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 describes and tries to reconcile trends in alternative work arrangements in the United States using data from the Contingent Worker Survey supplements to the Current Population Survey (CPS) for 1995 to 2017, the 2015 RAND-Princeton Contingent Work Survey (CWS), and administrative tax data from the Internal Revenue Service for 2000 to 2016. We conclude that there likely has been a modest upward trend in the share of the U.S. workforce in alternative work arrangements during the 2000s based on the cyclically-adjusted comparisons of the CPS CWS’s, measures using self-respondents in the CPS CWS, and measures of self-employment and 1099 workers from administrative tax data. We also present evidence from Amazon Mechanical Turk that suggests that the basic monthly CPS question on multiple job holding misses many instances of multiple job holding

Understanding Trends in Alternative Work Arrangements in the United States

Economists devote considerable effort to grad­ u­ate stu­dent edu­cation bu­t have condu­cted rela­ tively little research on the determinants of stu­dent performance or placement in the job market. Do gradu­ate stu­dents who do well in core microeco­ nomics (micro) cou­rses also do well in core mac­ roeconomics (macro) and econometrics (metrics) cou­rses? Are stu­dents who achieve higher grades in their first­year core classes or general exams more likely to complete their PhD and obtain higher ranked positions in the job market? In an attempt to answer these qu­estions, we assem­ bled a rich dataset on 1,029 economics gradu­ate stu­dents who enrolled at Harvard University, Massachu­setts Institu­te of Technology (MIT), Princeton University, Stanford University, or the University of Chicago in the 1990s. These schools were selected becau­se, in 1993, they had the five highest ranked economics PhD programs, accord­ ing to the National Research Cou­ncil (NRC). Ou­r resu­lts indicate that stu­dents’ grades in requ­ired core cou­rses are highly correlated across su­bjects. The PhD admissions com­ mittee’s evalu­ation of a stu­dent predicts first­ year grades and PhD completion, bu­t not job placement. First­year performance is a strong What Does Performance in Graduate School Predict? Graduate Economics Education and Student Outcomes

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What Does Performance in Graduate School Predict? Graduate Economics Education and Student Outcomes

Adults living in high-poverty neighborhoods often fare worse than adults in more advantaged neighborhoods on their physical health, mental health, and economic well-being. Although social scientists have observed this association for hundreds of years, they have found it difficult to determine the extent to which the neighborhoods themselves affect well-being versus the extent to which people at greater risk for adverse outcomes live in impoverished neighborhoods. In this article, we examine neighborhood effects using data from the 10- to 15-year evaluation of the Moving to Opportunity (MTO) for Fair Housing demonstration, which offered randomly selected families a housing voucher. The experimental design of MTO allows us to isolate the effects of neighborhoods from selection bias. We find that, 10 to 15 years after enrolling participants, the program had very few detectable effects on economic well-being but had some substantial effects on the physical and mental health of adults. For adults whose families received the offer of a housing voucher that could be used to move only to a low-poverty neighborhood, we find health benefits in terms of lower prevalence of diabetes, extreme obesity, physical limitations, and psychological distress. For adults offered a Section 8 voucher, we find benefits in terms of less extreme obesity and lower prevalence of lifetime depression.The contents of this article are the views of the authors and do not necessarily reflect the views or policies of the U.S. Department of Housing and Urban Development, the Congressional Budget Office, the U.S. government, or any state or local agency that provided data.IntroductionAdults living in disadvantaged neighborhoods have worse health and economic outcomes than people living in less distressed areas. Previous research has shown that living in a high-poverty or high-crime neighborhood is associated with an increased risk of poor overall health status, premature mortality, heart disease, obesity, serious mental illness, suicide, unemployment, and dropping out of school (Bagley, Jacobson, and Palmer, 1973; Diez-Roux and Mair, 2010; Kawachi and Berkman, 2003; Macintyre and Ellaway, 2003; Pickett and Pearl, 2001; Rezaeian et al, 2005; Weinberg, Reagan, and Yankow, 2004; Whitley et al., 1999).What remains less clear is the extent to which such variation in people's well-being across neighborhoods reflects the causal influence of the neighborhood environments themselves, beyond the variation explained by the nonrandom sorting of individuals among residential neighborhoods. People who are at elevated risk for adverse health or labor-market outcomes may face limited housing options and may be more likely to live in distressed neighborhoods. Observational (or nonexperimental) studies try to isolate the independent effects of neighborhoods on people's well-being by statistically controlling for observable risk and protective factors of individuals and their families. Studies that control for a larger number of individual factors tend to find smaller neighborhood effects than do other studies (Pickett and Pearl, 2001). In addition, substantial concerns remain that some of the key variables that directly affect both neighborhood selection and health or economic outcomes are missing from or are imperfectly measured in standard data sets. Observational studies may confound the influence of neighborhood environments with the effects of unmeasured individual or family attributes that directly affect outcomes and neighborhood selection, a problem that social scientists call selection bias.The existence, magnitude, and nature of any causal neighborhood effects on economic and health outcomes are of great relevance to housing policies that affect the geographic concentration of poverty in America. An understanding of neighborhood effects can inform policy choices related to: (1) whether to help low-income families meet their housing needs through project-based housing or, instead, through housing vouchers; (2) the types of relocation assistance and support to provide voucher holders; (3) where to locate new public housing projects; and (4) the potential value of mixed-income housing projects that subsidize nonpoor families to live near poor families. …

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The Long-Term Effects of Moving to Opportunity on Adult Health and Economic Self-Sufficiency

Sustaining U.S. Economic Growth

We examine whether online learning technologies have led to lower prices in higher education. Using data from the Integrated Postsecondary Education Data System, we show that online education is concentrated in large for-profit chains and less-selective public institutions. Colleges with a higher share of online students charge lower tuition prices. We present evidence that real and relative prices for full-time undergraduate online education declined from 2006 to 2013. Although the pattern of results suggests some hope that online technology can â€œbend the cost curveâ€ in higher education, the impact of online learning on education quality remains uncertain.

Lawrence F. Katz

Papers

Understanding Trends in Alternative Work Arrangements in the United States

What Does Performance in Graduate School Predict? Graduate Economics Education and Student Outcomes

The Long-Term Effects of Moving to Opportunity on Adult Health and Economic Self-Sufficiency

Sustaining U.S. Economic Growth

Can Online Learning Bend the Higher Education Cost Curve