The returns to cognitive and non-cognitive abilities in Germany

and
u it
is the remaining stochastic error term. Using the HTIV estimator comes with at least
two strong advantages. First, there is no need for model-external instruments:
serve as their own instruments,
x2,it
x
1,it
and
is instrumented by its deviation from individual means,
z 1i
x
− x , and z is instrumented by the individual average of
2, it 2i
2i
x
1,it
, x
1i
. If the model is
identified, i.e. as long as there are at least as many time-varying exogenous covariates as
there are time-invariant endogenous regressors, the resulting FGLS estimator is consistent
and efficient (Greene, 2008). The second advantage is that, as mentioned, this method allows
estimating the effects of time-invariant covariates that may be correlated with the individual
specific effects. This is not given in our case, since we assume that the vector of the
‘residualized’ personality and cognition indicators is time-invariant and exogenous. The fact
that we do not have time-invariant endogenous variables will only affect efficiency but not
consistency of the estimator. Using the HTIV is still justified because all other covariates are
to be treated as endogenous: A Hausman test after additional fixed effects regressions on the
set of time-varying covariates clearly rejects exogeneity. Year dummies finally are assumed
to be time-varying and exogenous.
Model sensitivity
Sensitivity of the results is examined using a variety of specifications that include
different sets of socio-economic control variables. First, the baseline specification comprises
a dummy on whether the individual is living in East Germany, a dummy for being married, a
dummy for not having German citizenship, the individual’s years of education, and a set of
job-related regressors: tenure and tenure squared, a dummy on whether the respondent has a
public employer, whether she works in a firm with 2000 employees or more, whether she has
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