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30050 APPLICATIONS FOR ECONOMICS, MANAGEMENT AND FINANCE (BIEMF) - 2015/2016

Applied Research Project and Report

Is there a difference in the level of satisfaction with life among immigrants and native-born?

Word Count: 3919

In submitting this assignment: 1. We declare that this written assignment is our own work and does not include (i) material from published sources used without proper acknowledgment or (ii) material copied from the work of other students. 2. We declare that this assignment has not been submitted for assessment in any other course at any university. 3. We have a photocopy and electronic version of this assignment in our possession.

Denis Kolev ………………………….

/1703578/

Dimitar Panchev ………………………….

/1708387/

Mariya Yotova ………………………….

/1717305/

Stanimira Marinova ………………………….

/1719350/

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Summary

In the following applied research project our group investigates if a difference exists in the level of satisfaction with life among immigrants and native- born. This analysis is based on all countries from the European Social Survey dataset, except Israel because we want to examine the topic in question across European countries as a whole. Through the observation of two factors, our report compares the extent to which the immigrants and the native-born citizens in Europe differ in their level of satisfaction with life. Using such a broad scope, we believe that this is a large enough sample to highlight the differences within immigrants and natives in Europe as a whole. We suppose that the tendency in various levels of satisfaction with life is built up from different variables defining the countries. For this reason, our group decided to choose eight different variables which we suppose are highly correlated to the level of satisfaction with life. As a next step we grouped the chosen variables into two factors which are as follows: satisfaction with public institutions and satisfaction with personal life. Using the factors stated, we performed various analyses, in order to compare the variation in their mean results. As a result, we observed that with respect to satisfaction with public institution there is a statistically significant difference among immigrants and natives. On the contrary, our analysis showed no evidence of a difference between immigrants and natives when considering the satisfaction with their personal life.

Introduction and Background

The European Union has always promoted goals and values such as equality and openness to people and ideas. Modern thinking and acceptance is an issue that Europe is trying to teach its citizens in order to form a better society. Each community combines separate different elements but nowadays efforts are concentrated on successfully coordinating them and operating as a harmonious whole. Nevertheless, Europe is facing the largest “Refugee crisis” since World War II with many of the refugees planning to establish themselves at the country they are currently in. Europe’s current immigration problem is to some extent familiar to everyone and prejudice is one of the most problematic issues that Europe faces when striving to form a uniform society that acts as one single country. Taking into account these uncontestable facts, our group here undertakes to analyze to what extent satisfaction with life differs between the immigrants and the native born residents of the country. We believe that the results obtained would give us some insight as to the extent to which Europe has managed to overcome its prejudices and has provided better living conditions for immigrants by treating them how an open and tolerant society would.

Immigration defined by the Oxford dictionary is: “The action of coming to live permanently in a foreign country”. Europe is facing a flood of migrants from the Middle East and Africa and this human “tsunami” seems unstoppable. However, what is left unsaid is whether immigrants could ever become assimilated Europeans, indistinguishable from the native populations. By performing our analysis, we aim to answer part of this broadly discussed issue by comparing satisfaction of life of immigrants to that of the native-born. For decades Europe has been a dream destination for many immigrants, because of the higher standard of life and the proximity of the continent. Taking into account the establishment of the European Union, free movement between people and capital in Europe has never been easier. But here comes the question “Does Europe guarantee better life? “ meaning whether

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the established better conditions on the continent would improve immigrants’ life by bringing them satisfaction with life. In considering that issue we must pay close attention to the anti-discrimination law that states: “people must be dealt with on an equal basis regardless of age, sex, race, ethnicity, nationality, sexual orientation, gender identity and sometimes religious and political opinions. “ .The promotion of equality between people has managed to encourage progress in dealing with the prejudices of Europeans that we have already talked about despite the fact that discrimination is still present across countries. To explore that issue our goal is to find whether the immigrants are more satisfied with the life they have in the country they have established given their social, cultural and economical origin compared to the native born population of the country. Our assumption is that immigrants have left countries with lower quality of life in search of better conditions. For this reason we expect them to be more satisfied with the country they live in than the native-born who don’t have a basis for comparison and are used to living in these conditions. Nevertheless, the immigrants may feel cultural and social differences from their native country which may affect their satisfaction with life. Furthermore, immigrants may feel left out of the society, because of the social and ethnic prejudices.

According to Shin and Johnson (1978) life satisfaction is defined as “a global assessment of a person’s quality of life according to his chosen criteria”. While keeping in mind these abstract ideas we’ve already discussed, we have decided to perform a more concentrated study by using the data available to set up factors and obtain results on the basis of which we could make conclusion for the aspect we are exploring. To do so, we focus on 8 variables which we group into two factors. The first factor we investigate is “Satisfaction with public institutions”. We regard this factor as having much explanatory power to overall satisfaction with life. What we are trying to explore here is whether our assumption, that institutions in the countries that immigrants have left were less efficient, is correct and if that would be a reason for them to judge their satisfaction with public institutions of the current country at a higher level. Our second factor concentrates on “Satisfaction with personal life” as we are interested in understanding how individuals asses their own life satisfaction in the two groups. We expect that these results would improve our understanding of whether Europeans have combated to a certain extent their prejudices and have managed to accomplish the important social goal of sharing values like tolerance, equality and acceptance of different people.

The focus of our paper is on an important aspect of contemporary Europe. It concerns immigration, one of the biggest problems it faces nowadays. We would like to offer a way to check whether the continent (using as an illustration both EU and non-EU countries) does indeed bring better life for immigrants by observing their satisfaction with life. This research gives answers to some of the question raised by the application of the following techniques. First of all, Factor Analysis is used to group the variables into the factors: “Satisfaction with public institutions” and “Satisfaction with personal life”. As a second step, we use Analysis of Variance to test for differences between immigrants and native residents for each of the factors. We believe our research will serve as a very convenient first step in distinguishing between the immigrants and natives with respect to two different aspects of satisfaction with life. Nevertheless, it is up to future research to track these differences.

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Data Description

The data set used for this analysis is drawn from Round 6 of the European Social Survey conducted in 2012 as being the most transparent and varied source on which we could focus our research. Since the inquiry is focused on the examination of the difference among immigrants and native-born in European countries as a whole, we use all the countries from the dataset while excluding Israel for obvious reasons. We define 8 variables that will be grouped into two factors later. For defining the two groups in our analysis- immigrants and native-born, we have transformed the Variable “Born in country” into “Immigrant status”, assuming that immigrants are the individuals not born in the country to which they correspond. Yet, we realize that there could be a small percentage of people who do not correspond to this way of classification, but we believe that this percentage would have a negligibly small effect on our analysis.

Taking into account that data sets may contain different problems such as: outliers, missing values and errors, we start with description of the variables we use and comment on data screening results:

Variable Label Scale Measurement

cntry Country {AL= Albania} ;{BE=Belgium};

{BG=Bulgaria}; {CH=Switzerland} …

Nominal

stfeco How satisfied with present state of

economy in country

{0,Extremely dissatisfied} ; {1,"1'"}; {2,"2"); {3,"3"} …

Ordinal

stfgov How satisfied with the national government

{0,Extremely dissatisfied};{1,"1'"};{2,"2");

{3,"3"}… Ordinal

stfdem How satisfied with the

way democracy works in country

{0,Extremely dissatisfied};{1,"1'"};{2,"2");

{3,"3"}… Ordinal

stfedu State of education in country nowadays

{0,Extremely bad}; {1,"1'"}; {2,"2"); {3,"3"};{4,"4"}…

Ordinal

stfhlth State of health

services in country nowadays

{0,Extremely bad}; {1,"1”};{2,"2");{3,"3"};{4,"4"}…

Ordinal

happy How happy are you {0,Extremely unhappy};

{1,"1'"};{2,"2");{3,"3"};{4,"4"}… Ordinal

sedirlf Have a sense of

direction in your life {0,Not at all};

{1,"1'"};{2,"2");{3,"3"};{4,"4"}… Ordinal

plinsoc Your place in society {0,Bottom of our society};

{1,"1'"};{2,"2");{3,"3"};{4,"4"}… Ordinal

immigrstatus Native-born or

immigrant {0,Immigrant};{1,native-born} Nominal

Table 1

Univariate Analysis

Given that sometimes datasets present problems such as missing values, outliers and other errors we start with screening the data as a first step. With regard to

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missing values, we obtain an acceptable overall percentage so we don’t have to exclude any variables that bring us sufficient information. We don’t observe the presence of outliers either.

Other patterns that we also check for are non-normality and skewness which in the case are not present. Yet, given the large size of the sample we use, they are not extremely relevant.

Univariate Statistics

N Mean Std. Deviation Missing No. of Extremes

a

Count Percent Low High

stfeco 51178 4.00 2.594 987 1.9 0 745

stfgov 50683 3.96 2.580 1482 2.8 0 521

stfdem 50162 5.13 2.578 2003 3.8 0 0

stfedu 49638 5.58 2.419 2527 4.8 1845 0

stfhlth 51583 5.10 2.650 582 1.1 0 0

happy 51760 7.08 2.105 405 .8 1841 0

plinsoc 51056 5.41 1.856 1109 2.1 1479 650

sedirlf 51201 6.99 2.186 964 1.8 2172 0

a. Number of cases outside the range (Mean - 2*SD, Mean + 2*SD). Table 2

Multivariate Analysis

For analyzing our research question, we use the following techniques: Factor Analysis (FA) and one-way ANOVA.

Factor Analysis

The basis of factor analysis is correlation. This interdependency model, that analyzes patterns in the data, is used to reduce a set of variables to a smaller set of factors. First of all, we have to check if there is sufficient correlation between the eight variables that we use. Pearson’s correlation coefficient, r, is related to the strength of the linear relationship that exist between two variables. In order to analyse these coefficients we take a look at the correlation matrix that shows us that a sufficient relationship between the variables exist. Besides, on the basis of the determinant of the correlation matrix that we obtain we conclude that sufficient correlation exists since the value we get is low. At the same time, this value is higher than 0 indicating that there is no multicollinearity. Table 3

We proceed with performing Kaiser-Meyer-Olkin Test and Bartlett’s test. We take that step because KMO test measures whether there is a linear correlation between the variables. It’s necessary to implement that test since obtaining results equal or close to zero signals absence of linear relation and there is no purpose with continuing the factor analysis. The result we get is satisfactory as it is above 0.8 revealing that the partial correlations coefficients are small, which indicates that significant correlation between the variables exists so we can form meaningful factors.

Moreover, we run Bartlett’s test for sphericity that shows the approximate Chi-Square, degrees of freedom and significance level. According to theory, at

Correlation Matrixa

a. Determinant = .063

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significance level of 0 we can reject the null hypothesis stating that there is no correlation between the variables. In our case, the significance level is .000 so the null hypothesis is rejected and we conclude that there is correlation between the variables.

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .836

Bartlett's Test of Sphericity

Approx. Chi-Square 126136.816

df 28

Sig. .000 Table 4

As a next step, we have to check whether there is sufficient common variance. To do so, we use Measure of Sampling Adequacy (MSA) for each individual variable, obtained by the Anti-Image Matrix. By performing this test we check if the proportion of common variance among variables is higher than the total sum of the squared partial correlations.In order for the model to be considered good, that proportion must be bigger than the sum of the squared partial correlations. By inspecting the diagonal of the anti-image correlation matrix, we observe that values range from 0.762 to 0.868, which shows that the matrix is suitable for factor analysis

Anti-image Matrices

How satisfied

with present state of

economy in

country

How satisfied with the national

government

How satisfied with the

way democracy

works in country

State of education in country nowadays

State of health

services in country nowadays

How happy

are you

Your place

in society

Have a sense of direction in your

life

Anti-image Correlation

How satisfied with present

state of economy in

country

.847a -.445 -.183 -.035 -.114 -.091 -.097 .001

How satisfied with the national

government

-.445 .807a -.376 -.036 -.069 .039 -.018 -.023

How satisfied with the way democracy

works in country

-.183 -.376 .865a -.203 -.105 -.089 -.034 -.012

State of education in

country nowadays

-.035 -.036 -.203 .838a -.399 -.046 .002 -.014

State of health services in

country nowadays

-.114 -.069 -.105 -.399 .850a -.101 -.070 .046

How happy are you

-.091 .039 -.089 -.046 -.101 .828a -.231 -.293

Your place in society

-.097 -.018 -.034 .002 -.070 -.231 .854a -.216

Have a sense of direction in

your life .001 -.023 -.012 -.014 .046 -.293 -.216 .760

a

a. Measures of Sampling Adequacy(MSA) Table 5

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On the basis of the performed tests we conclude that there is sufficient correlation and common variance in our sample dataset and therefore we proceed with the Factor Analysis. We are at the phase at which we have to define the number of factors. We do so by observing Eigen-values which are equal to the sum of the squared loadings for a given factor. These values represent the proportion of total variance explained by each factor in the initial solution. According to the Latent root criterion or Kaiser’s rule the optimal number of factors to use is those that have an eigen-value above 1. We take into account the fact that the use of a larger number of factors would not explain much more of the variation, because unique variance starts to dominate common variance. By observing the scree plot – a plot of the Eigen-values against the number of factors we consider extracting two factors as an adequate decision.

Chart 1

Our goal for this factor analysis is including only those variables for which the extracted factors explain an adequate amount of variance.For this reason we proceed with observation of the communalities. The “extraction” column of the table represents the percentage of common variance given the extracted factor model. Typically, it should be above 0.2. In our case, variables are above 0.36 which we believe is sufficiently high extraction so we can safely go on.

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Communalities

Extraction

How satisfied with present state of economy in country .585

How satisfied with the national government .623

How satisfied with the way democracy works in country .635

State of education in country nowadays .386

State of health services in country nowadays .423

How happy are you .482

Your place in society .363

Have a sense of direction in your life .364 Extraction Method: Principal Axis Factoring.

Table 6

We also observe the total sample variance explained by the two factors looking at the “Total variance explained” table. We obtain considerably satisfactory value close to 50 %.

Total Variance Explained

Factor Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.626 45.330 45.330 3.145 39.312 39.312 2.581 32.267 32.267

2 1.286 16.070 61.400 .716 8.950 48.262 1.280 15.994 48.262

3 .787 9.836 71.236

4 .636 7.950 79.186

5 .557 6.965 86.151

6 .437 5.462 91.613

7 .377 4.713 96.326

8 .294 3.674 100.000

Extraction Method: Principal Axis Factoring.

Table 7

An important procedure on which to focus now is rotation since the extraction procedure extracts that factor which explains the most variance, then that pattern is followed in a descending order with respect to variance but there exist many equivalent solutions. The one we get is the ‘first initial solution’ which is difficult to interpret. More accurately, we operate with the Varimax rotation technique, which rotates the axes but keeps them perpendicular. This orthogonal method of rotation reduces the amount of variables that have high loadings and thus we can interpret them easily. Furthermore Keiser’s experiment illustrates that the factor pattern realized with Varimax rotation tends to be more invariant with respect to other rotation techniques.

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Pattern Matrix

Factor

1 2

How satisfied with the national government

.775

How satisfied with the way democracy works in country

.769

How satisfied with present state of economy in country

.729

State of health services in country nowadays

.614

State of education in country nowadays .594

How happy are you .645

Have a sense of direction in your life .599

Your place in society .554

Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization.

a

a. Rotation converged in 3 iterations.

Table 8

On the basis of the table above we notice that each variable is strongly associated with only one of the factors. Then, each factor would be interpreted on the basis of the variables that load strongly onto it. The variables included in our first factor are: stfgov, stfdem, stfeco, stfhlth, stfedu. Our second factor includes the following variables: happy, sedirlf, plinsoc.

Factor 1 represents: “Satisfaction with public institutions”

Factor 2 represents: “Satisfaction with personal life”

After having defined the factors, we find factor scores and create a new latent variable for each factor that is used in the analysis.

ANOVA

We implement one-way Analysis of Variance for each of our two defined factors. By doing so, we measure the difference among the means of different sampled groups, in our case the difference in level of satisfaction with life between immigrants and natives.

The assumptions for ANOVA are that populations are normally distributed with equal variances and also that samples are randomly and independently drawn. So before we continue, we perform some tests to assure if these assumptions are met.

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First we run the test of variance homogeneity. The result shows p-values equal to zero for our first factor, while the value for our second factor is equal to 0.497. We interpret these results that for our first factor the assumption for homogeneity is not met since we reject the null-hypothesis that states that variances in the subpopulations are the same at a 0.05 significance level. Even though ANOVA is a fairly robust to violation of this assumption, we must interpret the results of the analysis with care especially since our two groups are not of approximately equal sizes.

Test of Homogeneity of Variances

Levene Statistic df1 df2 Sig.

Satisfaction with public institutions 23.013 1 48947 .000

Satisfaction with personal life .461 1 48947 .497

Table 9

We also include Normal P-P Plots in order to check if the other assumption of ANOVA, stating that populations are normally distributed, is satisfied:

Chart 2

From the plots above we conclude that each factor exhibits normal distribution with very little deviations. After we have checked if the assumptions of ANOVA are met, we would now want to proceed with testing whether there is a difference in the mean of level of satisfaction with life between the two groups we analyse: immigrants and natives. In order to make conclusions, we take as our null hypothesis the hypothesis that the two means are equal, and we hope that we will have sufficient information to reject the hypothesis, thus proving that the two means are different. The hypothesis we want to test is:

H0: For a given factor, μnatives = μimmigrants

H1: the two means are different

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ANOVA

Sum of Squares df Mean Square F Sig.

Satisfaction with public institutions

Between Groups 375.499 1 375.499 508.960 .000

Within Groups 36111.246 48946 .738 Total 36486.745 48947

Satisfaction with personal life

Between Groups 2.036 1 2.036 3.422 .064

Within Groups 29124.389 48946 .595 Total 29126.426 48947

Table 10

After performing the analysis of variance, we can see from the table above that p-value for our first factor it 0.000 therefore we can conclude that the null hypothesis is rejected. We interpret that result as the presence of significant difference between satisfaction with public institutions from the point of view of natives and from that of immigrants. On the other hand, the p-value for our second factor shows us the evidence that we cannot reject the null hypothesis. That result gives us the explanation that no significant difference in satisfaction with personal life exists between immigrants and natives.

Given the results that we obtain on the first tests we perform checking the assumptions for ANOVA in which we found that the assumption for homogeneity is not satisfied for our first factor, we decide to confirm our result with further test. We decide to run Robust Test of Equality of Means which does not require the assumption for homogeneity of variances to be met. From the table below, we can observe that we obtain similar results to those stated above regarding mean for immigrants and mean for natives for each factor.

Table 11

Conclusion

The analysis performed aims at investigating whether indeed a difference exists between how satisfied with his or her life would an immigrant be with respect to the native population of any country. In the course of our work, we distinguished two factors that we examined, considering them to have much explanatory power to the topic in question.

We explored the degree of satisfaction with life more in-depth by considering the results for “Satisfaction with public institutions” and “Satisfaction with personal life” as being our two factors. How people answer questions regarding the state of country and its public institutions together with people’s perception for their own life are diverse factors affecting the same general topic. As far as the former factor is considered, our analysis showed evidence of the existence of a difference between immigrant’s and natives’ satisfaction with public institutions. One of the main reasons

Robust Tests of Equality of Means

Statistica df1 df2 Sig.

Satisfaction with public institutions

Welch 462.282 1 4783.758 .000

Brown-Forsythe 462.282 1 4783.758 .000

Satisfaction with personal life Welch 3.382 1 4859.988 .066

Brown-Forsythe 3.382 1 4859.988 .066

a. Asymptotically F distributed.

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for immigration is the search for better life. Thus, we believe that this difference stems from the fact that immigrants come from countries with worse conditions so their perception of public institutions is higher than that of the natives who are used to be living at that quality. With regard to the latter factor, results were just the opposite – it showed no sign of a statistically significant difference. However, we must pay close attention to the interpretation of the result obtained since the data we used for analysis is quite subjective.

In conclusion, we believe that our analysis has presented some interesting observations. Nevertheless, the topic under research is quite complex and we would suggest a more in-depth research taking into account the results we already introduced.

References: 1. Power point lectures and slides

2. Shin & Johnson (1978)

3. Oxford Dictionaries – Immigration

4. Wikipedia – Anti-discrimination law

5. Europe’s Immigrant Problem – Robert Weissberg