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.

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Econometric Analysis of Cross Section and Panel Data

Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre”- and “post”-period and explicitly takes into account the effective sample size works well even for small numbers of states.

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How Much Should We Trust Differences-In-Differences Estimates?

https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

The future of employment: How susceptible are jobs to computerisation?

This paper examines the reflection problem that arises when a researcher observing the distribution of behaviour in a population tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group. It is found that inference is not possible unless the researcher has prior information specifying the compisition of reference groups. If this information is available, the prospects for inference depend critically on the population relationship between the variables defining reference groups and those directly affecting outcomes. Inference is difficult to implossible if these variables are functionally dependent or are statistically independent. The prospects are better if the variables defining reference groups and those directly affecting outcomes are moderately related in the population.

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Identification of Endogenous Social Effects: The Reflection Problem

▪ Abstract This paper assesses and synthesizes the cumulative results of a new “neighborhood-effects” literature that examines social processes related to problem behaviors and health-related outcomes. Our review identified over 40 relevant studies published in peer-reviewed journals from the mid-1990s to 2001, the take-off point for an increasing level of interest in neighborhood effects. Moving beyond traditional characteristics such as concentrated poverty, we evaluate the salience of social-interactional and institutional mechanisms hypothesized to account for neighborhood-level variations in a variety of phenomena (e.g., delinquency, violence, depression, high-risk behavior), especially among adolescents. We highlight neighborhood ties, social control, mutual trust, institutional resources, disorder, and routine activity patterns. We also discuss a set of thorny methodological problems that plague the study of neighborhood effects, with special attention to selection bias. We conclude with promising ...

ASSESSING "NEIGHBORHOOD EFFECTS": Social Processes and New Directions in Research

A simple supply and demand framework is used to analyze changes in the U. S. wage structure from 1963 to 1987. Rapid secular growth in the demand for more-educated workers, "more-skilled" workers, and females appears to be the driving force behind observed changes in the wage structure. Measured changes in the allocation of labor between industries and occupations strongly favored college graduates and females throughout the period. Movements in the college wage premium over this period appear to be strongly related to fluctuations in the rate of growth of the supply of college graduates.

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Changes in Relative Wages, 1963–1987: Supply and Demand Factors

* Introduction Part I: Economic Growth and Distribution * The Human Capital Century * Inequality across the Twentieth Century * Skill-biased Technological Change Part II: Education for the Masses in Three Transformations * The Origins of the Virtues * Economic Foundations of the High School Movement * America's Graduation from High School * Mass Higher Education in the Twentieth Century Part III. The Race * The Race between Education and Technology * How America Once Led and Can Win the Race for Tomorrow * Appendix A * Appendix B * Appendix C * Appendix D * Notes * References * A Note on Sources * Acknowledgments * Index

The race between education and technology

This chapter presents a framework for understanding changes in the wage structure and overall earnings inequality. The framework emphasizes the role of supply and demand factors and the interaction of market forces and labor market institutions. Recent changes in the US wage structure are analyzed in detail to highlight crucial measurement issues that arise in studying wage structure changes and to illustrate the operation of the supply-demand-institution framework. The roles of skill-biased technological change, globalization forces, changes in demographics and relative skill supplies, industry labor rents, unions, and the minimum wage in the evolution of the US wage structure are examined. Recent wage structure changes are placed in a longer-term historical perspective, and differences and similarities in wage structure changes among OECD nations are assessed. © 1999 Elsevier Science B.V. All rights reserved.

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Changes in the Wage Structure and Earnings Inequality

A recent “revisionist” literature characterizes the pronounced rise in U.S. wage inequality since 1980 as an “episodic” event of the first half of the 1980s driven by nonmarket factors (particularly a falling real minimum wage) and concludes that continued increases in wage inequality since the late 1980s substantially reflect the mechanical confounding effects of changes in labor force composition. Analyzing data from the Current Population Survey for 1963 to 2005, we find limited support for these claims. The slowing of the growth of overall wage inequality in the 1990s hides a divergence in the paths of upper-tail (90/50) inequality—which has increased steadily since 1980, even adjusting for changes in labor force composition—and lower-tail (50/10) inequality, which rose sharply in the first half of the 1980s and plateaued or contracted thereafter. Fluctuations in the real minimum wage are not a plausible explanation for these trends since the bulk of inequality growth occurs above the median ...

Trends in U.S. Wage Inequality: Revising the Revisionists

This paper examines the effect of technological change and other factors on the relative demand for workers with different education levels and on the recent growth of U.S. educational wage differentials. A simple supply-demand framework is used to interpret changes in the relative quantities, wages, and wage bill shares of workers by education in the aggregate U.S. labor market in each decade since 1940 and over the 1990 to 1995 period. The results suggest that the relative demand for college graduates grew more rapidly on average during the past 25 years (1970-95) than during the previous three decades (1940-70). The increased rate of growth of relative demand for college graduates beginning in the 1970s did not lead to an increase in the college/high school wage differential until the 1980s because the growth in the supply of college graduates increased even more sharply in the 1970s before returning to historical levels in the 1980s. The acceleration in demand shifts for more-skilled workers in the 1970s and 1980s relative to the 1960s is entirely accounted for by an increase in within- industry changes in skill utilization rather than between-industry employment shifts. Industries with large increases in the rate of skill upgrading in the 1970s and 1980s versus the 1960s are those with greater growth in employee computer usage, more computer capital per worker, and larger shares of computer investment as a share of total investment. The results suggest that the spread of computer technology may "explain" as much as 30 to 50 percent of the increase in the rate of growth of the relative demand for more-skilled workers since 1970.

Lawrence F. Katz

Papers

Changes in Relative Wages, 1963–1987: Supply and Demand Factors

The race between education and technology

Changes in the Wage Structure and Earnings Inequality

Trends in U.S. Wage Inequality: Revising the Revisionists

Computing Inequality: Have Computers Changed the Labor Market?