Cross-sectional data

About: Cross-sectional data is a research topic. Over the lifetime, 458 publications have been published within this topic receiving 53435 citations.

Econometric Analysis of Cross Section and Panel Data

Abstract: 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.

Analysis of Panel Data

Abstract: 1. Introduction 2. Homogeneity test for linear regression models (analysis of covariance) 3. Simple regression with variable intercepts 4. Dynamic models with variable intercepts 5. Simultaneous-equations models 6. Variable-coefficient models 7. Discrete data 8. Truncated and censored data 9. Cross-sectional dependent panel data 10. Dynamic system 11. Incomplete panel data 12. Miscellaneous topics 13. A summary view.

A New Data Set Measuring Income Inequality

Abstract: This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups' income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect inter temporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

Multiple regression as a general data-analytic system.

Abstract: Techniques for using multiple regression (MR) as a general variance-accounting procedure of great flexibility, power, and fidelity to research aims in both manipulative and observational psychological research are presented. As a prelude, the identity of MR and fixed-model analysis of variance/covariance (AV/ACV) is sketched. This requires an exposition of means of expressing nominal scale (qualitative) data as independent variables in MR. Attention is given to methods for handling interactions, curvilinearity, missing data, and covariates, for either uncorrelated or correlated independent variables in MR. Finally, the relative roles of AV/ACV and MR in data analysis are described, and the practical advantages of the latter are set forth.

Estimating the Social Return to Higher Education: Evidence From Longitudinal and Repeated Cross-Sectional Data

Abstract: Economists have speculated for at least a century that the social return to education may exceed the private return. In this paper, I estimate spillovers from college education by comparing wages for otherwise similar individuals who work in cities with different shares of college graduates in the labor force. OLS estimates show a large positive relationship between the share of college graduates in a city and individual wages, over and above the private return to education. A key issue in this comparison is the presence of unobservable individual characteristics, such as ability, that may raise wages and be correlated with college share. I use a confidential version of the National Longitudinal Survey of Youth (NLSY) to estimate a model of non-random selection of workers among cities. By observing the same individual over time, I can control for differences in unobserved ability across individuals and differences in the return to skills across cities. I then investigate the hypothesis that the correlation between college share and wages is due to unobservable city-specific shocks that may raise wages and attract more highly educated workers to different cities. To control for this source of potential bias, I turn to Census data and use two instrumental variables: the lagged city demographic structure and the presence of a land--grant college. The results from Census data are remarkably consistent with those based on the NLSY sample. A percentage point increase in the supply of college graduates raises high school drop-outs' wages by 1.9%, high school graduates' wages by 1.6%, and college graduates wages by 0.4%. The effect is larger for less educated groups, as predicted by a conventional demand and supply model. But even for college graduates, an increase in the supply of college graduates increases wages, as predicted by a model that includes conventional demand and supply factors as well as spillovers.