School Quality, Educational Attainment and Aggregation Bias

Data from 31 countries participating in the Programme for International Student Assessment (PISA) is used to estimate education production functions for reading literacy.The analysis suggests that the probability of finding statistically significant and correctly signed class size effects increases the higher the level of aggregation used to measure class size.Class size, PISA data, bias

Exceptional covers and bijections on rational points

We show that if f: X --> Y is a finite, separable morphism of smooth curves defined over a finite field F_q, where q is larger than an explicit constant depending only on the degree of f and the genus of X, then f maps X(F_q) surjectively onto Y(F_q) if and only if f maps X(F_q) injectively into Y(F_q). Surprisingly, the bounds on q for these two implications have different orders of magnitude. The main tools used in our proof are the Chebotarev density theorem for covers of curves over finite fields, the Castelnuovo genus inequality, and ideas from Galois theory.Comment: 19 pages; various minor changes to previous version. To appear in International Mathematics Research Notice

HACking at Non-linearity: Evidence from Stocks and Bonds

The implicit assumption of linearity is an important element in empirical finance. This study presents a hypothesis testing approach which examines the linear behaviour of the conditional mean between stock and bond returns. Conventional tests detect spurious non-linearity in the conditional mean caused by heteroskedasticity and/or autocorrelation. This study re-states these tests in a heteroskedasticity and autocorrelation consistent (HAC) framework and we find that stock and bond returns are indeed linear-in-the-mean in both univariate and bivariate settings. This study contends that previous research may have detected spurious non-linearity due to size distortions caused by heteroskedasticity and autocorrelation, rather than the presence of genuine non-linearity.linearity, nonlinear, heteroskedasticity-robust tests, autocorrelation-robust tests