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

This paper examines the impact of selected labor market changes on the decline in the unemployment rate in the 1990s. The first section provides an overview of aggregate unemployment trends, inflation, and price and wage Phillips curves. The second section examines the effect of demographic changes on the unemployment rate. The third section examines the impact of the 150 percent increase in the number of men in jail or prison since 1985 on the unemployment rate. The fourth section examines the impact of evolving labor market intermediaries (namely worker profiling by the Unemployment Insurance system and the growth of the temporary help industry) on the unemployment rate. The fifth section explores whether worker bargaining power has become weaker, allowing for low unemployment and only modest wage pressure, because of worker job anxiety, the decline in union membership, or increased competitive pressures. The final section examines the impact of the tightest labor market in a generation on poverty. Our main findings are that changes in the age structure of the labor force, the growth of the male prison population, and, more speculatively, the rise of the temporary help sector, are the main labor market forces behind the low unemployment rate in the late 1990s.(This abstract was borrowed from another version of this item.)

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The High-pressure U.S. Labor Market of the 1990s

Replication data for: The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment

FROM THE CLOSE OF WORLD WAR II TO 1970--the year the Brookings Papers on Economic Activity commenced publication--America enjoyed widespread prosperity. Not only did the national economy grow rapidly, driven by robust productivity growth, but all parts of the income distribution expanded at fairly similar rates. America was "growing together." But in the mid-1970s economic growth slowed. By the early 1980s the wage structure had begun a period of widening that has lasted until the present day. Even though productivity growth surged again starting in the mid-1990s, the benefits of economic growth have been concentrated at the top end of the distribution. (1) America has been "growing apart." These "growing together" and "growing apart" patterns are evident in figure 1, which compares real income growth across the family income distribution for the postwar period before 1973 with that after 1973. Before 1973, real income growth was a bit faster near the bottom of the distribution and somewhat slower near the top, making the changes modestly equalizing. In sharp contrast, from 1973 to 2005 family incomes virtually stagnated for the lowest quintile but grew more than three times as rapidly for the top 5 percent as for the middle quintile. [FIGURE 1 OMITTED] Since most Americans make their living from work, it should not come as a surprise that changes in the labor market and the distribution of wages have been the driving force behind the rising disparity in the economic fortunes of American families. (2) In this paper we document the nature of rising U.S. wage inequality since 1980 and place the recent changes within a century-long historical perspective to understand the sources of change. The widening of the wage structure that began in the early 1980s differed markedly from the wage structure changes of the early to mid-twentieth century. The wage structure narrowed substantially during the first half of the twentieth century and was relatively stable during the 1950s and 1960s. The spreading out of the wage structure since 1980 occurred in two stages. From 1980 to around 1987, wage inequality increased in a rapid and monotonic fashion: wages at the top grew most rapidly, those in the middle less rapidly, and those at the bottom the least of all. Since the late 1980s, wages at the upper end of the distribution have continued to grow rapidly relative to the middle, but the lower end has not lost out relative to the middle. These recent wage structure changes have been associated with a polarization of the labor market, with employment shifting into high- and low-wage jobs at the expense of middle-wage positions. (3) Why has the wage structure widened so much since 1980? A popular explanation attributes the primary role to an increase in the rate of growth in relative demand for more-skilled workers, due to skill-biased technological change and a reorganization of work driven by the spread of computer-based technologies. (4) Globalization pressures, eroding labor market institutions, and changes in the social norms that constrain pay disparities have also been offered as explanations, and each appears to have played some role? Our focus is on reassessing the skill-biased technological change hypothesis in a long-run historical context. Skill-biased technological change is not a new phenomenon. Rather, it has driven rapid secular growth in the relative demand for more-educated workers for at least a century. During most of the twentieth century, the narrowing of the wage structure came about largely because the supply of skills grew faster than did the demand for skills. Growth in the relative demand for skills was produced largely by skill-biased technological change. Growth in the supply of skills was due primarily to the rising educational attainment of successive cohorts. That, in turn, was fueled by increased access to public high schools and later to colleges and universities. …

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Long-Run Changes in the Wage Structure: Narrowing, Widening, Polarizing

During the early 1980s, earnings inequality in the U.S. labor market rose relatively uniformly throughout the wage distribution. But this uniformity gave way to a significant divergence starting in 1987, with upper-tail (90/50) inequality rising steadily and lower tail (50/10) inequality either flattening or compressing for the next 16 years (1987 to 2003). This paper applies and extends a quantile decomposition technique proposed by Machado and Mata (2005) to evaluate the role of changing labor force composition (in terms of education and experience) and changing labor market prices to the expansion and subsequent divergence of upper- and lower-tail inequality over the last three decades We show that the extended Machado-Mata quantile decomposition corrects shortcomings of the original Juhn-Murphy-Pierce (1993) full distribution accounting method and nests the kernel reweighting approach proposed by DiNardo, Fortin and Lemieux (1996). Our analysis reveals that shifts in labor force composition have positively impacted earnings inequality during the 1990s. But these compositional shifts have primarily operated on the lower half of the earnings distribution by muting a contemporaneous, countervailing lower-tail price compression. The steady rise of upper tail inequality since the late 1970s appears almost entirely explained by ongoing between-group price changes (particularly increasing wage differentials by education) and residual price changes.

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Rising wage inequality : the role of composition and prices

Private for-profit institutions have become an increasingly visible part of the U.S. higher education sector. Within that sector, they are today the most diverse institutions by program and size, have been the fastest growing, have the highest fraction of nontraditional students, and obtain the greatest proportion of their total revenue from federal student aid (loan and grant) programs. They are, as well, the subjects of high-profile investigations and are facing major regulatory changes. Today’s for-profit postsecondary schools were preceded a century ago by a group of proprietary schools that were also responding to an explosion in demand for technical, vocational, and applied subjects. Business, managerial, and secretarial skills were in great demand in the late nineteenth and early twentieth centuries, and a multitude of proprietary institutions emerged that taught accounting, management, real estate, stenography, and typing. The numbers and enrollments of these institutions were greatly reduced when public high schools expanded and increased their offerings in the business and vocational areas. But many survived and morphed into some of the current for-profits, such as Blair College (established 1897; now part of Everest College), Bryant and Stratton College (1854),

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Lawrence F. Katz

Papers

The High-pressure U.S. Labor Market of the 1990s

Replication data for: The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment

Long-Run Changes in the Wage Structure: Narrowing, Widening, Polarizing

Rising wage inequality : the role of composition and prices

The For-Profit Postsecondary School Sector: Nimble Critters or Agile Predators?