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Does political conflict with another country influence domestic consumers' daily consumption choices? We exploit the volatile US-China relations in 2018 and 2019 to analyze whether US consumers reduce their visits to Chinese restaurants when bilateral relations deteriorate. We measure the degree of political conflict through negativity in media reports and rely on smartphone location data to measure daily visits to over 190,000 US restaurants. A deterioration in US-China relations induces a significant decline in visits not only to Chinese but also to other foreign ethnic restaurants, while visits to typical American restaurants increase. We identify consumers' age, race, and cultural openness to moderate the strength of this ethnocentric effect.
External linkages allow nascent ventures to access crucial resources during the process of new product development. Forming external linkages can substantially contribute to a venture’s performance. However, little is known about the paths of external linkage formation, as well as the circumstances that drive the choice to pursue one rather than another path. This gap deserves further investigation, because we do not know whether insights developed for incumbent firms also apply to nascent ventures: To address this gap, we explore a novel dataset of 370 venture creation processes. Using sequence analyses based on optimal matching techniques and cluster analyses, we reveal that nascent ventures pursue one of overall four distinct paths of linkage formation activities during new product development. Contrary to the findings of the strategy literature, we find that if nascent ventures engage in external linkages at all, they do not combine exploration- and exploitation-oriented linkages but form either exploration- or exploitation-oriented linkages. Additional regression analyses highlight the circumstances that lead nascent ventures to pursue one rather than the other pathways. Taken together, our analyses point out that resource scarcity constitutes an important factor shaping the linkage formation activities of nascent ventures. Accordingly, we show that nascent ventures tend not to optimize by adding complementary knowledge to the firm’s knowledge base but rather to extend the existing knowledge base—a strategy which we call bricolage.
Nations are imposing unprecedented measures at a large scale to contain the spread of the COVID-19 pandemic. While recent studies show that non-pharmaceutical intervention measures such as lockdowns may have mitigated the spread of COVID-19, those measures also lead to substantial economic and social costs, and might limit exposure to ultraviolet-B radiation (UVB). Emerging observational evidence indicates the protective role of UVB and vitamin D in reducing the severity and mortality of COVID-19 deaths. This observational study empirically outlines the protective roles of lockdown and UVB exposure as measured by the ultraviolet index (UVI). Specifically, we examine whether the severity of lockdown is associated with a reduction in the protective role of UVB exposure. We use a log-linear fixed-effects model on a panel dataset of secondary data of 155 countries from 22 January 2020 until 7 October 2020 (n = 29,327). We use the cumulative number of COVID-19 deaths as the dependent variable and isolate the mitigating influence of lockdown severity on the association between UVI and growth rates of COVID-19 deaths from time-constant country-specific and time-varying country-specific potentially confounding factors. After controlling for time-constant and time-varying factors, we find that a unit increase in UVI and lockdown severity are independently associated with − 0.85 percentage points (p.p) and − 4.7 p.p decline in COVID-19 deaths growth rate, indicating their respective protective roles. The change of UVI over time is typically large (e.g., on average, UVI in New York City increases up to 6 units between January until June), indicating that the protective role of UVI might be substantial. However, the widely utilized and least severe lockdown (governmental recommendation to not leave the house) is associated with the mitigation of the protective role of UVI by 81% (0.76 p.p), which indicates a downside risk associated with its widespread use. We find that lockdown severity and UVI are independently associated with a slowdown in the daily growth rates of cumulative COVID-19 deaths. However, we find evidence that an increase in lockdown severity is associated with significant mitigation in the protective role of UVI in reducing COVID-19 deaths. Our results suggest that lockdowns in conjunction with adequate exposure to UVB radiation might have even reduced the number of COVID-19 deaths more strongly than lockdowns alone. For example, we estimate that there would be 11% fewer deaths on average with sufficient UVB exposure during the period people were recommended not to leave their house. Therefore, our study outlines the importance of considering UVB exposure, especially while implementing lockdowns, and could inspire further clinical studies that may support policy decision-making in countries imposing such measures.
The hierarchical feature regression (HFR) is a novel graph-based regularized regression estimator, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic patterns into the fitting process. The graph structure has the effect of shrinking parameters towards group targets, where the extent of shrinkage is governed by a hyperparameter, and group compositions as well as shrinkage targets are determined endogenously. The method offers rich resources for the visual exploration of the latent effect structure in the data, and demonstrates good predictive accuracy and versatility when compared to a panel of commonly used regularization techniques across a range of empirical and simulated regression tasks.
This cumulative dissertation contains four self-contained chapters on stochastic games and learning in intertemporal choice.
Chapter 1 presents an experiment on value learning in a setting where actions have both immediate and delayed consequences. Subjects make a series of choices between abstract options, with values that have to be learned by sampling. Each option is associated with two payoff components: One is revealed immediately after the choice, the other with one round delay. Objectively, both payoff components are equally important, but most subjects systematically underreact to the delayed consequences. The resulting behavior appears impatient or myopic. However, there is no inherent reason to discount: All rewards are paid simultaneously, after the experiment. Elicited beliefs on the value of options are in accordance with choice behavior. These results demonstrate that revealed impatience may arise from frictions in learning, and that discounting does not necessarily reflect deep time preferences. In a treatment variation, subjects first learn passively from the evidence generated by others, before then making a series of own choices. Here, the underweighting of delayed consequences is attenuated, in particular for the earliest own decisions. Active decision making thus seems to play an important role in the emergence of the observed bias.
Chapter 2 introduces and proves existence of Markov quantal response equilibrium (QRE), an application of QRE to finite discounted stochastic games. We then study a specific case, logit Markov QRE, which arises when players react to total discounted payoffs using the logit choice rule with precision parameter λ. We show that the set of logit Markov QRE always contains a smooth path that leads from the unique QRE at λ = 0 to a stationary equilibrium of the game as λ goes to infinity. Following this path allows to solve arbitrary finite discounted stochastic games numerically; an implementation of this algorithm is publicly available as part of the package sgamesolver. We further show that all logit Markov QRE are ε-equilibria, with a bound for ε that is independent of the payoff function of the game and decreases hyperbolically in λ. Finally, we establish a link to reinforcement learning, by characterizing logit Markov QRE as the stationary points of a game dynamic that arises when all players follow the well-established reinforcement learning algorithm expected SARSA.
Chapter 3 introduces the logarithmic stochastic tracing procedure, a homotopy method to compute stationary equilibria for finite and discounted stochastic games. We build on the linear stochastic tracing procedure (Herings and Peeters 2004), but introduce logarithmic penalty terms as a regularization device, which brings two major improvements. First, the scope of the method is extended: it now has a convergence guarantee for all games of this class, rather than just generic ones. Second, by ensuring a smooth and interior solution path, computational performance is increased significantly. A ready-to-use implementation is publicly available. As demonstrated here, its speed compares quite favorable to other available algorithms, and it allows to solve games of considerable size in reasonable times. Because the method involves the gradual transformation of a prior into equilibrium strategies, it is possible to search the prior space and uncover potentially multiple equilibria and their respective basins of attraction. This also connects the method to established theory of equilibrium selection.
Chapter 4 introduces sgamesolver, a python package that uses the homotopy method to compute stationary equilibria of finite discounted stochastic games. A short user guide is complemented with discussion of the homotopy method, the two implemented homotopy functions logit Markov QRE and logarithmic tracing, and the predictor-corrector procedure and its implementation in sgamesolver. Basic and advanced use cases are demonstrated using several example games. Finally, we discuss the topic of symmetries in stochastic games.