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I am a PhD student in Economics at Princeton University. I work in Empirical Industrial Organization, applied to market design and spatial settings.

Working Papers

(Click on paper descriptions to see abstracts)

Urban Economics

Market design, subsidies and supply: Interventions for efficient and equitable public housing
  • Joint work with Andrew Ferdowsian and Luther Yap.
  • Compares market design interventions, subsidies and supply expansion in the market for public housing in Singapore.
  • Chinese title: 机制设计、津贴与供应:公共住宅的最优分配
  • Presented at (by self or coauthor):
    • 15th North American Meeting, Urban Economics Association (2021); Summer School in Urban Economics, Urban Economics Association (2021); Young Economist Symposium (2021); 9th Warwick Economics PhD Conference (2021)

We study how public housing can be allocated more efficiently and equitably, comparing market design interventions to subsidies and changing the mix of apartments available. To this end, we combine tools from Urban Economics and Industrial Organization to formulate a dynamic choice model over housing lotteries. Our model is estimated on novel public housing data from the Singaporean mechanism, in which bigger apartments are sold at larger markdowns from their resale price to ensure affordability. We find that each rich household receives an average of 1.5 times the amount of subsidy that poor households receive, precisely because they opt for these larger apartments. Then, we simulate outcomes for households applying for apartments under various counterfactual mechanisms. We find that: first, if the social planner does not expand supply, it is difficult to reduce wait times in this mechanism. Second, to raise match rates for the poor, the government should subsidize them and increase prices on the largest apartments; in this policy regime, subsidies are redistributed 1-for-1 from the rich to the poor. Third, by prioritizing the poor, the government can improve their “match quality” without worsening that of the rich. Finally, building more, smaller apartments (in lieu of larger ones) results in congestion, raising wait times for the rich and lowering match rates for the poor.

Urban transit infrastructure and inequality: The role of access to non-tradable goods and services
  • Joint work with Brandon Joel Tan.
  • Presented at (by self or coauthor):
    • 15th North American Meeting, Urban Economics Association (2021); Urban Economics Workshop, NBER Summer Institute (2021); 10th European Meeting of the Urban Economics Association (2021)

With 68% of the world population projected to live in urban areas by 2050, mass transit networks are expanding faster than ever before. But how are the economic gains from such expansions being shared between low- and high-income workers? Existing research focuses on the role of commuting to work, however much of urban travel is related to the consumption of non-tradable goods and services (retail, F&B, personal services etc.). Since low-income workers are overwhelmingly employed in these non-tradable sectors, changes in consumption travel patterns in response to a transit expansion leads to a spatial re-organization of low-income jobs in the city which has important implications for inequality. This paper develops an urban spatial model with heterogeneous worker groups and incorporating travel to consume non-tradable goods and services. We estimate our model using detailed farecard and administrative data from Singapore to quantify the impact of the Downtown Line (DTL). We find large welfare gains for high-income workers, but near zero gains for low-income workers. All workers benefit from improved access to consumption opportunities, but low-income non-tradable sector jobs move to less attractive workplaces. Abstracting away from consumption travel results in a five-fold underestimation of the inequality effects and failure to capture the spatial re-organization of low-income jobs in the city.

Principal responsiveness in centralized mechanisms: Build to order
  • Joint work with Andrew Ferdowsian and Luther Yap.
  • Presented at (by self or coauthor):
    • 32nd Stony Brook International Conference on Game Theory (2021)

How should public housing be optimally allocated? Standard mechanisms treat the supply of goods in a matching problem as exogenous. However, in practice, designers can often control the inflow of goods as well. For instance, the Singaporean mechanism utilizes household applications to bias apartment supply. Taking a dynamic mechanism design approach, we show that endogenous supply radically changes the nature of the optimal mechanism. By endogenizing supply, we find that: (1) Inefficiency is substantially lowered relative to a setting with exogenous supply. (2) Whereas competition impedes incentive constraints in a setting with exogenous supply, it instead improves matching through weaker incentive constraints when supply is endogenous. (3) Increasing the thickness of the market through batching applications reduces welfare loss. (4) Optimal mechanisms with endogenous supply produce undesirable housing with probability beyond their share of the market to incentivize truthful reporting.

Economics of Digitization

Entry into two-sided markets shaped by platform-guided search
  • Joint work with Leon Musolff.
  • Previously circulated as “The firm as market designer: Evidence from Amazon”
  • Accepted at the 18th International Industrial Organization Conference (2020)

We evaluate the problem of firms that operate platforms matching buyers and sellers, while also selling goods on these same platforms. By being able to guide consumer search through algorithmic recommendations, these firms can influence market outcomes, a finding that has worried regulators. To analyze this phenomenon, we combine rich novel data about sales and recommendations on Amazon Marketplace with a structural model of intermediation power. In contrast to prior literature, we explicitly model seller entry. This feature enables us to assess the most plausible theory of harm from self-preferencing, i.e. that it is a barrier to entry. We find that recommendations are highly price elastic but favor Amazon. A substantial fraction of customers only consider recommended offers, and recommendations hence noticeably raise the price elasticity of demand. By preferring Amazon’s offer, the recommendation algorithm raises consumer welfare by approximately US$4.5 billion (since consumers also prefer these offers). However, consumers are made worse off if self-preferencing makes the company raise prices by more than 7.8%. Furthermore, we find no evidence of consumer harm from self-preferencing through the entry channel. Nevertheless, entry matters. The algorithm raises consumer welfare in the short and medium run by increasing the purchase rate and intensifying price competition. However, these gains are mostly offset by reduced entry in the long run.

Work in Progress

Urban Economics

Figures from my research

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