JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. University of Wisconsin Press andThe Board of Regents of the University of Wisconsin System are collaborating with JSTOR to digitize, preserve and extend access to The Journal of Human Resources. ABSTRACT This paper surveys the available methods for estimating models with sample selection bias. I initially examine the fully parameterized model proposed by Heckman (1979) before investigating departures in two directions. First, I consider the relaxation of distributional assumptions. In doing so I present the available semi-parametric procedures. Second, I investigate the ability to tackle different selection rules generating the selection bias. Finally, I discuss how the estimation procedures applied in the cross-sectional case can be extended to panel data. Symposium: Vella 129 be similar to the average characteristics of the population. Now consider where the decision to work is no longer random and consequently the working and nonworking samples potentially have different characteristics. Sample selection bias arises when some component of the work decision is relevant to the wage determining process. That is, when some of the determinants of the work decision are also influencing the wage. When the relationship between the work decision and the wage is purely through the observables, however, one can control for this by including the appropriate conditioning variables in the wage equation. Thus, sample selection bias will not arise purely because of differences in observable characteristics.If I now assume the unobservable characteristics affecting the work decision are correlated with the unobservable characteristics affecting the wage, however, I generate a relationship between the work decision and the process determining wages. Controlling for the observable characteristics when explaining wages is insufficient, as some additional process is influencing the wage, namely, the process determining whether an individual works. If these unobservable characteristics are correlated with the observables then the failure to include an estimate of the unobservables will lead to incorrect inference regarding the impact of the observables on wages. Thus, a bias will be induced due to the sample selection.This discussion highlights that sample selectivity operates through unobservable elements and their correlation with observed variables, although often one can be alerted to its possible presence through differences in observables across the two samples. However, this latter condition is by no means necessary, or even indicative, of selection bias. Although this example is only illustrative, it highlights the generality of the issues and their relevance to many economic examples. The...

This paper presents a simple two-step nonparametric estimator for a triangular simultaneous equation model.

Sample selection models provide an important way of accounting for economic decisions that combine discrete and continuous choices and of correcting for nonrandom sampling. Nonparametric estimators for these models are developed in this paper. These can be used for estimating shapes and important economic quantities, as in standard nonparametric regression. Endogeneity of regressors of interest is allowed for. Series estimators for these models are developed, which are useful for imposing additivity restrictions that arise from selection corrections. Convergence rates and asymptotic normality results are derived. An application to returns to schooling among Australian young females is given.

Using a sample of mother–child pairs from the National Longitudinal Survey of Youth 1979, we study the economics of cultural transmission regarding women's roles. We find that a mother's attitudes have a statistically significant effect on those of her children. Furthermore, we find a strong association between the attitudes of sons in their youth and their wives' labour supply as adults. For daughters, the association between their own attitudes and adult work outcomes is weaker and seems to operate through the educational channel. Our findings indicate that cultural transmission contributes to heterogeneity in the labour supply of women.

This paper surveys the growing literature on diagnostic testing of models based on unit record data. We argue that while many of these tests are produced in a Lagrange multiplier framework they are often more readily derived, and more easily applied, if approached from the conditional moment testing view of Newey (1985) and Tauchen (1985). In addition we propose some new tests based on comparisons of parametric estimators with nonparametric estimators which are consistent under certain forms of misspecification. To illustrate the utility of the tests we employ them in the examination of some existing published studies.

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SUMMARYThis paper formulates a likelihood-based estimator for a double-index, semiparametric binary response equation. A novel feature of this estimator is that it is based on density estimation under local smoothing. While the proofs differ from those based on alternative density estimators, the finite sample performance of the estimator is significantly improved. As binary responses often appear as endogenous regressors in continuous outcome equations, we also develop an optimal instrumental variables estimator in this context. For this purpose, we specialize the double-index model for binary response to one with heteroscedasticity that depends on an index different from that underlying the 'mean response'. We show that such (multiplicative) heteroscedasticity, whose form is not parametrically specified, effectively induces exclusion restrictions on the outcomes equation. The estimator developed exploits such identifying information. We provide simulation evidence on the favorable performance of the estimators and illustrate their use through an empirical application on the determinants, and affect, of attendance at a government-financed school.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Estimating a Class of Triangular Simultaneous Equations Models without Exclusion RestrictionsRoger Klein Francis Vella The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit company supported by Deutsche Post World Net. The center is associated with the University of Bonn and offers a stimulating research environment through its research networks, research support, and visitors and doctoral programs. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. ABSTRACT Estimating a Class of Triangular Simultaneous Equations Models Without Exclusion Restrictions *This paper provides a control function estimator to adjust for endogeneity in the triangular simultaneous equations model where there are no available exclusion restrictions to generate suitable instruments. Our approach is to exploit the dependence of the errors on exogenous variables (e.g. heteroscedasticity) to adjust the conventional control function estimator. The form of the error dependence on the exogenous variables is subject to restrictions, but is not parametrically specified. In addition to providing the estimator and deriving its large-sample properties, we present simulation evidence which indicates the estimator works well. JEL Classification:C14, C30