Globalization and Inequality: Evidence from Within China

In this paper, we provide a case study of the impact of globalization on income inequality using data across Chinese regions. The literature on cross-country studies has been criticized because differences in legal systems and other institutions across countries are difficult to control for, and the inequality data across countries may not be compatible. An in-depth case study of a particular country's experience can provide a useful complement to cross-country regressions. We construct a measure of urban-rural income ratio for 100 or so Chinese cities (urban areas and adjacent rural counties) over the period 1988-1993. The central finding is that cities that experience a greater degree of openness in trade also tend to demonstrate a greater decline in urban-rural income inequality. Thus, globalization has helped to reduce, rather than increase, the urban-rural income inequality. This pattern in the data suggests that inferences based solely on China's national aggregate figures (overall openness and overall inequality) can be misleading. The negative association between openness and inequality holds up when we apply a geography-based instrumental variable approach to correct for possible endogeneity of a region's trade openness.

A single-level random-effects cross-lagged panel model for longitudinal mediation analysis

Cross-lagged panel models (CLPMs) are widely used to test mediation with longitudinal panel data. One major limitation of the CLPMs is that the model effects are assumed to be fixed across individuals. This assumption is likely to be violated (i.e., the model effects are random across individuals) in practice. When this happens, the CLPMs can potentially yield biased parameter estimates and misleading statistical inferences. This article proposes a model named a random-effects cross-lagged panel model (RE-CLPM) to account for random effects in CLPMs. Simulation studies show that the RE-CLPM outperforms the CLPM in recovering the mean indirect and direct effects in a longitudinal mediation analysis when random effects exist in the population. The performance of the RE-CLPM is robust to a certain degree, even when the random effects are not normally distributed. In addition, the RE-CLPM does not produce harmful results when the model effects are in fact fixed in the population. Implications of the simulation studies and potential directions for future research are discussed

Efficient Estimation of Copula-based Semiparametric Markov Models

This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant (one-dimensional marginal) distributions and parametric bivariate copula functions; where the copulas capture temporal dependence and tail dependence of the processes. The Markov processes generated via tail dependent copulas may look highly persistent and are useful for financial and economic applications. We first show that Markov processes generated via Clayton, Gumbel and Student's $t$ copulas and their survival copulas are all geometrically ergodic. We then propose a sieve maximum likelihood estimation (MLE) for the copula parameter, the invariant distribution and the conditional quantiles. We show that the sieve MLEs of any smooth functionals are root-$n$ consistent, asymptotically normal and efficient; and that their sieve likelihood ratio statistics are asymptotically chi-square distributed. We present Monte Carlo studies to compare the finite sample performance of the sieve MLE, the two-step estimator of Chen and Fan (2006), the correctly specified parametric MLE and the incorrectly specified parametric MLE. The simulation results indicate that our sieve MLEs perform very well; having much smaller biases and smaller variances than the two-step estimator for Markov models generated via Clayton, Gumbel and other tail dependent copulas.Copula, Tail dependence, Nonlinear Markov models, Geometric ergodicity, Sieve MLE, Semiparametric efficiency, Sieve likelihood ratio statistics, Value-at-Risk