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This research analyzed car-ownership in the US during and after the COVID-19 pandemic, by utilizing a nationally representative household survey spanning from January 2020 to March 2022. Using a multilevel Hurdle model with month and state random effects, vehicle ownership trends are separately modeled for car-owning and carless households within the same modeling framework, while accounting for endogeneity and unobserved heterogeneity. An increase in the total amount of economic stimulus funding received increased the odds of vehicle ownership, while high car prices, high levels of housing expenditures, living in multi-family dwelling, and being from minority and low-skilled worker families negatively affected car-ownership. Greater household labor force participation, increases in household size and young persons in the household, living in states with high COVID caseloads and with moderately stringent Stay-at-Home social distancing mandates affected car-ownership propensities differently for car-owning and carless households. The significance of the research is that it disentangled pandemic-related and transportation policy variables from changes in household structure, living arrangements and employment-related characteristics. The analysis jointly considered how short-term pandemic-related influences as well as long-term demographic and occupational factors differently affect car-ownership for car-owning households and those without cars. Policy implications are drawn for consumer protection in the car ownership process, auto loan forbearance in future economic disruptions, strategies for public transportation which has continued to suffer from lower levels of use, and sustainability programs due to higher volumes of older used cars. • Increase in economic stimulus funding increased the odds of car-ownership. • High car prices and high housing costs negatively affected car-ownership. • Greater proportion of workers affected car-owning and carless households differently. • Stringency of Stay-at-Home levels and COVID caseloads also had different effects. • Pre-existing racial, occupational and dwelling effects persisted. [ABSTRACT FROM AUTHOR]

We show that search frictions in credit markets affect accepted interest rates and loan sizes and distort consumption. Using data on car loan applications and originations not intermediated by car dealers, we isolate quasi-exogenous variation in both the costs and benefits to searching for credit. After identifying lender-specific policies that price risk discontinuously, we study the differential response to offered interest rates by borrowers who face high and low search costs. High-search-cost borrowers are 10 |$\%$| more likely to accept loan offers with higher markups, consequently originating smaller loans and purchasing older and less expensive cars than lower-search-cost borrowers. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

COVID-19 altered travel patterns in the U.S. Studies have analyzed the effect of the pandemic on travel mode, including working from home, but few have focused on automobile ownership—a relationship with potentially long-term consequences for accessibility, household budgets and debt, and policy efforts to meet climate goals. To understand the association between the pandemic and automobile ownership, we rely on a unique credit panel dataset from Experian and examine three different automobile loan-related outcome measures: annualized growth rate of new automobile loan balances, average new loan size, and the number of new loans. We focus specifically on changes across loans in neighborhoods by race/ethnicity, hypothesizing larger increases in automobile debt in Black and Latino/a neighborhoods, where workers are less likely to be able to telework. The annualized growth rate of new automobile loans increased during the pandemic across all neighborhoods by race/ethnicity, increasing most rapidly in Latino/a neighborhoods. Controlling for other factors, loan size increased similarly across neighborhoods by race/ethnicity. The increase in automobile lending in Latino/a neighborhoods, therefore, likely was explained by a significant uptick in the number of new loans. The growth in automobile lending during the pandemic was potentially prompted by pandemic-induced changes in the need for automobiles and facilitated by an expanded social safety net. As the pandemic and its various forms of public financial assistance recede, the findings underscore the importance of ongoing assistance in enabling automobile ownership or shared access among households with limited means whose livelihoods depend on the access that vehicles provide. • Annualized growth rates of new automobile loans increased during the COVID-19 pandemic across all neighborhoods. • Annualized growth rates of new automobile loans increased most rapidly in Latino/a neighborhoods. • New automobile loan size increased similarly across neighborhoods by race/ethnicity. • The increase in lending in Latino/a neighborhoods likely was due to a significant uptick in the number of new loans. • Policies should enable automobile ownership or shared access among poor households whose livelihoods depend on this access. [ABSTRACT FROM AUTHOR]

In this paper, using data from indirect auto lending and a structural model of external sales representative (ESR) behaviour, we investigate (1) the role of commissions as a potential tool to influence ESRs' pricing decisions under limited authority, (2) the impact of optimized commissions on firm profitability, and (3) the implications for customer welfare. The results provide strong evidence for ESRs being strategic (vs. myopic) in their pricing and effort decisions; and in both cases, strategic behaviour is inversely proportional to customer risk. Moreover, once optimized, commissions are an effective tool for firms to bridge the profitability gap between centralized pricing and pricing delegation. Our analyses on social justice and fairness reveal that customer groups along the dimensions of customer risk, income class, and gender, which have been traditionally marginalized in society, suffer from inequities in the indirect-lending ecosystem. While, the optimization of commissions does not intensify these biases, we found females to be the exception, and that the inequities due to gender bias not only persist in the optimized regime, but also deepen. Through counterfactual simulations, we propose two policies for firms to minimize social inequity, which helps them balance immediate profit-maximizing goals with responsible AI initiatives. [ABSTRACT FROM AUTHOR]

Lower-income consumers who seek to increase their perceived social status or to emulate their wealthier peers may be motivated to purchase conspicuous luxury goods. Using a vehicle financing dataset, we find that non-prime consumers value vehicle prestige more than the average consumer. The stronger preferences for prestige lead non-prime consumers to purchase more expensive vehicles than they otherwise would. The preferences for prestige are driven both by status signaling and peer emulation motives. Furthermore, we show that larger vehicle purchases financed by auto loans lead to worse loan performance and credit standing for non-prime consumers. [ABSTRACT FROM AUTHOR]

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm's pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost "for free." That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization. This paper was accepted by Vishal Gaur, operations management. Funding: R. Momot acknowledges financial support from the HEC Paris Foundation and the Agence Nationale de la Recherche (French National Research Agency) "Investissements d'Avenir" [Grant LabEx Ecodec/ANR-11-LABX-0047] during the initial stages of this project. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4925. [ABSTRACT FROM AUTHOR]

We document racial disparities in auto lending. Combining credit bureau records with borrower characteristics, we find that Black and Hispanic applicants' approval rates are 1.5 percentage points lower, even after controlling for creditworthiness. In aggregate, this effect crowds out 80,000 minority loans each year. Results are stronger where racial biases are more prevalent and lending competition is lower. Minority borrowers pay 70-basis-point higher interest rates, but default less ceteris paribus, consistent with racial bias rather than statistical discrimination. A major antidiscrimination enforcement policy initiated in 2013, but halted in 2018, reduced unexplained racial differences in interest rates by 60 |$\%$|⁠. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Residents in every state in the United States can lose their driver’s license or car registration because they owe debt to the state. At least eleven million people across the United States suffer these debt-based driving restrictions at any given time. Because Americans overwhelmingly rely on personal automobiles for transportation, states, by controlling access to driver’s licenses and vehicle registrations, use debt policy to control where and how people travel. And because these laws disproportionately affect people of color, primarily Black people, this kind of regulation props up racial segregation in both location and opportunity. This Article’s first contribution is empirical, descriptive, and prescriptive: it catalogs debt-based driving restrictions across multiple categories for all fifty states and the District of Columbia, provides a long overdue analysis of their harms, and proposes corrective legislative action. The driving suspension laws that animate this project are not the first instance where the government has turned to debt policy as a mechanism of racial control. These laws are not an aberration, but rather the latest iteration of a long tradition of government actors using debt policy to expand physical mobility for White people and simultaneously constrict physical mobility for Black people. Tracing this thread across American history provides the basis for this Article’s second contribution—a theoretical one—that expands the traditional view of credit and debt as a mechanism of social and financial mobility to include an analysis of credit and debt as a force that acts on physical mobility. When we expand the lens to include debt policy’s effects on physical mobility, we also see debt policy’s differential effects on White and Black people in the United States. Thus, this Article begins a new conversation about debt and debt policy, one that interrogates debt policy’s racialized effects on physical mobility, freedom, and personhood. [ABSTRACT FROM AUTHOR]

In recent years, China's automobile industry has undergone rapid development, creating new opportunities for the auto loan industry. Currently, auto financing companies are actively seeking to expand their cooperation with banks. Therefore, improving the approval rate and scale of joint loan business is of significant practical importance. In this paper, we propose a Stacking‐based financial institution risk approval model and select the optimal stacking model by comparing its performance with other models. Additionally, we construct a bank approval model using deep learning techniques on a biased data set, with feature extraction performed using convolution neural networks (CNN) and feature‐based counterfactual augmentation used for balanced sampling. Finally, we optimize the model of the prediction of auto finance companies by selecting the optimal coefficients of loss function based on the features and results of the bank approval model. The proposed approach leads to an approximately 6% increase in the joint loan approval rate on the actual data set, as demonstrated by experimental results. [ABSTRACT FROM AUTHOR]

We study the connection between financialization and consumerism, and claim that greater availability of consumer loans affects consumerist culture and promotes the institution of conspicuous consumption. We extend the ideas of Thorstein Veblen who coined the term of "conspicuous consumption" to illustrate our thinking with the empirical evidence on car purchases financed through bank loans in Russia's Rostov Region. We find that leveraged car buying has become embedded over the past 20 years. Social positioning drives people to seek more expensive cars, whereas getting a bank loan allows to buy an otherwise unaffordable car. We see a sign of conspicuousness in the fact that household income grows slower than the average price of a new car financed by a car loan, as well as the amount of loans. Our contribution is that a basic concept of institutional theory (conspicuous consumption) is combined with elements from other research approaches, namely the social significance of bank lending, functional differentiation of credit, and the effects of financialization. [ABSTRACT FROM AUTHOR]

Contextual dynamic pricing aims to set personalized prices based on sequential interactions with customers. At each time period, a customer who is interested in purchasing a product comes to the platform. The customer's valuation for the product is a linear function of contexts, including product and customer features, plus some random market noise. The seller does not observe the customer's true valuation, but instead needs to learn the valuation by leveraging contextual information and historic binary purchase feedback. Existing models typically assume full or partial knowledge of the random noise distribution. In this paper, we consider contextual dynamic pricing with unknown random noise in the linear valuation model. Our distribution-free pricing policy learns both the contextual function and the market noise simultaneously. A key ingredient of our method is a novel perturbed linear bandit framework, in which a modified linear upper confidence bound algorithm is proposed to balance the exploration of market noise and the exploitation of the current knowledge for better pricing. We establish the regret upper bound and a matching lower bound of our policy in the perturbed linear bandit framework and prove a sublinear regret bound in the considered pricing problem. Finally, we demonstrate the superior performance of our policy on simulations and a real-life auto loan data set. Funding: Y. Liu and W.W. Sun acknowledge support from the National Science Foundation Division of Social and Economic Sciences [Grant NSF-SES 2217440]. Supplemental Material: The supplementary material is available at https://doi.org/10.1287/moor.2023.1369. [ABSTRACT FROM AUTHOR]

We document the existence of gender and racial gaps in dealer markups of auto financing interest rates. Most car buyers are unaware that dealers can mark up interest rates for auto loans, the third most prevalent U.S. consumer debt category, beyond the rate dictated by lenders. Dealers can set their markups without considering buyer risk profiles, potentially leading to discrimination. Using individual transaction-level data from a 20% random sample of U.S. car dealers between 2004 and 2015, this paper shows that women and minorities pay a statistically significant 0.6% and 2.6% greater interest rate markup than men and nonminorities, respectively. The racial and gender gaps in dealer markups largely attenuate over time. Additionally, the premia for minorities and women are larger for those buyers in census block groups with lower education levels, those not trading in a car, and those in markets with fewer financial institutions. Although we find no heterogeneity in the racial gap by age and political inclination, the gender gap increases for women who are relatively older and is significant only in majority Republican-voting counties. Our analyses do not provide a precise estimate of the interaction effect between race and gender. Based on the buyer characteristics considered, the most vulnerable group of women (minorities), accounting for 4.7% (8.5%) of car buyers, pay 7.6% (27.9%) more compared with men (nonminorities). The findings inform policymakers and consumers of the gender and racial gaps in auto financing, their underlying mechanisms, and situations most susceptible to unfair lending. History: Catherine Tucker served as the senior editor. This paper was accepted through the Marketing Science: Frontiers review process. Funding: O.C. Ozturk thanks the Darla Moore School of Business research grant program for support. P. K. Chintagunta thanks the Kits Center for Marketing at the University of Chicago for financial support. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mksc.2022.0295. [ABSTRACT FROM AUTHOR]