Publications

Should Pepsi reconsider its long-term focus on the youth market for the company's signature brand?

For decades, PepsiCo, the largest food company in the United States, marketed its Pepsi-Cola brand with a primary focus on the youth demographic, leveraging advertisements featuring teens and young adults. However,  sales of the brand struggled; the company had seen its market share erode over the 2010s, dropping even faster than the overall cola segment. Amidst this backdrop, Todd Kaplan, PepsiCo's new Chief Marketing Officer for U.S. Pepsi, faced the challenge of revitalizing the brand:

Youth Targeting: Persist with Pepsi's traditional approach of appealing to youth, despite the drop in youth consumption of cola products and the inherent risks of marketing missteps. Look for new users to drink their brand.

Increasing Share of Wallet: Look to increasing consumption among existing customers, regardless of their age. 

Kaplan's team considered the options. What marketing strategy should Pepsi take to improve its brand position? Would Pepsi reconsider its long-term focus on the youth market for the company's signature brand? If so, what options would succeed in the market as well as with a skeptical internal audience?

We study personalized pricing in a general oligopoly model. The impact of personalized pricing relative to uniform pricing hinges on the degree of market coverage. If market conditions are such that coverage is high (e.g., the production cost is low, or the number of firms is high), personalized pricing harms firms and benefits consumers, whereas the opposite is true if coverage is low. When only some firms have data to personalize prices, consumers can be worse off compared to when either all or no firms personalize prices.

Residential Property Assessed Clean Energy (PACE) loans are a new class of financial contract, whereby homeowners borrow to fund green residential projects and repay the loan via their local property tax payments. We assess equity-efficiency trade-offs of PACE using loan-level data from Florida merged to property transaction, tax, and permitting records. Consistent with the program's objectives, borrowers are more likely to obtain permits related to disaster-proofing homes, and loan takeup is concentrated in areas with higher ex ante and ex post natural hazard risk. Such investments are capitalized into home values, but expansions of the property tax base are partially offset by an uptick in tax delinquency rates among borrowers. Although PACE loans are super senior to other debt, lenders expand their provision of mortgage credit in PACE-enabled counties. Enabling PACE loans increases the fiscal income of participating local governments while closing the investment gap in projects which improve the climate resiliency of the housing stock.

We study the classic divide-and-choose method for equitably allocating divisible goods be- tween two players who are rational, self-interested Bayesian agents. The players have additive values for the goods. The prior distributions on those values are common knowledge. We con- sider both the cases of independent values and values that are correlated across players (as occurs when there is a common-value component). We describe the structure of optimal divisions in the divide-and-choose game and identify several cases where it is possible to efficiently compute equilibria. An approximation algorithm is presented for the case when the distribution over the chooser’s value for each good follows a normal distribution, along with a randomized approximation algorithm for the case of uniform distributions over intervals. A mixture of analytic results and computational simulations illuminates several striking differences between optimal strategies in the cases of known versus unknown preferences. Most notably, given unknown preferences, the divider has a compelling “diversification” incentive in creating the chooser’s two options. This incentive leads to multiple goods being divided at equilibrium, quite contrary to the divider’s optimal strategy when preferences are known. In many contexts, such as buy-and-sell provisions between partners, or in judging fairness, it is important to assess the relative expected utilities of the divider and chooser. Those utilities, we show, depend on the players’ levels of knowledge about each other’s values, the correlations between the players’ values, and the number of goods being divided. Under fairly mild assump- tions, we show that the chooser is strictly better off for a small number of goods, while the divider is strictly better off for a large number of goods.

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.