Trade liberalization and the evolution of skill earnings differentials in Brazil

Rio de Janeiro

Convex combinations of long memory estimates from different sampling rates

Convex combinations of long memory estimates using the same data observed at different sampling rates can decrease the standard deviation of the estimates, at the cost of inducing a slight bias. The convex combination of such estimates requires a preliminary correction for the bias observed at lower sampling rates, reported by Souza and Smith (2002). Through Monte Carlo simulations, we investigate the bias and the standard deviation of the combined estimates, as well as the root mean squared error (RMSE), which takes both into account. While comparing the results of standard methods and their combined versions, the latter achieve lower RMSE, for the two semi-parametric estimators under study (by about 30% on average for ARFIMA(0,d,0) series).

Trade liberalization and the evolution of skill earnings differentials in Brazil

Rio de Janeiro

Trade liberalization and the evolution of skill earnings differentials in Brazil

From 1988 to 1995, when trade liberalization was implemented in Brazil, relative earnings of skilled workers decreased. In this paper, we investigate the role of trade liberalization in explaining these relative earnings movements, by checking all the steps predicted by the HeckscherOhlin- style trade transmission mechanism. We find that: i) employment shifted from skilled to unskilled intensive sectors, and each Sector increased its relative share of skilled labor; ii) relative prices fell in skill intensive sectors; iii) tariff changes across sectors were not related to skill intensities, but the pass-through from tariffs to prices was stronger in skill intensive sectors; iv) the decline in skilled eamings differentials mandated by the price variation predicted by trade is very elose to the observed one. The results are compatible with trade liberalization, accounting for the observed rei ative eamings changes in Brazil.

Improved combinations of parametric and nonparametric Regression estimators

This paper provides a systematic and unified treatment of the developments in the area of kernel estimation in econometrics and statistics. Both the estimation and hypothesis testing issues are discussed for the nonparametric and semiparametric regression models. A discussion on the choice of windowwidth is also presented.

Bright minds, big rent: gentrification and the rising returns to skill

In 1980, housing prices in the main US cities rose with distance to the city center. By 2010, that relationship had reversed. We propose that this development can be traced to greater labor supply of high-income households through reduced tolerance for commuting. In a tract-level data set covering the 27 largest US cities, years 1980-2010, we employ a city-level Bartik demand shifter for skilled labor and find support for our hypothesis: full-time skilled workers favor proximity to the city center and their increased presence can account for the observed price changes, notably the rising price premium commanded by centrality.