Documento de Trabajo - 2016/04

“Cultural Persistence” of Health Capital: Evidence from European Migrants

Joan Costa-Font and Azusa Sato 兴

London School of Economics and Political Science (LSE)

Este trabajo ha sido realizado en el marco de la Cátedra CaixaBank de investigación sobre “Economía de la Salud y Hábitos de Vida”. Las opiniones y análisis que en él aparecen son responsabilidad de los autores y no coinciden necesariamente con los de CaixaBank.

Acknowledgements: We very grateful to Paola Giuliano, Berkay Ozcan, Stephen Jenkings, Emily Grundy, Chiara Orsini, Lucinda Platt, Owen Thomson, Martin Ljunge, Andreas Berg, Karin Edmark, Joacim Tåg, Henrik Horn, Assar Lindbeck, Mario Blazquez, Mireia Borrell, Frank Cowell, Mario Macis and all the participants of the CESIfo Conference on Social Economics March 21-22 2014, Munich Germany and the internal reading group participants at LSE Social Policy Department. All errors remain our own and the usual disclaimer applies.

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Resumen

El efecto de la cultural sanitaria sobre la producción de salud, y más concretamente el capital sanitaria, es una cuestión poco estudiada pero de elevada importancia. La salud depende de decisiones individuales que pueden estar altamente motivadas por valores sociales que son específicos del grupo cultural al que pertenece cada individuo. Este trabajo es el primero en analizar empíricamente la persistencia cultural del capital sanitario. Específicamente, en este artículo examinamos la persistencia entre la salud auto reportada del individuo que clasificamos como inmigrante de primera y segunda generación (en treinta países europeos de la muestra), con las salud auto reportada media de sus países de origen (más de 91 países del mundo). Una elevada asociación, especialmente en la segunda generación es un indicador de persistencia cultural ya que estos no han crecido en el país de residencia de sus padres, y por lo tanto han tenido la misma influencia de las instituciones del país de residencia que lo nativos de ese país. Asimismo, el análisis del efecto del tiempo de residencia (asimilación), y la comparación entre la primera y la segunda generación en la asociación entre la salud reportada y la media de la población del país de origen nos ofrece alguna idea sobre la persistencia cultural del capital sanitario. En este trabajo presentamos diversas especificaciones donde controlamos por variables socio-económicas (e.g., renta, educación), socio demográficas (e.g., composición de la población), en instituciones (e.g. ciudadanía, percepción del sistema sanitario). Los resultados nos muestran una evidencia muy robusta de persistencia cultural en la salud auto-reportada, que incluso es mayor para las segundas generaciones que las primeras. Asimismo, se observa que entre los inmigrantes de primera generación, el tiempo en el país, no solo no reduce la asociación con el país de origen sino que la aumenta. En cambio, ser una minoría en el país reduce esa distancia, lo que indica que los inmigrantes que forman parte de minorías tienen más tendencia a asimilarse. Los resultados son robustos a muestras de ciudadanos nacidos en otros países de la Unión

Europea, así como por residentes en Europa y por áreas específicas de Europa. Estimamos que un aumento de una desviación estándar de la percepción del capital salud (medido como salud auto-reportada) en el país de origen aumenta la percepción media de salud de la primera generación de inmigrantes en un 16%. De forma análoga, un aumento de una desviación estándar de la percepción del capital salud (medido como salud auto-reportada) en el país de origen aumenta al salud auto-reportada entre un 11% y un 25% dependiendo de si se evalúa la cultura del país por el linaje materno o paterno. Los resultados son heterogéneos por género (mayor persistencia entre hombres) y linaje (mayor persistencia entre el linaje paterno). Estos resultados confirman que la cultura, lejos de ser irrelevante, tiene un efecto persistente por lo menos hasta la segunda generación, y es por ello un factor de producción de salud a tener en cuenta en el diseño de incentivos en salud.

Palabras clave: cultura, persistencia cultural, transmisión cultural, salud, inmigrante de primera y segunda generación.

Abstract

Culture is an under-studied determinant of health production and seldom measured. This paper empirically examines the persistence and association of health capital assessments of first and second-generation migrants with that of their ancestral countries. We draw on European data from 30 countries, including over 90 countries of birth and control for timing of migration, selective migration and other controls including citizenship and cultural proxies. Our results show robust evidence of cultural persistence of health assessments. Culture persists, rather than fades, and further, appears to strengthen over generations. We estimate a one standard deviation increase in ancestral health assessment increases first generation migrant’s health assessments by an average of 16%, and that of second generation migrants between 11% and 25%. Estimates are heterogeneous by gender (larger for males) and lineage (larger for paternal lineage).

Key words: assimilation, health, health assessments, cultural persistence, first generation migrant, second generation migrant.

JEL: I18, H23, Z 13.

1. Introduction

Health status evaluations are employed as commonly used tools to assess cross country health capital. Health assessments highly correlate with objective measures of health status (Bound, 1991), and have been used widely as a measure of health capital which is potentially less sensitive to genetics. Similarly, Heiden (2015) shows that self-assessed health is correlated to historical, current, and future hospital records. However, such health assessments reflect, at least partially, cultural specific cues and judgements, and more generally, culturally specific reference points. Culture contains potentially portable dimensions (e.g., beliefs and social norms, inertial health behaviour etc). To date, we know little about how culturally transmission of health capital takes place although there exist some evidence which show cultural influence on health assessments. Specifically, some literature documents the effect of both language proficiency (Schachter et al, 2012) and generalised trust (Ljunge, 2014) on health.

Without doubt, the most obvious way to estimate such cultural persistence is by drawing on immigrant samples that are heterogeneous both in countries of ancestry and residence. If such information were available, it would be an ideal ‘quasi-natural experiment’. This is even more so if second generation immigrants could be included, given they would have been brought up under the institutions of their country of residence, and culture can be isolated form institutions after controlling for citizenship. Hence, the association between health assessments and that of the individual’s country of ancestry (or that of their parents’), is akin to measuring cultural persistence. Documenting cultural persistence of health capital, especially when measured across generations, adds to the existing literature which to date has focused on assimilation (Salant and Lauderdale, 2003). Assimilation studies typically face the problem of identifying the effect of local institutions as the country of destination is not established. Furthermore, the assimilation literature has not reached consensus on whether

‘health acculturation’ actually takes place (Antecol and Bedard, 2006; Subedi and Rosenberg, 2014, Ljunge, 2016).

This paper investigates further the hypothesis of the portability of ‘culture’ in explaining health capital. We estimate the cultural persistence of health assessments of first generation migrants, and second generation migrants who were both born and raised in the country of residence (Fernandez and Fogli 2006; Luttmer and Singhal 2011). Research on the cultural persistence of health capital goes against theories arguing that the transmission of human capital declines across generations (Becker and Tomes, 1986).

We take advantage of a rich data set containing data on worldwide (including European) migrants’ health. Data from 30 different European member states are available, allowing us to control for compositional effects and heterogeneous origins¹. Specifically, we examine the association between individual health assessments to the average health assessment of their country of origin for both first and second generation migrants over six waves of the European Social Survey (ESS). This allows us to identify the presence of cultural effects, taking into account a number of controls (e.g., citizenship, income etc) and immigrant’s self-selection. Given that immigrants’ own health assessments do not determine the mean health of the country of origin, the effect of average country of origin health assessment is not endogenous to the individual’s assessment.

We test the hypothesis that first and second generation migrants’ health is associated with health in their country of origin. The advantage of examining a second generation coefficient is that in addition to reporting on intergenerational transmission of health assessment, they provide a clean estimate of cultural persistence given that they have been raised under the same institutional arrangements as natives. In addition to estimating cultural persistence, we attempt to understand processes behind assimilation - which are often determined before adapting to host country values². This refers to the effect of sample selection, given that migrants tend to differ from the rest of the population in key socioeconomic dimensions. Paradoxically, immigrant health is often found to be better than natives at the point of immigration (Antecol and Bedard, 2005), hence assimilation to patterns of the country of residence cannot necessarily be assumed healthier or welfare improving. However, in the European context, this effect may be mitigated as populations are more homogenous and ethnic differences are less pronounced than in other parts of the world³. To control for potential selection problems, important health related characteristics of migrants need to be controlled for, to which we refer as ‘wellbeing controls’. Another challenge is that migration is institutionally induced by different regulation, hence in addition to controlling for citizenship, we examine subsample of migrants from certain European countries.

¹ As other studies have found (Ljunge, 2016), natives and immigrants’ show similar characteristics in predicting health in such a heterogeneous dataset, with the exception of Muslim immigrants. We therefore control for religion.

Our identification strategy follows the the so-called ‘epidemiological approach (Fernandez and Fogli 2006; Luttmer and Singhal 2011) which allows us to isolate the effect of culture from institutions and address omitted variable bias (including biases from measures of health knowledge, parental health, and parental specific characteristics). Our contribution lies in measuring the persistence of assessments of ancestral paternal and maternal country of birth which avoids the problem of potential reverse causality, as the child’s health evaluation cannot affect health evaluation in the father or mother’s country of origin. Results are reported in standardised coefficients, to compare the mean across first and second generations.

² It is important to test whether selective migration and other economic factors explain assimilation.

³ Assimilation is largely dependent on patterns of socialisation, to the extent that immigrants who network among themselves are shown to have reduced stress and improved self-esteem (Umberson and Montez 2010), but at the price of a slower rate of assimilation.

Focusing on other outcomes, earlier work explores beliefs across first and second generation immigrants. Alba and Nee, (1997 and Antecol (2000) find that cultural effects persist into the second generation. Similarly, Borjas (1992) finds that cultural persistence is strikingly higher for the second generation than for any further generation of immigrants. Lazear (1999) makes the case that the smaller the minority group the more likely it is an individual to assimilate Our empirical strategy will address the issues raised in previous research by examining the effect of time in the country of residence and minority status. Our results show very strong evidence of cultural persistence in the evaluation of health status. A one unit change in migrants’ self-assessed health increases one’s own self assessed health by 0.36 scale units (16%) irrespective of gender. The effect increases to 0.45 (or an average of 25%) on maternal lineage and 0.57 scale units (or an average of 25%) on paternal lineage. However, for second generation migrants, the effect is 0.24 scale units (or 11% on average) among maternal lineage and 0.32 scale units (or 14%) among paternal lineage. We run a number of robustness checks including potential differential effects by gender, the potential selection effect of migrants to EU countries, or those born in EU countries, and current residence location.

The structure of the paper is as follows. The next section provides the background. Section three addresses data and methods. Section four contains the results, followed by robustness checks and the final section concludes.

2. Migration and Culture

Migration is not a random process, but a costly one, and in many cases only healthy people are able to migrate. This gives rise to the so-called ‘healthy immigrant effect’, which argues that on average, migrants possess better health than native counterparts upon arrival (Antecol and Bedard, 2005, 2006, Palloni and Arias 2004). However, research outside the

United States, and in Europe (where migrants move from and to many different countries) shows that migrant health does not differ much from that of natives, apart from Muslim migrants (Ljunge, 2016).

One explanation for the healthy dividend of migrants is argued to stem from common beliefs, which economics labels as ‘culture’. Owusu-Daaku and Smith (2005) show that Ghanaian women who have moved to the UK uphold Ghanaian perspectives about health and illness while adapting to the British health system. That is, migrants come with ‘protective cultural factors’ towards healthier lifestyles (Scribner 1996). Consistently, some evidence show that a migrant’s health advantage declines with time spent in-country (Deri 2003). Antecol and Bedard (2005) show that immigrants to the US are less likely to report poor health, however, assimilation to poor health (as opposed to good or average health) takes place within ten years of arrival. In the US the health advantage for Latin American populations declines the longer they stay in the country, a sign of unhealthy adaptation to increased stress (Kaestner et al. 2009).

Yet,other evidence finds that immigrants become healthier the longer they remain in the country (Jasso et. al. 2004). Given this mixed evidence, it is difficult to predict the direction of change in immigrant assessments of health capital over time that results from changes in circumstance, including health care access. This is explained by the idea that exposure to a new environment can cause immigrants to adopt native-born behaviours (such as, diet and exercise), although some evidence also shows that health advantages are lost in childhood (Hamilton et al, 2011), and many health conditions worsen across generations (Mendoza 2009). Hence, an important gap in the literature is in understanding persistence in health capital assessments across generations. Similar studies have been carried out for other outcomes. For example, Luttmer and Singhal (2011) argue that culture is a strong determinant of redistribution preferences. By comparing immigrants’ redistributive

preferences with the average preferences of people in their birth countries, they find that immigrants from countries indicating high levels of preference of redistribution are more likely to vote for pro-redistributive parties.

3. Data and Empirical Strategy

3.1 Data

We draw upon data from the European Social Survey, Waves 1-6, representing every two years between 2002 and 2012 inclusive. All datasets across waves were first merged and variables made consistent. The data includes 30 participating countries and the survey contains information about the respondent’s country of birth and that of his/her father and mother. This allows us to collect information on over 90 countries and accordingly, individual level data can be matched with health measures constructed at the country level from the World Values Survey. Similarly, we can control for country of origin and residence country income (GDP per capita), mainly obtained from the World Bank database⁴. This strategy has been previously used by Lutmer and Singhal (2011) to study preference for redistribution. In our case, we have data on health assessment for all waves such that we are able to take advantage of variation in health assessments over time. However, unlike redistributive preferences, health measures are less reliant on changes in context (e.g., migration) and possibly more dependent on changes in individual specific circumstances. Dependent variables: we use self-reported health (subjective, measured on 5 levels (very good, good, fair, bad, very bad)). The question is asked as follows: “How is your health in general?” Would you say it is,.. (See Table A1 in the Appendix).

⁴ Available online at http://www.europeansocialsurvey.org. Other sources of GDP per capita are available form IMF and World Bank.

Independent variables: we use mean values of all dependent variables for the following: individual’s country of birth; father’s country of birth; mother’s country of birth and parents’ country of birth (where applicable, using values for where parents were born in the same country). The baseline specification includes population weights and wave controls but no other controls. Then we have include controls that we classify as those proxying for welfare (whether hampered in daily activities by illness, disability, infirmary or mental problem); level of happiness; opinion on state of health services in country nowadays; whether feel discriminated; socioeconomic and demographic status (gender, age, and household size) as well as religious denomination which has been shown to explain some health effects of migration in Europe (Ljunge, 2016). Our data contains records on how long individuals have lived in-country and whether they belong to a minority ethnic group in-country; alongside educational attainment, we include main occupational activity and household net income quintile. Finally, to control for institutions, we include the opinion on state of health services in their country of origin and their feeling about household’s income nowadays as well as citizenship information. Further details of all variables are available in Appendix 1.

From our master dataset, we have created two samples: one for the first generation (defined as people born in one country and moved to another) and another for second generation (defined as children of first generation immigrants – where parents are not born in the same country as the child).

3.2 Empirical Strategy

The broad range of immigrants from various countries in the ESS reduces the concern that estimates are driven by the effect of small number of ancestral backgrounds. We present the summary statistics in Table A1. As in other studies using the same data (Ljunge, 2016) we find that immigrants are similar to the general population on observable variables, with some differences in religion and education, which we control for along with a number of other controls.

Specifically, we examine the association between measures of health of immigrants and that of their country of origin. We rely on the following specification that measure cultural acculturation of first generation migrants:

\[H _ {i j} = \rho \overline {{H}} _ {j t} + \varphi X _ {i j t} + \gamma_ {j t} + \varepsilon_ {i j}\tag{1}\]

where of an individual i residing in country j’s health assessment, refers to the ancestral country j’s health assessment, and refers to individual specific controls that could upwardly bias the the effect of cultural persistence, specifically where indicates proxy measures for welfare and institutional controls, is a vector of an immigrant i’s socioeconomic and demographic status. We include a parameter which refers to a country-by-year fixed effect to account for the institutional setting and any other unobserved characteristics whether time invariant or country specific. Finally, can stand depending on the specification as picking indicates random shocks, which may include country of origin fixed effects. All standard errors are clustered by the individual’s country of origin to account for the arbitrary correlations of error terms among individuals from same country of origin. We have estimated linear probability models but the results are replicated using both ordered probit and logit models. We have standardised the regression parameters to allow for comparing effects’ sizes and interpreting coefficients as ‘the effect of one standard deviation on health assessments’.

These regressions are regarded as reduced form equations where measures cultural persistence, accounting for a number of other factors influencing assimilation, such as the time in the country. was close to zero, this would indicate full assimilation. However, one of the limitations of such a strategy is that migrants have been raised under the institutions of the country of origin, and hence inevitably, will pick up institutional effects and not the cultural effect alone. A common way to control for local institutions, in addition to controls, includes focusing on second generation migrants. In so doing, cultural transmission results from the parental transmission of preferences (from parents to children). We run two different specifications, one for the paternal lineage and one for maternal lineage. In addition to this we also run one regression where both parents are from the same country (and use father’s country to cluster).

In robustness checks, we restricted our analysis of culture to migrants from a country other than where the survey was undertaken. This way we can precisely estimate the effect for the country of origin of migrants. Further, given that mobility restrictions within Europe are less stringent than between Europe and other parts of the world, and rights and regulations differ, we take a sample of migrants who are just from Europe to overcome potential sources of unobserved heterogeneity that could not be entirely controlled for with destination country fixed effects.

4. Results

4.1 Descriptive Evidence

Figure 1 reports the association between the first generation’s assessed health capital and the average health capital in their country of origin. We show average health assessments and a circle represents the standard deviation of each measure. We show the fitted values of an association between the two measures. For the first generation there is a higher concertation of values around the same area, but this is not the case among second generations. Importantly, the fitted values indicate a steep and positive association between migrants’ health assessments and that of their ancestral countries. Further, we find that such associations are stronger for second generation migrants.

[Insert Figure 1 about here]

4.2 Assimilation

In Table 1 we begin by examining the association of individual health assessments and that of their countries of ancestry for first generation migrants only. For all samples examined we find evidence of very strong cultural persistence of migrants and that migrants bring with them some bias from their original institutional environment⁵. Once we control for welfare controls, the coefficient halves to 0.43, and when socio-economic and demographic controls are included (our preferred specification) the coefficient drops to 0.36. Importantly, the results are the same with and without clustering per country of origin. However, the most important finding of Table 1 is that unlike a standard cultural assimilation model, we find that time in the country increases the association with the culture of the country of ancestry. Up to ten years in the country increases cultural attachment to the country of ancestry by 0.2 scale units and the effect for those staying beyond ten years is on average 0.1 scale units. However, as suggested by some literature, minority groups are more likely to assimilate – as indicated by the corresponding coefficient.

[Insert Table 1 about here]

4.2 Cultural Effects: Second Generation

Table 2 reports the same estimates as for Table 1 but for second generation migrants (ie children of migrants). Importantly, we find that cultural persistence is higher for second generation migrants when measured along paternal lineage. That is, the association is higher for paternal country of ancestry (0.44 scale units) than for maternal country of ancestry (0.33 scale unit). The latter results do not change when time in the country and minority controls are added. Consistently with Table, 1, we find that spending up to 20 years or more in the country increases cultural association with ancestral country’s health, irrespective of lineage.

⁵ For example, individuals’ attitudes towards health systems in terms of trust or cultural differences

[Insert Table 2 about here]

4.4 Gender Specific Effects

Next in Table 3, results for both first and second generations, split by males and females, are presented. The literature has shown that assimilation effects can differ across men and women. Much like earlier results, we see that associations are still very strong and moreover, the size of coefficients does not differ significantly when comparing like for like. Our results show very strong evidence of cultural persistence in the evaluation of health status. A one unit change in migrants’ self-assessed health increases one’s own self assessed health by 0.36 (16%) irrespective of gender. The effect increases to 0.45 (or an average of 25%) on maternal lineage) and 0.57 (or an average of 25%) on paternal lineage among men. However, among women the effect is 0.24 (or 11% on average among maternal lineage) and 0.32 (or 14% among paternal lineage) for second generation migrants.

[Insert Table 3 about here]

5. Robustness checks

We run similar regressions on sub samples of the dataset to check robustness of results. Specifically, we check for potential selection effects using subsamples of migrants to EU countries, those born in EU countries, and current residence location. The results are shown in Table 4. The rationale for doing this is to test whether individuals in different parts of

Europe hold different cultural norms and beliefs. Again, across all regressions, the notion that current health is influenced by culture is strong. When we focus on individuals born in the EU (and hence those who are more likely to be comparable in terms of rights and institutions), we find that cultural persistence increases for second generations from 0.27 scale points to 0.44 and 0.34 for paternal and maternal lineage respectively. In contrast we find no strengthening (though not fading) cultural persistence when we restrict our sample to those residing in the EU.

We ran other robustness checks (unreported), including splitting the sample into those who were not born in Southern Europe⁶ and those who were not born in East or Central Europe. We use these samples because one could argue that long lasting genetic triggers may be location specific in Europe, and choose to present results for those ‘not born’ rather than ‘born’ in these areas due to limited sample size. Once again, all results are significant, with first generation coefficients being 0.432 for non-southerners and 0.516 for non-easterners. For the second generation, parents’ country has a large and significant effect irrespective of lineage.

6. Conclusion

We have drawn upon samples of first and second-generation immigrants to measure cultural persistence of health capital assessments. Our findings suggest robust evidence of cultural persistence, an effect which increases for second generation migrants. These results hold even while using a long list of countries (30) and an even larger list of countries of ancestry (91).

⁶ Country divisions were taken from the UN classification system. Non South means everyone not born in Albania, Bosnia and Herzegovnia, Serbia and Montenegro, Spain, Gibraltar, Greece, Italy, Macedonia, Montserrat, Malta, Portugal, and San Marino. Non Central and Eastern Europe implies everyone not born in Belarus, Cyprus, Czech Republic, Georgia, Croatia, Hungary, Kosovo, Moldova, Poland, Romania, Russia, Slovakia, and Ukraine.

The data have allowed us to control for compositional effects, selection and a number of other potential drivers that pick up the effect of institutions. Our strategy follows that of Luttmer and Singhal (2011), and resulting estimates extend those of Ljunge (2014).On the whole, we find that time in country of residence strengthens cultural association with the country of ancestry. This results can lead to different policy implications including the role of social norms in health production, and more generally understanding how one builds health capital in light of culture, which has been traditionally ignored in health production models.

References

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Figure 1. Cultural Persistence of Health Capital – Correlation of SAH between Country of Origin and Resident of- First and Second (Paternal and Maternal Lineage) Genenration Migrants

Note: This figure plots corrlelations between country of residence migrants’ health and that of their country of origin for first genenration migrants, and that of the country of origin of the mother and father among second genenration migrants.The plot contains in cricles the standard error of the estimates.

Table 1 Cultural Persistence of Health Status

Notes: All estimates include pweights and wave controls (essround). T refers to time in the country and M refers to belonging to the largest minority group. (1) Contains no controls. (2) Contains controls proxying for welfare (hlthhmp (whether hampered in daily activities by illness, disability, infirmary or mental problem; satisfaction with health services in country nowadays (stfhlth)); whether feel discriminated (dscrntn); socioeconomic and demographic status (rlgdnm (religious denomination); how long have lived in country (livecntr); whether belong to minority ethnic group in country (blgetmg); number of people in household (hhmmb); gender (gndr); marital status(marital); age group (age_gr); number of years of education (eduyrs_gr); main occupational activity (mnactic); household net income quintile (quintile); opinion on state of health services in their country of origin (trust_hs); feeling about household’s income nowadays (hincfel); whether citizen of country (ctzcntr); country variable; country income quintile (country quintile). Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 2. Cultural persistence of Health Status: Paternal and Maternal Lineage (OLS estimates)

Notes: All estimates include pweights and wave controls (essround). T refers to time in the country and M refers to belonging to the largest minority group. Controls includes variables proxying for welfare (hlthhmp (whether hampered in daily activities by illness, disability, infirmary or mental problem; satisfaction with health services in country nowadays (stfhlth)); whether feel discriminated (dscrntn); socioeconomic and demographic status (rlgdnm (religious denomination); how long have lived in country (livecntr); whether belong to minority ethnic group in country (blgetmg); number of people in household (hhmmb); gender (gndr); marital status(marital); age group (age_gr); number of years of education (eduyrs_gr); main occupational activity (mnactic); household net income quintile (quintile); opinion on state of health services in their country of origin (trust_hs); feeling about household’s income nowadays (hincfel); whether citizen of country (ctzcntr); country variable; country income quintile (country quintile). Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 3. Cultural Persistence of Health Evaluations by gender

Notes: All estimates include pweights and wave controls (essround). Contains controls proxying for welfare (hlthhmp (whether hampered in daily activities by illness, disability, infirmary or mental problem; satisfaction with health services in country nowadays (stfhlth)); whether feel discriminated (dscrntn); socioeconomic and demographic status (rlgdnm (religious denomination); how long have lived in country (livecntr); whether belong to minority ethnic group in country (blgetmg); number of people in household (hhmmb); gender (gndr); marital status(marital); age group (age_gr); number of years of education (eduyrs_gr); main occupational activity (mnactic); household net income quintile (quintile); opinion on state of health services in their country of origin (trust_hs); feeling about household’s income nowadays (hincfel); whether citizen of country (ctzcntr); country variable; country income quintile (country quintile). Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 4. Cultural Persistence of Health Evaluations by EU birth and EU residence

Notes: All estimates include pweights and wave controls (essround). Contains controls proxying for welfare (hlthhmp (whether hampered in daily activities by illness, disability, infirmary or mental problem; satisfaction with health services in country nowadays (stfhlth)); whether feel discriminated (dscrntn); socioeconomic and demographic status (rlgdnm (religious denomination); how long have lived in country (livecntr); whether belong to minority ethnic group in country (blgetmg); number of people in household (hhmmb); gender (gndr); marital status(marital); age group (age_gr); number of years of education (eduyrs_gr); main occupational activity (mnactic); household net income quintile (quintile); opinion on state of health services in their country of origin (trust_hs); feeling about household’s income nowadays (hincfel); whether citizen of country (ctzcntr); country variable; country income quintile (country quintile). Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Appendix

Table A1 Summary Table

*bold indicates omitted category

References

1. 2016-04: ““Cultural Persistence” of Health Capital: Evidence from European Migrants”, Joan Costa-Font y Azusa Sato.

References

1. 2016-03: “Like Mother, Like Father? Gender Assortative Transmission Of Child Overweight”, Joan Costa-Font y Mireia Jofre-Bonet.

References

1. 2016-02: “Health Capacity to Work at Older Ages: Evidence from Spain”, Pilar García-Gómez, Sergi Jimenez Martin y Judit Vall Castello.

References

1. 2016-01: “Monte Carlo evidence on the estimation of AR(1) panel data sample selection models”, Sergi Jiménez-Martín y José María Labeaga.

References

1. 2015-13: “On the Treatment of Foreigners and Foreign-Owned Firms in Cost–Benefit Analysis”, Per-Olov Johansson y Ginés de Rus.

References

1. 2015-12: “Evaluating Options for Shifting Tax Burden to Top Income Earners”, Jorge Onrubia, Fidel Picos y María del Carmen Rodado.

References

1. 2015-11: “Differences in Job De-Routinization in OECD countries: Evidence from PIAAC”, Sara De La Rica y Lucas Gortazar.

References

1. 2015-10: “Bad times, slimmer children?”, Cristina Belles-Obrero, Sergi Jimenez-Martín y Judit Vall-Castello.

References

1. 2015-09: “The Unintended Effects of Increasing the Legal Working Age on Family Behaviour”, Cristina Belles-Obrero, Sergi Jimenez-Martín y Judit Vall-Castello.

References

1. 2015-08: “Capital Humano y Productividad”, Ángel de la Fuente.

References

1. 2015-07: “The effect of changes in the statutory minimum working age on educational, labor and health outcomes”, Sergi Jiménez-Martín, Judit Vall y Elena del Rey.

References

1. 2015-06: “The Effects of Employment Uncertainty, Unemployment Insurance, and Wealth Shocks on the Retirement Behavior of Older Americans”, Hugo Benítez-Silva, J. Ignacio García-Pérez y Sergi Jiménez-Martín.

References

1. 2015-05: “Instruments, rules and household debt: The effects of fiscal policy”, J. Andrés, J.E. Boscá y J. Ferri.

References

1. 2015-04: “Can International Macroeconomic Models Explain Low-Frequency Movements of Real Exchange Rates?”, Pau Rabanal y Juan F. Rubio-Ramírez.

References

1. 2015-03: “Privatización, competencia y regulación aeroportuaria: Experiencia internacional”, Ofelia Betancor y María Paz Espinosa.

References

1. 2015-02: “La experiencia internacional en alta velocidad ferroviaria“, Daniel Albalate y Germà Bel.

References

1. 2015-01: “Household Debt and Fiscal Multipliers“, J. Andrés, J.E. Boscá y J. Ferri.

References

1. 2014-21: “Structural Estimation of a Model of School Choices: the Boston Mechanism vs. Its Alternatives”, Caterina Calsamiglia, Chao Fu y Maia Güell.

References

1. 2014-20: “Which club should I attend, Dad?: Targeted socialization and production”, Facundo Albornoz, Antonio Cabrales y Esther Hauk.

References

1. 2014-19: “The Informational Content of Surnames, the Evolution of Intergenerational Mobility and Assortative Mating”, Maia Güell, José V. Rodríguez Mora y Chris Telmer.

References

1. 2014-18: “Risk-sharing and contagion in networks”, Antonio Cabrales, Piero Gottardi y Fernando Vega-Redondo.

References

1. 2014-17: “A simple model of aggregate pension expenditure”, Ángel de la Fuente.

References

1. 2014-16: “The economic evaluation of infrastructure investment. Some inescapable tradeoffs”, Ginés de Rus.

References

1. 2014-15: “Cross-country data on the quantity of schooling: a selective survey and some quality measures”, Ángel de la Fuente y Rafael Doménech.

References

1. 2014-14: “Educational Attainmet in the OECD, 1960-2010, (version 3.1)”, Ángel de la Fuente y Rafael Doménech.

References

1. 2014-13: “The Systematic Component of Monetary Policy in SVARs: An Agnostic Identification Procedure”, Jonas E. Arias, Dario Caldara y Juan F. Rubio-Ramírez.

References

1. 2014-12: “Reforming the U.S. Social Security system accounting for employment uncertainty”, Hugo Benítez-Silva, J. Ignacio García-Pérez y Sergi Jiménez-Martín.

References

1. 2014-11: “Estimating Dynamic Equilibrium Models with Stochastic Volatility”, Jesús Fernández-Villaverde, Pablo Guerrón-Quintana y Juan F. Rubio-Ramírez.

References

1. 2014-10: “Efficiency and Endogenous Fertility”, Mikel Pérez-Nievas, J. Ignacio Conde-Ruiz y Eduardo L. Giménez.

References

1. 2014-09: “The Role of Global Value Chains during the Crisis: Evidence from Spanish and European Firms”, Aranzazu Crespo y Marcel Jansen.

References

1. 2014-08: “Can Fixed-Term Contracts Put Low Skilled Youth on a Better Career Path? Evidence from Spain”, J. Ignacio García Pérez, Ioana Marinescu y Judit Vall Castello.

References

1. 2014-07: “Gender Peer Effects in School, a Birth Cohort Approach”, Antonio Ciccone y Walter Garcia-Fontes.

References

1. 2014-06: “Delaying the Normal and Early Retirement Ages in Spain: Behavioural and Welfare Consequences for Employed and Unemployed Workers”, Alfonso R. Sánchez, J. Ignacio García-Pérez y Sergi Jiménez-Martín.

References

1. 2014-05: “Immigrant Selection over the Business Cycle: The Spanish Boom and the Great Recession”, Jesús Fernández-Huertas Moraga.