Weihuang Wong

I am a data scientist at NORC at the University of Chicago, where I work on social research in the education and public sectors. I earned my Ph.D. at MIT, where my research focused on political behavior as it relates to housing markets.


Our Town: Support for Housing Growth When Localism Meets Liberalism

Community resistance to new housing is a cause of housing supply shortfalls in cities around the United States. Citizens differ on the desirable rate and nature of housing growth. I argue that two types of political beliefs shape support for housing growth. Localism is the belief that the interests of established members of the local community should be privileged relative to those of newcomers and outsiders. Liberalism is the belief that economic outcomes should be equitable. I draw on an original survey experiment and rich observational data on land use ballot measures in San Francisco to show that liberals' support for housing growth is moderated by the type of housing being produced. Localism, on the other hand, is negatively associated with support for housing growth, regardless of the type of development being proposed.

Working Paper

The American Dream and Support for the Social Safety Net

I propose the status quo bias hypothesis, which predicts that housing wealth increases preference for status quo arrangements with respect to Social Security. I contrast the status quo bias hypothesis with the claim that housing wealth reduces support for social insurance, and test the hypothesis using an original survey experiment and observational data from the 2000-2004 ANES panel. The evidence suggests that housing wealth's conservatizing effect should be interpreted as a status quo preference, rather than opposition to redistributive social policies.

Working Paper

Trade Liberalization and Regime Type: Evidence from a New Tariff-line Dataset

with In Song Kim and Soubhik Barari

How does democracy shape trade policies? We collect 5.7 billion observations of applied tariff rates that 136 countries apply to their trading partners. We then develop a Bayesian multilevel estimator that distinguishes the effects of regime type across industries and trading partners. We find that democracies tend to have lower trade barriers than non-democracies but are more likely to protect their agricultural sectors. Pairs of democracies achieve greater tariff reductions than dyads with a democracy and a non-democracy because of shallower concessions granted by non-democratic importers to their democratic partners

Working Paper


mediation: R package for causal mediation analysis

Enhancements to the mediation package (Imai, Hirose, Keele, Tingley, and Yamamoto 2017). I implement parallelization for resampling-based uncertainty estimates and a two-stage least squares method for estimating causal mediation effects.

Github repo

multitestr: R package for multiple hypothesis testing

with F. Daniel Hidalgo

An implementation of Westfall and Young's (1993) free step-down method to adjust p-values in resampling-based multiple testing.

Github repo (requires MIT Touchstone authentication)

I'm a senior data scientist at NORC at the University of Chicago. I earned my Ph.D. in the Department of Political Science at MIT, where I was a member of the Political Methodology Lab. Prior to MIT, I worked in the Credit Markets Group at GIC. I earned a B.A. in Political Science and Economics from Stanford University.

Curriculum Vitae
Twitter: @weihuang
Github: weihuangwong
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