Abstract
The pursuit of happiness and life satisfaction mobilises individuals to create a dynamic, unique, everyday reality for themselves. The literature has placed particular emphasis on the study of immigrants on the basis that migration is triggered by discrepancies between prior expectations and realisations post-migration. However, the relationship between happiness and illegal migration has received little attention. This study contributes to the current literature by analysing a sample of both illegal and legal immigrants to test the established socio-economic drivers of well-being for each group. Additionally, for the first time, the subjective well-being (SWB) of the respondents is measured in a continuous scale utilising the Visual Analogue Scale technique. Our findings demonstrate that illegal immigrants not only experience greater increases in SWB, but they also report higher levels. Finally, the income effect is positively correlated (although weak) with SWB for illegal immigrants only and family size is positively correlated with greater levels of SWB for their legal counterparts.
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Notes
In this paper life satisfaction, happiness and subjective well-being will be used interchangeably, although we acknowledge that there are qualitative differences between these terms (Kim-Prieto et al. 2005).
In Italy, immigrants represent 8 % of the total population (Eurispes 2013).
The presence of linguistic mediators ensures that the questionnaires were filled correctly.
Illegal immigrants are entitled to, in public or private hospitals, all outpatient treatment and hospital care urgent or essential for illness and injury, as well as preventive medicine.
The IFO Institute is the Leibniz Institute for Economic Research at the University of Munich.
There are many ways in which VAS have been presented, including horizontal lines. Wewers and Lowe (1990) provide an informative discussion of the benefits and shortcomings for different styles of VAS. The advantage of the vertical line over the horizontal one is that it avoids confusion for people that are used to write from right to left.
Our dependent variable records subjective well-being and spans from −70 to 100. The negative values consist only 1.8 % of the sample and they have been re-coded as zero. An alternative approach is to drop the negative observations from the sample. The results are robust with the latter approach.
This problem arises when comparing immigrants to natives.
This is explained by the relative deprivation model of Stark and Taylor (1991).
The calculation of relative income differs from that of Ball and Chernova (2008) who consider the absolute income of other people in the country, implicitly comparing immigrants to natives.
Additional regressions substituting the absolute income with the relative income have been performed. However, the model estimates were identical for the two regressions and therefore are not presented in the paper.
The Appendix presents the distribution of schooling across legal status, as well as the distributions of other factor variables.
Regarding the variable Family Size Italy, we decided to merge the “1 to 3 relatives” and “more than 3 relatives” categories because the latter consisted a very small percentage in the sample.
Additionally Fig. 13 (found in the Appendix) shows that illegal immigrants weakly dominate the changes in SWB, meaning that illegal immigrants are better off relative to their legal counterparts. However, it has to be made clear that although this is the impression that one gets by looking the distribution of Fig. 13, the graph is not conclusive as the two lines overlap several times.
We have conducted a two-sample Kolmogorov–Smirnov test for the equality of the distributions and we find that the two distributions significantly differ above the median.
This variable is equivalent to the \(\Delta (Age)_{i}\) variable.
Table V shows for which variables the differences are statistically significant and towards which group.
There is no U-shaped pattern for age in our regressions. When age squared enters the regressions both age and age squared variables are insignificant. The answer to this can be found by going back to the data. Figure 12 in the Appendix shows that our sample is skewed towards young ages. Therefore, we are only able to verify the downward sloping part of the U-shaped pattern the age variable normally presents. A uniform distribution would possibly verify the U-shaped pattern.
This variable is measured with error since we only hold actual information about the family size after the individual migrates rather than before. During the immigrant's stay abroad, their family has most probably migrated as well and therefore this variable is likely to be downward biased. However, all we are interested in is the sign rather than the magnitude and this is most probably not affected as long as all three bands of the variable are equally downward biased.
To create the matched sample we identified which characteristics are correlated with the probability of being legal using a Probit model and then we used a nearest neighbour matching algorithm, with at least two nearest neighbours.
To test for the existence of such a bias one would need a counterfactual group. However, since the respondents were explicitly asked to report the level of their life satisfaction in general we expect that the extent of bias is somewhat restricted.
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Acknowledgments
The authors would particularly, like to thank Dr. Luciano Gualdieri, from ASL Napoli 1, for both making the data available and helping with the preparation of this paper. The authors would like to thank participants at the WISERD 2013, 4th Annual Conference, and at the “Svipuppo, Lavoro e Istituzioni” 2014, workshop for they helpful suggestions. Many thanks also to Michalis Drouvelis, Marco Musella, Stephen Drinkwater and Catherine Robinson for their useful comments on earlier versions of this paper. We would also like to thank three anonymous referees for their constructive feedback on earlier versions of this paper.
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Appendix
Appendix
See Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13 and Tables 10, 11 and 12.
Distribution of years of education. The educational bands are as follows: 0 “no education”, 1 “1–6 years”, 2 “7–12 years”, 3 “13–15 years”, 4 “16+ years”
Continental distribution. The Continent groups are as follows: 0 “Africa”, 1 “America”, 2 “Asia”, 3 “Europe”
Distribution of Health Status (Home). The Health status is a dummy variable that takes value 1 for those with a long-term illness and zero otherwise
Distribution of Health Status (Italy). The Health status is a dummy variable that takes value 1 for those with a long-term illness and zero otherwise
Distribution of family size (Home Country). The groups of relatives are as follows: 0 “no relatives”, 1 “1–3 kins”, 2 “More than 3 kins”
Distribution of family size (Italy). The groups of relatives are as follows: 0 “no relatives”, 1 “1–3 kins”, 2 “More than 3 kins”
Shares of employment status for the country of origin. The groups of employment are as follows: 0 “unemployed”, 1 “student”, 2 “employed”
Shares of employment status for the destination country. The groups of employment are as follows: 0 “unemployed”, 1 “student”, 2 “employed”
Distribution of the age in the sample
Cumulative distribution of the change of SWB by legal status
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D’Isanto, F., Fouskas, P. & Verde, M. Determinants of Well-being Among Legal and Illegal Immigrants: Evidence from South Italy. Soc Indic Res 126, 1109–1141 (2016). https://doi.org/10.1007/s11205-015-0924-7
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DOI: https://doi.org/10.1007/s11205-015-0924-7