We study the impact on consumer financial health of the widespread legalization of sports betting in the United States, which began in 2018. We use the state-by-state rollout of legal betting combined with a large representative dataset on consumer credit outcomes to identify effects. We find that states with legal sports betting, particularly online or mobile betting, experienced small decreases in credit scores, as well as large increases in some indicators of excessive debt, including bankruptcies and debt sent to collection agencies. These adverse outcomes are overwhelmingly concentrated among consumers with poor financial health prior to gambling legalization.

We provide new evidence that short-term rental (STR) platforms like Airbnb incentivize residential real estate investment. We exploit two complementary identification strategies. First, we use variation in the timing of STR regulations to estimate the effect of regulation on both Airbnb listings and residential permits. We find that over the first 12 months following the start of the regulation, STR regulations reduce Airbnb listings by 8.9% and residential permits by 10.8%. Second, we show that residential permits decline discontinuously across jurisdictional boundaries in which one side of the boundary has a STR regulation and the other side does not. The effect is especially striking for accessory dwelling units, which decline by 16.5% across regulatory boundaries. Our results imply that STRs incentivize residential investment, and especially so for housing units that are well suited for short-term renting.

We study the market for fake product reviews on Amazon.com. These reviews are purchased in large private internet groups on Facebook and other sites. We hand-collect data on these markets to characterize the types of products that buy fake reviews and then collect large amounts of data on the ratings and reviews posted on Amazon for these products, as well as their sales rank, advertising, and pricing behavior. We use this data to assess the costs and benefits of fake reviews to sellers and evaluate the degree to which they harm consumers. The theoretical literature on review fraud shows conditions when they harm consumers and conditions where they function as simply another type of advertising. Using detailed data on product outcomes before and after they buy fake reviews we can directly determine if these are low-quality products using fake reviews to deceive and harm consumers or if they are high-quality products that solicit reviews to establish reputations. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook leads to a significant increase in average rating and sales rank, but the effect disappears after roughly one month. After firms stop buying fake reviews their average ratings fall significantly and the share of one-star reviews increases significantly, indicating fake reviews are mostly used by low quality products and are deceiving and harming consumers. Finally, we observe that Amazon deletes large numbers of reviews and we document their deletion policy.

There is a rumbling debate over the impact of gentrification: presumed gentrifiers have been the target of protests and attacks in some cities, while they have been welcome as generators of new jobs and taxes in others. Census data fails to measure neighborhood change in real-time since it is usually updated every ten years. This work shows that Airbnb data can be used to quantify and track neighborhood changes. Specifically, we consider both structured data (e.g., number of listings, number of reviews, listing information) and unstructured data (e.g., user-generated reviews processed with natural language processing and machine learning algorithms) for three major cities, New York City (US), Los Angeles (US), and Greater London (UK). We find that Airbnb data (especially its unstructured part) appears to nowcast neighborhood gentrification, measured as changes in housing affordability and demographics. Overall, our results suggest that user-generated data from online platforms can be used to create socioeconomic indices to complement traditional measures that are less granular, not in real-time, and more costly to obtain.

In this paper, we empirically study whether firms improve their quality based on reviews left by their customers in a dynamic quality environment. We do so by analyzing the US hotel industry using data from two major online review platforms: TripAdvisor and Expedia. Using management response as a proxy for whether hotels read and listen to consumer reviews, and a difference-in-differences strategy, we demonstrate that hotels make improvements in quality by paying attention to the reviews they receive. Moreover, we show that these improvements are primarily made by low-rated hotels that have more room for improvement, and by chain hotels likely because of their lower operational marginal cost. To pin down the underlying mechanism, we analyze the text of reviews and responses using novel tools for natural language processing, and show that: (i) hotels that listen to customers improve on issues that are frequently mentioned in their reviews, and (ii) hotels that use canned responses are less likely to see improvements in quality. Overall, our results suggest that online user-generated reviews form a feedback mechanism through which consumers make themselves heard by businesses and contribute to changes in the quality of those businesses.

Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. However, it is computationally challenging to analyze product-level competition for the millions of products available on e-commerce platforms. We introduce Product2Vec, a method based on the representation learning algorithm Word2Vec, to study product-level competition, when the number of products is large. The proposed model takes shopping baskets as inputs and, for every product, generates a low-dimensional embedding that preserves important product information. In order for the product embeddings to be useful for firm strategic decision making, we leverage economic theories and causal inference to propose two modifications to Word2Vec. First of all, we create two measures, complementarity and exchangeability, that allow us to determine whether product pairs are complements or substitutes. Second, we combine these vectors with random utility-based choice models to forecast demand. To accurately estimate price elasticities, i.e., how demand responds to changes in price, we modify Word2Vec by removing the influence of price from the product vectors. We show that, compared with state-of-the-art models, our approach is faster, and can produce more accurate demand forecasts and price elasticities.

We assess the impact of home-sharing on residential house prices and rents. Using a dataset of Airbnb listings from the entire United States and an instrumental variables estimation strategy, we find that a 1% increase in Airbnb listings leads to a 0.018% increase in rents and a 0.026% increase in house prices at the median owner-occupancy rate zip code. The effect is moderated by the share of owner-occupiers, a result consistent with absentee landlords reallocating their homes from the long-term rental market to the short-term rental market. A simple model rationalizes these findings.

Over the last fifteen years, one of the major developments online has been the growth and proliferation of review websites such as TripAdvisor. The ready availability of independent information from past users poses interesting questions for marketing strategy. What role does advertising play in the new environment? How should firms adjust their advertising strategy to the presence of reviews? In this paper we address these questions in the context of the hotel industry. Using a data set of TripAdvisor hotel reviews and another describing hotels' advertising expenditures, we show, first, that overall ad spending decreased from 2002 to 2015, suggesting that online reviews have had the effect of displacing advertising. Second, there is a negative causal relationship between TripAdvisor ratings and advertising spending in the cross-section: hotels with higher ratings spend less. This suggests that user ratings and advertising are substitutes, not complements. Third, this relationship is stronger for independent hotels than for chains, and stronger in competitive markets than in noncompetitive markets. The former suggests that a strong brand name provides some immunity to reviews, and the latter suggests that when ratings are pivotal, the advertising response might be particularly strong. Finally, we show that the relationship between user ratings and advertising has strengthened over time, as websites such as TripAdvisor have become more influential. This provides further confirmation that the effect of online ratings on advertising operates through the demand side, and not the supply side. Hotels seem to react to reviews if and only if consumers react to them.

Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of ``algorithmic regulation''.

Failure to meet a consumer's expectations can result in a negative review, which can have a lasting, damaging impact on a firm's reputation, and its ability to attract new customers. To mitigate the reputational harm of negative reviews many firms now publicly respond to them. How effective is this reputation management strategy in improving a firm's reputation? We empirically answer this question by exploiting a difference in managerial practice across two hotel review platforms, TripAdvisor and Expedia: while hotels regularly respond to their TripAdvisor reviews, they almost never do so on Expedia. Based on this observation, we use difference-in-differences to identify the causal impact of management responses on consumer ratings by comparing changes in the TripAdvisor ratings of a hotel following its decision to begin responding against a baseline of changes in the same hotel's Expedia ratings. We find that responding hotels, which account for 56% of hotels in our data, see an average increase of 0.12 stars in the TripAdvisor ratings they receive after they start responding. Moreover, we show that this increase in ratings does not arise from hotel quality investments. Instead, we find that the increase is consistent with a shift in reviewer selection: consumers with a poor experience become less likely to leave a negative review when hotels begin responding.

Spurred by technological advancement, a number of decentralized peer-to-peer markets, now colloquially known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. A central question surrounding the sharing economy regards its long-term impact: will peer-to-peer platforms materialize as viable mainstream alternatives to traditional providers, or will they languish as niche markets? In this paper, we study Airbnb, a sharing economy pioneer offering short-term accommodation. Combining data from Airbnb and the Texas hotel industry, we estimate the impact of Airbnb's entry into the Texas market on hotel room revenue, and study the market response of hotels. To identify Airbnb's causal impact on hotel room revenue, we use a difference-in-differences empirical strategy that exploits the significant spatiotemporal variation in the patterns of Airbnb adoption across citylevel markets. We estimate that each 10% increase in Airbnb supply results in a 0:37% decrease in monthly hotel room revenue. In Austin, where Airbnb supply is highest, the impact on hotel revenue exceeds 10%. We find that Airbnb's impact is non-uniformly distributed, with lower-priced hotels, and hotels not catering to business travel being the most affected segments. Finally, we find that affected hotels have responded by reducing prices, an impact that benefits all consumers, not just participants in the sharing economy. Our work provides empirical evidence that the sharing economy is making inroads by successfully competing with, and acquiring market share from, incumbent firms.

We present an approach to differentially private computation in which one does not scale up the magnitude of noise for challenging queries, but rather scales down the contributions of challenging records. While scaling down all records uniformly is equivalent to scaling up the noise magnitude, we show that scaling records non-uniformly can result in substantially higher accuracy by bypassing the worst-case requirements of differential privacy for the noise magnitudes. This paper details the data analysis platform wPINQ , which generalizes the Privacy Integrated Query (PINQ) to weighted datasets. Using a few simple operators (including a non-uniformly scaling Join operator) wPINQ can reproduce (and improve) several recent results on graph analysis and introduce new generalizations ( e.g. , counting triangles with given degrees). We also show how to integrate probabilistic inference techniques to synthesize datasets respecting more complicated (and less easily interpreted) measurements.

No abstract available.

With the rapid evolution of networks and the widespread penetration of mobile devices with increasing capabilities, that have already become a commodity, we are getting a step closer to ubiquity. Thus, we are moving a great part of our lives from the physical world to the online world, i.e. social interactions, business transactions, relations with government administrations, etc. However, while identity verification is easy to handle in the real world, there are many unsolved challenges when dealing with digital identity management, especially due to the lack of user awareness when it comes to privacy. Thus, with the aim to enhance the navigation experience and security in multiservice and multiprovider environments the user must be empowered to control how her attributes are shared and disclosed between different domains.With these goals on mind, we leverage the benefits of the Infocard technology and introduce this usercentric paradigm into the emerging NGN architectures. This paper proposes a way to combine the gains of a SAML federation between service and identity providers with the easiness for the final user of the Inforcard System using the well known architectural schema of IP Multimedia Subsystem.