Following its opening to trade and foreign investment in the mid-1980s, Mexico's economic growth has been modest at best, particularly in comparison with that of China. Comparing these countries and reviewing the literature, we conclude that the relation between openness and growth is not a simple one. Using standard trade theory, we find that Mexico has gained from trade, and by some measures, more so than China. We sketch out a theory in which developing countries can grow faster than the United States by reforming. As a country becomes richer, this sort of catch-up becomes more difficult. Absent continuing reforms, Chinese growth is likely to slow down sharply, perhaps leaving China at a level less than Mexico's real GDP per working-age person.

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank’s customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R2’s of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk.