Estimation of labour supply functions using panel data: a survey

This survey aims at providing the reader with a thread through the literature on the topic of panel econometrics of labour supply, reporting also on the evaluation of the data used in these studies, and summarizing their substantive results. It documents the present trend away from models that take advantage of panel data almost exclusively in order to control for unobserved heterogeneity, towards fully dynamic models where wages become endogenous and consequently the concept of wage elasticity loses much of its appeal. --

Semiparametric Bayesian inference in smooth coefficient models

We describe procedures for Bayesian estimation and testing in cross-sectional, panel data and nonlinear smooth coefficient models. The smooth coefficient model is a generalization of the partially linear or additive model wherein coefficients on linear explanatory variables are treated as unknown functions of an observable covariate. In the approach we describe, points on the regression lines are regarded as unknown parameters and priors are placed on differences between adjacent points to introduce the potential for smoothing the curves. The algorithms we describe are quite simple to implement - for example, estimation, testing and smoothing parameter selection can be carried out analytically in the cross-sectional smooth coefficient model. We apply our methods using data from the National Longitudinal Survey of Youth (NLSY). Using the NLSY data we first explore the relationship between ability and log wages and flexibly model how returns to schooling vary with measured cognitive ability. We also examine a model of female labor supply and use this example to illustrate how the described techniques can been applied in nonlinear settings

Econometrics: A bird's eye view

As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledge and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks and forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of the "real time econometrics". This paper attempts to provide an overview of some of these developments

Does Product Market Competition Decrease Employers’ Training Investments? – Evidence from German Establishment Panel Data

Using a large panel data set of German manufacturing establishments, this paper investigates the impact of competition on training incidence as well as on the number of trained workers. According to theory, one would expect a negative relationship between product market competition and firms’ incentives to invest in employees’ general skills (Gersbach and Schmutzler 2006). In our empirical analysis, product market competition is approximated by various measures of competition such as the Herfindahl Index, the number of firms at the 3-digit industry level and the price cost margin. After controlling for unobserved heterogeneity across industries and establishments, there is no significant effect of competition on training. This result is robust towards different samples, model specifications and estimation techniques.Training, human capital, product market competition

Identification and Estimation of Nonlinear Dynamic Panel Data Models with Unobserved Covariates

This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved voariates. Including such unobserved covariates may control for both the individual-specific unobserved heterogeneity and the endogeneity of the explanatory variables. Without specifying the distribution of the initial condition with the unobserved variables, we show that the models are nonparametrically identified from three periods of data. The main identifying assumption requires the evolution of the observed covariates depends on the unobserved covariates but not on the lagged dependent variable. We also propose a sieve maximum likelihood estimator (MLE) and focus on two classes of nonlinear dynamic panel data models, i.e., dynamic discrete choice models and dynamic censored models. We present the asymptotic property of the sieve MLE and investigate the finite sample properties of these sieve-based estimator through a Monte Carlo study. An intertemporal female labor force participation model is estimated as an empirical illustration using a sample from the Panel Study of Income Dynamics (PSID).

The gap between male and female pay in the Spanish tourism industry

This paper analyzes wage differentials between male and female workers in the Spanish tourism industry, using a large, administratively matched employer-employee data set obtained from a representative sample of companies. This allows us to control for unobserved firm-specific factors likely to affect the magnitude of the gender wage gap. Our findings indicate that male workers earn on average 6.7% higher monthly wages than their socially comparable female counterparts. In particular, the type of contract held, the qualifications required for the job and the specific sub-sector of employment are very important variables in explaining this gender wage difference. We also find that only around 12% of the mean wage difference in the tourism industry cannot be explained by differences in observable characteristics, which is well below the average for the rest of the industries in Spain (87%). Our interpretation is that minimum wage legislation provides a particularly effective protection to women in the tourism industry, which is characterized by a large number of low-wage earners

Sequential Regression Multiple Imputation for Incomplete Multivariate Data using Markov Chain Monte Carlo

This paper discusses the theoretical background to handling missing data in a multivariate context. Earlier methods for dealing with item non-response are reviewed, followed by an examination of some of the more modern methods and, in particular, multiple imputation. One such technique, known as sequential regression multivariate imputation, which employs a Markov chain Monte Carlo algorithm is described and implemented. It is demonstrated that distributional convergence is rapid and only a few imputations are necessary in order to produce accurate point estimates and preserve multivariate relationships, whilst adequately accounting for the uncertainty introduced by the imputation procedure. It is further shown that lower fractions of missing data and the inclusion of relevant covariates in the imputation model are desirable in terms of bias reduction.Missing data; Item non-response; Missingness mechanism; Imputation; Regression; Markov chain Monte Carlo.

The gap between male and female pay in the Spanish tourism industry

This paper analyzes wage differentials between male and female workers in the Spanish tourism industry, using a large, administratively matched employer-employee data set obtained from a representative sample of companies. This allows us to control for unobserved firm-specific factors likely to affect the magnitude of the gender wage gap. Our findings indicate that male workers earn on average 6.7% higher monthly wages than their socially comparable female counterparts. In particular, the type of contract held, the qualifications required for the job and the specific sub-sector of employment are very important variables in explaining this gender wage difference. We also find that only around 12% of the mean wage difference in the tourism industry cannot be explained by differences in observable characteristics, which is well below the average for the rest of the industries in Spain (87%). Our interpretation is that minimum wage legislation provides a particularly effective protection to women in the tourism industry, which is characterized by a large number of low-wage earners.Spanish tourism industry, Wage discrimination, Blinder-Oaxaca decomposition, Censored models