Water stress, Water Transfer and Social Equity in Northern China: Implications for Policy Reforms

human development, water, sanitation

Rural Income Volatility and Inequality in China

Available data indicates a growing urban-rural income gap (the ratio of mean urban to rural incomes) with a significant increase from around 1.8 in the late 1980's to over 3 today. These estimates do not take into account the higher volatility of rural incomes in China. Current literature based on analyses of rural income volatility in China decomposes poverty into chronic and transient components using longitudinal survey data and assesses the fraction of the Foster, Greer and Thorbecke poverty gap attributable to mean income over time being below the poverty line. Resulting estimates of 40-50 % transient poverty point to the policy conclusion that poverty may be a less serious social problem than it appears in annual data due to rural income volatility. Here we use a direct method instead to adjust rural income for volatility using a certainty equivalent income measure and recompute summary statistics for the distribution of volatility corrected incomes, including the urban-rural income gap on which much of current poverty debate in China focuses. Since an uncertain income stream is worth less in utility terms than a certain income stream we argue that heightened rural volatility increases the effective urban-rural income gap and intensifies not weakens poverty concerns. Using Chinese longitudinal rural survey data for which current decompositions can be replicated, we make adjustments for certainty equivalence of rural household income streams which not only widen the urban-rural income gap in China but also increases other distributional summary statistics. Depending upon values used for the coefficient of relative risk aversion, the measured urban-rural income gap increases by 20-30% using a certainty equivalent measure to adjust rural incomes for volatility. We also conduct similar analyses using consumption data, for which slightly larger increases occur.

Information-Theoretic Distribution Test with Application to Normality

We derive general distribution tests based on the method of Maximum Entropy density. The proposed tests are derived from maximizing the differential entropy subject to moment constraints. By exploiting the equivalence between the Maximum Entropy and Maximum Likelihood estimates of the general exponential family, we can use the conventional Likelihood Ratio, Wald and Lagrange Multiplier testing principles in the maximum entropy framework. In particular, we use the Lagrange Multiplier method to derive tests for normality and their asymptotic properties. Monte Carlo evidence suggests that the proposed tests have desirable small sample properties and often outperform commonly used tests such as the Jarque-Bera test and the Komogorov-Smirnov-Lillie test for normality. We show that the proposed tests can be extended to tests based on regression residuals and non-iid data in a straightforward manner. We apply the proposed tests to the residuals from a stochastic production frontier model and reject the normality hypothesis.

Partially Adaptive Estimation via Maximum Entropy Densities

We propose a partially adaptive estimator based on information theoretic maximum entropy estimates of the error distribution. The maximum entropy (maxent) densities have simple yet flexible functional forms to nest most of the mathematical distributions. Unlike the nonparametric fully adaptive estimators, our parametric estimators do not involve choosing a bandwidth or trimming, and only require estimating a small number of nuisance parameters, which is desirable when the sample size is small. Monte Carlo simulations suggest that the proposed estimators fare well with non-normal error distributions. When the errors are normal, the efficiency loss due to redundant nuisance parameters is negligible as the proposed error densities nest the normal. The proposed partially adaptive estimator compares favorably with existing methods, especially when the sample size is small. We apply the estimator to a bio-pharmaceutical example and a stochastic frontier model.

China's Income Distribution and Inequality

We use a new method to estimate China’s income distributions based on publicly available interval summary statistics from China’s largest national household survey. We examine rural, urban, and overall income distributions for each year from 1985-2001. By estimating the entire distributions, we can show how the distributions change directly as well as examine trends in traditional welfare indices such as the Gini. We find that inequality has increased substantially in both rural and urban areas. Using an inter-temporal decomposition of aggregate inequality, we determine that increases in inequality within the rural and urban sectors and the growing gap in rural and urban incomes have been equally responsible for the growth in overall inequality over the last two decades. However, the rural-urban income gap has played an increasingly important role in recent years. In contrast, only the growth of inequality within rural and urban areas is responsible for the increase in inequality in the United States, where the overall inequality is close to that of China. As a robustness check, we show that consumption inequality (which may be a proxy for permanent income inequality) in urban areas also rose considerablyincome distribution, inequality, maximum entropy

CHINA'S INCOME DISTRIBUTION OVER TIME: REASONS FOR RISING INEQUALITY

We estimate China's rural, urban and overall income distributions using grouped data from 1985-2001. We show how the distributions evolve as well as examine trends in welfare indices. We find the growing rural-urban income gap and increases in inequality within either sector have been equally responsible for overall inequality growth.Consumer/Household Economics,