Root-N Consistent Semiparametric Estimators of a Dynamic Panel Sample Selection Model

This paper considers the problem of identification and estimation in panel-data sample-selection models with a binary selection rule when the latent equations contain possibly predetermined variables, lags of the dependent variables, and unobserved individual effects. The selection equation contains lags of the dependent variables from both the latent and the selection equations as well as other possibly predetermined variables relative to the latent equations. We derive a set of conditional moment restrictions that are then exploited to construct a three-step sieve estimator for the parameters of the main equation including a nonparametric estimator of the sample-selection term. In the second step the unknown parameters of the selection equation are consistently estimated using a transformation approach in the spirit of Berkson's minimum chi-square sieve method and a first-step kernel estimator for the selection probability. This second-step estimator is of interest in its own right. It can be used to semiparametrically estimate a panel-data binary response model with correlated random effects without making any distributional assumptions. We show that both estimators (second and third stage) are √n-consistent and asymptotically normal.This paper considers the problem of identification and estimation in panel-data sample-selection models with a binary selection rule when the latent equations contain possibly predetermined variables, lags of the dependent variables, and unobserved individual effects. The selection equation contains lags of the dependent variables from both the latent and the selection equations as well as other possibly predetermined variables relative to the latent equations. We derive a set of conditional moment restrictions that are then exploited to construct a three-step sieve estimator for the parameters of the main equation including a nonparametric estimator of the sample-selection term. In the second step the unknown parameters of the selection equation are consistently estimated using a transformation approach in the spirit of Berkson's minimum chi-square sieve method and a first-step kernel estimator for the selection probability. This second-step estimator is of interest in its own right. It can be used to semiparametrically estimate a panel-data binary response model with a nonparametric individual specific effect without making any other distributional assumptions. We show that both estimators (second and third stage) are √n-consistent and asymptotically normal.

Life-Cyle Fertility Behavior and Human Capital Accumulation

This paper develops and implements a semiparametric estimator for investigating, with panel data, the importance of human capital accumulation, non-separable preferences of females and child care costs on females life-cycle fertility and labor supply behaviors. It presents a model in which the agents' expectations are correlated with their future choices and provides a set of conditions under which statistical inferences are possible from a short panel. Under the assumption that observed allocations are Pareto optimal, a dynamic model of female labor supply, labor force participation and fertility decision is estimated. In that model, experience on the job raises future wages, time spent nurturing children affects utility, while time spent off the job in the past directly affects current utility(or, indirectly through productivity in the non-market sector). This paper then uses the estimates from the model to conduct different policy simulations which shows that human capital accumulation is the most important determinant of life-cycle fertility behavior.

Estimating a Dynamic Adverse Selection Model: Labor Force Experience and the Changing Gender Earnings Gap 1968-93.

This paper addresses two questions: What accounts for the gender gap in labor-market outcomes? What are the driving forces behind the changes in the gender-labor-market out- comes over the period 1968–97? It formulates a dynamic general equilibrium model of labor supply, occupational sorting and human capital accumulation in which gender discrimination and an earnings gap arise endogenously. It uses this model to quantify the driving forces behind the decline in the gender earnings gap and the increase in women’s labor-force participation, professional-occupation representation and hours worked. It …nds that labor-market experience is the most important factor explaining the gender earnings gap. In addition, statistical dis- crimination accounts for a large fraction of the observed gender earnings gap and its decline. It also …nds that a large increase in aggregate productivity in professional occupations plays a major role in the increase in women’s labor-force participation, professional-occupation repre- sentation and hours worked. Although of less importance, demographic changes account for a substantial part of the increase in female labor-force participation and hours worked, whereas home-production technology shocks do not.

Gender Differences in Executive Compensation and Job Mobility

Fewer women than men become executive managers. They earn less over their careers, hold more junior positions, and exit the occupation at a faster rate. We compiled a large panel data set on executives and formed a career hierarchy to analyze mobility and compensation rates. We find that, controlling for executive rank and background, women earn higher compensation than men, experience more income uncertainty, and are promoted more quickly. Amongst survivors, being female increases the chance of becoming CEO. Hence, the unconditional gender pay gap and job-rank differences are primarily attributable to female executives exiting at higher rates than men in an occupation where survival is rewarded with promotion and higher compensation.

Are There Glass Ceilings for Female Executives?

Fewer women than men become executive managers. They earn less, hold more junior positions, and attrit faster. We compiled a large panel data set on executives and formed a career hierarchy to analyze promotion and compensation rates. Given executive rank and background, women are paid more than men, experience less income uncertainty, and are promoted as quickly. Amongst survivors, being female increases the chance of becoming CEO. Hence the gender pay gap and job rank differences are primarily attributable to female executives attriting at higher rates than males in an occupation where survival is rewarded with promotion and higher compensation.

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist