The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

/pdf/econometric-analysis-of-cross-section-and-panel-data-3jzr8emmtf.pdf

Econometric Analysis of Cross Section and Panel Data

A selection of panel studies appearing in the American Sociological Review and the American Journal of Sociology between 1990 and 2003 shows that sociologists have been slow to capitalize on the advantages of panel data for controlling unobservables that threaten causal inference in observational studies. This review emphasizes regression methods that capitalize on the strengths of panel data for consistently estimating causal parameters in models for metric outcomes when measured explanatory variables are correlated with unit-specific unobservables. Both static and dynamic models are treated. Among the major subjects are fixed versus random effects methods, Hausman tests, Hausman-Taylor models, and instrumental variables methods, including Arrelano-Bond and Anderson-Hsaio estimation for models with lagged endogenous variables.

Panel Models in Sociological Research: Theory into Practice

This focuses on models and data that arise from repeated observations of a cross-section of individuals, households or companies. These models have found important applications within business, economics, education, political science and other social science disciplines. The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented graduate social science students as well as individual researchers. He emphasizes mathematical and statistical fundamentals but also describes substantive applications from across the social sciences, showing the breadth and scope that these models enjoy. The applications are enhanced by real-world data sets and software programs in SAS and Stata.

Longitudinal and Panel Data: Analysis and Applications in the Social Sciences

This chapter summarizes recent research in economics that investigates differentials by race and gender in the labor market. We start with a statistical overview of the trends in labor market outcomes by race, gender and Hispanic origin, including some simple regressions on the determinants of wages and employment. This is followed in Section 3 by an extended review of current theories about discrimination in the labor market, including recent extensions of taste-based theories, theories of occupational exclusion, and theories of statistical discrimination. Section 4 discusses empirical research that provides direct evidence of discrimination in the labor market, beyond “unexplained gaps” in wage or employment regressions. The remainder of the chapter reviews the evidence on race and gender gaps, particularly wage gaps. Section 5 reviews research on the impact of pre-market human capital differences in education and family background that differ by race and gender. Section 6 reviews the impact of differences in both the levels and the returns to experience and seniority, with discussion of the role of training and labor market search and turnover on race and gender differentials. Section 7 reviews the role of job characteristics (particularly occupational characteristics) in the gender wage gap. Section 8 reviews the smaller literature on differences in fringe benefits by gender. Section 9 is an extensive discussion of the empirical work that accounts for changes in the trends in race and gender differentials over time. Of particular interest is the new research literature that investigates the impact of widening wage inequality on race and gender wage gaps. Section 10 reviews research that relates policy changes to race and gender differentials, including anti-discrimination policy. The chapter concludes with comments about a future research agenda.

Chapter 48 Race and gender in the labor market

This paper reexamines the link between career interruptions and subsequent wages. Using a rich new Swedish data set, we are able to disaggregate time out of work into several components. In both cross-sectional and panel estimations, regressing log wages on total time out results in a negative coefficient on total time out, which has been interpreted in other studies as evidence for human capital depreciation. However, we find that different types of time out have different effects on wages and that these effects vary by gender. This suggests that human capital depreciation is not the entire explanation for the negative effect of career interruptions on subsequent wages.

Career Interruptions and Subsequent Earnings: A Reexamination Using

Panel estimates of the gender earnings gap: Individual-specific intercept and individual-specific slope models

This paper applies single and simultaneous equation fixed-effects (FE) and random-effects (RE) panel data estimation techniques to obtain male and female earnings function parameters. Using the Panel Study of Income Dynamics (PSID), the paperfinds that earnings appreciation with experience and depreciation with labor market intermittency are comparable for men and women. Further, skill atrophy rates increase not decrease once one controls for heterogeneity and endogeneity. Finally the unexplained male-female wage differential declines from 40 percent to 20 percent when one adjusts for heterogeneity. Adjusting for endogeneity depends very much on the choice of instruments. However, when adjusting for endogeneity the gender earnings gap falls and approaches zero percent. These results hold for two separate subsamples so that the estimates appear robust independent of sample selectivity.

Panel Estimates of Male-Female Earnings Functions

ABSTRACT We all like to consider ourselves helpful people, but are we always quick to lend a hand whenever the opportunity arises? This panel discussion focused on bystander intervention approaches within the context of academic libraries. What strategies and resources are available? What are the limitations of bystander intervention? How do you cultivate a culture of active bystanders in an environment that is not conducive? The panelists focused on how one can become an active mediator through sharing their experiences and thoughts on bystander intervention approaches.

Moon-Kak Kim

Papers

Panel estimates of the gender earnings gap: Individual-specific intercept and individual-specific slope models

Panel Estimates of Male-Female Earnings Functions

Equity and Inclusion Panel