Do White Police Officers Unfairly Target Black Suspects?

Using a unique data set we link the race of police officers who kill suspects with the race of those who are killed across the United States. We have data on a total of 2,706 fatal police killings for the years 2013 to 2015. This is 1,333 more killings by police than is provided by the FBI data on justifiable police homicides. We conducted three tests of discrimination. The results of these tests are different. In the first test we find some evidence that white officers are more likely to kill a black suspect who is later found to be unarmed than they are to kill an unarmed white suspect. However, this result could not be confirmed using a fixed effects model on panel data aggregated to the city level. In the second test, we find that white police officers are no more likely to kill an unarmed black suspect than are black or Hispanic officers. The results of this test are confirmed by the panel data version of the test. The third discrimination test indicated that black suspects, whether armed or not, are no more likely to be killed by a white officer than they are to be killed by black or Hispanic officers. Similarly, Hispanic suspects are no more likely to be killed by white offices than officers of other races. These results are also confirmed by panel data analyses. We find that when there is more than one officer on the scene, unarmed black suspects are not more likely to be killed by white police officers than unarmed white suspects. This could be evidence supporting a policy of reducing the number of officers working alone. Also, we find no evidence that body cameras affect either the number of police killings or the racial composition of those killings.

Intrahousehold Bargaining Power in Spain: An Empirical Test of the Collective Model

This paper analyzes the intrahousehold bargaining power of spouses in Spanish families, in a collective framework. We estimate household labor supply equations and, under certain testable restrictions, we obtain a theoretically derived sharing rule for household income, which characterizes intrahousehold bargaining power. Then, using unique data on decision-making in the household, we construct Pareto weights, and study the validity of the collective model by comparing the theoretical sharing rule and the constructed Pareto weight. The results reveal that both the observed Pareto weight and the theoretical sharing rule display qualitative similarities, thus providing direct empirical support to the collective model. Furthermore, the results suggest that Spanish wives behave more altruistically, while husbands behave more egoistically. This should be taken into account by policy makers and researchers when analyzing inequality in the household, and contemplating specific policies affecting the household.

Impact of Gender and Age on Claim Rates of Dread Disease and Cancer Insurance Policies in Taiwan

In this paper, the impact of both gender and age on the claim rates of dread disease and cancer insurance policies were examined using unique data taken from Taiwan’s private health insurance policies issued by non-life insurers during the 2012 to 2015 policy years. Those aged 30–39 served as the reference group. For the total number of dread disease policies, male insureds had a higher non-cancer claim probability than female insureds, while an age under 20 was associated with much lower claim rates for dread disease policies than for ages over 50. The claim rate for dread disease policies increased rapidly beginning at age 40 for both cancerous and non-cancerous diseases amongst male insureds. Amongst female insureds, those under 20 had much lower claim rates for dread disease policies. Only those aged 50–59 had a higher claim rate for non-cancerous diseases. For the total number of cancer insurance policies, male insureds had lower claim rates than female insureds, with an upward trend being associated with age. For male (female) insureds aged over 40 (20), the claim rates of cancer increased with age.

Economics of savings-based finance: an interest-free model of rotating savings and credit association in Turkey

Purpose The emerging markets have been witnessing a remarkable revival of rotating savings and credit associations (ROSCAs) serving as alternative informal financing and investment platforms, also known as savings-based finance (SBF) in Turkey. The purpose of this study is to present the SBF model mathematically, analyse the performance of the SBF sector and propose a new Sharīʿah-compliant SBF model for the acquisition of durables. Design/methodology/approach The paper thoroughly reviews the concept and practice of ROSCA across the globe, mathematically models and empirically analyses the performance of Turkish SBF companies using a unique data set. Findings The study formulates a two-person SBF model and proposes a Mudarabah-Wakalah hybrid model with a new investment feature. It is found that the concept of ROSCA is being operationalized in 105 countries across the globe under different names with slight business model modifications. The research also reveals that the demand for financing of durables in Turkey significantly increased in recent years with the demand for housing is twice greater compared to vehicles. Most importantly, a strong significant inter- and intra-comovement is observed between these durables implying that the success of the sector in one segment has attracted the customers to other SBF products. It shows that the SBF institutions can effectively serve as the alternative financing houses for pooling savings and financing the durables, and they have strong potential to capture a larger financial market share in Turkey and even globally. Originality/value The study constructs mathematical models and proposes a new investment wing to an existing SBF wealth fund.

Helpful Behavior and the Durability of Collaborative Ties

Long-term collaborations are crucial in many creative domains. Although there is ample research on why people collaborate, our knowledge about what drives some collaborations to persist and others to decay is still emerging. In this paper, we extend theory on third-party effects and collaborative persistence to study this question. We specifically consider the role that a third party’s helpful behavior plays in shaping tie durability. We propose that when third parties facilitate helpfulness among their group, the collaboration is stronger, and it persists even in the third’s absence. In contrast, collaborations with third parties that are nonhelpful are unstable and dissolve in their absence. We use a unique data set comprising scientific collaborations among pairs of research immunologists who lost a third coauthor to unexpected death. Using this quasi-random loss as a source of exogenous variation, we separately identify the effect of third parties’ traditional role as an active agent of collaborative stability and the enduring effect of their helpful behavior—as measured by acknowledgments—on the persistence of the remaining authors’ collaboration. We find support for our hypotheses and find evidence that one mechanism driving our effect is that helpful thirds make their coauthors more helpful.

Quantifying the Endogeneity in Online Donations

Charitable crowdfunding provides a new channel for people and families suffering from unforeseen events, such as accidents, severe illness, and so on, to seek help from the public. Thus, finding the key determinants which drive the fundraising process of crowdfunding campaigns is of great importance, especially for those suffering. With a unique data set containing 210,907 crowdfunding projects covering a period from October 2015 to June 2020, from a famous charitable crowdfunding platform, specifically Qingsong Chou, we will reveal how many online donations are due to endogeneity, referring to the positive feedback process of attracting more people to donate through broadcasting campaigns in social networks by donors. For this aim, we calibrate three different Hawkes processes to the event data of online donations for each crowdfunding campaign on each day, which allows us to estimate the branching ratio, a measure of endogeneity. It is found that the online fundraising process works in a sub-critical state and nearly 70–90% of the online donations are endogenous. Furthermore, even though the fundraising amount, number of donations, and number of donors decrease rapidly after the crowdfunding project is created, the measure of endogeneity remains stable during the entire lifetime of crowdfunding projects. Our results not only deepen our understanding of online fundraising dynamics but also provide a quantitative framework to disentangle the endogenous and exogenous dynamics in complex systems.

What’s in a “Username”? The Effect of Perceived Anonymity on Herding in Crowdfunding

This research investigates whether and how predecessors’ usernames—as evaluated from a perspective of perceived anonymity—affect successors’ herding momentum through the varying extent of perceived source credibility. Using a unique data set collected from a leading debt-based crowdfunding platform, we classify lenders’ usernames as either anonymous or real-seeming, with the latter referring to usernames that seem to reveal one’s legal name. We find that successors demonstrate weaker herding momentum toward predecessors who are presented with real-seeming usernames than anonymous ones. This finding, which we attribute to a lower extent of perceived credibility resulting from a nonconforming behavior, challenges the conventional wisdom that considers anonymity a negative factor for source credibility. Further, we demonstrate the importance of risk-related factors, in that the uncovered positive effect of perceived anonymity on herding is accentuated in the early stage of the fundraising period. Our findings provide actionable insights for platform owners to utilize the user heterogeneity with respect to perceived anonymity and hence perceived credibility in herding. These findings are also informative for borrowers who desire to exert effort to encourage participation from the crowd.

Negative Binomial Time Series Regression – Random Forest Ensemble in Intermittent Data

Intermittent dataset is a unique data that will be challenging to forecast. Because the data is containing a lot of zeros. The kind of intermittent data can be sales data and rainfall data. Because both sometimes no data recorded in a certain period. In this research, the model is created to overcome the problem. The approach that is used in this research is the ensemble method. Mostly the intermittent data comes from the Negative Binomial because the variance is over the mean. We use two datasets, which are rainfall and sales data. So, our approach is creating the base model from the time series regression with Negative Binomial based, and then we augmented the base model with a tree-based model which is random forest. Furthermore, we compare the result with the benchmark method which is The Croston method and Single Exponential Smoothing (SES). As the result, our approach can overcome the benchmark based on metric value by 1.79 and 7.18.

Race for Second Place? Explaining East-West Differences in Anti-Muslim Sentiment in Germany

It has been shown that anti-Muslim sentiment is more pronounced in East Germany than in West Germany. In this paper, we discuss existing explanations and add to them. We argue that some East Germans see themselves as a disadvantaged group in competition with other minorities, such as Muslims, for social recognition by West Germans; they are in what we call a “race for second place”. Based on social identity theory, we expect that this might be particularly true for those who explicitly self-identify as East Germans. The theoretical discussion carves out the role of “perceived non-recognition” and “outgroup mobility threat” as important concepts within the conflicts of belonging. We use unique data from the survey “Postmigrant Societies: East-Migrant Analogies” for a comprehensive empirical analysis. We find that factors related to pre-existing arguments – such as socioeconomic and demographic variables, personality traits, or contact – can capture much of the group differences in anti-Muslim sentiment, but that they do not fully apply to those who were born and still live in the East and who explicitly self-identify as East Germans. For this subgroup, perceived non-recognition adds to the empirical models and outgroup mobility threat has a stronger effect.