The Empowerment Zone and Enterprise Community Initiative of 1993 offered targeted funding and tax incentives to distressed urban and rural communities. This initiative required a community-involvement component, setting it apart from more traditional economic development initiatives of the Reagan and Bush administrations. Using reports required by the U.S. Department of Housing and Urban Development (HUD) and census data, this study examines the programmatic emphases of four of the original six urban zones and evaluates the overall impact of zone programs on socioeconomic trends. These trends are evaluated by matching zone-designated census tracts to nonzone tracts through a propensity-score matching model using 1990 census data. Trends in poverty and other socioeconomic outcomes are measured by 1990-2000 change at the census tract level for individual zones, as well as across all zones using a series of fixed-effect models. Findings indicate that community building and involvement initiatives received the least amount of funding. Traditional economic development programs received the most emphasis but this did not translate into positive socioeconomic outcomes. With the exception of a few isolated incidences where individual zones fared better than comparison areas, zone initiatives had little impact.

Model selection strategies play an important, if not explicit, role in quantitative research. The inferential properties of these strategies are largely unknown, therefore, there is little basis for recommending (or avoiding) any particular set of strategies. In this paper, we evaluate several commonly used model selection procedures [Bayesian information criterion (BIC), adjusted R2, Mallows' Cp, Akaike information criteria (AIC), AICc, and stepwise regression] using Monte-Carlo simulation of model selection when the true data generating processes (DGP) are known. We find that the ability of these selection procedures to include important variables and exclude irrelevant variables increases with the size of the sample and decreases with the amount of noise in the model. None of the model selection procedures do well in small samples, even when the true DGP is largely deterministic; thus, data mining in small samples should be avoided entirely. Instead, the implicit uncertainty in model specification should be explicitly discussed. In large samples, BIC is better than the other procedures at correctly identifying most of the generating processes we simulated, and stepwise does almost as well. In the absence of strong theory, both BIC and stepwise appear to be reasonable model selection strategies in large samples. Under the conditions simulated, adjusted R2, Mallows' Cp AIC, and AICc are clearly inferior and should be avoided.model selection, BIC, AIC, stepwise regression,

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This article provides a demographic exposition of the changes in the U.S prison population during the period of mass incarceration that began in the late twentieth century. By drawing on data from the Survey of Inmates in State Correctional Facilities (1974–2004) for inmates 17–72 years of age (N = 336), we show that the age distribution shifted upward dramatically: Only 16 percent of the state prison population was 40 years old or older in 1974; by 2004, this percentage had doubled to 33 percent with the median age of prisoners rising from 27 to 34 years old. By using an estimable function approach, we find that the change in the age distribution of the prison population is primarily a cohort effect that is driven by the “enhanced” penal careers of the cohorts who hit young adulthood—the prime age of both crime and incarceration—when substance use was at its peak. Period-specific factors (e.g., proclivity for punishment and incidence of offense) do matter, but they seem to play out more across the life cycles of persons most affected in young adulthood (cohort effects) than across all age groups at one point in time (period effects).