Research Papers

We study how much an ancestor’s social status in 19th-century China shaped the fortunes of descendants. Using a population dataset from Liaoning province (CMGPD-LN), we track families across six generations. We apply Markov chain models and measure mobility in two ways, addressing how long the memory of family origins shapes descendants’ outcomes, and how often sons moved into a different class than their fathers.

Late Qing China is a unique historical period, encompassing eras of relative stability as well as conflict and political crisis, making it a rare test case for whether crises reshape ordinary families’ chances of improving or losing social standing. The CMGPD-LN registry’s six generations let us observe five transition matrices, allowing us to model mobility as a nonhomogeneous Markov chain that evolves over time.

Most of what we know about intergenerational mobility comes from stable, modern economies. Studying how mobility evolves under upheaval, over a long historical horizon, broadens our understanding of mobility itself. Our findings echo existing theories linking declining upward mobility to political instability, leaving open a number of questions about the underlying mechanisms for future research.

We study intergenerational class mobility using synthetic dynasties modeled using Markov chains. This yields measures of movement (class change across generations) and memory (how long class origins persist). Applied to U.S. cohorts born 1945-1990, we find overall stability masking offsetting trends: upper/lower classes show declining movement and rising memory; middle classes show the reverse.

Existing research relies on origin–destination associations from log-linear models, capturing only one mobility dimension with restrictive assumptions. Prior evidence is mixed, limited by small samples or volatile income measures. We develop a dynamic, nonparametric framework using Markov chains that separately identifies movement and memory across generations.

When inequality grows but mobility stays constant, stable mobility may reduce public concern about inequality and weaken redistribution support. Yet it amplifies mobility’s significance: moving up feels more rewarding, moving down carries steeper penalties. These divergent high-stakes experiences can deepen social and political divisions.

We examine intergenerational educational and occupational mobility in China and Russia during their transitions from centrally planned to market economies. We focus on the extent new generations can escape institutional and structural constraints shaping their parents’ outcomes. We estimate overall and steady-state mobility and decompose it to reveal mechanisms driving intergenerational change.

Analysis of intergenerational mobility in China and Russia must account for the fact that observed mobility arises within an evolving dynamic system. We offer the first attempt to measure intergenerational mobility in these transition economies, isolating the transmission of educational and occupational status between parents and children from the direct effects of exogenous structural change.

We offer a novel perspective on intergenerational mobility in transition economies. Our measures address limits of traditional approaches that overstate mobility in contexts like China and Russia. By isolating transmission of social status from structural changes, this method allows more accurate assessments and enhances comparability of mobility across countries.

We conducted the first systematic empirical analysis of income inequality across Chinese provinces using CHFS data and a semiparametric model to estimate Gini indices from 2012 to 2018, identifying human capital differences as key drivers.

We aimed to understand intraprovincial inequality, which accounts for 90% of national income variation and contributes to economic disparity, slower growth, and potential instability.

Our findings suggest that government policies promoting educational development in disadvantaged provinces and reducing barriers to labor mobility can facilitate labor market integration, narrow income gaps, and support sustainable and balanced regional economic development in China.

I estimate income inequality in China from 2012–2018 using new methods that adjust for sparse top-income data in the China Household Finance Survey. I find Gini index estimates of 0.616 (2012), 0.604 (2014), 0.581 (2016), and 0.590 (2018). I also document rural-urban, regional, and provincial inequality patterns in Shanghai, Guangdong, Liaoning, Henan, and Gansu.

Academic and policy discussions are complicated by a lack of consensus on income inequality in China. The existing discrepancies result from the data and methodological issues. In response, I provide estimates of income inequality levels in China using a dataset designed to oversample rich households, and by developing new robust measurement procedures applicable to the datasets with extreme high-income values.

Most researchers share the view that existing high levels of income inequality pose significant warning signs for future economic progress and remain an important challenge for Chinese society and its policymakers. New empirical evidence on the levels and trends of income inequality in China can help inform the design of effective policy interventions.