State executions, deterrence, and the incidence of murder

This study employs a panel of U.S. state-level data over the years 1978-1997 to estimate the deterrent effect of capital punishment. Particular attention is paid to problems of endogeneity bias arising from the non-random assignment of death penalty laws across states and a simultaneous relationship between murders and the deterrence probabilities. The primary innovation of the analysis lies in the estimation of a simultaneous equations system whose identification is based upon the employment of instrumental variables motivated by the theory of public choice. The estimation results suggest that structural estimates of the deterrent effect of capital punishment are likely to be downward biased due to the influence of simultaneity. Correcting for simultaneity, the estimates imply that a state execution deters approximately fourteen murders per year on average. Finally, the results also suggest that the announcement effect of capital punishment, as opposed to the existence of a death penalty provision, is the mechanism actually driving the deterrent effect associated with state executions.capital punishment, deterrence, executions, murder

Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting

Current AI regulations require discarding sensitive features (e.g., gender, race, religion) in the algorithm's decision-making process to prevent unfair outcomes. However, even without sensitive features in the training set, algorithms can persist in discrimination. Indeed, when sensitive features are omitted (fairness under unawareness), they could be inferred through non-linear relations with the so called proxy features. In this work, we propose a way to reveal the potential hidden bias of a machine learning model that can persist even when sensitive features are discarded. This study shows that it is possible to unveil whether the black-box predictor is still biased by exploiting counterfactual reasoning. In detail, when the predictor provides a negative classification outcome, our approach first builds counterfactual examples for a discriminated user category to obtain a positive outcome. Then, the same counterfactual samples feed an external classifier (that targets a sensitive feature) that reveals whether the modifications to the user characteristics needed for a positive outcome moved the individual to the non-discriminated group. When this occurs, it could be a warning sign for discriminatory behavior in the decision process. Furthermore, we leverage the deviation of counterfactuals from the original sample to determine which features are proxies of specific sensitive information. Our experiments show that, even if the model is trained without sensitive features, it often suffers discriminatory biases

The zooarchaeological application of quantifying cranial shape differences in wild boar and domestic pigs (Sus scrofa) using 3D geometric morphometrics

Peer reviewedPublisher PD

Regional trade policy options for Tanzania : the importance of services commitments

Despite the growing importance of commitments to foreign investors in services in regional trade agreements, there are no applied general equilibrium models in the literature that assess these regional impacts. This paper develops a 52 sector applied general equilibrium model of Tanzania with foreign direct investment, and uses that model to assess Tanzania's regional and multilateral trade options. The model incorporates the features of the modern theory of international trade that has shown empirically that trade and foreign direct investment can increase productivity, and trade and foreign direct investment with technologically advanced countries is especially valuable for that purpose. To assess the sensitivity of the results to parameter values, the model is executed 30,000 times, and the results are reported as confidence intervals of the sample distributions. The analysis finds that a 50 percent preferential reduction in the ad valorem equivalents of barriers in all business services by Tanzania with respect to its African regional partners would be slightly beneficial for Tanzania. But wider liberalization, with larger partners or multilaterally, it will yield much larger gains due to providing access to a much wider set of service providers. Finally, the results show that the largest gains in services would be derived from reduction of regulatory barriers that are geographically non-discriminatory.Economic Theory&Research,Transport Economics Policy&Planning,Emerging Markets,Environmental Economics&Policies,Banks&Banking Reform

Algorithms that "Don't See Color": Comparing Biases in Lookalike and Special Ad Audiences

Today, algorithmic models are shaping important decisions in domains such as credit, employment, or criminal justice. At the same time, these algorithms have been shown to have discriminatory effects. Some organizations have tried to mitigate these effects by removing demographic features from an algorithm's inputs. If an algorithm is not provided with a feature, one might think, then its outputs should not discriminate with respect to that feature. This may not be true, however, when there are other correlated features. In this paper, we explore the limits of this approach using a unique opportunity created by a lawsuit settlement concerning discrimination on Facebook's advertising platform. Facebook agreed to modify its Lookalike Audiences tool - which creates target sets of users for ads by identifying users who share "common qualities" with users in a source audience provided by an advertiser - by removing certain demographic features as inputs to its algorithm. The modified tool, Special Ad Audiences, is intended to reduce the potential for discrimination in target audiences. We create a series of Lookalike and Special Ad audiences based on biased source audiences - i.e., source audiences that have known skew along the lines of gender, age, race, and political leanings. We show that the resulting Lookalike and Special Ad audiences both reflect these biases, despite the fact that Special Ad Audiences algorithm is not provided with the features along which our source audiences are skewed. More broadly, we provide experimental proof that removing demographic features from a real-world algorithmic system's inputs can fail to prevent biased outputs. Organizations using algorithms to mediate access to life opportunities should consider other approaches to mitigating discriminatory effects

Sector concentration in loan portfolios and economic capital

The purpose of this paper is to measure the potential impact of business-sector concentration on economic capital for loan portfolios and to explore a tractable model for its measurement. The empirical part evaluates the increase in economic capital in a multi-factor asset value model for portfolios with increasing sector concentration. The sector composition is based on credit information from the German central credit register. Finding that business sector concentration can substantially increase economic capital, the theoretical part of the paper explores whether this risk can be measured by a tractable model that avoids Monte Carlo simulations. We analyze a simplified version of the analytic value-at-risk approximation developed by Pykhtin (2004), which only requires risk parameters on a sector level. Sensitivity analyses with various input parameters show that the analytic approximation formulae perform well in approximating economic capital for portfolios which are homogeneous on a sector level in terms of PD and exposure size. Furthermore, we explore the robustness of our results for portfolios which are heterogeneous in terms of these two characteristics. We find that low granularity ceteris paribus causes the analytic approximation formulae to underestimate economic capital, whereas heterogeneity in individual PDs causes overestimation. Indicative results imply that in typical credit portfolios, PD heterogeneity will at least compensate for the granularity effect. This suggests that the analytic approximations estimate economic capital reasonably well and/or err on the conservative side. --sector concentration risk,economic capital

Singular patterns of skull shape and brain size change in the domestication of South American camelids

Patterns of selection in South American camelids (Lamini) and their unique demographic history establish the llama and alpaca as remarkable cases of domestication among large herd animals. Skull shape is implicated in many changes reported between wild and domestic taxa. We apply 3D geometric morphometric methods to describe skull shape, form, and size, differences among the four species of Lamini. In so doing, we test if domesticated Lamini exhibit changes similar to those in other domesticated groups: not only in the skull, but also in brain and body size. In contrast to other domesticated artiodactyls, very little change has occurred in domestic alpacas and llamas compared to their wild counterparts. Nevertheless, their differences are statistically significant and include a flatter cranium, inclined palate and increased airorhynchy in the domestics. Selection pressures that contrast with those on other herd animals, as well as recent population bottlenecks, likely have influenced the morphological patterns we note in Lamini. High-resolution 3D morphospace allows skull size, shape, and form (shape + size), to discriminate all four species, with form providing the greatest separation. These results help differentiate morphologically the Lamini, which in nature are distinguished mainly by body size, and provide an additional tool to archaeologists for distinction of wild and domestic remains. Most of our shape analyses suggest a marginally closer relationship between the alpaca and vicuña, to the exclusion of the guanaco, supporting the genetic relationships for this group. The expected brain size change between wild and domestic populations is lower than previously thought, with a 15.4% reduction in llama, and 6.8% reduction in alpaca. This is the lowest reduction in brain size thus far reported among domesticated Artiodactyla

Does trade openness increase firm-level volatility?

From a theoretical point of view, greater trade openness affects firm-level volatility by changing the exposure and the reaction of firms to macroeconomic shocks. The net effect is ambiguous, though. This paper provides firm-level evidence on the link between openness and volatility. Using two novel datasets on German firms, we analyze the evolution of firm-level output volatility and the link between volatility and trade openness. We find that firm-level output volatility displays patterns similar to those found in aggregated data for Germany. Also, smaller firms and firms that grow faster are more volatile. Increased trade openness tends to lower volatility. --firm-level volatility,trade openness