The role of the entrepreneurial encounter in the emergence of opportunities: Vallée’s Dallas Buyers Club

ABSTRACT

This paper aims to explore the conceptual potential of the Deleuzian notion of the encounter in order to better understand the genealogy of opportunities. We adopt a processual perspective of opportunities. In order to translate this notion to the domain of entrepreneurship, we analysed and interpreted Jean-Marc Vallée’s Dallas Buyers Club. This film follows the creation of the first club in the United States that illegally allowed HIV-positive people to supply themselves with foreign antiretroviral drugs from Mexico or Japan. The article highlights encounters in this process that disturb the entrepreneur’s belief systems and allow him or her to be open to potential opportunity. It finally explores how the encounter may improve our understanding of the political becoming of opportunities within the entrepreneurial process.     

Ambiguity in election games

We construct a model in which the ambiguity of candidates allows them to increase the number of voters to whom they appeal. We focus our analysis on two points that are central to obtain ambiguity in equilibrium: restrictions on the beliefs that candidates can induce in voters, and intensity of voters' preferences. The first is necessary for a pure strategy equilibrium to exist, while the second is necessary for ambiguity in equilibrium when there exists a Condorcet winner in the set of pure alternatives (e.g. the spatial model of electoral competition), and when candidates' only objective is to win the election. In this last case, an ambiguous candidate may offer voters with different preferences the hope that their most preferred alternative will be implemented. We also show that if there are sufficiently many candidates or parties, ambiguity will not be possible in equilibrium, but a larger set of possible policies increases the chance that at least one candidate will choose to be ambiguous in equilibrium. We would like to thank Alberto Alesina, Antonio Cabrales, Steve Coate, Olivier Compte, Tim Feddersen, Itzhak Gilboa, Joe Harrington, Michel Le Breton, Alessandro Lizzeri, George Mailath, Steve Matthews, Steve Morris, Ignacio Ortuno, Tom Palfrey, Larry Samuelson, Murat Sertel, Fernando Vega, Eyal Winter and an anonymous referee for helpful comments. The first author acknowledges financial support from DGICYT-PB 95-0983. This work was done while the first author was visiting the Center in Political Economy at Washington University, and visiting the Center for Basic Research in the Social Sciences at Harvard University. Their hospitality is gratefully acknowledged. The support of the second author's research by the National Science Foundation is also gratefully acknowledged.     

Using technology to improve the management of development impacts on biodiversity

The mitigation hierarchy (MH) is a prominent tool to help businesses achieve no net loss or net gain outcomes for biodiversity. Technological innovations offer benefits for business biodiversity management, yet the range and continued evolution of technologies creates a complex landscape that can be difficult to navigate. Using literature review, online surveys, and semi-structured interviews, we assess technologies that can improve application of the MH. We identify six categories (mobile survey, fixed survey, remote sensing, blockchain, data analysis, and enabling technologies) with high feasibility and/or relevance to (i) aid direct implementation of mitigation measures and (ii) enhance biodiversity surveys and monitoring, which feed into the design of interventions including avoidance and minimization measures. At the interface between development and biodiversity impacts, opportunities lie in businesses investing in technologies, capitalizing on synergies between technology groups, collaborating with conservation organizations to enhance institutional capacity, and developing practical solutions suited for widespread use.     

Does Team-Based Compensation Give Rise to Problems When Agents Vary in Their Ability?

This paper reports the results of an experiment on how team heterogeneity in terms of productivity influences both the revenue sharing proposed by the principal to the team and the employees' performance. Experimental evidence shows that when the team is heterogeneous, the principal does not try to motivate the agents through her sharing offer. Regardless of the level of team-based compensation, a large amount of free riding occurs since each agent is mainly influenced by his teammate's behavior. In contrast, when the team is homogeneous, agents are better able to cooperate, reciprocating the principal's offer.     

Some results about averaging in stochastic approximation

The paper presents some results concerning the averaging approach in a general linear regression model in one dimension under suitable conditions about the martingale structure of errors. At first asymptotics of the primary and averaged estimators are discussed. Then it is shown that variances of estimators can be consistently estimated by appropriate integrated squared deviations functionals. Finally applications to the construction of confidence regions are considered.     

ON‐THE‐JOB SEARCH,PRODUCTIVITY SHOCKS,AND THE INDIVIDUAL EARNINGS PROCESS*

Individual labor earnings observed in worker panel data have complex, highly persistent dynamics. We investigate the capacity of a structural job search model with on‐the‐job search, wage renegotiation by mutual consent, and i.i.d. productivity shocks to replicate salient properties of these dynamics, such as the covariance structure of earnings, the evolution of individual earnings mean, and variance with the duration of uninterrupted employment, or the distribution of year‐to‐year earnings changes. Structural estimation of our model on a 12‐year panel of highly educated British workers shows that our simple framework produces a dynamic earnings structure that is remarkably consistent with the data.     

Environmental practices in the wine industry: An empirical application of the theory of reasoned action and stakeholder theory in the United States and New Zealand

Industry transformation related to environmental stewardship has received significant scholarly attention over the past decade. However, limited theoretical and empirical work examines the motivations for improving environmental performance in an industry in different countries. In this paper, we develop a set of hypotheses, based in the theory of reasoned action and stakeholder theory, regarding drivers of the adoption of environmental practices in the wine industries of New Zealand and the United States. We test our hypotheses using data from survey questionnaires collected in each country. Our findings suggest that subjective norms and internal stakeholder pressures are common drivers of the adoption of environmental practices in these two countries. However, managerial attitudes and external stakeholder pressures are not significant drivers. We also find that managerial attitudes and export dependence are stronger determinants of environmental practice adoption in New Zealand compared to the U.S.     

Twenty years of economic transition in Russia: Results and lessons learned

The present article studies the events and processes that took place during the socioeconomic transformation in Russia. The positive and negative consequences of the reforms are considered, and their macroeconomic results are assessed. The article reveals the causes of the weak points in the present-day Russian economy and focuses on mistakes and wrong solutions that should be avoided in the future.     

Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black–Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed form formula for integration. Moreover, these two methods solve the backward dynamic programing problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of the binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite that this model is only bidimensional, the whole history of the process impacts on the price, and how to handle all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.