Publications

This paper studies an emerging subscription model called ship-then-shop. Leverag- ing its predictive analytics and artificial intelligence (AI) capability, the ship-then-shop firm curates and ships a product to the consumer, after which the consumer shops (i.e., evaluates product fit and makes a purchase decision). The consumer first pays the up-front ship-then- shop subscription fee prior to observing product fit and then pays the product price afterward if the consumer decides to purchase. We investigate how the firm balances the subscription fee and product price to maximize its profit when consumers can showroom. A key finding is the ship-then-shop firm’s nonmonotonic surplus extraction strategy with respect to its prediction capability. As prediction capability increases, the firm first switches from ex ante to ex post sur- plus extraction (by lowering fees and raising prices). However, if the prediction capability increases further, the firm reverts to ex ante surplus extraction (by raising fees and capping prices). We also find that the ship-then-shop model is most profitable when (i) the prediction capability is advanced, (ii) the search friction in the market is large, or (iii) the product match potential is large. Finally, we show that the marginal return of AI capability on the firm’s profit decreases in search friction but increases in product match potential. Taken together, we pro- vide managerially relevant insights to help guide the implementation of the innovative sub- scription model.

A signal is privacy-preserving with respect to a collection of privacy sets, if the posterior probability assigned to every privacy set remains unchanged conditional on any signal realization. We characterize the privacy-preserving signals for arbitrary state space and arbitrary privacy sets. A signal is privacy-preserving if and only if it is a garbling of a reordered quantile signal. These signals are equivalent to couplings, which in turn lead to a characterization of optimal privacy-preserving signals for a decision- maker. We demonstrate the applications of this characterization in the contexts of algorithmic fairness, price discrimination, and information design.

We examine property tax reduction as a tool for increasing housing affordability. Analyzing various tax reduction policies through the lens of property tax incidence reveals a complex relationship between affordability and property taxes, with differential effects across demographic groups. Many policies often fail to improve affordability for young first-time homebuyers and renters, sometimes worsening affordability. We present a new nationwide atlas documenting the prevalence of local measures altering property tax burdens. Quasi-experimental evidence from Georgia's homestead exemption valuation freezes suggests strong capitalization of assessment limits into home values, reinforcing that property tax relief may worsen affordability for first-time buyers.

Sharing about charity online or in personal conversations can help raise awareness and bolster fundraising efforts for good causes. However, when deciding whether to tell others about their charitable giving, donors may focus more on possible risks to their reputation (e.g., of seeming braggy, inauthentic) than on potential word-of-mouth benefits for the charity. In a large, preregistered field experiment, we tested a post-donation intervention designed to encourage word-of-mouth by reorienting donors to the idea that sharing about charity means doing more good; 77,485 donors received either a control or treatment message asking them to share a link to the cause via social media, text, or email. Compared with the organization’s standard solicitation (“Please share your donation…”), our intervention emphasized consequences of sharing for the cause (“Your donation can start a chain reaction…”). This brief message increased click-through by 5.1% and likelihood of recruiting at least one later donation via word-of-mouth by 12.4%. Exploratory follow-up analyses suggest that these effects are most pronounced among larger-gift donors; the more donors gave, the more responsive they were to the intervention. Whereas many field experiments aim to increase giving directly, we test an intervention designed to boost word-of-mouth for worthy causes. We discuss approaches for encouraging sharing in the domain of charity and beyond.

Attention-deficit/hyperactivity disorder (ADHD) is America’s most common childhood disorder. Although an ADHD diagnosis can bring positives, recent research uncovers potential negatives associated with diagnosis. This study examines understudied racial-ethnic heterogeneity in the relationships between an early elementary school ADHD diagnosis—with or without medication treatment—and children’s future perceived self-competence, teacher-rated school behaviors, and parent-rated educational expectations. Findings are consistent with the notion that diagnosis can trigger racialized patterns of stigma. That is, relative to undiagnosed matches of the same social class and regardless of medication use, diagnosed Black children demonstrate worse teacher-rated school behaviors, diagnosed White children report poorer perceived self-competence, and parents of diagnosed Hispanic children report poorer educational expectations. Racialized patterns of stigma might amplify the consequences of negative-ability stereotyping on Black children, academic pressure on White children, and mental health stigma on Hispanic children. Findings also highlight the challenges of identification posed by differential unobserved selection into diagnosis.

In this article, we review recent developments in macroeconomics and finance on the relationship between financial risk and the real economy. We focus on three specific topics: (a) the term structure of uncertainty, (b) time variation—specifically, the long-term decline—in the variance risk premium, and (c) time variation in conditional skewness. We also introduce two new data series: implied volatility from one-day options on grains for the period 1906–1936 and prices of cliquet options, which provide insurance against single-day crashes on the S&P 500. Both series give some context to the recent rise in trade in extremely short-dated options. Finally, we discuss new avenues for future research.

Low- and middle-income nations host 76 percent of the world's refugees. This study uses original data to explore within-country spatial variability in refugee-hosting responsibilities. We find that hosting responsibilities for the displaced Rohingya people in Bangladesh are allocated in similarly unequal fashion when analyzed at the national, regional, and microregional levels. Refugee camps are placed in socioeconomically disadvantaged communities relative to both Bangladesh as a whole and surrounding areas. Our findings underscore the importance of considering host communities in the coordination of humanitarian responses to refugee crises to prevent economic hardship and political backlash.

An important debate exists around the extent to which retail investors make sustainable investments and, if they do, why. We contribute to this debate by investigating the aggregate trading patterns of retail investors around a comprehensive sample of key environmental, social, and governance (ESG) news events for U.S. firms. We show that ESG news events appear to be an important factor in retail investors’ portfolio allocation decisions. Yet, inconsistent with arguments about retail investors’ nonpecuniary preferences, our evidence shows that retail investors mainly trade on this information when they deem it financially material to a company’s stock performance. We also find their net trading demand predicts abnormal returns in the subsample of financially material events, consistent with retail traders benefiting from incorporating ESG-related information into their decision-making when it influences firm value. Overall we conclude that the average U.S. retail investor cares about firms’ ESG activities but primarily to the extent these activities matter for company financial performance.

In vertical markets, eliminating double marginalization with a two-part tariff may not be possible due to risk aversion. When demand is uncertain, contracts with large fixed fees expose the downstream firm to more risk than contracts that are more reliant on variable fees. In equilibrium, contracts may thus rely on variable fees, giving rise to double marginalization. Coun- terintuitively, however, we show that increased demand risk or risk aversion can actually mitigate double marginalization. We also characterize several sufficient conditions under which increased risk or risk aversion is guar- anteed to exacerbate double marginalization. We conclude by discussing potential applications and extensions.

Loyalty programs are pervasive across numerous markets, offering members rewards based on their past purchases for future benefits. This study explores the dynamics of loyalty pro- grams within a repeated ordered search framework, where consumers sequentially search for the optimal product across multiple firms over two periods. Our findings reveal that firms strategically use price discounts and rewards to influence consumer behaviors. Price discounts discourage further search in the current shopping period, while rewards encourage consumer loyalty by inducing prominence in subsequent visits. As search costs increase, firms tend to offer lower price discounts but higher rewards. This strategy increases industry profit but reduces consumer surplus. Compared with its absence, loyalty programs decrease both indus- try profit and consumer welfare, leading to a lose-lose outcome. Moreover, we demonstrate that when the market is heterogeneous, high-type firms, with larger networks, offer lower re- wards but achieve higher second-period prices and greater consumer loyalty, contrasting with low-type firms that compensate with higher rewards for their smaller networks. This study offers new insights into the strategic use of loyalty programs and their impact on market competition.

We study a multi-location model with financial market segmentation that permits
self-fulfilling fluctuations. Such fluctuations are necessarily idiosyncratic, but their
volatility varies systematically with an aggregate latent factor. We thus provide a
coordination-based microfoundation for time-varying idiosyncratic risk. A key as-
sumption of our analysis is that cash flow growth rates (e.g., firm profit growth,
asset dividend growth, or country output growth) rise with valuations. We con-
sider three applications: (i) firm dynamics and their risk factor structure; (ii) law
of one price violations in finance; and (iii) exchange rate disconnect in international
macroeconomics.

The SELCO Foundation, established by Harish Hande in 2010, focuses on providing decentralized renewable energy (DRE) solutions to rural, impoverished communities, emphasizing a user-centered, ecosystem-based approach. The Foundation aims to integrate sustainable energy solutions within the daily lives and activities of users through comprehensive support structures, involving technical, financial, and community partnerships.

SELCO's methodology is characterized by a design-thinking approach that considers the entire ecosystem surrounding the end-user. This involves understanding user needs, developing customized devices, securing appropriate financing, and ensuring maintenance and support through local networks. The Foundation works closely with rural households, small technical enterprises, financial institutions, and community organizations to pilot and scale interventions, creating what Hande terms "Lego Blocks" of innovation that can be adapted by other development organizations globally.

Despite its innovative model, the SELCO Foundation faces several challenges in diffusing its approach to other organizations. One significant challenge is the fragmented and often outdated support ecosystems in under-developed regions, such as India’s Northeast. Often, SELCO must incubate new support organizations rather than merely aligning existing ones, complicating project scopes, especially in regions lacking the necessary ecosystem components entirely.

Cultural and geographical specificity presents another difficulty, requiring tailored interventions unique to each region. What works in one area may not be directly replicable elsewhere, complicating diffusion efforts. Financially, other organizations may find that securing patient, long-term funding to support iterative, ecosystem-building approach is difficult, as traditional funders often prioritize immediate results.

he proliferation of harmful content and misinformation on social networks necessitates content moderation policies to maintain platform health. One such policy is shadow banning, which limits content visibility. The danger of shadow banning is that it can be misused by social media platforms to manipulate opinions. Here we present an optimization based approach to shadow banning that can shape opinions into a desired distribution and scale to large networks. Simulations on real network topologies show that our shadow banning policies can shift opinions and increase or decrease opinion polarization. We find that if one shadow bans with the aim of shifting opinions in a certain direction, the resulting shadow banning policy can appear neutral. This shows the potential for social media platforms to misuse shadow banning without being detected. Our results demonstrate the power and danger of shadow banning for opinion manipulation in social networks.

Even if a model is not globally sparse, it is possible for decisions made from that model to be accurately and faithfully described by a small number of features. For instance, an application for a large loan might be denied to someone because they have no credit history, which overwhelms any evidence towards their creditworthiness. In this work, we introduce the Sparse Explanation Value (SEV), a new way of measuring sparsity in machine learning models. In the loan denial example above, the SEV is 1 because only one factor is needed to explain why the loan was denied. SEV is a measure of decision sparsity rather than overall model sparsity, and we are able to show that many machine learning models -- even if they are not sparse -- actually have low decision sparsity, as measured by SEV. SEV is defined using movements over a hypercube, allowing SEV to be defined consistently over various model classes, with movement restrictions reflecting real-world constraints. We proposed the algorithms that reduce SEV without sacrificing accuracy, providing sparse and completely faithful explanations, even without globally sparse models.

This paper studies access to services across geographical regions, using both theoretical and empirical analyses. We model and examine the effects of economies of density in ridesharing markets. Our model predicts that (i) economies of density skew access to rideshareing service away from less dense regions, (ii) the skew will be more pronounced for smaller platforms (i.e., "thinner markets"), and (iii) rideshare platforms do not find this skew efficient and thus use prices and wages to mitigate (but not eliminate) it. We show that these insights are robust to whether the source of economies of density is the supply-side or the demand-side. We then calibrate our model using ride-level Uber data from New York City. We devise an identification strategy based on relative flows of rides among regions which allows us to infer unobsrevable potential demand in different boroughs. We use the model to simulate counterfactual scenarios providing insights on platform optimal pricing with and without spatial price discrimination, the role of market thickness, the impact of prices/wages on access to rides, and the effects of minimum-wage regulations on access equity across regions.

This paper argues that firms proactively use innovation decisions to influence the mobility and human capital accumulation of their workers. We develop a dynamic model in which workers conduct R&D projects, accumulating both general and firm-specific human capital. Firms choose the scope of innovation, influencing the type of human capital workers accumulate during the process. Pursuing more general innovation leads to increased knowledge redeployability for the firm at the cost of more difficult employee retention. We estimate the model using granular innovation production and mobility data of three million inventors. Our model closely matches the observed mobility and innovation specificity over inventors' life cycles. Empirical estimates of the model parameters imply that 24% of observed innovation specificity among U.S. firms is driven by their labor market considerations, which enhances the firm value but lowers the inventors' surplus.