THE EFFECTS OF SECONDARY SCHOOL QUALITY ON THE DISTRIBUTION OF EARNINGS

DAVID P. SIMS (*) I. INTRODUCTION AND OVERVIEW The link between measures of school quality and students' educational and labor market outcomes has been a source of much debate among education researchers and is central to the ongoing public discussion over how to improve public schooling in the United States. Much of the work by economists in this literature has examined whether or not improvements in school resources, such as increasing per-pupil expenditures or lowering class size, raise postschooling earnings. (1) Existing work (e.g., Betts [1995, 1996a], Card and Krueger [1996, 1992], Grogger [1996a, 1996b]) on the correlation between school resources (sometimes referred to as "school quality") and earnings has focused on "mean" effects, thus addressing the question: does an increase in school resources increase average earnings? This article broadens the inquiry to ask whether school resources measurably affect other portions of the conditional earnings distribution. For example, it might be the case that individual schools and districts raise per-pupil expenditures or lower pupil/teacher ratios to target the more disadvantaged students, thus leading to disproportionately beneficial effects for the bottom tail of the earnings distribution. Such effects may be difficult to detect using estimation techniques that focus on the mean of the conditional earnings distribution. In this article we estimate the effect of school resource measures on earnings at different points of the conditional earnings distribution using quantile regressions. That differential distributional effects are potentially important is suggested by the work of Eide and Showalter (1998) who find that test score gains for the bottom 5% of the test score distribution are disproportionately improved by increased funding. In other work, Eide and Showalter (1999) find that the bottom part of the conditional earnings distribution disproportionally benefits from college completion. This article presents and explores the empirical effects of school resources on the earnings distribution using standard specifications and resting on well-established economic theory. (2) The remainder of the article is as follows: section II gives a brief introduction to the empirical methodology, section III discusses the data, section IV presents the results, and section V concludes. II. EMPIRICAL METHODOLOGY We begin with a brief description of quantile regression, since it has only recently seen widespread application in applied econometrics. Our use of quantile regression can best be understood by analogy to ordinary least squares, where an ordinary least squares (OLS) coefficient estimate is typically interpreted as the marginal effect on the conditional mean of the dependent variable due to a change in an explanatory variable. Similarly, quantile regression produces parameter estimates that can be interpreted as the marginal effect on some defined point of the conditional distribution of the dependent variable ("quantiles") due to a change in the explanatory variables. For example, a researcher can estimate how a change in an explanatory variable changes the median outcome (of the dependent variable), holding all other variables constant. Or estimates can be computed for any other percentile of the conditional distribution. Quantile regressions were initially introduced as a robust regression technique enabling consistent estimation where the typical assumption of normality of the error term might not be strictly satisfied (Koenker and Bassett, 1978). It has also been used as a means of estimating models with censoring (Powell, 1984, 1986; Buchinsky, 1994, 1995), for estimating duration models (Koenker and Bilias, 2000), and for detecting heteroskedasticity (Koenker and Bassett, 1982). Recently there has been an increased use of quantile regressions simply to get information about points in the distribution of the dependent variable other than the conditional mean. …