The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry
TL;DR: In this article, the authors study the impact of the short-term accommodation market on the hotel industry and find that the impact is non-uniformly distributed, with lower-priced hotels and those hotels not catering to business travelers being the most affected.
Abstract: Peer-to-peer markets, collectively known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. A central question regards the impact of these sharing economy platforms on incumbent firms. We study the case of Airbnb, specifically analyzing Airbnb’s entry into the short-term accommodation market in Texas and its impact on the incumbent hotel industry. We first explore Airbnb’s impact on hotel revenue, by using a difference- in-differences empirical strategy that exploits the significant spatiotemporal variation in the patterns of Airbnb adoption across city-level markets. We estimate that in Austin, where Airbnb supply is highest, the causal impact on hotel revenue is in the 8-10% range; moreover, the impact is non-uniformly distributed, with lower-priced hotels and those hotels not catering to business travelers being the most affected. We find that this impact materializes through less aggressive hotel room pricing, an impact that benefits all consumers, not just participants in the sharing economy. The impact on hotel prices is especially pronounced during periods of peak demand, such as SXSW. We find that by enabling supply to scale – a differentiating feature of peer-to-peer platforms – Airbnb has significantly crimped hotels’ ability to raise prices during periods of peak demand. Our work provides empirical evidence that the sharing economy is making inroads by successfully competing with, differentiating from, and acquiring market share from incumbent firms.
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TL;DR: This article found that the more trustworthy the host is perceived to be from her photo, the higher the price of the listing and the probability of its being chosen, and that a host's reputation, communicated by her online review scores, has no effect on listing price or likelihood of consumer booking.
Abstract: ‘Sharing economy’ platforms such as Airbnb have recently flourished in the tourism industry. The prominent appearance of sellers' photos on these platforms motivated our study. We suggest that the presence of these photos can have a significant impact on guests' decision making. Specifically, we contend that guests infer the host's trustworthiness from these photos, and that their choice is affected by this inference. In an empirical analysis of Airbnb's data and a controlled experiment, we found that the more trustworthy the host is perceived to be from her photo, the higher the price of the listing and the probability of its being chosen. We also find that a host's reputation, communicated by her online review scores, has no effect on listing price or likelihood of consumer booking. We further demonstrate that if review scores are varied experimentally, they affect guests' decisions, but the role of the host's photo remains significant.
818 citations
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TL;DR: The authors found that the more trustworthy the host is perceived to be from her photo, the higher the price of the listing and the probability of its being chosen, and that a host's reputation, communicated by her online review scores, has no effect on listing price or likelihood of consumer booking.
794 citations
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13 May 2016
TL;DR: Sundararajan et al. as mentioned in this paper introduced the concept of crowd-based capitalism, a new way of organizing economic activity that may supplant the traditional corporate-centered model.
Abstract: Sharing isn't new. Giving someone a ride, having a guest in your spare room, running errands for someone, participating in a supper club -- these are not revolutionary concepts. What is new, in the "sharing economy," is that you are not helping a friend for free; you are providing these services to a stranger for money. In this book, Arun Sundararajan, an expert on the sharing economy, explains the transition to what he describes as "crowd-based capitalism" -- a new way of organizing economic activity that may supplant the traditional corporate-centered model. As peer-to-peer commercial exchange blurs the lines between the personal and the professional, how will the economy, government regulation, what it means to have a job, and our social fabric be affected? Drawing on extensive research and numerous real-world examples -- including Airbnb, Lyft, Uber, Etsy, TaskRabbit, France's BlaBlaCar, China's Didi Kuaidi, and India's Ola, Sundararajan explains the basics of crowd-based capitalism. He describes the intriguing mix of "gift" and "market" in its transactions, demystifies emerging blockchain technologies, and clarifies the dizzying array of emerging on-demand platforms. He considers how this new paradigm changes economic growth and the future of work. Will we live in a world of empowered entrepreneurs who enjoy professional flexibility and independence? Or will we become disenfranchised digital laborers scurrying between platforms in search of the next wedge of piecework? Sundararajan highlights the important policy choices and suggests possible new directions for self-regulatory organizations, labor law, and funding our social safety net.
774 citations
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TL;DR: In this article, the authors develop a conceptual framework that allows us to define the sharing economy and its close-cousins and understand its sudden rise from an economic-historic perspective.
Abstract: We develop a conceptual framework that allows us to define the sharing economy and its close cousins and we understand its sudden rise from an economic-historic perspective. We then assess the sharing economy platforms in terms of the economic, social and environmental impacts. We end with reflections on current regulations and future alternatives, and suggest a number of future research questions.
761 citations
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TL;DR: In this article, the authors identify how the use of peer-to-peer accommodation leads to changes in travelers' behavior and identify how these changes can affect expansion in destination selection, increase in travel frequency, length of stay, and range of activities participated in tourism destinations.
Abstract: As a result of the phenomenal growth of the sharing economy in the travel industry, investigating its potential impacts on travelers and tourism destinations is of paramount importance. The goal of this study was to identify how the use of peer-to-peer accommodation leads to changes in travelers’ behavior. Based on two online surveys targeting travelers from the United States and Finland, it was identified that the social and economic appeals of peer-to-peer accommodation significantly affect expansion in destination selection, increase in travel frequency, length of stay, and range of activities participated in tourism destinations. Travelers’ desires for more meaningful social interactions with locals and unique experiences in authentic settings drive them to travel more often, stay longer, and participate in more activities. Also, the reduction in accommodation cost allows travelers to consider and select destinations, trips, and tourism activities that are otherwise cost-prohibitive. Implications for tourism planning and management are provided.
597 citations
References
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TL;DR: In this article, the authors randomly generate placebo laws in state-level data on female wages from the Current Population Survey and use OLS to compute the DD estimate of its "effect" as well as the standard error of this estimate.
Abstract: Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre”- and “post”-period and explicitly takes into account the effective sample size works well even for small numbers of states.
9,397 citations
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TL;DR: In this paper, the authors build a model of platform competition with two-sided markets and reveal the determinants of price allocation and end-user surplus for different governance structures (profit-maximizing platforms and not-for-profit joint undertakings), and compare the outcomes with those under an integrated monopolist and a Ramsey planner.
Abstract: Many if not most markets with network externalities are two-sided. To succeed, platforms in industries such as software, portals and media, payment systems and the Internet, must “get both sides of the market on board.” Accordingly, platforms devote much attention to their business model, that is, to how they court each side while making money overall. This paper builds a model of platform competition with two-sided markets. It unveils the determinants of price allocation and end-user surplus for different governance structures (profit-maximizing platforms and not-for-profit joint undertakings), and compares the outcomes with those under an integrated monopolist and a Ramsey planner. (JEL: L5, L82, L86, L96)
3,317 citations
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TL;DR: This article offers an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner.
Abstract: Social Scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. In this article, we offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner. Using this technique requires some expertise, which we try to provide herein, but its application should make the results of quantitative articles more informative and transparent. To illustrate our recommendations, we replicate the results of several published works, showing in each case how the authors' own conclusions can be expressed more sharply and informatively, and, without changing any data or statistical assumptions, how our approach reveals important new information about the research questions at hand. We also offer very easy-to-use Clarify software that implements our suggestions.
2,938 citations
"The Rise of the Sharing Economy: Es..." refers methods in this paper
...In this section, we employ statistical simulation to convey our results more intuitively while incorporating the uncertainty built into our estimates of model parameters (see King et al. (2000))....
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TL;DR: It is shown that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use.
Abstract: We discuss a method for improving causal inferences called ‘‘Coarsened Exact Matching’’ (CEM), and the new ‘‘Monotonic Imbalance Bounding’’ (MIB) class of matching methods from which CEM is derived. We summarize what is known about CEM and MIB, derive and illustrate several new desirable statistical properties of CEM, and then propose a variety of useful extensions. We show that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use. We focus on the connection between theoretical properties and practical applications. We also make available easy-to-use open source software for R, Stata, and SPSS that implement all our suggestions.
2,425 citations
"The Rise of the Sharing Economy: Es..." refers methods in this paper
...While various matching methods exist, here we use the Coarsened Exact Matching (CEM) procedure (Iacus et al. 2012), because it is intuitive and works well with categorical data (like most hotel characteristics)....
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...Finally, in a separate analysis, we combine DD with coarsened exact matching (Iacus et al. 2012) to further reduce endogeneity concerns....
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