The Distributional Impact of the Sharing Economy on the Housing Market

Revise and Resubmit, American Economic Review, 2022.

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What is the impact of the sharing economy, pioneered by companies such as Airbnb, on the housing market? In this paper, I estimate the welfare and distributional impact of Airbnb on the residents of New York City. I develop a model of an integrated housing market, where a landlord can offer a housing unit for rent on either the traditional long-term rental market or the newly available short-term rental market. By estimating a structural model of residential choice and linking it to detailed Airbnb usage data, I estimate the effect of such reallocation on the equilibrium rent across different housing types and demographic groups. In addition, to evaluate the gains from direct home-sharing, I estimate a supply system featuring heterogeneous costs. Overall, renters in New York City suffer a loss of $178mm per annum, as the losses from the rent channel dominate the gains from the host channel. I find that the increased rent burden falls most heavily on high-income, educated, and white renters, because they prefer housing and location amenities most desirable to tourists. Moreover, there is a divergence between the median and the tail, where a few enterprising low-income households obtain substantial gains from home-sharing, especially during demand peaks. Thus, this paper delivers a more nuanced characterization of the winners and losers of the sharing economy, and provides a framework for understanding the consequences of regulating such technological innovations.

Coordinated vs Efficient Prices: The Impact of Algorithmic Pricing on Multifamily Rental Markets

(with Gi Heung Kim), 2023.

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Algorithmic pricing can improve efficiency by helping firms set prices more responsive to changing market conditions. However, widespread adoption of the same algorithm could also lead to price coordination, resulting in elevated prices. In this paper, we examine the impact of algorithmic pricing on the U.S. multifamily rental housing market using hand-collected adoption decisions of property management companies merged with the data of market-rate multifamily apartments from 2005 to 2019. First, our findings suggest that algorithm adoption indeed helps building managers set more responsive prices: buildings with the software increase prices during booms and lower prices during busts, compared to non-adopters in the same market. Second, when compared across markets, we find markets with greater algorithm penetration also experienced higher rents and lower occupancy in the post-crisis period. Such empirical patterns are consistent with either price coordination through the algorithm or widespread pricing error among non-adopters. Lastly, we estimate a structural model of housing demand and perform a test of conduct to evaluate the "algorithmic coordination" hypothesis.

Diversity and Performance in Entrepreneurial Teams

(with Paul Gompers, Kanyuan Kevin Huang), NBER Working Paper, 2021.

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We study the role of diversity on the performance of entrepreneurial teams by exploiting a unique experimental setting of over 3,000 MBA students who participated in a business course to build startups. First, we quantify the strong selection based upon shared attributes when students are allowed to choose teammates. Team formation based upon shared endowed demographic characteristics such as gender, race, and ethnicity is stronger than team formation based upon acquired characteristics such as education and industry background. Second, when team memberships are randomly assigned, greater racial/ethnic diversity leads to significantly worse performance. Interestingly, the negative performance effect of diversity is partially alleviated in cohorts where teams are formed voluntarily. Finally, we find that teams with more female members performed substantially better when their faculty section leader was female. These findings suggest that policy interventions targeting greater diversity should consider match-specific qualities in forming teams to prevent the potential negative impact of diversity. Our results on vertical diversity suggest that capital allocators could also play an important role in the mentoring and advising of minority entrepreneurs.

Venture Capital's Me Too Moment

(with Paul Gompers, Patrick Sweeney) NBER Working Paper, 2021.

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In this paper, we document the historically low rate of hiring of women in the venture capital sector. We find that the high-profile Ellen Pao v. Kleiner Perkins gender discrimination trial had dramatic treatment effects. In difference-in-differences regressions, we find that the rate of hiring of female venture capitalists increased substantially after the trial and that the hiring was more pronounced in states that were more receptive to the exposure. We use the state-level mandated maternity benefits as an instrument for the receptivity to the treatment effects of the Pao Trial. We also show that the fraction of founders who are female increases after the Pao Trial, but that the increase is driven entirely by the hiring of female venture capitalists. There is no increase in the propensity of male venture capitalists to invest in female founders in the post-Pao Trial period.

Diversity in Innovation

(with Paul Gompers) NBER Working Paper, 2017.

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In this paper we document the patterns of labor market participation by women and ethnic minorities in venture capital firms and as founders of venture capital-backed startups. We show that from 1990-2016 women have been less than 10% of the entrepreneurial and venture capital labor pool, Hispanics have been around 2%, and African Americans have been less than 1%. This is despite the fact that all three groups have much higher representation in education programs that lead to careers in these sectors as well as having higher representation in other highly-compensated professions. Asians, on the other hand, have much higher representation in the venture capital and entrepreneurial sector than their overall percentages in the labor force. We explore potential supply side explanations including both education attainment as well as relevant prior job experience. We also explore the correlation between diversity and state-level variations. Finally, we discuss how these patterns are consistent with homophily-based hiring and homophily-induced information flows about career choices. We end the paper by discussing areas for future research.

Pricing Neighborhood Amenities: A Proxy-Based Approach

(with Alex Bell, and Shusheng Zhong), Working Paper, 2023.

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Understanding how housing markets price neighborhood amenities is key to unpacking residential socioeconomic disparities. Yet prior approaches to amenity pricing have suffered from the key confounder of unmeasured neighborhood quality, often leading to wrong-signed estimates. In this paper, we develop a novel proxy-based method that allows us to estimate the price of housing amenities in the midst of unobserved housing quality. Our method shows, as long as one can find at least one "proxy" relevant variable for housing quality that satisfies conditional independence, the price of amenity can be identified as the ratio of two coefficients. Using detailed migration data, we construct an innovative measure of locational desirability–Geographic PageRank–to use as the proxy variable for quality. We show that this new approach can successfully correct the “wrong-signed” problem in the amenities valuation literature when applied to a standard measure of environmental air quality. The estimated amenity prices are key inputs to evaluating returns on investment in local public goods or environmental policies, including their role in reducing housing disparities.

Unobserved Heterogeneity, State Dependence, and Health Plan Choices

(with Ariel Pakes, Jack Porter, and Mark Shepard), Reject and Resubmit, American Economic Review, 2021.

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In this paper, we propose a new approach for the structural estimation of switching costs in a panel data discrete choice model with individual choice-specific fixed effects, by leveraging the recent developments in moment inequality methods. We achieve this by comparing the choice probabilities of the same individual over two different points in time. With non-parametric i.i.d. distribution of the error terms, we develop a set estimator for the switching cost by constructing a set of improving choices over time. Next, when allowing for generic parametric assumptions on the error term, we develop conditional moment inequalities by making inference on the revealed preferences of a pair of choices. We show that in the CommCare Health Plan Choice context, they provide particularly informative upper bounds on the switching cost, rejecting a simplistic model when unobserved heterogeneity is omitted. In addition, if the errors are assumed to be logit, we make further improvements as the ratio of odds-ratio is independent of individual heterogeneity. Overall, these new sets of inequalities provide an intuitive and easy-to-implement test to assess the robustness of state-dependence models when concerns about unobserved heterogeneity loom significant.

The Value of Information: Why You Should Add the Second Order Conditions

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When conducting estimation based on agent optimization, I show that one can improve the performance of the estimator when information such as the second-order condition is incorporated as moment inequality restrictions, especially when there are weak instruments. I run a simulation study to demonstrate the effectiveness of this approach in both continuous and discrete choice problems.

And the Children Shall Lead: Gender Diversity and Performance in Venture Capital

(with Paul Gompers)   Journal of Financial Economics, 2021.

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With an overall lack of gender and ethnic diversity in the innovation sector, we ask the next question: Does increased diversity lead to better firm performances? In this paper, we answer this question using a unique dataset of the gender of venture capital partners’ children. First, we find strong evidence that parenting more daughters leads to an increased propensity to hire female partners by venture capital firms. Second, using an instrumental variable set-up, we show that improved gender diversity, induced by parenting more daughters, improves deal and fund performances. These effects concentrate overwhelmingly on the daughters of senior partners than junior partners. Taken together, our findings have profound implications on how the capital markets could function better with improved diversity.

A Set-Valued Approach to Utility Maximization under Transaction Cost

(with Andreas H. Hamel)  Decision in Economics and Finance 2017, (1-19).

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A set optimization approach to multi-utility maximization is presented, and duality results are obtained for discrete market models with proportional transaction costs. The novel approach allows us to obtain results for non-complete preferences, where the derived formulae closely resemble but generalize the scalar case.

Diversity in Venture Capital

(with Paul Gompers, Kanyuan Huang, and William Levinson) The Palgrave Encyclopedia of Private Equity, 2023.

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This review article first highlights some key statistics on diversity in venture capital. In particular, it establishes that the fraction of women in VC and entrepreneurship has remained quite low throughout the past three decades. Even as of 2019, fewer than 15% of new entrants into venture capital and entrepreneurship were women. In addition, it also documents that the racial/ethnic composition of this population is highly skewed toward Whites and Asian Americans, with only about 10% of them being under-represented minorities. This article also highlights that the performance implications of diversity in venture capital are likely context-dependent. Diversity improvements due to reduced structural frictions are likely performance-enhancing. In contrast, diversity achieved at the expense of sacrificing team-specific match quality degrades entrepreneurial performance. Such findings also suggest that policy interventions on the issue of diversity in venture capital, and in corporate settings at large, should be careful about how to design the methods through which diversity is achieved.