Visible hands
A deterministic worldview promotes approval of state paternalism
Ivar Hannikainen et al.
Journal of Experimental Social Psychology, May 2017, Pages 251-259
Abstract:
The proper limit to paternalist regulation of citizens' private lives is a recurring theme in political theory and ethics. In the present study, we examine the role of beliefs about free will and determinism in attitudes toward libertarian versus paternalist policies. Throughout five studies we find that a scientific deterministic worldview reduces opposition toward paternalist policies, independent of the putative influence of political ideology. We suggest that exposure to scientific explanations for patterns in human behavior challenges the notion of personal autonomy and, in turn, undermines libertarian arguments against state paternalism appealing to autonomy and personal choice.
Political Influence and Merger Antitrust Reviews
Mihir Mehta, Suraj Srinivasan & Wanli Zhao
University of Michigan Working Paper, March 2017
Abstract:
We document that firms linked to powerful U.S. politicians that oversee merger antitrust regulators receive favorable mergers and acquisition antitrust review outcomes. When acquirers are constituents of these politicians, mergers are likely to encounter a more favorable review process. In contrast, when targets are constituents, the merger antitrust review outcomes are dependent on the target's partiality towards the merger. To establish identification, we exploit a subset of politician turnover events that are plausibly exogenous as well as a falsification test using politicians with no jurisdiction over antitrust regulators. Politician incentives to influence merger antitrust review outcomes appear to be driven by lobbying, contributions, and prior business connections. Our findings suggest that merger antitrust reviews are not independent of self-serving political intervention.
The Digital Privacy Paradox: Small Money, Small Costs, Small Talk
Susan Athey, Christian Catalini & Catherine Tucker
MIT Working Paper, February 2017
Abstract:
This paper uses data from the MIT digital currency experiment to shed light on consumer behavior regarding commercial, public and government surveillance. The setting allows us to explore the apparent contradiction that many cryptocurrencies offer people the chance to escape government surveillance, but do so by making transactions themselves public on a distributed ledger (a 'blockchain'). We find three main things. First, the effect of small incentives may explain the privacy paradox, where people say they care about privacy but are willing to relinquish private data quite easily. Second, small costs introduced during the selection of digital wallets by the random ordering of featured options, have a tangible effect on the technology ultimately adopted, often in sharp contrast with individual stated preferences about privacy. Third, the introduction of irrelevant, but reassuring information about privacy protection makes consumers less likely to avoid surveillance at large.
Are Ideas Getting Harder to Find?
Nicholas Bloom et al.
Stanford Working Paper, March 2017
Abstract:
In many growth models, economic growth arises from people creating ideas, and the long-run growth rate is the product of two terms: the effective number of researchers and the research productivity of these people. We present a wide range of evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore's Law. The number of researchers required today to achieve the famous doubling every two years of the density of computer chips is more than 75 times larger than the number required in the early 1970s. Across a broad range of case studies at various levels of (dis)aggregation, we find that ideas - and in particular the exponential growth they imply - are getting harder and harder to find. Exponential growth results from the large increases in research effort that offset its declining productivity.
Entrepreneurship, Innovation, and Political Competition: How the Public Sector Helps the Sharing Economy Create Value
Yongwook Paik, Sukhun Kang & Robert Seamans
Washington University in St. Louis Working Paper, February 2017
Abstract:
With the recent growth of the sharing economy, regulators must frequently strike the right balance between private and public interests to maximize value creation. In this paper, we argue that political competition is a critical ingredient that explains whether cities accommodate or ban ridesharing platforms and that this relationship is moderated in more populous cities or in cities with higher unemployment rate. We empirically test our arguments using archival data covering ridesharing bans in various U.S. cities during the 2011-2015 period, which we supplement with semi-structured interviews. We find broad support for our arguments while mitigating potential endogeneity concerns. Our study has important implications for the nonmarket strategy, entrepreneurship and innovation, and public-private partnership literatures, in addition to informing policy debates on the sharing economy.
What is a Patent Worth? Evidence from the U.S. Patent "Lottery"
Joan Farre-Mensa, Deepak Hegde & Alexander Ljungqvist
NBER Working Paper, March 2017
Abstract:
We provide evidence on the value of patents to startups by leveraging the random assignment of applications to examiners with different propensities to grant patents. Using unique data on all first-time applications filed at the U.S. Patent Office since 2001, we find that startups that win the patent "lottery" by drawing lenient examiners have, on average, 55% higher employment growth and 80% higher sales growth five years later. Patent winners also pursue more, and higher quality, follow-on innovation. Winning a first patent boosts a startup's subsequent growth and innovation by facilitating access to funding from VCs, banks, and public investors.
Compulsory Licensing and Innovation - Historical Evidence from German patents after WWI
Joerg Baten, Nicola Bianchi & Petra Moser
Journal of Development Economics, May 2017, Pages 231-242
Abstract:
Compulsory licensing allows governments to license patented inventions without the consent of patent owners. Intended to mitigate the potential welfare losses from enforcing foreign-owned patents, many developing countries use this policy to improve access to drugs that are covered by foreign-owned patents. The effects of compulsory licensing on access to new drugs, however, are theoretically ambiguous: Compulsory licensing may encourage innovation by increasing competition or discourage innovation by reducing expected returns to R&D. Empirical evidence is rare, primarily because contemporary settings offer little exogenous variation in compulsory licensing. We address this empirical challenge by exploiting an event of compulsory licensing as a result of World War I when the US Trading with the Enemy Act made all German-owned patents available for licensing to US firms. Firm-level data on German patents indicate that compulsory licensing was associated with a 30 percent increase in invention by German firms whose inventions were licensed.
Can mergers increase output? Evidence from the lodging industry
Arturs Kalnins, Luke Froeb & Steven Tschantz
RAND Journal of Economics, Spring 2017, Pages 178-202
Abstract:
We find that hotel mergers increase occupancy. In some specifications, price also rises. Because these effects occur only in markets with high capacity utilization and high uncertainty, we reject simple models of price or quantity competition in favor of models of "revenue management," where firms price to fill available capacity in the face of uncertain demand.
Regulating by Robot: Administrative Decision Making in the Machine-Learning Era
Cary Coglianese & David Lehr
Georgetown Law Journal, forthcoming
Abstract:
Machine-learning algorithms are transforming large segments of the economy, underlying everything from product marketing by online retailers to personalized search engines, and from advanced medical imaging to the software in self-driving cars. As machine learning's use has expanded across all facets of society, anxiety has emerged about the intrusion of algorithmic machines into facets of life previously dependent on human judgment. Alarm bells sounding over the diffusion of artificial intelligence throughout the private sector only portend greater anxiety about digital robots replacing humans in the governmental sphere. A few administrative agencies have already begun to adopt this technology, while others have the clear potential in the near-term to use algorithms to shape official decisions over both rulemaking and adjudication. It is no longer fanciful to envision a future in which government agencies could effectively make law by robot, a prospect that understandably conjures up dystopian images of individuals surrendering their liberty to the control of computerized overlords. Should society be alarmed by governmental use of machine learning applications? We examine this question by considering whether the use of robotic decision tools by government agencies can pass muster under core, time-honored doctrines of administrative and constitutional law. At first glance, the idea of algorithmic regulation might appear to offend one or more traditional doctrines, such as the nondelegation doctrine, procedural due process, equal protection, or principles of reason-giving and transparency. We conclude, however, that when machine-learning technology is properly understood, its use by government agencies can comfortably fit within these conventional legal parameters. We recognize, of course, that the legality of regulation by robot is only one criterion by which its use should be assessed. Obviously, agencies should not apply algorithms cavalierly, even if doing so might not run afoul of the law, and in some cases, safeguards may be needed for machine learning to satisfy broader, good-governance aspirations. Yet in contrast with the emerging alarmism, we resist any categorical dismissal of a future administrative state in which key decisions are guided by, and even at times made by, algorithmic automation. Instead, we urge that governmental reliance on machine learning should be approached with measured optimism over the potential benefits such technology can offer society by making government smarter and its decisions more efficient and just.
Does Quality Matter in Local Consumption Amenities? An Empirical Investigation with Yelp
Chun Kuang
Journal of Urban Economics, forthcoming
Abstract:
The possibility that local consumption amenities provided by bars, restaurants, and other retail services improve neighborhood or city attractiveness has received increasing attention in the literature. Empirical research thus far has focused on the number of establishments in an area. This paper proposes and tests a method for differentiating consumption amenities along a quality dimension, based on either consumer ratings or price estimates from Yelp.com. Appealing to the implicit market model of Rosen (1974), consumption amenity is capitalized in the value of nearby housing. The results demonstrate that both the quantity and quality aspects of consumption amenities matter, and that consumer ratings are more informative about unobservable restaurant amenity than price estimates. Furthermore, comparisons between the results for the pre- and post-Yelp periods show that such capitalization differentials are more significant when information on quality is readily available to and widely used by the public. The method used in this paper to measure the quality of consumption amenities could be applied to other private retail businesses or even local public goods.
Does Peer Review Work? An Experiment of Experimentalism
Daniel Ho
Stanford Law Review, January 2017, Pages 1-119
Abstract:
Ensuring the accuracy and consistency of highly decentralized and discretionary decisionmaking is a core challenge for the administrative state. The widely influential school of "democratic experimentalism" posits that peer review - the direct and deliberative evaluation of work product by peers in the discipline - provides a way forward, but systematic evidence remains limited. This Article provides the first empirical study of the feasibility and effects of peer review as a governance mechanism based on a unique randomized controlled trial conducted with the largest health department in Washington State (Public Health-Seattle and King County). We randomly assigned half of the food safety inspection staff to engage in an intensive peer review process for over four months. Pairs of inspectors jointly visited establishments, separately assessed health code violations, and deliberated about divergences on health code implementation. Our findings are threefold. First, observing identical conditions, inspectors disagreed 60% of the time. These joint inspection results in turn helped to pinpoint challenging code items and to develop training and guidance documents efficiently during weekly sessions. Second, analyzing over 28,000 independently conducted inspections across the peer review and control groups, we find that the intervention caused an increase in violations detected and scored by 17% to 19%. Third, peer review appeared to decrease variability across inspectors, thereby improving the consistency of inspections. As a result of this trial, King County has now instituted peer review as a standard practice. Our study has rich implications for the feasibility, promise, practice, and pitfalls of peer review, democratic experimentalism, and the administrative state.
Do Markets Make Good Commissioners?: A Quasi-Experimental Analysis of Retail Electric Restructuring in Ohio
Noah Dormady, Zhongnan Jiang & Matthew Hoyt
Ohio State University Working Paper, February 2017
Abstract:
Empirical support for the purported benefits of retail electric deregulation is mixed at best. Prior studies that refer to states as simply "retail deregulated" overlook the fact that efforts in many states to introduce retail competition have been muddied by various degrees of regulatory intervention. Those studies are often based upon Energy Information Administration (EIA) 826 data that does not account for large costs that end-customers incur - which amount to more than 50 percent of the total bill in states like Ohio. Using robust time series household final bill survey data from the Public Utilities Commission of Ohio (PUCO), this paper provides a quasi-experimental analysis of the price impacts of retail electric restructuring in Ohio. The results suggest that residential electricity prices have increased following retail restructuring in all service territories in Ohio, with the exception of the Cincinnati area. We also provide welfare impact estimates for each utility service territory - which indicate a statewide net loss of approximately a billion dollars to residential standard service offer (SSO) customers since retail restructuring.
Even good bots fight: The case of Wikipedia
Milena Tsvetkova et al.
PLoS ONE, February 2017
Abstract:
In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful. In this article, we analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other's edits over the period 2001-2010, model how pairs of bots interact over time, and identify different types of interaction trajectories. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other's edits and these sterile "fights" may sometimes continue for years. Unlike humans on Wikipedia, bots' interactions tend to occur over longer periods of time and to be more reciprocated. Yet, just like humans, bots in different cultural environments may behave differently. Our research suggests that even relatively "dumb" bots may give rise to complex interactions, and this carries important implications for Artificial Intelligence research. Understanding what affects bot-bot interactions is crucial for managing social media well, providing adequate cyber-security, and designing well functioning autonomous vehicles.