Stata's graphics capabilities--while certainly powerful--have always struck me as unduly complicated, certainly when it comes to coding graph commands. Consequently, it is with trepidation that I note a recent Stata Blog posting that illustrates Stata's animated graphics possibilities. (I also note that such efforts will necessarily involve video editing software (such as Camtasia or FFmpeg)). For those willing to invest the time/effort, however, the end results can be quite clever and interesting. Those inclined (and with time to burn) might want to look at some examples here.
“Whether as a result of low crime rates, the financial pressures of the 2008 credit crunch, or other factors, policymakers on both sides of the aisle are trying to rein or even reduce the US incarceration rate after an unprecedented forty-year expansion. Unfortunately, reforms are hampered by the fact that we do not have a solid empirical understanding of what caused the explosion in the first place. In fact, the "Standard Story" of prison growth generally overemphasizes less important factors and overlooks more important ones. This essay thus does two things. First, it points out the flaws in five key aspects of the Standard Story: its argument that the War on Drugs is of central importance, that trends in violent and property crimes are relatively unimportant, that longer sentence lengths drive growth, that the "criminal justice system" is a fairly coherent entity advancing specific goals, and that the “politics of crime control” is uniquely dysfunctional. And second, it argues that an increased willingness of the part of prosecutors to file charges — a causal factor almost completely overlooked by the Standard Story — is likely the most important force behind prison growth, at least for the past two decades.”
In an interesting exchange between Jacob Felson and Andrew Gelman, Gelman recommends resisting the urge to "adjudicate" competing interpretations of results (or, more specifically, "picking" one significant variable as a "cause" and another significant variable as a "moderator"). Gelman writes: "Rather than trying to isolate a single causal path, consider different cases of forward casual inference. My guess is that the different stories regarding moderators etc. could motivate different thought experiments (and, ultimately, different observational studies) regarding different potential interventions."
The SELS blog includes a link to a recent paper in JELS (11:1, 2014) that explores data breach litigation. In Empirical Analysis of Data Breach Litigation, Romanosky (NYU) et al., take an initial look into a decade's worth of privacy litigation with a particular eye towards disposition differences. The abstract follows.
"In recent years, many lawsuits have been filed by individuals seeking legal redress for harms caused by the loss or theft of their personal information. However, very little is known about the drivers, mechanics, and outcomes of those lawsuits, making it difficult to assess the effectiveness of litigation at balancing organizations’ usage of personal data with individual privacy rights. Using a unique and manually collected database, we analyze court dockets for more than 230 federal data breach lawsuits from 2000 to 2010. We investigate two questions: Which data breaches are being litigated? And which data breach lawsuits are settling? Our results suggest that the odds of a firm being sued are 3.5 times greater when individuals suffer financial harm, but 6 times lower when the firm provides free credit monitoring. Moreover, defendants settle 30 percent more often when plaintiffs allege financial loss, or when faced with a certified class action suit. By providing the first comprehensive empirical analysis of data breach litigation, our findings offer insight into the debate over privacy litigation versus privacy regulation."
By addressing important causation challenges, identical twins provide unique research design opportunities. A well-designed study exploiting identical twins facilitates the need to control for unobservables--including as an important subset, genetic endowments--in analyses that explore the impact of one variable on outcomes of interest. An article in today's NYT (here) describes NASA's current effort to increase understanding of the effects of living in space by studying identical twin astronauts, one of whom will soon return from a full year aboard the International Space Station.