Wealth Mobility, Statistical Transformation, and the Political Economy of Economic Inequality

Description of Research Project:

I am working on multiple projects investigating two of the most important trends in sociopolitical development since the late 20 th century: the concentration of wealth distribution among the top and the pendulum motion of statistical knowledge between counting and sampling. More specifically, I am interested in two sets of questions: (1) how much of the rising wealth inequality and the stagnating wealth mobility we’ve observed can be attributed to the redistribution of old money, and how much to the distribution of new money? How is the empirical concentration of wealth linked to changes in the perception of inequality and in the social class landscape, if we can appropriately measure them? (2) how does the back-and-forth between counting (as in socialist statistics and in modern machine learning) and sampling (as in probabilistic statistics and ethnographic observations) in statistical knowledge casts a long shadow on sociopolitical development through institutional and cognitive legacies? For both projects, I am interested in the case of US, China, and other post-socialist settings like Russia and Eastern Europe.

Description of Work that will be Assigned:

Since both projects are in their early stage, I hope to work with research assistants in searching for and reviewing academic literature and policy reports. I will work with research assistants to develop literature management skills using Zotero at the beginning of the spring-summer term.

For the wealth project, we will work on the following topics (1) description trend of wealth inequality, mobility, and perception; (2) the link between empirical wealth inequality/mobility and the subjective perception of them; (3) the formation of political support given the matched/unmatched pattern of empirical and subject inequality/mobility, with a special focus onage, period, cohort decomposition and on the mechanism through cultural schema.

For the statistical transformation project, we will work on the following topics (1) the similarity and difference between socialist statistics, frequential statistics, Bayesian statistics, and modern machine learning; (2) the morphological and causal connection between the development of statistics and state building; (3) the cognitive legacies of quantification and statistical presentation and the empirical strategy of measuring them.

Supervising Faculty Member: Fabian Pfeffer

Graduate Student: Junchao Tang

Contact Information: junchaot@umich.edu

Average Hours Per Week: 3 weekly per credit; flexible when students have more coursework

Range of Credits Students can Earn: 1-3

Number of Positions Available: 2