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.

/pdf/econometric-analysis-of-cross-section-and-panel-data-3jzr8emmtf.pdf

Econometric Analysis of Cross Section and Panel Data

This paper considers whether it is possible to devise a nonexperimental procedure for evaluating a prototypical job training programme. Using rich nonexperimental data, we examine the performance of a two-stage evaluation methodology that (a) estimates the probability that a person participates in a programme and (b) uses the estimated probability in extensions of the classical method of matching. We decompose the conventional measure of programme evaluation bias into several components and find that bias due to selection on unobservables, commonly called selection bias in econometrics, is empirically less important than other components, although it is still a sizeable fraction of the estimated programme impact. Matching methods applied to comparison groups located in the same labour markets as participants and administered the same questionnaire eliminate much of the bias as conventionally measured, but the remaining bias is a considerable fraction of experimentally-determined programme impact estimates. We test and reject the identifying assumptions that justify the classical method of matching. We present a nonparametric conditional difference-in-differences extension of the method of matching that is consistent with the classical index-sufficient sample selection model and is not rejected by our tests of identifying assumptions. This estimator is effective in eliminating bias, especially when it is due to temporally-invariant omitted variables.

Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme

Using 6 longitudinal data sets, the authors estimate links between three key elements of school readiness--school-entry academic, attention, and socioemotional skills--and later school reading and math achievement In an effort to isolate the effects of these school-entry skills, the authors ensured that most of their regression models control for cognitive, attention, and socioemotional skills measured prior to school entry, as well as a host of family background measures Across all 6 studies, the strongest predictors of later achievement are school-entry math, reading, and attention skills A meta-analysis of the results shows that early math skills have the greatest predictive power, followed by reading and then attention skills By contrast, measures of socioemotional behaviors, including internalizing and externalizing problems and social skills, were generally insignificant predictors of later academic performance, even among children with relatively high levels of problem behavior Patterns of association were similar for boys and girls and for children from high and low socioeconomic backgrounds

/pdf/school-readiness-and-later-achievement-3b0grfzdeu.pdf

School Readiness and Later Achievement

Identification of Causal effects Using Instrumental Variables

We outline a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable. To address the problems associated with comparing subjects by the ignorable assignment—an “intention-to-treat analysis”—we make use of instrumental variables, which have long been used by economists in the context of regression models with constant treatment effects. We show that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers. Without these assumptions, the IV estimand is simply the ratio of intention-to-treat causal estimands with no interpretation as an average causal effect. The advantages of embedding the IV approach in the RCM are that it clarifies the nature of critical assumptions needed for a...

/pdf/identification-of-causal-effects-using-instrumental-16wp7tkypl.pdf

Identification of Causal Effects Using Instrumental Variables

On April 1, 1992 New Jersey's minimum wage rose from $4.25 to $5.05 per hour. To evaluate the impact of the new law we surveyed over 400 fast food restaurants in New Jersey and Pennsylvania before and after the rise in the minimum. Comparisons of the changes in wages, employment, and prices at stores in New Jersey relative to stores in Pennsylvania (where the minimum remained constant at $4.25 per hour) provide simple robust estimates of the effect of the increased minimum wage. Our empirical findings challenge the conventional notion that a rise in the minimum causes employment to decline. Relative to stores in Pennsylvania, fast food restaurants in New Jersey increased employment by 2.5 employees per store. We also compare employment changes at stores in New Jersey that were initially paying $5.00 per hour or more (and were therefore largely unaffected by the new law) to the employment changes at lower-wage stores, where the new law raised wages by 10-15 percent. Stores that were unaffected by the minimum wage had the same employment growth as stores in Pennsylvania, while stores that had to increase their wages increased their employment. Finally, we evaluate theoretical models that might explain these results.

/pdf/minimum-wages-and-employment-a-case-study-of-the-fast-food-pn68vox4a6.pdf

Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania

This paper surveys the recent literature on the causal relationship between education and earnings. I focus on four areas of work: theoretical and econometric advances in modelling the causal effect of education in the presence of heterogeneous returns to schooling; recent studies that use institutional aspects of the education system to form instrumental variables estimates of the return to schooling; recent studies of the earnings and schooling of twins; and recent attempts to explicitly model sources of heterogeneity in the returns to education. Consistent with earlier surveys of the literature, I conclude that the average (or average marginal) return to education is not much below the estimate that emerges from a standard human capital earnings function fit by OLS. Evidence from the latest studies of identical twins suggests a small upward "ability" bias -- on the order of 10%. A consistent finding among studies using instrumental variables based on institutional changes in the education system is that the estimated returns to schooling are 20-40% above the corresponding OLS estimates. Part of the explanation for this finding may be that marginal returns to schooling for certain subgroups -- particularly relatively disadvantaged groups with low education outcomes -- are higher than the average marginal returns to education in the population as a whole.

The causal effect of education on earnings

This paper reviews a set of recent studies that have attempted to measure the causal effect of education on labor market earnings by using institutional features of the supply side of the education system as exogenous determinants of schooling outcomes. A simple theoretical model that highlights the role of comparative advantage in the optimal schooling decision is presented and used to motivate an extended discussion of econometric issues, including the properties of ordinary least squares and instrumental variables estimators. A review of studies that have used compulsory schooling laws, differences in the accessibility of schools, and similar features as instrumental variables for completed education reveals that the resulting estimates of the return to schooling are typically as big or bigger than the corresponding ordinary least squares estimates. One interpretation of this finding is that marginal returns to education among the low-education subgroups typically affected by supply-side innovations tend to relatively high, reflecting their high marginal costs of schooling, rather than low ability that limits their return to education.

Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems

Preface ix CHAPTER 1 Introduction and Overview I CHAPTER 2 Employer Responses to the Minimum Wage: Evidence from the Fast-Food Industry 20 CHAPTER 3 Statewide Evidence on the Effect of the 1988 California Minimum Wage 78 CHAPTER 4 The Effect of the Federal Minimum Wage on Low-Wage Workers: Evidence from Cross-State Comparisons 113 CHAPTER 5 Additional Employment Outcomes 152 CHAPTER 6 Evaluation of Time-Series Evidence 178 CHAPTER 7 Evaluation of Cross-Section and Panel-Data Evidence 208 CHAPTER 8 International Evidence 240 CHAPTER 9 How the Minimum Wage Affects the Distribution of Wages, the Distribution of Family Earnings, and Poverty 276 CHAPTER 10 How Much Do Employers and Shareholders Lose? 313 CHAPTER 11 Is There an Explanation? Alternative Models of the Labor Market and the Minimum Wage 355 CHAPTER 12 Conclusions and Implications 387 References 401 Index 415

Myth and Measurement: The New Economics of the Minimum Wage.

The recent rise in wage inequality is usually attributed to skill‐biased technical change (SBTC), associated with new computer technologies. We review the evidence for this hypothesis, focusing on the implications of SBTC for overall wage inequality and for changes in wage differentials between groups. A key problem for the SBTC hypothesis is that wage inequality stabilized in the 1990s despite continuing advances in computer technology; SBTC also fails to explain the evolution of other dimensions of wage inequality, including the gender and racial wage gaps and the age gradient in the return to education.

/pdf/skill-biased-technological-change-and-rising-wage-inequality-3fpsltvgxm.pdf

David Card

Papers

Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania

The causal effect of education on earnings

Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems

Myth and Measurement: The New Economics of the Minimum Wage.

Skill‐Biased Technological Change and Rising Wage Inequality: Some Problems and Puzzles