Accounting for dynamic selection bias in educational transitions: The Cameron-Heckman latent class estimator and its generalizations

The sequential logit model of educational transitions has long been the dominant modeling framework for the study of inequality of educational opportunity ever since the seminal works of [Mare, 1980] and [Mare, 1981]. But conventional applications of the model are known to be biased by the ubiquitous presence of unobserved heterogeneity. Cameron and Heckman (1998) propose a logit model that allows for two or three latent classes if the selection bias is solely generated by a person-specific component of stable unobserved heterogeneity. To evaluate the latent class logit regression estimator, this study makes use of simulated data to eliminate the influences of other problems of transition modeling. The simulation is based on five independent pairs of large samples generated from standard distributional assumptions of transition modeling. The new estimator appears to be an effective way to adjust for dynamic selection bias when family background effects are transition-invariant and sample size is in the order of ten thousand or above. By contrast, the conventional sequential logit model produces results that are very different from the data generating models. This study also considers two alternative ways to improve statistical efficiency: (1) incorporate a crude indicator of stable unobserved heterogeneity; (2) pool the effect estimates across transitions, background variables, and alternative estimators to smooth out noise under the null hypothesis of transition invariance. In addition, this study examines the impact of indicator reliability and sample size on the performance of the latent class regression models and suggests practical guidelines.     

农村大龄未婚男性的婚姻困境:基于性别不平等视角的认识

大龄未婚男性的婚姻困境是在中国农村普婚制社会下长期存在的现象。文章利用全国性的专项调查、主流媒体报道和实地调查数据,从婚姻市场上的性别不平等视角来研究农村大龄男性的婚姻困境。宏观数据分析显示,用性别结构失衡来解释农村大龄男性的婚姻剥夺显得原发性因果关系不强和实证性不足,而简单地以贫困概括农村大龄男性婚姻困境的主因并没有充分考虑婚姻的本质和功能。基于性别不平等的理论视角和实证研究,认为婚姻市场上性别不平等对贫困男性婚姻困境的影响远比人口性别结构失衡的影响更为直接和重要,农村大龄男性的婚姻困境具有独立于个体内因的社会经济结构性的成因。     

Household wealth and the measurement of economic well-being in the United States

We analyze the level and distribution of economic well-being in the United States during the 1980s and 1990s based on the standard measure of money income and a measure in which income from wealth is calculated as the sum of lifetime annuity from nonhome wealth and imputed rental-equivalent for owner-occupied homes. Over the 1982–2000 period, median well-being increases faster when these adjustments are made than when standard money income is used. This adjustment also widens the income gap between African-Americans and whites but increases the relative well-being of the elderly. Adding imputed rent and annuities from household wealth to household income considerably increases measured inequality and the share of income from wealth in inequality. However, both measures show about the same rise in inequality over the period. We also find an increasing share of wage and salary income in our expanded definition of income among the richest 1% over the period but do not find that the “working rich” have largely replaced rentiers at the top of the economic ladder.      

U-Statistics and Their Asymptotic Results for Some Inequality and Poverty Measures

U-statistics form a general class of statistics that have certain important features in common. This class arises as a generalization of the sample mean and the sample variance, and typically members of the class are asymptotically normal with good consistency properties. The class encompasses some widely used income inequality and poverty measures, in particular the variance, the Gini index, the poverty rate, the average poverty gap ratios, the Foster-Greer-Thorbecke index, and the Sen index and its modified form. This paper illustrates how these measures come together within the class of U-statistics, and thereby why U-statistics are useful in econometrics.     

Nonparametric regression for functional data: Automatic smoothing parameter selection

We study regression estimation when the explanatory variable is functional. Nonparametric estimates of the regression operator have been recently introduced. They depend on a smoothing factor which controls its behavior, and the aim of our work is to construct some data-driven criterion for choosing this smoothing parameter. The criterion can be formulated in terms of a functional version of cross-validation ideas. Under mild assumptions on the unknown regression operator, it is seen that this rule is asymptotically optimal. As by-products of this result, we state some asymptotic equivalences for several measures of accuracy for nonparametric estimate of the regression operator. We also present general inequalities for bounding moments of random sums involving functional variables. Finally, a short simulation study is carried out to illustrate the behavior of our method for finite samples.     

Knowledge and beliefs about national development and developmental hierarchies: The viewpoints of ordinary people in thirteen countries

Scholars and policy makers have for centuries constructed and used developmental hierarchies to characterize different countries. The hypotheses motivating this paper are that such social constructions have been circulated internationally, are constructed similarly in various countries, and follow the social constructions of elite international organizations, such as the United Nations. This paper uses data from 15 surveys in 13 diverse countries to study how developmental hierarchies are understood in everyday life. Our research shows that most people have constructions of developmental hierarchies that are similar across countries and are similar to the developmental hierarchies constructed by the United Nations. These findings suggest that developmental hierarchies are widely understood around the world and are widely available to ordinary people as they make decisions about many aspects of life.     

Does globalization drive long-run inequality within OECD countries? A guide to policy making

We analyse around four decades of annual time-series data revisiting the long-run relationship between globalization and income inequality for 24 OECD member countries across different geographical regions, applying the Yamamoto-Kurozumi multivariate vector autoregression (VAR) framework. We observe that rapid globalization is not the key cause of rising long-run intra country inequality. This result is obtained by controlling for growth, terms of trade, minimum wage legislation, and unionization and found robust by further controlling education. Most of the countries in our study with a long-run relationship reveal the robust reverse causal impact of rising globalization on reducing inequality. Our impulse response breakdown across various sub-components of globalization suggests that economic globalization is not a primary contributor to long-run inequality for developed industrialized countries. Our framework guides future research to concentrate more on country-specific relationships, with policy guidance tailored for each country based on their level of economic development and institutional quality.