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

We examine long-term neighborhood effects on low-income families using data from the Moving to Opportunity (MTO) randomized housing-mobility experiment. This experiment offered to some public-housing families but not to others the chance to move to less-disadvantaged neighborhoods. We show that ten to 15 years after baseline, MTO: (i) improves adult physical and mental health; (ii) has no detectable effect on economic outcomes or youth schooling or physical health; and (iii) has mixed results by gender on other youth outcomes, with girls doing better on some measures and boys doing worse. Despite the somewhat mixed pattern of impacts on traditional behavioral outcomes, MTO moves substantially improve adult subjective well-being.

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Long-Term Neighborhood Effects on Low-Income Families: Evidence from Moving to Opportunity

Moving to Opportunity: Interim Impacts Evaluation

U.S. educational and occupational wage differentials were exceptionally high at the dawn of the twentieth century and then decreased in several stages over the next eight decades. But starting in the early 1980s the labor market premium to skill rose sharply and by 2005 the college wage premium was back at its 1915 level. The twentieth century contains two inequality tales: one declining and one rising. We use a supply-demand-institutions framework to understand the factors that produced these changes from 1890 to 2005. We find that strong secular growth in the relative demand for more educated workers combined with fluctuations in the growth of relative skill supplies go far to explain the long-run evolution of U.S. educational wage differentials. An increase in the rate of growth of the relative supply of skills associated with the high school movement starting around 1910 played a key role in narrowing educational wage differentials from 1915 to 1980. The slowdown in the growth of the relative supply of college workers starting around 1980 was a major reason for the surge in the college wage premium from 1980 to 2005. Institutional factors were important at various junctures, especially during the 1940s and the late 1970s.

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The Race between Education and Technology: The Evolution of U.S. Educational Wage Differentials, 1890 to 2005

This paper empirically examines the importance of explicitly accounting for the layoff-rehire process in the analysis of unemployment outcomes in the United States. We find that the spells of individuals' who initially expect to be recalled account for much more of the unemployment of unemployment insurance (UI) recipients than do spells actually ending in recall. Our results indicate that the recall and new job escape rates from unemployment have quite different time patterns and are often affected in opposite ways by explanatory variables. We also find that the probability of leaving unemployment both through recalls and new job finding increases greatly around the time that UI benefits lapse. Over the past fifteen years many studies have empirically analyzed the determinants of individual unemployment spell durations in the United States.' Much of this literature has used a job search framework in which the unemployed are viewed as permanently displaced workers who are unattached to jobs. The possibility of recall to a former job, a process not requiring search, has been omitted from explicit consideration in the majority of these studies.2 An explicit treatment of the layoff-rehire process is necessary for a proper understanding of the determinants of unemployment durations and of the impacts of unemployment policies in the United States for at least three reasons. First, temporary layoffs in which workers are often rehired by their original employers are a quantitatively quite important feature of the U. S. labor market.3 Second, expectations concerning the likelihood of recall are likely to affect the responses of employers and unemployed workers to labor market interventions such as reemployment bonuses and subsidies

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Unemployment Insurance, Recall Expectations, and Unemployment Outcomes

During the past two decades, wages of skilled workers in the United States rose while those of unskilled workers fell; less-educated young men in particular have suffered unprecedented losses in real earnings. These twelve original essays explore whether this trend is unique to the United States or is part of a general growth in inequality in advanced countries. Focusing on labor market institutions and the supply and demand forces that affect wages, the papers compare patterns of earnings inequality and pay differentials in the United States, Australia, Korea, Japan, Western Europe, and the changing economies of Eastern Europe. Cross-country studies examine issues such as managerial compensation, gender differences in earnings, and the relationship of pay to regional unemployment. From this rich store of data, the contributors attribute changes in relative wages and unemployment among countries both to differences in labor market institutions and training and education systems, and to long-term shifts in supply and demand for skilled workers. These shifts are driven in part by skill-biased technological change and the growing internationalization of advanced industrial economies.

Lawrence F. Katz

Papers

Long-Term Neighborhood Effects on Low-Income Families: Evidence from Moving to Opportunity

Moving to Opportunity: Interim Impacts Evaluation

The Race between Education and Technology: The Evolution of U.S. Educational Wage Differentials, 1890 to 2005

Unemployment Insurance, Recall Expectations, and Unemployment Outcomes

Differences and Changes in Wage Structures