a b s t r a c tWe consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. Assuming that appropriate instruments are available, we propose several tests for selection bias and two estimation procedures that correct for selection in the presence of endogenous regressors. The tests are based on the fixed effects two-stage least squares estimator, thereby permitting arbitrary correlation between unobserved heterogeneity and explanatory variables. The first correction procedure is parametric and is valid under the assumption that the errors in the selection equation are normally distributed. The second procedure estimates the model parameters semiparametrically using series estimators. In the proposed testing and correction procedures, the error terms may be heterogeneously distributed and serially dependent in both selection and primary equations. Because these methods allow for a rather flexible structure of the error variance and do not impose any nonstandard assumptions on the conditional distributions of explanatory variables, they provide a useful alternative to the existing approaches presented in the literature.

Does personality affect earnings? If so, are there gender differences in personality that explain part of the gender wage gap? We use survey data collected from over 2600 Russian employees between 2000 and 2003 to evaluate the impact on earnings of two personality traits: locus of control and challenge-affiliation. We find that gender differences in personality traits are significant. Men are more likely to exhibit an internal locus of control and need for challenge, while women are more likely to exhibit an external locus of control and need for affiliation. Moreover, there are differences in the effect of personality on earnings by gender -women's earnings are strongly affected by personality, while the effect of personality on men's earnings is small and not always significant. Among the employees participating in our study, personality traits explain as much as 8% of the gender wage gap.

We propose a new method for estimating dynamic panel data models with selection. The method uses backward substitution for the lagged dependent variable, which leads to an estimating equation that requires correcting for contemporaneous selection only. The estimator is valid under relatively weak assumptions about errors and permits avoiding the weak instruments problem associated with differencing. We also propose a simple test for selection bias that is based on the addition of a selection term to the first-difference equation and subsequent testing for significance of this term. The methods are applied to estimating dynamic earnings equations for women.

Using data collected from over 9400 employees in Armenia, Kazakhstan, Kyrgyzstan, Russia, and Serbia, across a wide variety of workplaces and sectors, we identify the extrinsic and intrinsic rewards that workers desire and expectations of receiving these rewards. We use ordered probit regression analysis to evaluate the association between anticipated rewards and job satisfaction, hypothesizing that reward desirability matters most for extrinsic rewards linked to numeric values. Data strongly support our hypothesis in the case of expected job security; limited support is found in the case of expected promotion. For non-numeric extrinsic and intrinsic rewards, a strong positive link between job satisfaction and the reward variables often is observed, even if the expected reward is not highly desired. While own earnings typically are positively linked to job satisfaction, peers' earnings may be positively (Kazakhstan, Armenia, Russia) or negatively (Krygyzstan, Serbia) linked to job satisfaction, but not always statistically significant.

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