Standard economic theory predicts that if property rights to pollute are clearly established, equilibrium outcomes in an efficient emissions permit market will be independent of how the emissions permits are initially distributed. This so-called independence property has important implications for policy design and implementation. Past studies document a strong positive correlation between the initial permit allocation and firm-level emissions, raising concerns that the independence property is failing to hold in real-world settings. We exploit the random assignment of firms to different permit allocation cycles in Southern California's RECLAIM program in order to test the independence of permit allocation and emissions. Our results lend empirical support to the independence hypothesis.

We employ a new method to estimate China's income distribution using publicly available interval summary statistics. We examine rural, urban, and overall income distributions from 1985 to 2001. We show how the distributions change directly, and we examine trends in inequality. Using an intertemporal decomposition of aggregate inequality, we determine that increases in inequality within rural and urban sectors and the growing rural-urban income gap have been equally responsible for the growth in overall inequality over the last two decades. However, the rural-urban gap has played an increasingly important role in recent years. We also show that the urban consumption inequality rose considerably.

We use a nonlinear, nonparametric method to forecast unemployment rates. This method is an extension of the nearest-neighbor method but uses a higher-dimensional simplex approach. We compare these forecasts with several linear and nonlinear parametric methods based on the work of Montgomery et al. (1998) and Carruth et al. (1998). Our main result is that, due to the nonlinearity in the data-generating process, the nonparametric method outperforms many other well-known models, even when these models use more information. This result holds for forecasts based on quarterly and on monthly data.

A new information-based approach for estimating systems of many equations with nonnegativity constraints is presented. This approach, called generalized maximum entropy (GME), is more practical and efficient than traditional maximum-likelihood methods. The GME method is used to estimate an almost ideal demand system for five types of meat using cross-sectional data from Mexico, where most households did not buy at least one type of meat during the survey week. The system of demands is shown to vary across demographic groups.