Non-monetary returns to education: Estimating the causal effect of education on the social participation of migrants and non-migrants
Author(s):
Marina Trebbels (presenting / submitting)
Conference:
ECER 2016
Format:
Paper

Session Information

09 SES 04 A, A Closer Look at Migration. Findings on Attitudes, Outcomes, and Returns on Education

Paper Session

Time:
2016-08-24
09:00-10:30
Room:
NM-F101
Chair:
Paulina Korsnakova

Contribution

Empirical research on the effects of education has almost exclusively been confined to the estimation of monetary educational returns, for example in the form of income at the individual level and fiscal revenue at the societal level. However, there is also evidence of systematic associations between level of education and different non-monetary dimensions, such as physical health, psychological well-being and social participation (Engles et al. 2011; Nieminen et al. 2008; Lelkes 2011; Pichler & Wallace 2009). However, the question whether these associations can in fact be interpreted in terms of non-monetary returns to education – implying the existence of an actual causal effect of education net of monetary educational outcomes – remains to be addressed.

The project REdMig (Non-monetary Returns to Education in the Form of Social Inclusion: Estimation and Interdependence of Private and Social Returns to Education of Migrants and Non-migrants) has been funded by the Federal Ministry of Education and Research with the objective to identify and (further) develop indicators to measure the long-term development of non-monetary educational returns for national reporting purposes. Building on the Second Federal Integration Indicators Report (Engels et al. 2011), which has identified several areas of life in which a convergence of the living conditions of migrants and non-migrants is considered a main priority, the project specifically aims to identify indicators for non-monetary educational returns in the form of the social inclusion (comprising the life areas “social participation” and “social networks”) of migrants and non-migrants.

The present contribution focuses on modeling educational effects on social participation specifically. In the Second Integration Indicators Report, several indicators have been defined to measure social participation, such as the membership in non-profit organizations and individual volunteerism. Also, the report provides results from OLS regressions to explain level of social participation, which point to a significant positive estimate of level of educational attainment even when factors like income and the family’s financial situation are controlled for. Further, the social participation of migrants is significantly lower compared to the participation of non-migrants even when level of education, income and other factors such as religious orientations are taken into account.

Yet, the question whether these indicators in fact constitute non-monetary returns to education remains open due to several methodological difficulties that have been neglected in the estimation of educational effects. More specifically, the estimates derived from OLS regressions must be expected to be systematically biased due to the negligence to explicitly take into account endogeneity and self-selection effects. In other words, the effects estimated in the Second Integration Indicators Report unlikely reflect the true causal effects of education on the different indicators for social participation. For instance, it is most likely not only the case that level of educational attainment influences social participation, but that social participation in turn influences level of education (Grootaert et al. 2004). Also, it is reasonable to assume that level of educational attainment is influenced by unobserved characteristics, such as social skills, that have a direct influence on social participation, which is – if not explicitly modeled – falsely ascribed to the effect of education (Greene 2008).

The contribution presents and discusses results obtained from alternative approaches to estimate the true causal effect of education on different forms of social participation on the one hand, and to identify systematic variations in this effect in the population with and without a migration background on the other hand.

Method

The project REdMig uses data from the German Socio-economic Panel (GSOEP), from the IAB-SOEP Migration Sample and from the National Educational Panel Study to estimate non-monetary educational returns. We use alternative estimation techniques and model specifications in order to learn more about the true causal effect of education on different forms of social participation: We use instrumental variable estimators (Two-Stage Least Squares/ Three-Stage Least Squares) to explicitly model endogeneity effects. The panel structure of the data base is particularly suited for this purpose due to the opportunity to estimate dynamic models, i.e., to use lagged variable values as instruments. To take into account endogeneity that may result from omitted variables, we further make use of the panel structure of the data by estimating panel models that provide consistent estimates even in the presence of unobserved time-invariant heterogeneity across individuals (random and fixed effects, choice depending on whether the orthogonality assumption is met). Further, we employ the Heckman correction to explicitly model self-selection effects. Further, we not only use level of educational attainment as an explaining variable but also consider other dimensions of education. In case the orthogonality assumption is not met (which is likely due to the large number and interdependence of potential influences on social participation), the estimation of fixed effects models does not allow the estimation of effects of time-constant variables like the highest level of education an individual has attained. To still be able to estimate educational effects in this event, we also use data from younger cohorts that are still in education (implying changes in level of education over time). Also, we use results from different competence tests. Against the background that the systematically lower social participation of migrants cannot fully be explained by factors like level of education, income and religious orientations, we further take into consideration forms of education that may be of particular importance in the migration context, such as majority and heritage language skills. In all models, we introduce interaction effects to identify systematic variations in the effects of different forms of education in the population with and without a migration background. We compare our results to OLS estimates, and provide insight into the stability of our results by comparing results derived from different model specifications (including different instruments and different operationalizations of central constructs, such as migration background) and estimation techniques as well as using different data sets and age cohorts.

Expected Outcomes

Following the assumption that OLS estimates are biased by endogeneity and self-selection effects, we expect the true causal effect of education on the different forms of social participation to deviate from the results obtained from OLS regressions. We further expect systematic variations in the effect of education on social participation in the population with and without a migration background as a result of migration-specific mechanisms such as language barriers and perceived and/or experienced discrimination. Also, we expect different levels of variation in the effect of education depending on the form of social participation that is modeled and depending on the precise operationalization of education and migration background.

References

Engels, D., Köller, R., Koopmans, R., & Höhne, J. (2011). Zweiter Integrationsindikatorenbericht. Erstellt für die Beauftragte der Bundesregierung für Migration, Flüchtlinge und Integration. Köln, Berlin. Greene, W. H. (2008). Econometric analysis (6th ed). Upper Saddle River, N.J: Prentice Hall. Grootaert, C., Narayan, D., Jones, V. N., & Woolcock, M. (2004). Measuring Social Capital: An Integrated Questionnaire. Working Paper No. 18. Washington, D.C. Lelkes, O. (2011). Social Isolation. In Bundesministerium für Arbeit und Soziales (Hrsg.), Vergleichende Analysen der Teilhabechancen in Europa (S. 102–113). Bonn: BMAS. Nieminen, T., Martelin, T., Koskinen, S., Simpura, J., Alanen, E., Härkänen, T., & Aromaa, A. (2008). Measurement and socio-demographic variation of social capital in a large population-based survey. Social Indicators Research, 85(3). Pichler, F., & Wallace, C. (2009). Social capital and social class in Eurpoe: The role of social networks in social stratification. European Sociological Review, 25(3).

Author Information

Marina Trebbels (presenting / submitting)
Universität Hamburg, Germany

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