Returns to Education and to Experience within the EU
|
Returns to Education and to Experience within the EU: An Instrumental Variable Approach for Panel Data |
|
(Referencia completa: García-Mainar, I. y Montuenga-Gómez, V. (2011), “Returns to education and to experience within the EU: An instrumental variable approach for panel data”, in Advances in Econometrics-Theory and Applications. Miroslav Verbic ed. Intech, págs. 79-96). |
|
|
|
Estimating the returns to education and to experience has been the target of labor economists for decades, with a very significant volume of research having been devoted to appraising the causal effect of schooling on earnings. One of the main centers of interest when estimating these returns has been to study whether differences exist across several demographic sectors; essentially, distinguishing between males and females, whites and non-whites or natives and immigrants to assess the possibility of wage discrimination, and to compute the extent of the wage gap between the groups (Harmon et al., 2003; Heckman et al., 2003). In this line, a rapidly growing literature examines the differences in the return to education, distinguishing between the self-employed and wage earners (Evans & Leighton, 1989; Hamilton, 2000; Lazear & Moore, 1984; Rees & Shah, 1986). Fundamentally, these studies have set out not only to investigate earning differentials between the two employment groups per se, but also to test competing views about the relationship between earnings and education, on the basis that these groups face different economic incentives. In this context, the self-employed can be used as a control group to discriminate between the human capital or sorting models of wage determination, provided that signaling or screening functions are much less relevant for the self-employed (Riley, 1979; Wolpin, 1977). |
|
Returns to experience for the self-employed have also been estimated against those for wage earners in order to test different theories of the labor market, such as those of agency and risk hypotheses, against the learning and matching models, or against the compensating differentials premises, for example. Thus, as long as the self-employed have less incentives to shirk in the job or to quit from it, they should exhibit flatter earnings-experience profiles, since wage earners obtain higher earnings than self-employed when getting older (f.i., Salop and Salop, 1976). |
|
In the paper we set out to estimate the returns to education and to experience for the self-employed and wage earners, with our aim being to address some on the issues raised above. In doing so, we provide evidence on such returns for three EU countries, namely Germany, Italy and the UK, using panel data information. Studying different countries is helpful in identifying common features that are not considered in a single-country analysis. Moreover, these countries cover a wide range of variation across Europe. Germany can be considered as representing those countries with self-employment rates well below the EU average. Italy characterizes those Southern and peripheral countries with rates over 20%. Finally, the UK stands for those countries on the average. Table 1 shows the evolution over 20 years of the self-employment rate within the EU-15. |
|
Using a homogeneous database, we investigate whether differences exist in the profitability of schooling and experience both between the two employment statuses and across the three sample countries. The database used in this work has been obtained from the European Community Household Panel (ECHP) from 1994 through 2000. This provides abundant information about both the personal and labor characteristics of individuals, and has the advantage that this information is homogenous across the sample countries, given that the questionnaire is the same and the elaboration process is coordinated by the Statistical Office of the European Communities (EUROSTAT). Additionally, the application of panel data techniques offers some clear advantages over the traditional cross-sectional approaches. First, individual unobserved heterogeneity is controlled for. Secondly, biases arising from aggregation, selectivity, measurement error and endogeneity can be dealt with in a proper form. Thirdly, dynamic behavior, such as the movements into and out of self-employment, may be explicitly accounted for. |
|
The two habitual estimators used in panel data, that is to say fixed and random effects, are, however, not adequate in this setting if the objective is to obtain consistent and efficient measures of the profitability of education and experience. Thus, the presence of time-invariant, (possibly endogenous) regressors, f.i. education, would make it impossible to estimate their associated parameters when a time or mean-differencing approach, i.e. the fixed effects, is applied. Additionally, the probable correlation between these time-invariant regressors and the unobserved effects causes the random effects estimator to be inconsistent. Altogether, this points out to the advisability of using a hybrid technique that lies between both extreme alternatives. Moreover, the potential existence of measurement errors and/or endogeneity requires instrumental variables (IV) to obtain consistent estimates of the coefficients. As a consequence, the Hausman & Taylor (1981) estimator is the most adequate, since it has been shown to be an Efficient Generalised Instrumental Variables (EGIV). This procedure allows simultaneously controlling for the correlation between regressors and unobserved individual effects (as fixed effects) and to identify the estimates for the time-invariant covariates, such as education (as a random effects estimator). Furthermore, it avoids the uncertainty associated to the choice of the instruments, since exogenous included variables, and their means over time, are used as efficient instruments. |
|
|
|
Our results show that returns to education are greater for workers in paid work, with non-linearities in the relationship between wages and educational levels (the so-called sheepskin effect). Both findings point out to the relevance of signalling in the earnings of workers. Earnings experience profiles are, however, not very different across groups, especially when experience is not very large, indicating absence of delays in remuneration for workers. |
|
|
|
PODÉIS DESCARGAR EL TEXTO COMPLETO AQUÍ: |
|
|
Español 
English (UK)