CSO linkages, the more likely it is they will be involved in activities toward economic development or poverty alleviation. Interpretation of the Outcomes for Hypothesis 3: “Less investment by TCs if more conflict is perceived”. The variable perceiving of conflict has a negative influence on the economic involvement of the respondents. The B-coefficient is negative which means that respondents who perceive the conflict as more worsened have less chance of being economic involved compared to respondents who perceive the conflict as improved. To put it the other way around: the less conflict is perceived, the more one is likely to be involved in economic activities toward economic development or poverty alleviation.

35  |  Factors Influencing Migrants’ Engagement


Interpretation of Other Independent Variables: The variables view on integration, date of arrival and frequent travel have a significant influence on economic involvement. For view on integration the category ‘no integration’ is used as reference category. The fact that view on integration has a significant influence on the dependent variable lies in the fact that the differences between 0 (no integration) and 1 (language / communication) and 0 (no integration) and 3 (accept and respect) are significant. The category ‘language’ has the highest B-score, which means that respondents who define integration as ‘language’ have the highest chance to score yes, they are economic involved (compared to the other categories). The second highest is the category ‘no integration’. The third is ‘adapt / participate’. The respondents who define integration as ‘accept / respect’ have the lowest chance of being involved in economic involvement. The ranking order of the B-coefficients (high-low): Original ranking order (most integrated till least integrated):

1023 3210

From these ranking orders one can find a trend towards the more integrated, the less one is involved in economic involvement. The variable date of arrival also has a significant influence on economic involvement of respondents. In this case the category 3 (less than 10 years in country) is used for the reference category. The fact that the date of arrival has a significant influence on economic involvement, lies in the fact that the difference between 3 (less than 10 years) and 1 (more than 30 years in country) is significant. In addition, the category ‘less than 10 years in country’ has the highest B-score. This means that respondents who arrived less than 10 years in the country of settlement are the ones who have the highest chance of being economic involved (compared to the other categories). The second highest is the category ‘between 10 and 30 years’. The category which has the lowest chance to score a 1 on economic involvement is ‘more than 30 years in country’. The ranking order of the B-coefficients (high-low): 321 Original ranking order: 3 2 1 Concluding can be stated: the more recent one arrived in the country of settlement, the more he / she is likely to be involved in economic involvement toward the country of origin. Finally, the variable frequent travel has a significant influence on economic involvement. The B-coefficient is positive. This means that respondents who stated to travel back to their country of origin are more likely to be involved in economic involvement. Dependent variable: • Attract investment (0 = no / 1 = yes) Independent variables: • Perceiving of conflict (1 = improved /  2 = worsened) • CSO linkages (1 = low or average intensity /  2 = high intensity)

Factors Influencing Migrants’ Engagement  |  36


Table A.3.2.  Significant determinants for attracting investment / economic assistance Variables (n = 93) Perceiving of conflict (total) CSO linkages (total) Frequent travel (total) Reason for migration (total) Violence/war (3) b Family/born (0) b Socio-economic (1) b Political situation (2) b Cox and Snell R² Nagelkerke R²

b ‒2.834 1.049 0.724

se 1.248 0.880 1.140

Wald 5.152 1.422 0.403 4.199

‒1.360 ‒2.266 ‒0.957 0.595 0.794

1.243 1.186 1.033

1.198 3.651 0.859

Sign.(2-sided) a Sign.(1-sided) a 0.023** 0.012** 0.233 0.117 0.525 0.263 0.241 0.121 0.257 0.056* 0.384

0.129 0.028** 0.192

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category.

Table A.3.2 only shows the outcomes for four of the six independent variables. This is because the variables date of arrival and view on integration showed to have 0 outcomes in some options. Reducing the number of categories does not make a difference. In that case, spss is not able to run the logistic regression. Interpretation of the Outcomes for Hypothesis 2: “More attracting of investment /  economic assistance by TCs with more / less intensive CSO linkages”. The results from Table A.2.1 show that the independent variable CSO linkages does not has a significant influence on attracting investment. Interpretation of the Outcomes for Hypothesis 3: “Less attracting of investment /  economic assistance by TCs if more conflict is perceived”. The results from Table A.2.2 show that the independent variable perceiving of conflict has a significant influence on attracting investment. The B-coefficient is negative which means that respondents who perceive the conflict as worsened have less chance of being economic involved compared to respondents who perceive the conflict as improved. To put it the other way around: the less conflict is perceived, the more one is likely to be involved in activities aimed at attracting investment / economic assistance. Interpretation of the Outcomes of Other Independent Variables: Frequent travel does not show to have a significant influence on attracting of investment. And the variable reason for migration also does not have a significant influence. However, between the different categories there appears to be 1 significant difference. The difference between 3 (violence / war) and 1 (socio-economic) is significant. But, in total no significant influence of reason for migration on attracting investment is found. A.3.3.  Testing Hypothesis 1, 4 and 5 Dependent variable: • Sending remittances (0 = no /  1 = yes) 37  |  Factors Influencing Migrants’ Engagement


Independent variables: • View on integration (0 = no integration /  1 = language / communication /  2 = adapt and participate /  3 = accept and respect) • Date of arrival (1 = > 30 years ago /  2 = between 10 and 30 years ago /  3 = < 10 years ago) • Frequent travel (0 = no /  2 = yes) • Table A.3.3.  Significant determinants for sending remittances Variables (n = 95)

Constant Perceiving of conflict (total) CSO linkages (total) View on integration (total) No integration (0) b Language (1) b Adapt/participate (2) b Accept/respect (3) b Date of arrival (total) More than 30 years in country (1) b Between 10 and 30 years in country (2) b Less than 10 years in country (3) b Frequent travel (total) Reason for migration (total) Socio-economic (1) b Family/born (0) b Political situation (2) b Violence/war (3) b Cox and Snell R² Nagelkerke R²

b 0.201 ‒1.620 0.965 7.271

se 2.102 0.943 0.729 0.064*

Wald Sign. (2-sided) a Sign.(1-sided) a 0.009 0.924 0.462 2.951 0.086* 0.043** 1.752 0.186 0.092* 0.032**

0.386 ‒1.456 ‒3.250

1.367 1.232 1.724

0.080 1.396 3.555 4.946

0.778 0.237 0.059* 0.084*

0.389 0.119 0.030** 0.042**

2.776 3.113 ‒1.453

1.269 1.632 0.813

4.789 3.641 3.198 8.177

0.029*** 0.056* 0.074* 0.042**

0.015** 0.028** 0.037** 0.021**

‒0.257 ‒2.885 ‒1.572 0.28 0.443

1.011 1.047 0.871

0.065 7.599 3.260

0.799 0.006*** 0.071*

0.400 0.003*** 0.036**

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category.

Interpretation of the Outcomes for Hypothesis 1: “Better integrated members of TCs are more / less involved in sending remittances”. From the outcomes in Table A.2.3 can be found that ones view on integration has a significant influence on sending of remittances (sign.  =  0.064). However, no further statements can be made about the nature of the influence. It is thus needed to look at the separately categories to find out in what way the view on integration has a significant influence. Looking at the categories separately it can be stated that respondents who define integration as ‘language / communication (1)’ have the highest chance to answer yes to the question whether they send remittances to their home country compared to the other categories (resulting from the B values). Secondly, come the respondents who define integration as ‘no integration’, third the respondents who define integration as ‘adaptation and / or participation’. Respondents who define integration as ‘accept and respect’ have the lowest chance of all groups to be involved in sending remittances. The ranking order will be as follows (from highest till lowest B-coefficient): 1 0 2 3 The original ranking order (from high till low integration) is: 3210

Factors Influencing Migrants’ Engagement  |  38


The difference between 0 (no integration) and 1 (language) and the difference between 0 (no integration) and 2 (adapt / participate) is not significant. The difference between 0 (no integration) and 3 (accept / respect) is significant. So, concluded can be stated that the fact that the variable ‘view on integration’ has a significant influence on sending of remittances lies in the fact that the difference between 0 (no integration) and 3 (accept / respect) is significant. Herein, respondents who report integration as ‘language / communication’ are most likely to answer yes to the question whether they send remittances. In addition, because the ranking order of the B-coefficients is not in line with the original ranking order it cannot be stated that respondents with a more in-depth view on integration (or being more integrated) results in more / less sending of remittances. However, when taking category 0 and 1 together (the differences between the B-coefficients are rather small), a trend towards such a statement can be seen. The more one is integrated, the less this respondent is likely to be involved in the sending of remittances. One remark about the method of logistic regression must be made. I think that for this particular hypothesis, this method is not completely sufficient. Due to the outcomes, we cannot state that the higher the integration, the more sending of remittances. A trend towards this outcome is noticed, but in fact it is not a very sufficient conclusion. Interpretation of the Outcomes for Hypothesis 4: “More / less sending of remittances if conflict is perceived as more conflictive”. From the outcomes of Table A.2.2 it can be concluded that ‘perceiving of conflict’ has a significant influence on sending of remittances. Because the variable ‘perceiving of conflict’ is a dichotome variable, a positive B indicates that an increase in the independents variable score will result in an increased probability of the cases recording a score of 1 in the dependent variable. In this case, the B-value is negative which means that the more conflict is perceived, the less one is likely to be involved in sending of remittances. Interpretation of the Outcomes for Hypothesis 5: “More remittances sending with recent arrival and frequent travel”. Frequent travel shows to have a significant influence on sending of remittances. The B-coefficient is negative, which means that the more frequent one is traveling to the country of origin, the less likely one will be involved in the sending of remittances. Recent arrival also shows a significant influence on sending of remittances. In this respect the referent category (1) is ‘more than 30 years in the country’. When we look at the category, which scores the highest B-coefficient (meaning the highest chance of scoring 1) we see that respondents who arrived less than 10 years ago have the highest chance of sending remittances. Respondents who are in the country between 10 and 30 years have the second highest B score. The category with the lowest chance of a score of yes is more than 30 years in country of settlement. The ranking order from the highest B-coefficient till the lowest is as follows: With an original ranking ranging from (high-low):

321 321

All differences with the reference category are significant. Thus, the fact that recent arrival has a significant influence on sending of remittances lies in the fact that the differences between 1 (before 30 years) and 2 (between 10 and 30 years) and 3 (less than 10 years) are significant. Concluding can be stated that (regarding the B-order and the original order) the more recent one arrived in the country of settlement, the more likely he / she will be involved in the sending of remittances.

39  |  Factors Influencing Migrants’ Engagement


Interpretation of Other Independent Variables: The independent variable CSO linkages has a significant influence on the sending of remittances. The B-coefficient is positive. This means that the more intensive the CSO linkages, the more one is likely to be involved in the sending of remittances. This is in line with the influence of CSO linkage on economic involvement in general. The independent variable reason for migration also has a significant influence on the sending of remittances. In this case category 1 (socio-economic reasons) is being used as the reference category. The fact that reasons for migration has a significant influence on the sending of remittances, lies in the fact that the differences between 1 (socio-economic) and 2 (political) and 1(socioeconomic) and 3 (violence / war) are significant. Respondents who mentioned socio-economic reasons for migration have the highest B-coefficient, which means they have the highest chance in being involved in the sending of remittances compared to the other categories. The second highest is the category family / born, however the B-coefficient between these two is rather small. Third is category ‘violence / war’. Respondents who mention ‘political’ as the reason for migration have the lowest chance of being involved in the sending of remittances. Ranking order B-coefficients (high-low): Original ranking order (high-low)

1032 3210

No final conclusions from these outcomes can be made. Perhaps a trend towards a difference between pull and push factors can be found where pull factors have the highest chance in being involved in the sending of remittances. When respondents are pushed out of their country of origin (by war or political instability) they will be less likely to be involved in the sending of remittances. A reason for this can be that these people are more likely to go back to their country of origin when it is possible. A.3.4.  Comparing Outcomes of Different Dependent Variables When comparing the three tables together the following can be concluded: • The variable “perceiving of conflict” shows to have a significant influence on all three dependent variables. For all outcomes, the same conclusion is found: the more conflict is perceived, the less one is likely to be involved in any economic activities (in general, in remittances or in attracting investment). The strongest influence is on attracting investment. The influence on the other two variables is almost the same. • The variable “view on integration” shows to have a significant influence on economic involvement in general and specific on sending remittances. For both dependent variables, the trend toward the higher the view on integration /  the more integrated the less involvement in activities toward economic development or poverty alleviation or sending remittances is found. • The variable “date of arrival” shows to have a significant influence on economic involvement in general and specific on sending remittances. Also here, the same outcome is found for both dependent variables: the more recent one arrived in the country of settlement, the more one is likely to be involved in economic involvement and sending of remittances. • The variable “intensity of the CSO linkages” only influences economic involvement in general significantly. • “Frequent travel” and “reason for migration” only influences sending of remittances significantly. Factors Influencing Migrants’ Engagement  |  40


Annex B Data Analysis country of origin (Great Lakes, Kosovo and Turkey)

Nienke Regts & Ruerd Ruben


B.1. PROFILES1 Variable Country of Origin Rwanda Burundi Congo1 Kosovo Turkey Most Important Public Role Head Organization Member / Employee Religious Type Organization One Is Member of NGO Political Movement Business Public Perceiving of Conflict Improved No Change Worsened Tensions Felt in Europe Yes No Role European Community in Situation Country None Share Knowledge / Skills etc. Socio-Economical Support Political Support Influence European Community On International Actors None Share Knowledge Organizing Projects / Fund Raising Dialogue / Lobbying Create Network Organization Cooperated with European Community in Those Efforts No Yes

Received Assistance

Received Remittances

16.2% 35.1% 13.5% 24.3% 10 / 8%

19.2% 26.9% 11.5% 26.9% 15.4%

54.1% 43.2% 2.70%

46.2% 53.8%

59.5% 5.4% 13.5% 2.7%

65.4% 7.7% 7.7% –

56.8% 24.3% 13.5%

53.8% 19.2% 19.2%

29.7% 43.2%

34.6% 38.5%

27% 29.7% 29.7% 5.4%

26.9% 38.5% 30.8% –

16.2% 24.3% 13.5% 24.3% 2.7%

19.2% 23.1% 15.4% 23.1% 3.8% 21.6% 78.4%

1. No. 95675 (Great-Lakes) and no. 84201 (Kosovo) were removed from the file country of origin and thus not used for any analyses. Answers were whether of the record or based on just some remarks.

43  |  Factors Influencing Migrants’ Engagement


Variable Contribution European Migrant Community to Support Efforts in Home Country No Yes:   Share Knowledge / Skills   Organize Activities   (Financial) Support for Development European Community Role in Mitigation Conflict No Share Knowledge / Skills Cooperation Return to Country Invest Involved in Commercial Activities No Support / Set Up Business Obstacles for Economic Activities None Communication Socio-Economical Political Most Striking Event in Recent History (After ‘45) War / Rebellion Socio-Economical / Refugees Political Changes / Coups Stability of Political Situation Unstable Not Unstable / Not Stable Rather Stable Stable Identity Card Ethnicity Nationality Profession / Education Civilian of The World Personal Links with Organizations in Europe No Yes Personal Contacts with Persons Who Live in Europe No Yes

Factors Influencing Migrants’ Engagement  |  44

Received Received Assistance Remittances 43.2%

50,0%

24.3% 13.5% 8.1%

19.2% 11.5% 11.5%

35.1% 13.5% 2.7% 10.8% 21.6%

42.3% 15.4% 3.8% 7.7% 15.4%

70.3% 10.8%

65.4% 7.7%

24.3% 10.8% 29.7% 18.9%

15.4% 11.5% 38.5% 15.4%

16.2% 13.5% 37.8%

19.2% 11.5% 34.6%

29.7% 27% 18.9% 16.2%

26.9% 26.9% 19.2% 19.2%

8.1% 21.6% 29.7% 2.7%

7.7% 19.2% 30.8% –

24.3% 62.2%

23.1% 57.7%

13.5% 73%

11.5% 73.1%


In relation to the profiles of the migrants who were active in any economic activities towards their country of origin the profiles of the respondents who received any assistance are in line with each other. More than half of the respondents live in the Great Lakes and more than 70% received assistance in the form of money. Respondents who have received assistance are most of the time member of an NGO or private organization. More than half of them have a leading position. And almost 80% of the organization they are involved in cooperates with the community in Europe. Most of them (more than one third) also have personal contacts with organizations and persons in Europe. For the respondents who receive remittances, almost the same outcomes are apparent. Vision on the influence of community in Europe: A great deal of the respondents feels that the community in Europe has a significant role or influence related to the conflict and international actors. Sharing knowledge/skills and socio-economical support are the most common ways of influencing by the community. About the conflict: more than half of the respondents perceive the conflict in their country as improved. However, looking at the political stability, only 35.1% and 38.4% of the respondents thinks that it is (rather) stable. Overall It can be stated that respondents who received assistance (which consist of around 70% money) from their community in Europe, have rather strong links with the European community (in terms of having contacts with people / organizations and cooperating with the community in Europe). Moreover, it seems that the majority of these respondents are positive about the influence of the community in Europe on the conflict or international actors. Regarding to their own involvement in the community, the leading position and being a member of an NGO are the most common categories. So it seems that these respondents are rather high involved in their community. The conflict in their country is perceived as mostly improved (which is also in line with the view of the ‘senders’). But, a great deal does not feel that the political situation is very much stable. So, it looks like that respondents who have received assistance and remittances, have rather strong links with their European community: their own organization cooperates with this community and personally they know organizations and persons who live in Europe. Besides, most of these respondents feel that the European community is active in playing a mitigating kind of role in the conflict. In addition, the biggest part of these respondents are highly involved in their own community (the public role is rather high). Being involved in any commercial activities however, is not very common.

45  |  Factors Influencing Migrants’ Engagement


B.2. CROSS-TABS B.2.1.  Country of Origin Received Assistance

Country Of Origin Not Specific

Total

Received Assistance? No 7 Count Great Lakes Adjusted Residual ‒1,4 2 Count Kosovo Adjusted Residual ‒1,0 7 Count Turkey Adjusted Residual 2,7 Count 16

Yes 24 1,4 9 1,0 4 ‒2,7 37

Total 31 11 11 53

Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Fisher’s Exact Test Linear-by-Linear Association N of Valid Cases

Value

df

7,443 a 6,951 6,659 4,926 b 53

2 2

Asymp. sig. (2-sided) ,024 ,031

1

,026

Exact sig. (2-sided) ,030 ,051 ,051 ,028

Exact sig. Point (1-sided) Probability

,022

,013

Notes: (a) 2 cells (33,3%) have expected count less than 5. The minimum expected count is 3,32. (b) The standardized statistic is ‒2,219.

B.2.2.  Country of Origin Received remittances

Great Lakes Country of Origin Kosovo Not Specific Turkey Total

Count Adjusted Residual Count Adjusted Residual Count Adjusted Residual Count

Factors Influencing Migrants’ Engagement  |  46

Received Remittances No Yes 16 15 ,3 ‒,3 3 7 ‒1,4 1,4 7 4 1,0 ‒1,0 26 26

Total 31 10 11 52


Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Fisher’s Exact Test Linear-by-Linear Association N of Valid Cases

Value

df

2,450 a 2,507 2,373 ,114 b 52

2 2

Asymp. sig. (2-sided) ,294 ,286

1

,735

Exact sig. (2-sided) ,348 ,348 ,348 ,866

Exact sig. Point (1-sided) Probability

,433

,126

Notes: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 5,00. (b) The standardized statistic is ‒,338.

B.2.3.  Country of Origin Perceiving of Conflict

Great Lakes Country of Origin Kosovo Not Specific Turkey Total

CountAdjusted Residual CountAdjusted Residual CountAdjusted Residual Count

Situation / Conflict Worsened or Improved Last 20 Years Improved No change Worsened 22 4 7 3,1 ‒3,5 ,2 5 7 2 ‒1,0 1,5 -,6 6 12 5 ‒2,5 2,4 ,3 33 23 14

Total 33 14 23 70

Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Fisher’s Exact Test Linear-by-Linear Association N of Valid Cases

Value

df

13,689 a 14,680 14,089 3,849 b 70

4 4

Asymp. sig. (2-sided) ,008 ,005

1

,050

Exact sig. (2-sided) ,007 ,009 ,005 ,055

Exact sig. (1-sided)

Point Probability

,031

,010

(a) 3 cells (33,3%) have expected count less than 5. The minimum expected count is 2,80. (b) The standardized statistic is 1,962.

47  |  Factors Influencing Migrants’ Engagement


B.2.4.  Country of Origin Tensions Felt in Europe Know about Tensions Happening in European Countries? No Yes 16 14 Count Great Lakes Adjusted Residual 1,0 ‒1,0 5 1 Country of Origin Count Kosovo Not Specific Adjusted Residual 1,9 ‒1,9 2 10 Count Turkey Adjusted Residual ‒2,5 2,5 Total Count 23 25

Total 30 6 12 48

Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Fisher’s Exact Test Linear-by-Linear Association N of Valid Cases

Value

df

8,064 a 8,783 7,868 3,216 b 48

2 2

Asymp. sig. (2-sided) ,018 ,012

1

,073

Exact sig. (2-sided) ,018 ,023 ,018 ,094

Exact sig. Point (1-sided) Probability

,051

,027

Notes: (a) 2 cells (33,3%) have expected count less than 5. The minimum expected count is 2,88. (b) The standardized statistic is 1,793.

B.2.5.  Country of Origin Role eu community in country

Great Lakes Country of Origin Kosovo Not Specific Turkey Total

Count Adjusted Residual Count Adjusted Residual Count Adjusted Residual Count

Factors Influencing Migrants’ Engagement  |  48

Community in Europe significant role in situation in country? Total Yes No 9 24 33 ‒2,2 2,2 3 11 14 ‒1,6 1,6 16 6 22 3,7 ‒3,7 28 41 69


Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Fisher’s Exact Test Linear-by-Linear Association N of Valid Cases

Value

df

13,982 a 14,187 13,540 10,087 b 69

2 2

Asymp. sig. (2-sided) ,001 ,001

1

,001

Exact sig. (2-sided) ,001 ,001 ,001 ,002

Exact sig. Point (1-sided) Probability

,001

,001

Notes: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 5,68. (b) The standardized statistic is ‒3,176.

B.2.6.  Country of Origin Cooperation with eu Community

Great Lakes Country of Origin Kosovo Not Specific Turkey Total

Count Adjusted Residual Count Adjusted Residual Count Adjusted Residual Count

Has Organization Cooperated with Community in Europe in Any of the Efforts No Yes 2 31 ‒4,7 4,7 12 4 3,9 ‒3,9 10 11 1,5 ‒1,5 24 46

Total 33 16 21 70

Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Fisher’s Exact Test Linear-by-Linear Association N of Valid Cases

Value

df

25,097 a 27,859 26,545 12,361 b 70

2 2

Asymp. sig. (2-sided) ,000 ,000

1

,000

Exact sig. (2-sided) ,000 ,000 ,000 ,000

Exact sig. Point (1-sided) Probability

,000

,000

Notes: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 5,49. (b) The standardized statistic is ‒3,516.

49  |  Factors Influencing Migrants’ Engagement


B.2.7.  Country of Origin Political Stability

Great Lakes Country of Origin Kosovo Not Specific Turkey Total

Not Stable 17 3,1 2 ‒1,8 5 ‒1,7 24

Count Adjusted Residual Count Adjusted Residual Count Adjusted Residual Count

Stability New Middle 6 ‒2,4 4 -,4 13 2,9 23

Stable 7 -,7 7 2,4 4 ‒1,2 18

Total 30 13 22 65

Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Fisher’s Exact Test Linear-by-Linear Association N of Valid Cases

Value

df

15,716 a 15,123 14,317 2,066 b 65

4 4

Asymp. sig. (2-sided) ,003 ,004

1

,151

Exact sig. (2-sided) ,003 ,007 ,005 ,165

Exact sig. Point (1-sided) Probability

,089

,025

Notes: (a) 3 cells (33,3%) have expected count less than 5. The minimum expected count is 3,60. (b) The standardized statistic is 1,437.

B.3. Testing Hypotheses B.3.1.  General Information For the country of origin file I maintained two different dependent variables. The first dependent is a general one and the second is more specific: Dependent variables: • Received assistance (0=no / 1=yes) • Receive remittances (0=no / 1=yes) Independent variables which might be interesting for analyses: • Perceiving of conflict (C.1) • Tensions felt in Europe (C.3) • Community in Europe played significant role in situation country (D.1) • Organization cooperated with community in Europe (D.4) • Community in Europe role in mitigating the situation in country (D.5) • View on political stability in country (F.2) • Personal links with organization in Europe (F.8) Tested hypotheses: 1. More perceiving of conflict, more/less receiving assistance Factors Influencing Migrants’ Engagement  |  50


2. Tensions felt in Europe leads to less receiving of any assistance/ remittances 3. Involvement European community in country, more/less receiving assistance/ remittances 4. Cooperation with European community leads to more assistance. 5. Less political stability in country of origin leads to less receiving of remittances. 6. Less receiving of remittances if more conflict is perceived. 7. More/less receiving of remittances if eu communities mitigate more/less in conflict. 8. I tried to use all independent variables in one matrix. The problem was that when more variables were used, the total number of included case reduced (less than 40). I tried to find the best fitting model. These attempts are described in the following sections. This means that some variables (like F.8 and D.5) were not included in the analysis. I started with one model, using as much as possible independent variables. For the dependent variable received remittances, this worked out. For the dependent variable received remittances, I also used single analyses and multiple imputation. B.3.2.  Testing Hypotheses 1–5 with Dependent Variable “Received Assistance” Dependent variable: • Received assistance (0=no / 1=yes) Independent variables: • C.1: Perceiving of conflict (1=improved /  2=no change /  3=worsened) • C.3: Knowing about tensions felt in Europe (0=no /  1=yes) • D.1: Community in Europe played significant role in situation country (0=no /  1=yes) • D.4: Has organization cooperated with community in Europe (in efforts like dialogue, peace building etc.) (0=no / 1=yes) • Political stability (1=unstable / 2=middle / 3=stable) Option 1: Outcomes Logistic Regression Using All Independent Variables Table B.3.1.  Significant Determinants for Receiving Assistance Variables (N = 36)

Constant Perceiving of Conflict (Total) Worsened (3) b Improved (1) b No Change (2) b Tensions in Europe (Total) Role eu Community (Total) Cooperation eu Community (Total) Political Stability (Total) Stable (3) b Not Stable (1) b Middle (2) b Cox And Snell R² Nagelkerke R²

b 0.795

se 2.031

Wald 0.153 4.818

Sign. (2-sided) a 0.695 0.090*

Sign.(1-sided) a 0.348 0.045**

3.254 4.335 ‒3.651 2.989 ‒0.545

1.667 2.151 1.693 1.593 1.398

3.809 4.059 4.648 3.52 0.152 0.376

0.051* 0.044** 0.031** 0.061* 0.696 0.829

0.026** 0.022** 0.016** 0.031** 0.348 0.415

‒0.709 ‒1.052 0.417 0.589

1.824 1.743

0.151 0.364

0.697 0.546

0.349 0.237

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category (removed outliers 31 and 21)

51  |  Factors Influencing Migrants’ Engagement


The number of selected cases becomes very small (N=36) when above 5 independent variables are used in one regression model. Just to show the outcomes I decided to depict this model here. In this model, political stability does not have a significant influence on the receiving of assistance. For a more detailed explanation of the outcomes I refer to the next pages. Option 2: Outcomes Regression Leaving Out Political Stability (Removed Outlier 31) In this option, the independent variable political stability is being left out. When this variable is left out, the total number of included cases runs from 36 to 40. Table B.3.2.  Significant determinants for receiving assistance Variables (N = 40)

Constant Perceiving of Conflict (Total) Worsened (3) b Improved (1) b No Change (2) b Tensions in Europe (Total) Role eu Community (Total) Cooperation eu Community (Total) Cox And Snell R² Nagelkerke R²

b ‒0.362

se 1.073

Wald 0.114 5.833

2.319 2.817 ‒2.068 1.642 ‒0.162 0.319 0.446

1.028 1.453 1.013 0.98 1.062

5.089 3.855 4.166 2.807 0.023

Sign.(2-sided) a Sign. (1-sided) a 0.736 0.368 0.054* 0.027** 0.024** 0.050* 0.041** 0.094* 0.879

0.012** 0.025** 0.021** 0.047** 0.44

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category (removed outliers 31 and 21).

Interpretation of the Outcomes For Hypothesis 1: “More perceiving of conflict, more/less receiving assistance”. The independent variable ‘perceiving of conflict’ shows to have a significant influence on the receiving of assistance. Because no judgments can be made about the direction of this influence, it is needed to look at the separate categories. In this perspective, category 3 (worsened) is the reference category. The outcomes in the table show that the separate categories 1 (improved) and 2 (no change) both have significant differences with the reference category. This means that the fact that the variable ‘perceiving of conflict’ has a significant influence on the receiving of assistance, lies in the fact that the differences between the categories 1 (improved) and 3 (worsened) and 2 (no change) and 3 (worsened) are significant. Category 2 (no change) has the highest B-score. This means that respondents who define the conflict as ‘no change’ have the highest chance of a score on ‘yes, they do receive assistance’. The second highest B-score is for category 1 (improved). Respondents, who perceive the conflict as worsened, have the lowest chance of a score on ‘yes, they receive assistance’. The ranking order from highest-lowest B-coefficient: Original ranking order (high-low):

213 321

Deriving from the outcomes above, no clear conclusion for the hypothesis can be found. Using the method of logistic regression cannot give an unambiguous outcome for the influence of ‘perceiving of conflict’ on the receiving of assistance.

Factors Influencing Migrants’ Engagement  |  52


Interpretation of the Outcomes for Hypothesis 2: “Tensions felt in Europe leads to less receiving of any assistance / remittances”. The independent variable ‘tensions’ has a significant influence on the receiving of assistance. The B-coefficient is negative, which means that the more respondents know about tensions happening in Europe (regarding the conflict), the less they are likely to receive any assistance. Interpretation of the Outcomes for Hypothesis 3: “Involvement European community in conflict, more / less receiving assistance / remittances”. The independent variable D.1 (community in Europe played significant role in situation country) also has a significant influence on the receiving of assistance. The B-coefficient is positive, which means that the more involvement of the European community in the situation of the country of origin, the more the respondents are likely to receive any assistance. Interpretation of the Outcomes for Hypothesis 4: “Cooperation with European community leads to more assistance”. The independent variable D.4 (has organization cooperated with community in Europe) does not has a significant influence on receiving of assistance. B.3.3.  Testing Hypotheses 1–5 Dependent Variable “Received Remittances” For the testing of the hypotheses 1–5 with the dependent variable received remittances, I follow the same procedure in section 3.2. Option 3: Outcomes Logistic Regression Using All Independent Variables Table B.3.3.  Significant Determinants for Receiving Remittances Variables (n = 38) Constant Perceiving of Conflict (Total) Worsened (3) a Improved (1) a No Change (2) a Tensions in Europe (Total) Role eu Community (Total) Cooperation eu Community Political Stability (Total) Stable (3) a Unstable (1) a Middle (2) a Cox and Snell R² Nagelkerke R²

b 0.104

se 1.181

Wald 0.008

Sign. (2-sided) Sign.(1-sided) 0,930 0.465 0.919 0.46

0,271 ‒0.093 ‒0.4 0.614 ‒0.199

0.908 1.019 0.728 0.759 0.929

0.089 0.008 0.302 0.653 0.046 0.462

0.765 0.928 0.583 0.419 0.831 0.794

0.383 0.464 0.292 0.21 0.416 0.397

‒0.635 ‒0.187 0.064 0.086

0.986 0.982

0.415 0.036

0.519 0.849

0.26 0.425

Note: (a) reference category.

53  |  Factors Influencing Migrants’ Engagement


As can be concluded from the table above, there is no single variable which has a significant influence on the receiving of remittances. Also when excluding the variable political stability (which results in N=41) does not give any significant outcomes (see option 4). spss did not find any outliers. None of the independent variables have a significant influence on the receiving of remittances. Option 4: Outcomes Regression Leaving Out Political Stability (Removed Outlier 31) In this option, the independent variable political stability is being left out. When this variable is left out, the total number of included cases runs from 38 to 41. Table B.3.4.  Significant Determinants for Receiving Assistance Variables (n = 41) Constant Perceiving of Conflict (Total) Worsened (3) a Improved (1) a No Change (2) a Tensions in Europe (Total) Role Eu Community (Total) Cooperation Eu Community Cox And Snell R² Nagelkerke R²

B ‒0.609

SE 0.963

Wald 0.4 0.9

0.695 0.152 ‒0.152 0.447 ‒0.184 0.044 0.058

0.789 0.982 0.652 0.699 0.805

0.778 0.024 0.054 0.41 0.052

Sign. (2-sided) Sign. (1-sided) 0.527 0.264 0.638 0.319 0.378 0.877 0.816 0.522 0.819

0.189 0.439 0.408 0.261 0.41

Note: (a) reference category.

Also here there are no significant outcomes. None of the independent variables have a significant influence on the receiving of remittances. Because there are no significant outcomes, I tried to find some with single analyses (using just one predictor). See tables below. The tables show that only the variables role of the eu community in the country of origin and the political stability have significant influences on the receiving of remittances. Option 5: Outcomes Regression Single Analyses Table B.3.5.  Significant Determinants for Receiving Assistance Variables (n = 50) Constant Perceiving of Conflict (Total) Worsened (3) a Improved (1) a No change (2) a Cox and Snell R² Nagelkerke R²

B ‒0.47

SE 0.57

Wald 0.68 1.295

0.711 0.134 0.026 0.035

0.698 0.817

1.038 0.027

Sign. (2-sided) Sign.(1-sided) 0.41 0.205 0.523 0.262 0.308 0.87

0.154 0.435

Note: (a) reference category.

Table B.3.5 shows that the independent variable perceiving of conflict has no significant influence on the receiving of remittances. Factors Influencing Migrants’ Engagement  |  54


Table B.3.6.  Significant Determinants for Receiving Assistance Variables (n = 41) Constant Tensions in Europe (Total) Cox and Snell R² Nagelkerke R²

B 0 ‒0.288 0.005 0.007

SE 0.447 0.628

Wald 0 0.21

Sign. (2-sided) Sign.(1-sided) 1 0.5 0.647 0.324

Table B.3.6 shows that the independent variable tensions felt in Europe has no significant influence on the receiving of remittances. Table B.3.7.  Significant Determinants for Receiving Assistance Variables (n = 52) Constant Role eu Community (Total) Cox and Snell R² Nagelkerke R²

B ‒0.619 0.999 0.055 0.074

SE 0.469 0.591

Wald 1.744 2.854

Sign.(2-sided) Sign.(1-sided) a 0.187 0.094* 0.091 0.046**

Note: (a) * = significant at 5%; ** = significant at 10%.

Table B.3.7 shows that the independent variable role of eu community in the country of origin has a significant influence on the receiving of remittances. The B-coefficient is positive, which means that an increase of the role of the eu community leads to an increase of the receiving of remittances. Table B.3.8.  Significant Determinants for Receiving Assistance Variables (n = 52)

Constant Cooperation eu Community (Total) Cox and Snell R² Nagelkerke R²

B 0 0 0 0

SE 0.535 0.625

Wald 0 0

Sign.(2-sided) 1 1

Sign.(1-sided) 0.5 0.5

Table B.3.8 shows that the independent variable cooperation with the eu community does not has a significant influence on the receiving of remittances. Table B.3.9.  Significant Determinants for Receiving Assistance Variables (n = 48) Constant Political Stability (Total) Stable (3) b Unstable (1) b Middle (2) b Cox and Snell R² Nagelkerke R²

B 0.693

SE 0.548

Wald 1.602 3.422

‒1.312 ‒0.539 0.072 0.096

0.721 0.781

3.313 0.477

Sign.(2-sided) Sign. (1-sided) a 0.206 0.103 0.181 0.091* 0.068 0.49

0.034** 0.245

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category.

55  |  Factors Influencing Migrants’ Engagement


Table B.3.9 shows that the independent variable political stability has a significant influence on the receiving of remittances (spss tests 2-sided). The fact that this variable has a significant influence lies in the fact that the difference between unstable (1) and the reference category (stable) is significant. In this case, respondents who define the political stability in their country as stable, have the highest chance of receiving of remittances. Second are the respondents who define the political stability as middle. Respondents who define the political stability as unstable have the lowest chance of receiving of remittances (compared to the other groups). Order of B-coefficient (highest-lowest): Original order (high-low):

321 321

Concluding: the more stable the political situation in the country (according to the respondents), the more chance for receiving of remittances.

B.4.  MULTIPLE IMPUTATION B.4.1. Method Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analysis. No matter which complete-data analysis is used, the process of combining results from different imputed data sets is essentially the same. This results in statistically valid inferences that properly reflect the uncertainty due to missing values.

In § B.3, the options for the dependent varaibel received assistance gave some nice outcomes. Especially, option 2 was rather ok because the number of included cases was just enough and some significant outcomes were found. Using the method of multiple imputation is new for me. It is also a rather new method in spss (included upward from version 17). Because I do not have version 17, I was not able to try out all possible options (it takes a lot of extra time because average numbers have to be calculated). I decided to try and find a better fitting model for the dependent variable receving remittances. In the sections below I explain the steps which are use in multiple imputation. § B.4.4 gives an overview of the final outcomes. It is argued that the independent variables role of eu communty in country of origin and the political stability have significant influences on the receivng of remittances.

Factors Influencing Migrants’ Engagement  |  56


B.4.2.  Step 1: Pattern of Missing Values

B.4.3.  Step 2: Multiple Imputation This step imputes missing values 10 times based on the incomplete (with missing values) matrix. In the table below, you can find how many times values are imputed for each independent variable. Imputation Models Model

Type

C.1.impwor Situation / Conflict Worsened or Improved Last 20 Logistic Regression Years D.4cooporg Has Organization Cooperated with Community in Logistic Regression Europe in Any of the Efforts D.1rolecomm Community in Europe Significant Role in Logistic Regression Situation in Country? Stabilitypolit Stability New

Logistic Regression

Remit Received Remittances

Logistic Regression

C.3Tensions Know About Tensions Happening in European Countries?

Logistic Regression

Effects D.4cooporg, D.1rolecomm, stabilitypolit, remit, C.3tensions C.1.impwor, D.1rolecomm, stabilitypolit, remit, C.3tensions C.1.impwor, D.4cooporg, stabilitypolit, remit, C.3tensions C.1.impwor, D.4cooporg, D.1rolecomm, remit, C.3tensions C.1.impwor, D.4cooporg, D.1rolecomm, stabilitypolit, C.3tensions C.1.impwor, D.4cooporg, D.1rolecomm, stabilitypolit, remit

Missing Imputed Values Values 2

20

2

20

3

30

7

70

20

200

24

240

B.4.4.  Step 3: The Analyses Running the analyses with multiple imputation will give you, in this case, the outcomes in tenfold. The total number of included cases is as much as the total number of respondents, because no missing values are apparent anymore. So, in this case the total number of included cases is not 38

57  |  Factors Influencing Migrants’ Engagement


(see Table B.3.3) but 72. Because the outcomes are in tenfold, the first table shows the outcomes of the average numbers (B, average sign and min/max sign). The second table is the original one. Table 4.1.  Average Outcomes with Dependent Variable Received Remittances Variables (n = 72)

Constant Perceiving of Conflict (Total) Worsened (3)¹ Improved (1) No Change (2) Tensions In Europe (Total) Role Eu Community (Total) Cooperation Eu Community (Total) Political Stability (Total) Stable (3)¹ Unstable (1) Middle (2) Cox And Snell R² Nagelkerke R²

B 0,117

Sign. (2/1-sided) 0,703 / 0.352 0,688 / 0.344

Sign. max 0,993 0,965

Sign. min 0,360 0,217

0,089 0,244 ‒0,454 0,865 ‒0,123

0,736 / 0.368 0,513 / 0.257 0,413 / 0.207 0,198 / 0.099* 0,632 / 0.316 0,183 / 0.092*

0,985 0,820 0,819 0,580 0,991 0,515

0,305 0,147 0,090 0,046 0,172 0,030

‒1,111 0,070 0.064 0.086

0,213 / 0.101 0,567 / 0.284

0,861 0,988

0,026 0,078

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category.

Interpretation Outcomes for the Hypotheses: The variables ‘perceiving of conflict, tensions felt in Europe and cooperation eu community’ do not have significant influences on the dependent variable. Based on the hypotheses, the significance can be divided by two (spss tests 2-sided). For this reason, political stability has a significant influence on the receiving of remittances (sign.= 0.092). However, the independent categories do not show a significant difference with the reference category. So, how to interpretate this? For the same reason, the role of the eu community on average has a significant influence on the sending of remittances (sign. = 0.099), with a between sign.= 0.580 and sign.= 0.046 (both 2-sided). Because the difference between the max and min is rather large, it means that the influence of the missing values also is large. The B-coefficient is positive, which means that (on average) the more respondents feel that the eu community plays a role in the country, the more they are likely to receive remittances.

Factors Influencing Migrants’ Engagement  |  58


Codes Used from spss Files for Analyses 1.  City of Settlement Dependent Variables Economic Involvement Remittances Attract Investment Independent Variables Perceiving of Conflict: CSO Linkages: View on Integration: Date of Arrival: Frequent Travel: Reason for Migration:

Name in spss E.1econdev (row 34) remittances (row 59) E.4invest (row 39) Name in spss impwornew (row 61) intensitynew (row 60) C.7meanint (row 23) G.2datearrivalnew (row 53) G.3back (row 54) G.1reason (row 51)

2.  Country of Origin Dependent Variables Receiving Assistance: Receiving Remittances: Independent Variables Perceiving of Conflict Know about Tensions Felt in Europe European Community Played Significant Role Has Organization Cooperated with Community In Europe. Political Stability

Name in spss E.1receive (row 30) remit (row 51) Name in spss C.1.impwor (row 13) C.3tensions (row 18) D.1rolecomm (row 20) D.4cooporg (row 26) stabilitypolit (row 58)

59  |  Factors Influencing Migrants’ Engagement

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