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

Boston Site Findings: The Early Impacts of Moving to Opportunity

This paper examines the short-run impacts of a change in residential neighborhood on the well-being of low-income families, using evidence from a program in which eligibility for a housing voucher was determined by random lottery. We examine the experiences of households at the Boston site of Moving To Opportunity (MTO), a demonstration program in five cities. Families in high poverty public housing projects applied to MTO and were assigned by lottery to one of three groups: Experimental-offered mobility counseling and a Section 8 subsidy valid only in a Census tract with a poverty rate of less than 10 percent; Section 8 Comparison-offered a geographically unrestricted Section 8 subsidy; or Control-offered no new assistance, but continued to be eligible for public housing. Our quantitative analyses of program impacts uses data on 540 families from a baseline survey at program enrollment, a follow-up survey administered l to 3.5 years after random assignment, and state administrative data on earnings and welfare receipt. 48 percent of the Experimental group and 62 percent of the Section 8 Comparison group moved through the MTO program. One to three years after program entry, families in both treatment groups were more likely to be residing in neighborhoods with low poverty rates and high education levels than were families in the Control group. However, while members of the Experimental group were much more likely to be residing in suburban communities than were those in the Section 8 group, the lower program take-up rate among the Experimental group resulted in more families remaining in the most distressed communities. Households in both treatment groups experienced improvements in multiple measures of well-being relative to the Control group including increased safety, improved health among household heads, and fewer behavior problems among boys. Experimental group children were also less likely to be a victim of a personal crime, to be injured, or to experience an asthma attack. There are no significant impacts of either MTO treatment on the employment, earnings, or welfare receipt of household heads in the first three years after random assignment.

Moving to Opportunity in Boston: Early Results of a Randomized Mobility Experiment

This paper shows the importance of explicitly accounting for the possibility of recalls when analyzing the determinants of unemployment spell durations and the effects of unemployment insurance (UI) on unemployment outcomes in the United States These issues are examined using a unique sample of UI recipients from Missouri and Pennsylvania covering unemployment spells in the 1979- 1981 period We find that those expecting recall who are not recalled tend to have quite long unemployment spells Furthermore, ex-ante temporary layoff spells (the spells of individuals' who initially expect to be recalled) may account for over 60 percent of the unemployment of UI recipients and appear to account for much more unemployment than ex-post temporary layoff spells (spells actually ending in recall) We estimate a competing risks model in which the finding of a new job and recall are treated as alternate routes of leaving unemployment Our results using this approach show that the recall and new job exit probabilities 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

Unemployment Insurance, Recall Expectations, And Unemployment Outcomes

The United States has produced rapid employment growth (almost entirely in the private sector) and an impressive unemployment record relative to most other advanced nations over the past two decades. The contrast of strong US labour market performance to persistent high unemployment throughout much of the OECD has been particularly striking in the 1990s. From 1990 to the fourth quarter of 1997, total employment in the US increased by 10.9 per cent, and the US unemployment rate declined from 5.5 per cent to 4.7 per cent. Over the same period in OECD Europe, total employment actually declined by 1.7 per cent and the unemployment rate increased from 8.0 per cent to 10.2 per cent (OECD 1998, pp. 40–41). Furthermore, the United States has combined strong employment growth with low and declining inflation in the 1990s.

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Reflections on US Labour Market Performance

The United States led all other nations in the development of universal and publicly-funded secondary school education and much of the growth occurred from 1910 to 1940. The focus here is on the reasons for the high school movement' in American generally and why it occurred so early and swiftly in America's heartland - a region we dub the 'education belt.' At the center of this belt' was the state of Iowa and we use information from the unique 1915 Iowa State Census to explore the factors, at both the county and individual levels, that propelled states like Iowa to embrace secondary school education very early. Iowa's small towns, as well as those across the nation, were the loci of the high school movement. In an analysis at the national level, we find that greater homogeneity of income or wealth, a higher level of wealth, greater community stability, and more ethnic and religious homogeneity fostered high school expansion from 1910 to 1930. The pecuniary returns to secondary school education were high - on the order of 12 percent per year in 1914 - providing substantial private incentives for high school attendance. State-level measures of social capital today are strongly correlated with economic and schooling variables from 1900 to 1930. The social capital assembled locally in the early part of the century, which apparently fueled part of the high school movement, continues to contribute to human capital formation.

Lawrence F. Katz

Papers

Boston Site Findings: The Early Impacts of Moving to Opportunity

Moving to Opportunity in Boston: Early Results of a Randomized Mobility Experiment

Unemployment Insurance, Recall Expectations, And Unemployment Outcomes

Reflections on US Labour Market Performance

Human Capital and Social Capital: The Rise of Secondary Schooling in America, 1910 to 1940