It is well known that most of the standard speci¯cation tests are not valid when the alternative hypothesis is misspeci¯ed. This is particularly true in the error component model, when one tests for either random e®ects or serial correlation without taking account of the presence of the other e®ect. In this paper we study the size and power of the standard Rao's score tests analytically and by simulation when the data is contaminated by local misspeci¯cation. These tests are adversely a®ected under misspeci¯cation. We suggest simple procedures to test for random e®ects (or serial correlation) in the presence of local serial correlation (or random e®ects), and these tests require ordinary least squares residuals only. Our Monte Carlo results demonstrate that the suggested tests have good¯nite sample properties and are capable of detecting the right direction of the departure from the null hypothesis. We also provide some empirical illustrations to highlight the usefulness of our tests.

Considerable effort has been exercised recently in estimating mean returns to education while carefully considering biases arising from unmeasured ability and measurement error. Some of this work has also attempted to determine whether there are variations from the "mean" return to education across the population with mixed results. In this paper, we use recent extensions of instrumental variables techniques to quantile regression on a sample of twins to estimate an entire family of returns to education at different quantiles of the conditional distribution of wages while addressing simultaneity and measurement error biases. We test whether there is individual heterogeneity in returns to education against the alternative that there is a constant return for all workers. Our estimated model provides evidence of two sources of heterogeneity in returns to schooling. First, there is evidence of a differential effect by which more able individuals become better educated because they face lower marginal costs of schooling. Second, once this endogeneity bias is accounted for, our results provide evidence of the existence of actual heterogeneity in market returns to education consistent with a non-trivial interaction between schooling and unobserved abilities in the generation of earnings. The evidence suggests that higher ability individuals (those further to the right in the conditional distribution of wages) have higher returns to schooling but that returns vary significantly only along the lower quantiles to middle quantiles. In our final approach, the resulting estimated returns are never lower than 9 percent and can be as high as 13 percent at the top of the conditional distribution of wages, thus providing rather tight bounds on the true return to schooling. Our findings have meaningful implications for the design of educational policies.JEL Classification: C14, I2, J24, J31

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We use recent unconditional quantile regression methods (UQR) to study the distributive effects of education in Argentina. Standard methods usually focus on mean effects, or explore distributive effects by either making stringent modeling assumptions, and/or through counter-factual decompositions that require several temporal observations. An empirical case shows the flexibility and usefulness of UQR methods. Our application for the case of Argentina shows that education contributed positively to increased inequality in Argentina, mostly due to the effect of strongly heterogeneous effects of education on earnings.