Factors of the numbers of homicides in spainpdf

FACTORS OF THE NUMBERS OF HOMICIDES IN SPAIN Applied Econometrics

Grado en Economía

Pablo Fernández Ruiz Diego Antonio Hernández Ronquillo Juan Gabriel González Morales

Index: 1. Introduction………………………………………………………………………………………………………………. 2 2. Literature…………………………………………………………………………………………………………………… 3 3. The model………………………………………………………………………………………………………………….. 4 4. Data Description………………………………………………………………………………………………………… 4 5. Econometric Model……………………………………………………………………………………………………. 6 6. Results……………………………………………………………………………………………………………………….. 12 7. Conclusion………………………………………………………………………………………………………………….. 15 8. Bibliography........................................................................................................... 16

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1. Introduction The aim of this paper is to test if the inmigration affects the level of homicides in the spanish provinces from 2000 to 2014. It is a panel data of 3 years(2000, 2013 and 2014).

There is no investigation on this topic in Spain. There are a few papers for the United States talking about crimes rates in relation with ethnicity and inmigration but crimes in general not only homicides.

Answering this question may tell us if we need to control our inmigration policy or is better to keep doing what we have done from previous years. Homicides are not a big deal in our country due to the restrictions on the guns but after looking at the data we have noticed that there are more murders in Spain than we thought it could be and moreover our days inmigration is in the edge of the social media and we wanted to link both ideas.

To answer this question we have proposed a model gathering information of the provinces through the three years, collection information of inmigrants but dividing this variable in three groups: south americans, africans and eastern european. We have chosen this three groups because they are the most common in Spain.

We get the data from the INE, Consejo General de la PsicologĂ­a en EspaĂąa and IDESCAT Apart from the inmigration variables we add some others that are relevant for the homicides levels like: number of inhabitants in each province, PIBpc, number of psychologists and the unemployment rate.

Our results can not be compared with other paper because there are none about this topic in Spain. Our final conclusions are that none of the inmigrants are significant on the homicides levels because the high correlation between inmigration and the number of inhabitants on each province. Also we found that the unemployment and PIBpc are important to reduce the level of homicides.

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2. Literature Those two papers are directly related to the main topic discused in our paper but for US and UK respectively: Kristin F. Butcher, Anne Morrison Piehl (1998) Cross-city evidence on the relationship between immigration and crime Michael Tonry(1997) Ethnicity, Crime, and Immigration The conclusions of the first paper are that inmigration levels are significant on the crime rates because cities with high crime rates tend to have large inmigration flows but recent inmigrant have no effect on the crime rate. Moreover youth born abroad are statistically less likely to commit a crime than native born inmigrants.

The second one talks about disadvantaged groups which tend to be arrested more times, they give us example for some western countries but they written about the example of England where Afro-Caribbean and Indians arrived at the same time, 50s and 60s, but nowadays AfroCaribbean are more likely to be arrested than Indians who are now in a better socioeconomical status.

Our paper it is about inmigration and homicide rate in Spain, we could not find papers on this topic for our country. Moreover we divided the inmigration variable in three group to see the differences between them. We used a model with more variables which are relevant.

This paper may be important for Spain due to the big inmigrant community who lives in our country and the problems we see sometimes on the media between the police and inmigrant or native population and inmigrants and our conclusions are that inmigration is not an important issue for homicide rates.

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3. The model We will start doing a theoretical analysis of the variables that we have finally included in the model, based on assumptions that we have about them: -Nhomicidiosdolososasesinato: that is our endogeneous variable. We want to explain what are the reason why in some provinces there are more murders than other using different exogenous variables.

- Unemployment: people unemployed have no income apart from the one coming from the government and maybe capital investment, Spain had experienced long term unemployment and this people may tend to commit murder in a desperate situation -PIBpc: higher levels of income prevent from commiting crimes -Inmigrants from South America: one of the bigger groups of inmigrants in Spain, they have been coming since the 90s.

-Inmigrants from Africa: other of the big groups, they come through Melilla jumping the border and to the Canary Islands on illegal boats called "cayucos".

-Inmigrants from Eastern Europe: last of the big groups some of the mafias coming from Romania, Albania and Kosovo have been commiting crimes here and moreover we have a huge gipsy community in Spain.

-Number of Psychologists: psychologist may treat this kind of people before comiting the crime and can be a way to prevent it.

4. Data description For the realization of this model we have collected data from different sources. First of all, our principal source is INE (Instituto Nacional de Estadística) but we had to take some data from IDESCAT and “Consejo General de la Psicología en España”. Our data shows us what is the change in exogenous variables for 2000 to 2014 using a panel data with 3 years 2000, 2013 and 2014.

The variables that we have described before, are the one that we use as explanatory variables of our dependent variable (Nhomicidiosdolosos). So, we have 156 observations for each variable except for PIBpc that we haven’t data for all provinces of Catalonia community (150) so our panel data is unbalanced.

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So, in the tables below we are going to describe each variable.

First of all, our endogenous variable. As we mentioned before “nhomicidiosdolososasesinato” refers to numbers of murder in each province in ages of study (2000,2012,2014). The results that we have obtained are in the following table:

Now, group of exogenous variables.: -

PIBpc: Shows what is gross domestic product for each province

-

Inmigrants from south America: Number of inmigrants that come to Spain from south America.

-

Inmigrants from África: Number of inmigrants that come to Spain from Africa.

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-

Unemployment: That shows what is the unemployment rate of each province in our country.

-

Number of psychologists:

On the other hand, all variables that we use (as we can see) are numerical variables. It implies is not operative to do a frequency analysis because it would not say nothing relevant for our study that we must include.

5. Econometric model: We have used a panel data model. The main reason for using this type of model is that we have data for the same sample in different years, maybe there are some data that we loose from one year to another but it does not affect the results. For getting good results the years that we include in this study are 2000,2013 and 2014 because we need a significance distance between years in that period.

In terms of estimation, we have a static panel data model so first step is to identify if we have fixed effect or random effects. The difference among this two method is the relation between individual fixed effects (ai) and x. In other words: Random effects Fixed effects

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First, we are going see if in our data we have differences between the same province through the different years or the opposite; the main difference is among the provinces in the same year. This is why we use between and within estimation:

As we see, there are more difference between provinces than the same province in the different years of the study.

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Now, we do our first estimation:

First of all we have change the name of “ndehabitantes” and “ninmigrantessudamerica” by “inhabitants” and “southinm” because we have changed the meaning of this two variables i.e. unit of measure of inhabitants is in million of people and the number of inmigrant coming from south America have been express as each 100.000 habitants.

The number of habitants and south American inmigrants are significant at 5 % and at 10% we have PIBpc and inmigrants from eastern of Europe. On the other hand we observe that number of habitants estimator is negative. It is quite strange cause normally we may think that bigger cities have more crime but in this case no. Further we will explain that better.

Another strange thing is the sign of Eastern Europe inmigrant. In that case it is positive, an increasing quantity of inmigrant means that number of crimes in Spain will be reduce according to our model.

By the other hand, we have deleted all variables that are not significant in our model. Is true that unemployment rate at the beginning for us was a variable that can explain number of crimes but after the results, it is not clear. However after some changes in the model it will change.

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However, as we explain before, the result of eastern inmigrants is not the one we expected so, for that reason we decide to drop it and include the number of psychologists because we realize that after delete that variables this one become significant for our analysis.

But it is not correct at all. Maybe we can have some problems with multicollinearity between “inhabitants” and “southin”. The main reason is that in big cities number of inmigrants will be higher than small cities so, it´s a good idea to check if we have correlation between this two variables.

That is what we obtain:

It is clear that we have high multiconillearity among this two variables so we decide to drop southinm i.e. numbers of inmigrants coming from south America.

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Then, we obtain that:

As we see, eastern inmigrants and Africans inmigrants are not significant, then we conclude that we do not have inmigrants effects in number of crimes in Spain. By the way we have a multicollinearity as we saw before.

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This is the model we obtain:

After changes “npsicologos” still is with a T-static close to 0 so finally we decide to delete it.

Regarding to “tasadeparo” we decide to change functional form in order to look for a higher level of significance. So we apply logarithm and obtain:

We accept unemployment as logarithm form at 10%.

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This is our final model. All the variables included are significant and it makes senses. The next step is to clarify this specification i.e. at the beginning of this point, we explain the difference between fixed effects and random effects so, we have to check what is the relation among individual fixed effects and each x.

For that we are going to estimate Haussmann test under null hypothesis of not relation between ai and x or alternative hypothesis of relation among that. In other words:

The way to check that hypothesis is using Haussmann test.

(b-B) is the vector of the difference so, we strongly reject Ho of cov (ai,x)=0, so we have to use fixed effects. Then we were right in our fixed effects estimation.

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6. Results: Before in the last point, we developed a model to look for an explanation to number of crimes in Spain and we get some approximation about what is the right assumption about that. For getting a good interpretation to the reader, we are going to take our final model from the last point:

First “logunemp”. At the beginning this variable was underlined as one of the most important to explain number of murders. In all the world we identify unemployment as a good index to show what is the social and economic situation whatever country is so we consider that it have to be included. So, in the table we can see a positive sign of this variable. It means that when level of unemployment increase, numbers of crimes increase too. Precisely an increasing in 1% unemployment rate, level of crimes will be increased in 2 unit more or less.

Sign of this result is what we expect because it is clear to think that an index as unemployment is a good unit of measure of society so, like we explain, this positive relation between unemployment and number of murders is the right one.

The following variable is “inhabitants”. While we were developing the model this variable was the reason to delete all inmigrants effects in murders because high correlation but we did not care about that. The main reason is that it have a high T-statics and it is strongly significant so we are analyzing resilient effect variable in the model.

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In terms of sign, to the reader maybe is quite confuse that it is negative. The main thinking about that is an increasing in number of inhabitants produce more crimes. According to our model, it is not correct. The fact is that when you are increasing population in any cities, at the same time you have to consider that the investment in security or simply quality of authorities are higher than in regions where the number of people is less. So, the effect of quality or security is higher than the first. In fact, the impact of number of inhabitants is fairly higher (9.2891) so, an increasing in one million (remember unit of measure of “inhabitants”) produce a reducing of number of murders of more or less 9.

Finally our last variable that is “PIBpc”. Maybe that is the easier one to explain cause even in some cases is not happening, normally the poor countries have higher levels of crime than richer ones, instead with less number of inhabitants. In fact there are evidence of countries in South America where the level of crimes is higher that the same country (or village) with more or less the same inhabitants, in Europe.

However, the sign of Beta show us that we are in the correct way but the coefficient is not to much relevant so the effect is not as high as we expected. Anyway the problem maybe is because a unit of measure so if GDP per capita grows up in 10.000, final results is that number of crimes will decrease in more or less 4.

But... Is this the normal behavior? A good way to understand it is looking at next graph:

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In this graph it has been described the relation between numbers of crimes and GDP per capita for countries of the entire world. We notice that the variable are in different unit of measure (than in our model) but it does not matter because finally what we have to interpret is behavior of this two variables.

Before in our model, the sign of the variable was negative showing us a decreasing level of crimes based in more GDP per capita. That is what line “valores ajustados� show us. Trend, when the level of GDP per capita increase, it goes down with a minor level of murders. And that is what normally happens but we observe some outliers that do not follow the trend of the majority of the countries.

For example we can stand out countries as Honduras, Jamaica or Venezuela that have a strong higher level of crimes even with more GDP per capita than another countries included in the line of normal trend. It is difficult to explain; maybe the main reason is the culture about justice in this countries, maybe is a security reason but the fact is that the level of murders are too high.

7. Conclusions: Today inmigration is a big issue. Since the start of the financial crisis on 2007 lot of people from politicians to normal workers have been yelling that inmigrants are one of the main reasons of the economical problems in Spain. Moreover, nowadays we have the problem of syrian refugees that want to come to Europe to find a new beginning and part of the spanish population do not want them here because of the possible increase on crime rates.

With this model we wanted to answer this question and see if inmigration is really linked to homicides rates, even if that murders are not a big deal in Spain. Apart from inmigration we introduced other variables which are quite relevant to study the reasons of murdering in Spain.

At first we thought that inmigrants from eastern europe and south america would be significant due to the gangs like: Latin Kings and Mara Salvatrucha that operate here and also because of drug market. On the other side we have eastern european like Romanian, Albanian and people from Kosovo that have been comitting crimes here like selling guns or robbery on banks and jewelry shops.

At the end we concluded that inmigration is not relevant to study the number of assassinations in Spain but the key is on economical variables like unemployment, PIBpc and demographic variable like the number of habitant on each province.

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8. Bibliography: [1]

http://www.ine.es/jaxiT3/Tabla.htm?t=9936&L=0 (Assassinations)

[2] http://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176781& menu=resultados&idp=1254735573175 (Psychologists) [3] [4]

http://www.ine.es/jaxiT3/Tabla.htm?t=10272&L=0 (Habitants) http://www.ine.es/jaxi/Tabla.htm?path=/t20/e245/p04/provi/l0/&file=0ccaa002.px&L=0

(Inmigrants) [5]

http://www.cop.es/

[6] Kristin F. Butcher, Anne Morrison Piehl (1998) Cross-city evidence on the relationship between immigration and crime [7] Michael Tonry(1997) Ethnicity, Crime, and Immigration

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