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

Roland Fryer has emerged during the last decade as a leading scholar of the US racial divide and as a major figure in the evaluation of education policies to narrow the racial achievement gap and improve the prospects of low-income and minority children. He has been bold and fearless in his willingness to apply rigorous economic theory, to collect new data, and to develop and implement appropriate and compelling empirical strategies (including randomized field experiments) to assess any serious hypothesis that might shed light on racial inequality and that may provide policy tools to improve the academic achievement and long-run outcomes of disadvantaged children. Fryer’s work is marked by a creative and entrepreneurial edge that has allowed him to carry out large-scale evaluations and interventions in the context of large school districts like New York, Chicago, Houston, and Dallas, often in the face of political opposition and bureaucratic inertia. His theoretical and empirical work on the “acting white” hypothesis of peer effects provides new insights into the barriers to increasing the educational investments of minorities and the socially excluded. Fryer’s research output related to racial inequality, the US racial achievement gap, and the design and evaluation of educational policies make him a worthy recipient of the 2015 John Bates Clark Medal. He was born in Daytona Beach, Florida. His tumultuous childhood and youth experiences in Florida and Texas have been well-documented in a profile in the New York Times Magazine (Dubner 2005). Roland went on to earn his BA from the University of Texas at Arlington in 1998 and his PhD in economics from Pennsylvania State

https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.30.1.207

Roland Fryer: 2015 John Bates Clark Medalist

Imagining a future of work that fosters mobility for all

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.

Reflections on US Labour Market Performance

Nearly 9 million Americans live in extreme-poverty neighborhoods, places that also tend to be racially segregated and dangerous. Yet the effects on the well-being of residents of moving out of such communities into less-distressed areas remain uncertain. Using data from Moving to Opportunity, a unique randomized housing mobility experiment, we find that moving from a high-poverty to lower-poverty neighborhood leads to long-term (10 to 15 year) improvements in adult physical and mental health and subjective well-being, despite not affecting economic self-sufficiency. A 1 standard deviation decline in neighborhood poverty (13 percentage points) increases subjective well-being by an amount equal to the gap in subjective well-being between people whose annual incomes differ by $13,000, a large amount given that the average control group income is $20,000. Subjective well-being is more strongly affected by changes in neighborhood economic disadvantage than racial segregation, which is important because racial segregation has been declining since 1970 but income segregation has been increasing.

Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults

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Lawrence F. Katz

Papers

Roland Fryer: 2015 John Bates Clark Medalist

Imagining a future of work that fosters mobility for all

Reflections on US Labour Market Performance

Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults

Supplementary Material for Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults