1. What Do CDC’s Surveys Say About the Frequency of Defensive Gun Uses? by Gary Kleck (Florida State University – College of Criminology and Criminal Justice)
2. Detection of False Investment Strategies Using Unsupervised Learning Methods by Marcos Lopez de Prado (Lawrence Berkeley National Laboratory) and Michael J. Lewis (New York University (NYU) – Courant Institute of Mathematical Sciences)
We were inspired by an apparent paradox: the academic literature on entrepreneurs largely describes them as “misfits” who can’t find, can’t stand, or can’t stay in, conventional employment. And yet, history and the popular press portray entrepreneurs as highly productive self-made visionaries, rejected by employers who could not discern their exceptional talents. But are these anecdotes merely a biased sample of successful entrepreneurs? We wanted to dig deeper into why individuals become entrepreneurs and what makes some successful.
In this paper, we formally derive and empirically confirm that asymmetric information about worker ability can drive anyone, who believes her productive capabilities to be greater than employers can perceive, to become an entrepreneur. This explanation holds over a spectrum of entrepreneurs: from immigrant food vendors (whose foreign credentials often go unrecognized) to college dropout technology moguls. Identifying this novel driver of entrepreneurial choice also moves us a step toward cracking the entrepreneurial earnings puzzle. Why individuals choose entrepreneurship likely influences their income—those choosing it for non-pecuniary benefits may reasonably earn less than employees, while those strategically responding to asymmetric information in the labor market about their ability, should earn more. – Deepak Hegde and Justin Tumlinson
4. Some Simple Economics of the Blockchain by Christian Catalini (Massachusetts Institute of Technology (MIT) – Sloan School of Management) and Joshua S. Gans (University of Toronto – Rotman School of Management)
The paper relies on economic theory to surface two key costs affected by blockchain technology: the cost of verification of transaction attributes, and the cost of bootstrapping and operating a digital marketplace without the need for a traditional intermediary. When combined with a native token (as in Bitcoin and Ethereum), a blockchain allows a decentralized network of economic agents to agree, at regular intervals, about the true state of shared data.This shared data can represent exchanges of currency, intellectual property, equity, information or other types of contracts and digital assets – making blockchain a general purpose technology that can be used to trade scarce, digital property rights and create novel types of digital platforms. The resulting marketplaces are characterized by increased competition, lower barriers to entry and innovation, lower privacy and censorship risk, and allow participants within the same ecosystem to make investments to support and operate shared infrastructure without assigning market power to a platform operator. – Christian Catalini