David A. Jaeger

Bio: David A. Jaeger is an academic researcher from University of Cologne. The author has contributed to research in topics: Population & Cycle of violence. The author has an hindex of 25, co-authored 65 publications receiving 7107 citations. Previous affiliations of David A. Jaeger include National Bureau of Economic Research & College of William & Mary.

Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak

Abstract: We draw attention to two problems associated with the use of instrumental variables (IV), the importance of which for empirical work has not been fully appreciated. First, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation. Second, in finite samples, IV estimates are biased in the same direction as ordinary least squares (OLS) estimates. The magnitude of the bias of IV estimates approaches that of OLS estimates as the R 2 between the instruments and the endogenous explanatory variable approaches 0. To illustrate these problems, we reexamine the results of a recent paper by Angrist and Krueger, who used large samples from the U.S. Census to estimate wage equations in which quarter of birth is used as an instrument for educational attainment. We find evidence that, despite huge sample sizes, th...

Degrees matter: new evidence on sheepskin effects in the returns to education.

Abstract: The effects of diploma receipt in the returns to education were examined. The sheepskin effects of the increase in wages due to receiving a degree were documented by various researchers. A unique data set with information on both years of education and diplomas received drawn from a matched sample of the 1991 and 1992 March Current Population Survey of the US Bureau of the Census was used to estimate the diploma effects. The sample comprised individuals whose race was Black or White and were 25-64 years old in 1992. The estimates of sheepskin effects of high school and college receipt based only on information on years of education suffered from substantial biases. However the diploma receipt was a much more important determinant of wages than found by previous research: completing a bachelors degree was worth more than the human capital acquired during 3 years of college. Minority men and women appeared to receive a higher return to completing 16 or more years of education than their White counterparts. Significant positive effects for a high school diploma were found only for White men. The wage returns to high school for Black men and for White women were smaller than those for White men. The marginal returns to receiving either an academic or an occupational associates degree were statistically significant for White women raising wages by 10-20%. Significant differences between Black and White women existed for both types of associates degrees. The marginal effects of receiving a bachelors degree were positive for all groups and statistically significant for all but Black men. Masters degrees delivered a very high return for Black men which was significantly different from that for White men and Black women. Professional and doctoral degrees yielded an increase in wages above those for bachelors degrees for White men and women.

Direct Evidence on Risk Attitudes and Migration

Abstract: Geographic mobility is important for the functioning of labor markets because it brings labor resources to where they can be most efficiently used. It has long been hypothesized that individuals' migration propensities depend on their attitudes towards risk, but the empirical evidence, to the extent that it exists, has been indirect. In this paper, we use newly available data from the German Socio-Economic Panel to measure directly the relationship between migration propensities and attitudes towards risk. We find that individuals who are more willing to take risks are more likely to migrate between labor markets in Germany. This result is robust to stratifying by age, sex, education, national origin, and a variety of other demographic characteristics, as well as to the level of aggregation used to define geographic mobility. The effect is substantial relative to the unconditional migration propensity and compared to the conventional determinants of migration. We also find that being more willing to take risks is more important for the extensive than for the intensive margin of migration.

Reconciling the Old and New Census Bureau Education Questions: Recommendations for Researchers

Abstract: Beginning with the 1990 Census and the January 1992 Current Population Survey (CPS), the Bureau of the Census changed the emphasis of its educational-attainment question from years of education to degree receipt. Using a matched sample from the 1991 and 1992 March CPS, this article addresses how to reconcile the old and new questions. The effects of those methods on the estimated return(s) to education are then examined. Both the estimated linear return and the estimated college–high-school wage differential are slightly larger using information from the new question.

Shift-Share Instruments and the Impact of Immigration

Abstract: A large literature exploits geographic variation in the concentration of immigrants to identify their impact on a variety of outcomes. To address the endogeneity of immigrants' location choices, the most commonly-used instrument interacts national inflows by country of origin with immigrants' past geographic distribution. We present evidence that estimates based on this "shift-share" instrument conflate the short- and long-run responses to immigration shocks. If the spatial distribution of immigrant inflows is stable over time, the instrument is likely to be correlated with ongoing responses to previous supply shocks. Estimates based on the conventional shift-share instrument are therefore unlikely to identify the short-run causal effect. We propose a "multiple instrumentation" procedure that isolates the spatial variation arising from changes in the country-of-origin composition at the national level and permits us to estimate separately the short- and long-run effects. Our results are a cautionary tale for a large body of empirical work, not just on immigration, that rely on shift-share instruments for causal inference.

Econometric Analysis of Cross Section and Panel Data

Abstract: 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.

How Much Should We Trust Differences-In-Differences Estimates?

Abstract: 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.

The Colonial Origins of Comparative Development: An Empirical Investigation

Abstract: 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

Instrumental variables regression with weak instruments

Abstract: 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%).

Testing for Weak Instruments in Linear IV Regression

Abstract: 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.