An Empirical Analysis of the Impacts of the Sharing Economy Platforms on the U.S. Labor Market

Each generation of digital innovation has caused a dramatic change in the way people work. Sharing economy is the latest trend of digital innovation, and it has fundamentally changed the traditional business models. In this paper, we empirically examine the impacts of the sharing economy platforms (specifically, Uber) on the labor market in terms of labor force participation, unemployment rate, supply, and wage of low-skilled workers. Combining a data set of Uber entry time and several microdata sets, we utilize a difference-in-differences (DID) method to investigate whether the above measures before and after Uber entry are significantly different across the U.S. metropolitan areas. Our empirical findings show that sharing economy platforms such as Uber significantly decrease the unemployment rate and increase the labor force participation. We also find evidence of a shift in the supply of low skill workers and consequently a higher wage rate for such workers in the traditional industries

Adding evidence to the debate: quantifying Airbnb's disruptive impact on ten key hotel markets

Airbnb's entry into the lodging landscape has dramatically increased the available supply of rooms for accommodating prospective visitors at a destination. In a competitive market, an increase in supply while keeping demand relatively constant would decrease prices and revenues. While Airbnb is expected to negatively impact the hotel industry, the effects of Airbnb on the performance of the hotel industry have not been extensively quantified. Also, existing studies on Airbnb's economic impacts are limited in their inferential, temporal, and/or geographical scope. In view of this gap in the literature, the present study examines the effects of Airbnb supply on key hotel performance metrics: room revenues (RevPAR), average daily rates (ADR), and occupancy rates (OCC) in ten major U.S. hotel markets for the period between July 2008 and June 2017. The results demonstrate that an increasing Airbnb supply negatively impacts all three performance metrics within the hotel industry. Moreover, while previous research has demonstrated a negative impact on lower-end hotels, our findings provide evidence of Airbnb's growing impact on the mainstream market across hotel class segments, signaling a high level of consistency with the tenets of the theory of disruptive innovation. The magnitude of these effects is not only statistically but also economically significant. Theoretical and practical implications are discussed.Accepted manuscrip

The rise of the sharing economy: estimating the impact of Airbnb on the hotel industry

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. We explore the economic impact of the sharing economy on incumbent firms by studying the case of Airbnb, a prominent platform for short-term accommodations. We analyze Airbnb's entry into the state of Texas, and quantify its impact on the Texas hotel industry over the subsequent decade. 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-uniform, with lower-priced hotels and those hotels not catering to business travelers being the most affected. The impact manifests itself primarily through less aggressive hotel room pricing, an impact that benefits all consumers, not just participants in the sharing economy. The price response is especially pronounced during periods of peak demand, such as SXSW, and is due to a differentiating feature of peer-to-peer platforms -- enabling instantaneous supply to scale to meet demand.Accepted manuscrip

Domestic Outsourcing in the United States: A Research Agenda to Assess Trends and Effects on Job Quality

The goal of this paper is to develop a comprehensive research agenda to analyze trends in domestic outsourcing in the U.S. -- firms' use of contractors and independent contractors -- and its effects on job quality and inequality. In the process, we review definitions of outsourcing, the available scant empirical research, and limitations of existing data sources. We also summarize theories that attempt to explain why firms contract out for certain functions and assess their predictions about likely impacts on job quality. We then lay out in detail a major research initiative on domestic outsourcing, discussing the questions it should answer and providing a menu of research methodologies and potential data sources. Such a research investment will be a critical resource for policymakers and other stakeholders as they seek solutions to problems arising from the changing nature of work

The Enigma of Digitized Property A Tribute to John Perry Barlow

Compressive Sensing has attracted a lot of attention over the last decade within the areas of applied mathematics, computer science and electrical engineering because of it suggesting that we can sample a signal under the limit that traditional sampling theory provides. By then using dierent recovery algorithms we are able to, theoretically, recover the complete original signal even though we have taken very few samples to begin with. It has been proven that these recovery algorithms work best on signals that are highly compressible, meaning that the signals can have a sparse representation where the majority of the signal elements are close to zero. In this thesis we implement some of these recovery algorithms and investigate how these perform practically on a real video signal consisting of 300 sequential image frames. The video signal will be under sampled, using compressive sensing, and then recovered using two types of strategies, - One where no time correlation between successive frames is assumed, using the classical greedy algorithm Orthogonal Matching Pursuit (OMP) and a more robust, modied OMP called Predictive Orthogonal Matching Pursuit (PrOMP). - One newly developed algorithm, Dynamic Iterative Pursuit (DIP), which assumes and utilizes time correlation between successive frames. We then performance evaluate and compare these two strategies using the Peak Signal to Noise Ratio (PSNR) as a metric. We also provide visual results. Based on investigation of the data in the video signal, using a simple model for the time correlation and transition probabilities between dierent signal coecients in time, the DIP algorithm showed good recovery performance. The main results showed that DIP performed better and better over time and outperformed the PrOMP up to a maximum of 6 dB gain at half of the original sampling rate but performed slightly below the PrOMP in a smaller part of the video sequence where the correlation in time between successive frames in the original video sequence suddenly became weaker.Compressive sensing har blivit mer och mer uppmarksammat under det senaste decenniet inom forskningsomraden sasom tillampad matematik, datavetenskap och elektroteknik. En stor anledning till detta ar att dess teori innebar att det blir mojligt att sampla en signal under gransen som traditionell samplingsteori innebar. Genom att sen anvanda olika aterskapningsalgoritmer ar det anda teoretiskt mojligt att aterskapa den ursprungliga signalen. Det har visats sig att dessaaterskapningsalgoritmer funkar bast pa signaler som ar mycket kompressiva, vilket innebar att dessa signaler kan representeras glest i nagon doman dar merparten av signalens koecienter ar nara 0 i varde. I denna uppsats implementeras vissa av dessaaterskapningsalgoritmer och vi undersoker sedan hur dessa presterar i praktiken pa en riktig videosignal bestaende av 300 sekventiella bilder. Videosignalen kommer att undersamplas med compressive sensing och sen aterskapas genom att anvanda 2 typer av strategier, - En dar ingen tidskorrelation mellan successiva bilder i videosignalen antas genom att anvanda klassiska algoritmer sasom Orthogonal Matching Pursuit (OMP) och en mer robust, modierad OMP : Predictive Orthogonal Matching Pursuit (PrOMP). - En nyligen utvecklad algoritm, Dynamic Iterative Pursuit (DIP), som antar och nyttjar en tidskorrelation mellan successiva bilder i videosignalen. Vi utvarderar och jamfor prestandan i dessa tva olika typer av strategier genom att anvanda Peak Signal to Noise Ratio (PSNR) som jamforelseparameter. Vi ger ocksa visuella resultat fran videosekvensen. Baserat pa undersokning av data i videosignalen visade det sig, genom att anvanda enkla modeller, bade for tidskorrelationen och sannolikhetsfunktioner for vilka koecienter som ar aktiva vid varje tidpunkt, att DIP algoritmen visade battre prestanda an de tva andra tidsoberoende algoritmerna under visa tidsekvenser. Framforallt de sekvenser dar videosignalen inneholl starkare korrelation i tid. Som mest presterade DIP upp till 6 dB battre an OMP och PrOMP

Concentration and Platform Growth in the Sharing Economy: A Resource Partitioning Perspective

The emergence and growth of sharing economy platforms have engendered significant research interests recently. These platforms have witnessed increased entry of professional service providers, who have large amounts of excess assets and standardized business practices. Meanwhile, sharing economy platforms have witnessed an astounding growth, much of which is not attributed to professional service providers. This paper examines two seemingly contradictory phenomena – increased concentration among professional service providers and rapid growth of non-professionals on sharing economy platforms. Using the resource partitioning theory from the organizational literature, we explain how these two phenomena are inherently related. We further emphasize the role of income inequality that affects the resource partitioning process. The empirical analysis uses 1.4 million zip-code level Airbnb data, with Airbnb Plus policy as a natural experiment. Findings reveal that professional service provider concentration facilitates non-professional growth but reduces their performance, and the effects are significantly moderated by income inequality

Political Cycles : The Opposition Advantage

We propose a two dimensional infinite horizon model of public consumption in which investments are decided by a winner-take-all election. Investments in the two public goods create a linkage across periods and parties have different specialities. We show that the incumbent party vote share decreases the longer it stays in power. Parties chances of winning do not converge and, when the median voter is moderate enough, no party can maintain itself in power for ever. Finally, the more parties are specialized and the more public policies have long-term effects, the more political cycles are likely to occur.Cycles, Alternation, Public goods, Advantage, Opposition

Uber Effort: The Production of Worker Consent in Online Ride Sharing Platforms

The rise of the online gig economy alters ways of working. Mediated by algorithmically programmed mobile apps, platforms such as Uber and Lyft allow workers to work by driving and completing rides at any time or in any place that the drivers choose. This hybrid form of labor in an online gig economy which combines independent contract work with computer-mediated work differs from traditional manufacturing jobs in both its production activity and production relations. Through nine interviews with Lyft/Uber drivers, I found that workers’ consent, which was first articulated by Michael Burawoy in the context of the manufacturing economy, is still present in the work of the online gig economy in post-industrial capitalism. Workers willingly engage in the on-demand work not only to earn money but also to play a learning game motivated by the ambiguity of the management system, in which process they earn a sense of self-satisfaction and an illusion of autonomous control. This research points to the important role of technology in shaping contemporary labor process and suggests the potential mechanism which produces workers’ consent in technology-driven workplaces

Dinner at Your Doorstep: Service Innovation via the Gig Economy on Food Delivery Platforms

Boosted by greater demand for convenience and then turbocharged by the coronavirus disease 2019 pandemic, online food delivery (OFD) has witnessed rapid growth over the past several years. Despite such growth, however, it is still unclear how incentives and payoffs of various parties are affected by the three-sidedness of the OFD market, which involves consumers, restaurants, and gig drivers—beyond the traditional two-sided setting. In this paper, we study the OFD platforms’ optimal choices in a competitive setting where the platforms compete on both prices and service quality. Our analysis shows that conventional insights from two-sided platforms do not completely carry over to OFD markets. Specifically, we find that the three-sidedness may either soften or intensify the price competition in the buyer-seller market, consequently altering the subsidizing conditions of OFD platforms. Although two-sided platforms generally get hurt by network effects because of the pressure to induce participation, OFD platforms are able to mitigate such negative impact by flexibly adjusting their service strategies. Yet, OFD platforms may not always be better off by introducing gig labor because additional leverage for competing platforms could lead to a prisoner’s dilemma situation. We show further how the platforms’ pricing and service strategies critically depend on the strength of network effects. With the rising of the gig economy, the question of employment status for gig workers has become an increasingly controversial issue in the United States and elsewhere. We address this by showing that the introduction of minimum wage regulation, although benefiting the gig drivers, may be welfare diminishing to society at large. Our results can thus provide guidance to policy makers seeking a compromise between the interests of gig workers and society as a whole