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

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

Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like 'what to do if there is choice-based sampling?' or 'when to measure effects?' can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes.

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Some practical guidance for the implementation of propensity score matching

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

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

be responsible for poor health and low levels of schooling among the children of young mothers. This paper uses special disability and grade repetition questions from the school enrollment supplement to the 1992 Current Population Survey to estimate the effect of maternal age and single parenthood on children's disability status and school progress. Our results suggest that there is little association between maternal age at birth and children's disabilities. But the children of teen mothers are much more likely to repeat one or more grades than other children, and within-household estimates of this relationship are even larger than OLS estimates. The grade repetition findings from the CPS are replicated using a smaller sample from the National Longitudinal Survey of Youth. Another finding of interest is that having a father in the household is associated with lower disability prevalence and fewer grade repetitions. But many of the effects of single parenthood on disability, as well as the effect on grade repetition, appear to be explained by higher incomes in two-parent families.

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The Effect of Teen Childbearing and Single Parenthood on Childhood Disabilities and Progress in School

Between 1898 and 1948, English was the language of instruction for most post-primary grades in Puerto Rican public schools. Since 1949, the language of instruction in all grades has been Spanish. We use this policy change to estimate the effect of English-intensive instruction on the English-language skills of Puerto Ricans. Although naive estimates suggest that English instruction increased English-speaking ability among Puerto Rican natives, estimates that allow for education-specific cohort trends show no effect. This result is surprising in light of the strong presumption by American policymakers at the time that instruction in English was the best way to raise English proficiency. This has implications for medium of instruction policy in former colonies as well as U.S. education policy toward immigrant children.

Is Spanish-Only Schooling Responsible for the Puerto Rican Language Gap?

The nation's largest charter management organization is the Knowledge is Power Program (KIPP) KIPP schools are emblematic of the No Excuses approach to public education, a highly standardized and widely replicated charter model that features a long school day, an extended school year, selective teacher hiring, strict behavior norms, and a focus on traditional reading and math skills No Excuses charter schools are sometimes said to focus on relatively motivated high achievers at the expense of students who are most diffiult to teach, including limited English proficiency (LEP) and special education (SPED) students, as well as students with low baseline achievement levels We use applicant lotteries to evaluate the impact of KIPP Academy Lynn, a KIPP school in Lynn, Massachusetts that typifies the KIPP approach Our analysis focuses on special needs students that may be underserved The results show average achievement gains of 036 standard deviations in math and 012 standard deviations in reading for each year spent at KIPP Lynn, with the largest gains coming from the LEP, SPED, and low-achievement groups The average reading gains are driven almost completely by SPED and LEP students, whose reading scores rise by roughly 035 standard deviations for each year spent at KIPP Lynn

Who Benefits from KIPP

A combination of voluntary enlistment, armed forces eligibility criteria, and the failure of draftees to avoid conscription jointly determined the racial composition of the Vietnam era armed forces. Administrative data show that men with draft lottery numbers that put them at high risk of conscription are overrepresented among men who voluntarily enlisted in the military but that the effect of the lottery on enlistment is stronger for Whites than for non-Whites. Minimum chi-squared estimates of enlistment models for the 1971 draft lottery suggest that non-Whites were more likely than Whites to prefer enlistment to a civilian career. This finding appears to explain racial differences in the effect of the lottery on enlistment. Contrary to the findings of a recent congressional study, the Vietnam era estimates presented here suggest that conscription of a relatively small number of Whites and non-Whites in a manner proportional to their prevalence in the population might substantially reduce non-Wh...

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Joshua D. Angrist

Papers

The Effect of Teen Childbearing and Single Parenthood on Childhood Disabilities and Progress in School

Is Spanish-Only Schooling Responsible for the Puerto Rican Language Gap?

Who Benefits from KIPP

The Draft Lottery and Voluntary Enlistment in the Vietnam Era

Chapter 4. Instrumental Variables in Action: Sometimes You Get What You Need