Child Abuse and the Experience of Violence in College Dating Relationships: Examining the Moderating Effect of Gender and Race

Prior research suggests that being abused during childhood significantly increases one’s risk for the involvement in violent intimate relationships across the life course. The current study contributes to the literature by using a large sample of college students to explore the moderating effects of race and gender on the relationship between child abuse and later experiences of intimate partner violence. Results indicate that for both men and women, child abuse increases the risk for dating violence. Findings also suggest that specific race and gender combinations moderate the relationship between child abuse and later experiences of IPV, specifically regarding the risk for mutual or bidirectional violence. Implications for prevention and intervention and directions for future research are discussed.     

Political Choice, Public Policy, and Distributional Outcomes

I address the functioning of the U.S. governing system by analyzing distributional outcomes from 1947 to 2000. The key question is whether public policy influences distributional outcomes. The macropolitics model and power resource theory suggest that left policies should equalize the distribution of income. I utilize single equation error correction models to assess the impact of policy on income inequality through two mechanisms—market conditioning and redistribution. Since nearly every government action influences markets in some way, I examine policy in the aggregate rather than focusing only on policies explicitly designed to redistribute income. The analysis indicates that policy influences inequality through both mechanisms, with left policy producing more equality. The results are consistent with power resource theory and strongly support the macropolitics model. Furthermore, I find that market conditioning is as important as, and works in tandem with, explicit redistribution.     

What is an algorithm? Financial regulation in the era of high-frequency trading

Abstract

In response to the flash crashes and market manipulations blamed on high-frequency trading (HFT), algorithms have been brought inside the regulatory perimeter. This paper focuses on the most ambitious regulation directed at the practice: the algorithm-tagging rule in the German High-Frequency Trading Act. Fifteen interviews with stakeholders in the Act’s implementation serve to reconstruct how regulators defined an algorithm and help pose the question of to what extent regulatory definitions and data need accurately to represent financial practices to be useful. Although tentative in its findings, the research suggests that the algorithm-tagging rule may be providing valuable signals in the noise to trade surveillance officers and having virtuous effects on the cultures of trading firms. The conclusion argues that sociologists of finance should adopt a more balanced approach when evaluating regulatory technologies and heed MacKenzie’s 2005 call to open up their black boxes.