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?

In Acemoglu, Johnson, and Robinson, henceforth AJR, (2001), we advanced the hypothesis that the mortality rates faced by Europeans in different parts of the world after 1500 affected their willingness to establish settlements and choice of colonization strategy. Places that were relatively healthy (for Europeans) were—when they fell under European control—more likely to receive better economic and political institutions. In contrast, places where European settlers were less likely to go were more likely to have “extractive” institutions imposed. We also posited that this early pattern of institutions has persisted over time and influences the extent and nature of institutions in the modern world. On this basis, we proposed using estimates of potential European settler mortality as an instrument for institutional variation in former European colonies today. Data on settlers themselves are unfortunately patchy—particularly because not many went to places they believed, with good reason, to be most unhealthy. We therefore followed the lead of Curtin (1989 and 1998) who compiled data on the death rates faced by European soldiers in various overseas postings. 1 Curtin’s data were based on pathbreaking data collection and statistical work initiated by the British military in the mid-nineteenth century. These data became part of the foundation of both contemporary thinking about public health (for soldiers and for civilians) and the life insurance industry (as actuaries and executives considered the

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The Colonial Origins of Comparative Development: An Empirical Investigation

This paper develops asymptotic distribution theory for instrumental variable regression when the partial correlation between the instruments and a single included endogenous variable is weak, here modeled as local to zero. Asymptotic representations are provided for various instrumental variable statistics, including the two-stage least squares (TSLS) and limited information maximum- likelihood (LIML) estimators and their t-statistics. The asymptotic distributions are found to provide good approximations to sampling distributions with just 20 observations per instrument. Even in large samples, TSLS can be badly biased, but LIML is, in many cases, approximately median unbiased. The theory suggests concrete quantitative guidelines for applied work. These guidelines help to interpret Angrist and Krueger's (1991) estimates of the returns to education: whereas TSLS estimates with many instruments approach the OLS estimate of 6%, the more reliable LIML and TSLS estimates with fewer instruments fall between 8% and 10%, with a typical confidence interval of (6%, 14%).

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Instrumental variables regression with weak instruments

Weak instruments can produce biased IV estimators and hypothesis tests with large size distortions. But what, precisely , are weak instruments, and how does one detect them in practice? This paper proposes quantitative definitions of weak instruments based on the maximum IV estimator bias, or the maximum Wald test size distortion, when there are multiple endogenous regressors. We tabulate critical values that enable using the first-stage F-statistic (or, when there are multiple endogenous regressors, the Cragg-Donald (1993) statistic) to test whether give n instruments are weak.

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Testing for Weak Instruments in Linear IV Regression

Using recently-available data from the New Immigrant Survey, we find that previous self-employment experience in an immigrant’s country of origin is an important determinant of their self-employment status in the U.S., increasing the probability of being self-employed by about 7 percent. Our results improve on the previous literature by measuring home-country selfemployment directly rather than relying on proxy measures. We find little evidence to suggest that home-country self-employment has a significant effect on U.S. wages in either paid employment or self employment.

The Persistence of Self-Employment Across Borders: New Evidence on Legal Immigrants to the United States

This paper presents suggested matches for the geographical coding (geocoding) of metropolitan areas in the 1970, 1980, and 1990 Censuses. The Census Bureau used different definitions and taxonomies to describe the geography of metropolitan areas in these three Census years. As a result, the geographical areas referred to by the standard Census Bureau definitions differ among the three Census data sets. The geographic matching scheme explained in this paper attempts to maximize consistency over time for metropolitan areas in the U.S.

Coding Geographic Areas Across Census Years: Creating Consistent Definitions of Metropolitan Areas

Reconciling Educational Attainment Questions in the CPS and the Census

Natives, the Foreign-Born and High School Equivalents: New Evidence on the Returns to the GED. Discussion Paper.

While it is well known that some areas of the United States receive more immigrants than others, less is understood about the extent to which the character of immigration varies as well. There is much broader geographic variation in the skill and demographic composition of immigrants than natives, with important implications for their economic effects. This paper provides a new perspective by focusing on heterogeneity in outcomes such as the share of population growth due to immigration, the presence of immigrant children in schools, and the effect of immigration on the age, sex, language, and educational composition of the local population and workforce.

David A. Jaeger

Papers

The Persistence of Self-Employment Across Borders: New Evidence on Legal Immigrants to the United States

Coding Geographic Areas Across Census Years: Creating Consistent Definitions of Metropolitan Areas

Reconciling Educational Attainment Questions in the CPS and the Census

Natives, the Foreign-Born and High School Equivalents: New Evidence on the Returns to the GED. Discussion Paper.

The Economic Diversity of Immigration Across the United States