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

Preface Chapter 1 The challenge Chapter 2 The nature of the evidence: A synthesis of meta-analyses Chapter 3 The argument: Visible teaching and visible learning Chapter 4: The contributions from the student Chapter 5 The contributions from the home Chapter 6 The contributions from the school Chapter 7 The contributions from the teacher Chapter 8 The contributions from the curricula Chapter 9 The contributions from teaching approaches - I Chapter 10 The contributions from teaching approaches - II Chapter 11: Bringing it all together Appendix A: The 800 meta-analyses Appendix B: The meta-analyses by rank order References

Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement

This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and compariso...

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Propensity score-matching methods for nonexperimental causal studies

Considerable controversy surrounds the impact of schools and teachers on the achievement of students. This paper disentangles the separate factors influencing achievement with special attention given to the role of teacher differences and other aspects of schools. Unique matched panel data from the Harvard/UTD Texas Schools Project permit distinguishing between total effects and the impact of specific, measured components of teachers and schools. While schools are seen to have powerful effects on achievement differences, these effects appear to derive most importantly from variations in teacher quality. A lower bound suggests that variations in teacher quality account for at least 7« percent of the total variation in student achievement, and there are reasons to believe that the true percentage is considerably larger. The subsequent analysis estimates educational production functions based on models of achievement growth with individual fixed effects. It identifies a few systematic factors a negative impact of initial years of teaching and a positive effect of smaller class sizes for low income children in earlier grades but these effects are very small relative to the effects of overall teacher quality differences.

Teachers, Schools, and Academic Achievement

This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical researchers. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental" design, and summarizes different ways (with their advantages and disadvantages) of estimating RD designs and the limitations of interpreting these estimates. Concepts are discussed using examples drawn from the growing body of empirical research using RD.

Regression Discontinuity Designs in Economics

I identify the effects of class size on student achievement using longitudinal variation in the population associated with each grade in 649 elementary schools. I use variation in class size driven by idiosyncratic variation in the population. I also use discrete jumps in class size that occur when a small change in enrollment triggers a maximum or minimum class size rule. The estimates indicate that class size does not have a statistically significant effect on student achievement. I rule out even modest effects (2 to 4 percent of a standard deviation in scores for a 10 percent reduction in class size).

The Effects of Class Size on Student Achievement: New Evidence from Population Variation

Peer effects are potentially important for understanding the optimal organization of schools, jobs, and neighborhoods, but finding evidence is difficult because people are selected into peer groups based, in part, on their unobservable characteristics. I identify the effects of peers whom a child encounters in the classroom using sources of variation that are credibly idiosyncratic, such as changes in the gender and racial composition of a grade in a school in adjacent years. I use specification tests, including one based on randomizing the order of years, to confirm that the variation I use is not generated by time trends or other non-idiosyncratic forces. I find that students are affected by the achievement level of their peers: a credibly exogenous change of 1 point in peers' reading scores raises a student's own score between 0.15 and 0.4 points, depending on the specification. Although I find little evidence that peer effects are generally non-linear, I do find that peer effects are stronger intra-race and that some effects do not operate through peers' achievement. For instance, both males and females perform better in math in classrooms that are more female despite the fact that females' math performance is about the same as that of males.

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Peer Effects in the Classroom: Learning from Gender and Race Variation

Tiebout choice among districts is the most powerful market force in American public education. Naive estimates of its effects are biased by endogenous district formation. I derive instruments from the natural boundaries in a metropolitan area. My results suggest that metropolitan areas with greater Tiebout choice have more productive public schools and less private schooling. Little of the effect of Tiebout choice works through its effect on household sorting. This finding may be explained by another finding: students are equally segregated by school in metropolitan areas with greater and lesser degrees of Tiebout choice among districts.

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Does Competition among Public Schools Benefit Students and Taxpayers

Many school choice proposals would enable parents to choose among public school districts in their area, though not among private schools. Theory predicts three reactions to easier choice among public schools: increased sorting of students and parents among schools; easier choice will encourage competition among schools, forcing them into higher productivity (better student performance per input); easier choice among public schools will give parents less incentive to send their children to private schools. I examine easing choice among public schools using exogenous variation in the concentration of public school districts in metropolitan areas measured by a Herfindahl index on enrollment shares. The exogenous variation is generated by topography: I derive instruments for concentration from natural boundaries (rivers) that partially determine district size. I find evidence that easier choice leads to greater productivity. Areas with greater opportunities for choice among public schools have lower per-pupil spending, lower teacher salaries, and larger classes. The same areas have better average student performance, as measured by students' educational attainment, wages, and test scores. Performance improvements are concentrated among white non-Hispanics, males, and students who have a parent with at least a high school degree. However, student performance is not worse among Hispanics,African-Americans, females, or students who do not have a parent with a high school degree.Also, student performance improves at both ends of the educational attainment distribution and test score distribution.

https://papers.ssrn.com/sol3/Delivery.cfm/nber_w4979.pdf?abstractid=226578&mirid=2

Does Competition Among Public Schools Benefit Students and Taxpayers

We show that the vast majority of very high-achieving students who are low-income do not apply to any selective college or university. This is despite the fact that selective institutions would often cost them less, owing to generous financial aid, than the resource-poor two-year and non-selective four-year institutions to which they actually apply. Moreover, high-achieving, low-income students who do apply to selective institutions are admitted and graduate at high rates. We demonstrate that these low-income students' application behavior differs greatly from that of their high-income counterparts who have similar achievement. The latter group generally follows the advice to apply to a few "par" colleges, a few "reach" colleges, and a couple of "safety" schools. We separate the low-income, high-achieving students into those whose application behavior is similar to that of their high-income counterparts ("achievement-typical" behavior) and those whose apply to no selective institutions ("income-typical" behavior). We show that income-typical students do not come from families or neighborhoods that are more disadvantaged than those of achievement-typical students. However, in contrast to the achievement-typical students, the income-typical students come from districts too small to support selective public high schools, are not in a critical mass of fellow high achievers, and are unlikely to encounter a teacher or schoolmate from an older cohort who attended a selective college. We demonstrate that widely-used policies–college admissions staff recruiting, college campus visits, college access programs–are likely to be ineffective with income-typical students, and we suggest policies that will be effective must depend less on geographic concentration of high achievers.

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Caroline M. Hoxby

Papers

The Effects of Class Size on Student Achievement: New Evidence from Population Variation

Peer Effects in the Classroom: Learning from Gender and Race Variation

Does Competition among Public Schools Benefit Students and Taxpayers

Does Competition Among Public Schools Benefit Students and Taxpayers

The Missing "One-Offs": The Hidden Supply of High-Achieving, Low-Income Students