Following a 2018 ruling of the U.S. Supreme Court, 38 states have legalized sports gambling. We study how this policy has impacted consumer financial health using the state-by-state rollout of legal sports gambling and a large and comprehensive dataset on consumer financial outcomes. Our main finding is that overall consumers' financial health is modestly deteriorating as the average credit score in states that legalize sports gambling decreases by roughly 0.3%. The decline in credit score is associated with changes in indicators of excessive debt. We find a substantial increase in bankruptcy rates, debt collections, debt consolidation loans, and auto loan delinquencies. We also find that financial institutions respond to the reduced creditworthiness of consumers by restricting access to credit. These results are stronger for states that allow sports gambling online compared to states that restrict access to in-person betting and larger for young men in low-income counties. Together, these results indicate that the ease of access to sports gambling is harming consumer financial health by increasing their level of debt. 

As online sports gambling (OSG) expands rapidly across the United States—30 states and the District of Columbia now have some form of OSG—policymakers face critical questions regarding its potential to generate state revenue. This paper examines whether the introduction of OSG affects consumer demand for existing state lotteries, which continue to be a vital source of funding for education, scholarships, and other public programs. Leveraging the staggered adoption of OSG across states, we analyze changes in lottery demand using a county-level lottery sales dataset across 19 states and a separate transaction-level dataset covering 80% of all independent convenience stores in all 50 US states. We find consistent evidence that OSG significantly cannibalizes lottery demand, with sales declining by 4.6% to 6.7% in the 16 months following the introduction of OSG. We show that the substitution is driven by heavy lottery players (“whales”) reducing consumption and primarily impacts stores in neighborhoods with high concentrations of young Black men. Given the low state revenue margins on OSG sportsbook taxes compared to state lotteries, we find that less than half (45%) of sportsbook tax revenue is new revenue; the other 55% would have been generated through state lotteries. Our findings challenge the prior notion that OSG can generate a significant fiscal windfall for states. Given the financial harm OSG is also known to cause consumers, the incremental revenue also comes with additional social costs.  

We study how improvements to search engine interpretation algorithms and the information signals they generate affect sponsored search markets. We focus on two outcomes: the number of advertisers bidding for a query (i.e., competition) and cost-per-click (CPC). We start by developing a theoretical auction model. We find that as the quality of a search engine's interpretation algorithm improves, the number of bidders allocated to auctions generally increases for all queries. Despite this, we find that changes in CPC depend on the prevalence of context in a query. For queries lacking context (e.g., shorter queries), CPC increases. However, CPC may decrease for queries with more contextual information (e.g., longer queries). This can occur when the new algorithm significantly improves contextual interpretation capabilities, leading to more precise advertiser relevancy scores. These scores can lower CPC but increase the average click-through rate (CTR). We then test the model predictions using a monthly dataset of competition scores and CPC for 12,000 queries, leveraging Google's October 2019 rollout of Bidirectional Encoder Representations from Transformers as a natural experiment. We find results consistent with the theoretical model. Our results provide insight into the economic impact of AI and Large Language Models on advertising markets.

It is unknown if, how, and to what extent digital ad clutter impacts ad effectiveness, leaving managers wondering how to advertise in cluttered digital spaces. In this work, researchers conduct a series of eye-tracking and psychological experiments to learn if and how digital clutter impacts ad viewership and psychological measures of ad effectiveness. Experimental findings establish that digital ad clutter is a meaningful digital advertising moderator. Across two eye-tracking studies, clutter is found to meaningfully alter how consumers distribute their limited attention across advertisements but not meaningfully cause consumers to avoid viewing advertisements altogether. While aggregate ad viewership increases with clutter, suggesting ad attention is a function of the number of ads on the page, premium advertising space suffers from viewership cannibalization, while middle-of-the-page advertisements are heterogeneously affected. Using a large panel dataset detailing the interactions between nearly one million consumers, 85 thousand publishers, and 19 advertisers, we then study how clutter correlates with ad effectiveness in the real world, finding that a one standard deviation increase in clutter associates with as much as an 85% reduction in average conversion probabilities across campaigns. The paper discusses implications for advertisers, publishers, and marketing researchers.