Estimating Heterogeneous Returns to Education in Germany Via Conditional Heteroskedasticity

Abstract

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Heterogeneous returns to education over the wage distribution: Who profits the most?

“…First, previous literature on the topic is scarce. Existing studies either compare higher tracks with lower tracks (Saniter, 2012) or focus on non-monetary returns (Hanushek, Woessmann, and Zhang, 2011) and returns to qualifications (Dearden, McIntosh, Myck, and Vignoles, 2002). Second, inserting this topic into a distributional framework creates an additional challenge, because-as in the case of returns to education in general-the returns to the vocational (academic) path may be heterogeneous over the wage distribution.…”

Analyzing Zero Returns to Education in Germany: Heterogeneous Effects and Skill Formation

“…Like our paper, Saniter (2012) wants to broaden the understanding of education and wage by explaining the differences between the works of Pischke and von Wachter (2008) as well as Winter-Ebmer (1999, 2004) and Becker and Siebern-Thomas (2007). Saniter (2012) does not use IV but a control function approach with conditional heteroskedasticity as proposed by Klein and Vella (2010).…”

“…Saniter (2012) does not use IV but a control function approach with conditional heteroskedasticity as proposed by Klein and Vella (2010). Using this approach, the model is not identified through an exclusion restriction but because the impact of the error term of the education equation is assumed to vary across the covariates.…”

“…Using this approach, the model is not identified through an exclusion restriction but because the impact of the error term of the education equation is assumed to vary across the covariates. By employing the approach for different sub-samples, Saniter (2012) finds that OLS regression over-estimates the effect for basic school students and under-estimates the returns for academic school students. As the author points out, these causal findings are only true within a sample.…”

Estimating the returns to education using a parametric control function approach: evidences for a developing country

“…relies on heterosckedasticity of the error terms, which yields nonlinearity to the control term, allowing for non-biased estimates of the coefficient of interest. As Saniter (2012) points out, this approach makes use of second moment restrictions (variance) instead of first moment exclusion restrictions (inherent to any IV approach) and therefore has the key advantage of allowing inference to be made at any moment in time (as long as our identifying assumptions hold). This is relevant, since IV estimates are often bounded by the time of the variation induced by the instrument.…”