Abstract. Quantile regression is used to estimate the cross sectional relationship
between high school characteristics and student achievement as measured
by ACT scores. The importance of school characteristics on student achievement
has been traditionally framed in terms of the effect on the expected
value. With quantile regression the impact of school characteristics is allowed
to be di¤erent at the mean and quantiles of the conditional distribution. Like
robust estimation, the quantile approach detects relationships missed by traditional
data analysis. Robust estimates detect the influence of the bulk of the
data, whereas quantile estimates detect the influence of co-variates on alternate
parts of the conditional distribution. Since our design consists of multiple
responses (individual student ACT scores) at fixed explanatory variables
(school characteristics) the quantile model can be estimated by the usual regression
quantiles, but additionally by a regression on the empirical quantile
at each school. This is similar to least squares where the estimate based on the
entire data is identical to weighted least squares on the school averages. Unlike
least squares however, the regression through the quantiles produces a different
estimate than the regression quantiles.