The Effects of Intraregional Disparities on Regional Development in China: Inequality Decomposition and Panel-Data Analysis

This paper analyzes the development and effects of intra-provincial regional disparities in China between 1989 and 2001. A decomposition analysis shows that intraprovincial disparities contribute significantly to total regional inequality. In the second part of the paper, the impact of the observed intraprovincial disparities on regional economic development is addressed. Using provincial panel data on industrial growth, capital and employment, the impact of inequality on industrial growth is estimated as affecting technical efficiency and level of technology. The results show a significant positive effect of intra-provincial disparities on provincial industrial growth, with causality from inequality to growth. Moreover, it appears that the inequality-growth relationship is not a linear one, but rather that the impact of extreme changes in inequality is stronger and more significant than a moderate increase in inequality. These outcomes are robust to alternative model specifications and control variablInequality, Decomposition, Growth, Panel Data, China

ETHNICITY AND CONFLICT: AN EMPIRICAL STUDY

This paper examines the impact of ethnic divisions on conflict. The analysis relies on a theoretical model of conflict (Esteban and Ray, 2010) in which equilibrium conflict is shown to be accurately described by a linear function of just three distributional indices of ethnic diversity: the Gini coefficient, the Hirschman-Herfindahl fractionalization index, and a measure of polarization. Based on a dataset constructed by James Fearon and data from Ethnologue on ethno-linguistic groups and the "linguistic distances" between them, we compute the three distribution indices. Our results show that ethnic polarization is a highly significant correlate of conflict. Fractionalization is also significant in some of the statistical exercises, but the Gini coefficient never is. In particular, inter-group distances computed from language and embodied in polarization measures turn out to be extremely important correlates of ethnic conflict.

Diversity and Equitability Ordering Profiles Applied to the Study of Forest Structure

Diversity and Equitability Ordering Profiles Applied to the Study of Forest Structur

Feasibility of Using Classification Analyses to Determine Tropical Cyclone Rapid Intensification

Tropical cyclone intensity techniques developed by Dvorak have thus far been regarded by tropical meteorologists as the best identification and forecast schemes available using satellite imagery. However, in recent years, several ideologies have arisen which discuss alternative means of determining typhoon rapid intensification or weakening in the Pacific. These theories include examining channel outflow patterns, potential vorticity superposition and anomalies, tropical upper tropospheric trough interactions, environmental influences, and upper tropospheric flow transitions. It is now possible to data mine these atmospheric parameters thought partly responsible for typhoon rapid intensification and weakening to validate their usefulness in the forecast process. Using the latest data mining software tools, this study used components of NOGAPS analyses along with selected atmospheric and climatological predictors in classification analyses to create conditional forecast decision trees. The results of the classification model show an approximate R2 of 0.68 with percent error misclassifications of 13.5% for rapidly weakening typhoon events and 21.8% for rapidly intensifying typhoon events. In addition, a merged set of suggested forecast splitting rules was developed. By using the three most accurate predictors from both intensifying and weakening storms, the results validate the notion that multiple parameters are responsible for rapid changes in typhoon development

Comparing Economic Mobility with Heterogeneity Indices: an Application to Education in Peru

The long literature on intergenerational transmission of well-being has largerly been driven by concerns for inequality of opportunity and the persistence of low levels of wellbeing among certain social groups.A comparative strand of this literature seeks to compare indicators of these transmission mechanisms, i.e. mobility regimes, across societies, regions or time. In this paper I contribute to this literature by suggesting an additional way of comparing mobility regimes with indices of heterogeneity across distributions based on a traditional homogeneity test of multinomial distributions, which is helpful to compare discrete-time transition matrices. The indices measure the degree of dissimilarity between two or more transition matrices controlling for population size and the dimensions of the matrix. The indices provide a good alternative to between-group comparisons based on linear parametric models (chiefly OLS) in which either slope coefficients are compared directly or group dummy variables are interacted with parameters from the models. They also provide complementary information to comparisons based on summary indicators of transition matrices. An application to educational mobility in Peru shows that the transition matrices of males and females are more similar among the youngest cohorts of adults.

The challenge of Inequality

.Poverty, challenge , Inequality

Measuring inequality: Statistical inference theory with applications

In this dissertation we develop statistical inference for the Atkinson index, one of the measures of inequality used in studying economic inequality. Specifically, we construct empirical estimators for the Atkinson index, both in the parametric and nonparametric case, and derive formulas for the asymptotic variances for the estimators. These statistics are used for testing hypothesis and constructing confidence intervals for the Atkinson index. We test the validity and the robustness of the asymptotic theory, by simulations (using R, a language and environment for statistical computing and graphics), in the case of one and two populations. In addition to proving asymptotic normality for the theory, we develop a nonparametric bootstrap theory, as an alternative to the asymptotic theory, and present some of the advantages for this method. It is natural, when studying income inequality, to analyze the distributions of the data sets and make statistical inference about various parameters of interest, such as means, medians, variances, etc. In trying to condense the information into a few parameters, one certainly faces a problem of constructing measures or indices that would give a proper idea about what happens in the society under consideration. The mean, as a statistical measures of distribution is useful in some instances, but not particularly relevant when, for example, we have outhers and/or skewed distributions. In addition, the mean does not tell us if inequality changes by transfers of wealth from the rich to the poor or from the poor to the rich. Hence, the need for constructing measures with various properties like transfer sensitivity, scale and/or location invariance. Indeed, some measures, like the Gini index, are not sensitive to the transfers at the lower and upper ends of the distribution, whereas other measures like the Atkinson index are more sensitive to such transfers. In this dissertation we have chosen to work with the Atkinson index because of its econometric properties described in Chapter 1 and the lack of inferential statistics results in the literature pertaining to this index

Quasi U-statistics of innite order and applications to the subgroup decomposition of some diversity measures

In several applications, information is drawn from quali- tative variables. In such cases, measures of central tendency and dis- persion may be highly inappropriate. Variability for categorical data can be correctly quantied by the so-called diversity measures. These measures can be modied to quantify heterogeneity between groups (or subpopulations). Pinheiro et al. (2005) shows that Hamming distance can be employed in such way and the resulting estimator of hetero- geneity between populations will be asymptotically normal under mild regularity conditions. Pinheiro et al. (2009) proposes a class of weighted U-statistics based on degenerate kernels of degree 2, called quasi U-statistics, with the property of asymptotic normality under suitable conditions. This is generalized to kernels of degree m by Pinheiro et al. (2011). In this work we generalize this class to an innite order degenerate kernel. We then use this powerful tools and the reverse martingale nature of U-statistics to study the asymptotic behavior of a collection of trans- formed classic diversity measures. We are able to estimate them in a common framework instead of the usual individualized estimation procedures. MSC 2000: primary - 62G10; secondary - 62G20, 92D20.In several applications, information is drawn from quali- tative variables. In such cases, measures of central tendency and dis- persion may be highly inappropriate. Variability for categorical data can be correctly quantied by the so-called diversity measures. These measures can be modied to quantify heterogeneity between groups (or subpopulations). Pinheiro et al. (2005) shows that Hamming distance can be employed in such way and the resulting estimator of hetero- geneity between populations will be asymptotically normal under mild regularity conditions. Pinheiro et al. (2009) proposes a class of weighted U-statistics based on degenerate kernels of degree 2, called quasi U-statistics, with the property of asymptotic normality under suitable conditions. This is generalized to kernels of degree m by Pinheiro et al. (2011). In this work we generalize this class to an innite order degenerate kernel. We then use this powerful tools and the reverse martingale nature of U-statistics to study the asymptotic behavior of a collection of trans- formed classic diversity measures. We are able to estimate them in a common framework instead of the usual individualized estimation procedures. MSC 2000: primary - 62G10; secondary - 62G20, 92D20