Education and Earnings for Poverty Reduction : Short-Term Evidence of Pro-Poor Growth from the Mexican Oportunidades Program

Education, as an indispensable component of human capital, has been acknowledged to play a critical role in economic growth, which is theoretically elaborated by human capital theory and empirically confirmed by evidence from different parts of the world. The educational impact on growth is especially valuable and meaningful when it is for the sake of poverty reduction and pro-poorness of growth. The paper re-explores the precious link between human capital development and poverty reduction by investigating the causal effect of education accumulation on earnings enhancement for anti-poverty and pro-poor growth. The analysis takes the evidence from a well-known conditional cash transfer (CCT) program — Oportunidades in Mexico. Aiming at alleviating poverty and promoting a better future by investing in human capital for children and youth in poverty, this CCT program has been recognized producing significant outcomes. The study investigates a short-term impact of education on earnings of the economically disadvantaged youth, taking the data of both the program’s treated and untreated youth from urban areas in Mexico from 2002 to 2004. Two econometric techniques, i.e. difference-in-differences and difference-in-differences propensity score matching approach are applied for estimation. The empirical analysis first identifies that youth who under the program’s schooling intervention possess an advantage in educational attainment over their non-intervention peers; with this identification of education discrepancy as a prerequisite, further results then present that earnings of the education advantaged youth increase at a higher rate about 20 percent than earnings of their education disadvantaged peers over the two years. This result indicates a confirmation that education accumulation for the economically disadvantaged young has a positive impact on their earnings enhancement and thus inferring a contribution to poverty reduction and pro-poorness of growth.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.