In this article, we describe qregsel, a community-contributed command that implements a copula-based sample-selection correction for quantile regression recently proposed by Arellano and Bonhomme (2017, Econometrica 85: 1–28). The command allows the user to model selection in quantile regressions by using either a Gaussian or a one-dimensional Frank copula. We illustrate the use of qregsel with two examples. First, we apply the method to the fictional dataset used in the Stata Base Reference Manual for the heckman command. Second, we replicate part of the empirical application of the original article using data for the United Kingdom that cover the period 1978–2000 to compare wages of males and females at different quantiles.
The reform to capital market in Chile in 2001 enacted, among other things, a capital gain tax exemption for stocks highly traded in stock markets. The goals of the reform were mainly to increase participation, depth and liquidity in the local stock market. However, it is not clear what the effect of a tax reduction is on stock prices because there exists two effects working on opposite directions. On the one hand, there exists a capitalization effect that produces an increase in prices. O the other hand, there exists a lock-in effect that leads a reduction in prices. To determine which of the two effects dominates is, therefore, an empirical question. This work contributes to answering this question, estimating for this purpose the effect on stock prices of the capital gains tax reform in Chile in 2001. Using a difference-indifference estimator, the results show an average anticipated effect of around-15% on stock prices traded in the Santiago Stock Market. The Price elasticity with respect to the tax rate in the economic literature for similar tax reforms in other countries ranges between-0.20 and-0.27, higher in magnitud than the one found in this study which ranges between-0.006 and-0.01. However, the estimated magnitude is quite close to the cases where the lock-in effect dominates.
We provide novel evidence of the impact of coresidence bias on a large set of indicators of intergenerational mobility in education. We begin re-examining a recent claim that the correlation coecient is less biased than the regression coecient. Then, we expand our analysis to show that there are indicators with varying average levels of coresidence bias going from less than 1% to more than 10%. However, some indicators with minimal bias produce high levels of re-ranking that make them uninformative to rank populations by the level of mobility. In contrast, other indicators with large bias generate more reliable rankings.
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