Trade Shocks in Brazil: An Investigation of Effects on Regional Manufacturing Wages

Brazil has experienced two trade shocks in the 90’s: unilateral liberalization, which weighted average nominal tariff reduced from 37.7% in 1988 to 10.2% in 1994; drastically real devaluation of 47% in the exchange rate in 1999. These two effects has influenced the location of industry in Brazil, since the industry center of Brazil, Sao Paulo State, reduced its participation in the industry sector from 52% in 1985 to 43% in 2002. This occurs when the dispersion forces overcome the agglomeration ones. The main dispersion force evidenced by the literature is the increase of competition, not only in the goods market (a new product), but also in the factor market (demand of labor, which increases wages). In a trade agreement, the most common trade shock, these two forces occurred simultaneously. At this case, it is possible to distinguish between two dispersion forces: competition of the imported goods (first shock); competition in the labor market (second shock). One way to evaluate these effects can be by investigating the effectiveness of transport cost to understand the regional differences in wages and if it has reduced (or increased) its explanation power after the trade shock. In order to do that, the methodology of Hanson 1997 will be used as a basic framework. It is possible to analyze the effects of these trade shocks in the disparities of regional wages in Brazil with his methodology. However, there will be some differences to his framework. First, Hanson uses state level data and this paper has a more disaggregated regional data (microregion, which divides Brazil into more than 500 parts). Second, Hanson doesn’t take into account any change in educational level, infrastructure improvement or government intervention, which are considered in this investigation. The first results show that transport cost is important to understand differences in wages between Brazilian microregions and trade shocks have influenced in some sense these disparities, but not so consistently as transport costs. Moreover, it seems that dispersion force of the second shock was greater than the first one, therefore, competition to hire new employees expel more plants to lower wages regions than comptetion with new products.

Statistical mechanics of sparse generalization and model selection

One of the crucial tasks in many inference problems is the extraction of sparse information out of a given number of high-dimensional measurements. In machine learning, this is frequently achieved using, as a penality term, the $L_p$ norm of the model parameters, with $p\leq 1$ for efficient dilution. Here we propose a statistical-mechanics analysis of the problem in the setting of perceptron memorization and generalization. Using a replica approach, we are able to evaluate the relative performance of naive dilution (obtained by learning without dilution, following by applying a threshold to the model parameters), $L_1$ dilution (which is frequently used in convex optimization) and $L_0$ dilution (which is optimal but computationally hard to implement). Whereas both $L_p$ diluted approaches clearly outperform the naive approach, we find a small region where $L_0$ works almost perfectly and strongly outperforms the simpler to implement $L_1$ dilution.Comment: 18 pages, 9 eps figure

Contamination source inference in water distribution networks

We study the inference of the origin and the pattern of contamination in water distribution networks. We assume a simplified model for the dyanmics of the contamination spread inside a water distribution network, and assume that at some random location a sensor detects the presence of contaminants. We transform the source location problem into an optimization problem by considering discrete times and a binary contaminated/not contaminated state for the nodes of the network. The resulting problem is solved by Mixed Integer Linear Programming. We test our results on random networks as well as in the Modena city network