Estimating household responses to trade reforms : net consumers and net producers in rural Mexico

This paper explores an empirical methodology to assess the impacts of trade reforms on household behavior in developing countries. It focuses on consumption and income responses: when price reforms take place, households modify consumption and production decisions and local labor markets adjust. The paper proposes a joint estimator of demand and wage price elasticities from survey data. The method uses an empirical model of demand to extract price information from unit values, and uses this information to estimate the response of households to price reforms. By correcting unit values for quality effects and measurement error, the method overcomes the problem of the endogeneity of unit values. By endogeneizing household income, the model corrects potential biases in the estimation of own- and cross-price elasticities in consumption. The paper applies the method to an expenditure and income survey for rural Mexico. It shows that the corrections suggested in this paper are empirically important. In particular, it shows that allowing for consumption and income responses is a key element of an accurate empirical assessment of trade policy.Environmental Economics&Policies,Economic Theory&Research,Access to Markets,Markets and Market Access,Inequality

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence