Progressive governments in poor countries fear that if they undertake measures to enhance real wages and working conditions, rising labor costs would cause wealthier countries to import from and invest elsewhere. Yet if the world trading system were designed to facilitate or even reward measures to promote labor standards, poor countries could undertake them without fear. In this book, Christian Barry and Sanjay G. Reddy propose ways in which the international trading system can support poor countries in promoting the well-being of their peoples. Reforms to the trading system can lessen the collective-action problem among poor countries, increasing their freedom to pursue policy that better serves the interests of their people. Incorporating the right kind of linkage between trading opportunities and the promotion of labor standards could empower countries, allowing them greater effective sovereignty and enabling them to improve the circumstances of the less advantaged. Barry and Reddy demonstrate how linkage can be made acceptable to all players, and they carefully defend these ideas against those who might initially disagree. Their volume is accessible to general readers but draws on sophisticated economic and philosophical arguments and includes responses from leading labor activists, economists, and philosophers, including Kyle Bagwell, Robert Goodin, Rohini Hensman, and Roberto Mangabeira Unger.
We analyze the performance of kernel density methods applied to grouped data to estimate poverty (as applied in Sala-i-Martin, 2006, QJE). Using Monte Carlo simulations and household surveys, we find that the technique gives rise to biases in poverty estimates, the sign and magnitude of which vary with the bandwidth, the kernel, the number of datapoints, and across poverty lines. Depending on the chosen bandwidth, the $1/day poverty rate in 2000 varies by a factor of 1.8, while the $2/day headcount in 2000 varies by 287 million people. Our findings challenge the validity and robustness of poverty estimates derived through kernel density estimation on grouped data.
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