Welfare Reform and Food Stamp Caseload Dynamics

We use state-level panel data for federal fiscal years 1980–1998 to estimate the impacts of welfare reform and the business cycle on food stamp caseloads. The model we employ is a dynamic function of past caseloads, economic factors, AFDC and Food Stamp Program policies, political factors, AFDC caseload levels, and unobserved fixed and trending heterogeneity. Our results suggest that the robust economy has substantially influenced the recent decline in food stamp caseloads, but that the estimated aggregate effect of welfare reform is modest—we attribute around 45 percent of 1994–1998 decline to the macroeconomy and about 5 percent to welfare reform. We do find substantial heterogeneity in the impact of AFDC waiver policies. States with JOBS sanctions policies but not family cap or earnings disregard waivers can expect a larger long-run decline in caseloads than those states with all three policies. In addition, we do find some evidence, albeit weaker, that states with waivers for unemployed able-bodied adults without dependents can expect higher caseload levels than states without the waivers and that the Electronic Benefits Transfer program is leading to food stamp caseload declines. An important finding of this study is that modeling food stamp caseload dynamics has implications for the estimated effects of policy changes and economic factors—when dynamic models are employed, we observe substantially reduced welfare-reform effects but significantly increased effects of the macroeconomy on food stamp caseloads. These results are robust to models that permit the simultaneous determination of AFDC and food stamp caseloads.

Accounting for the Decline in AFDC Caseloads: Welfare Reform or Economic Growth?

Nationwide, AFDC caseloads have decreased by about 18 percent since March 1994, while some states, such as Wisconsin, Indiana, and Oregon, have seen declines of 40 percent or more. Two factors are frequently suggested as possible causes: state-level experiments with welfare reform and strong economic growth. In this paper, we use state-level monthly panel data from 1987 to 1996 to assess the importance of each of these factors by estimating a model of AFDC caseloads as a dynamic function of time-dependent state welfare reform variables (welfare waivers) and economic variables such as per capita employment. Our results from the dynamic model suggest that the decline in per capita AFDC caseloads is attributable largely to the economic growth of states and not to waivers from federal welfare policies. In the 26 states experiencing at least a 20 percent decline in per capita AFDC caseloads between 1993 and 1996, we attribute 78 percent of the decline to business-cycle factors and 6 percent to welfare waivers.

Expanding Economic Opportunity for More Americans: Bipartisan Policies to Increase Work, Wages, and Skills

Many workers today find themselves lacking the skills and training necessary to thrive in the modern economy. Most low- and middle-income workers have not seen meaningful wage increases in many years. Millions of men and women are missing from the workforce altogether. These challenges stem from profound shifts in the American economy and necessitate a dedicated policy response.Over the course of the past year, the Aspen Economic Strategy Group collected policy ideas to address the barriers to broad-based economic opportunity and identified concrete proposals with bipartisan appeal. These proposals are presented here

An Information Theory Approach to Hypothesis Testing in Criminological Research

Background: This research demonstrates how the Akaike information criterion (AIC) can be an alternative to null hypothesis significance testing in selecting best fitting models. It presents an example to illustrate how AIC can be used in this way. Methods: Using data from Milwaukee, Wisconsin, we test models of place-based predictor variables on street robbery and commercial robbery. We build models to balance explanatory power and parsimony. Measures include the presence of different kinds of businesses, together with selected age groups and social disadvantage. Results: Models including place-based measures of land use emerged as the best models among the set of tested models. These were superior to models that included measures of age and socioeconomic status. The best models for commercial and street robbery include three measures of ordinary businesses, liquor stores, and spatial lag. Conclusions: Models based on information theory offer a useful alternative to significance testing when a strong theoretical framework guides the selection of model sets. Theoretically relevant ‘ordinary businesses’ have a greater influence on robbery than socioeconomic variables and most measures of discretionary businesses

QuantCrit: education, policy, ‘Big Data’ and principles for a critical race theory of statistics

Quantitative research enjoys heightened esteem among policy-makers, media and the general public. Whereas qualitative research is frequently dismissed as subjective and impressionistic, statistics are often assumed to be objective and factual. We argue that these distinctions are wholly false; quantitative data is no less socially constructed than any other form of research material. The first part of the paper presents a conceptual critique of the field with empirical examples that expose and challenge hidden assumptions that frequently encode racist perspectives beneath the façade of supposed quantitative objectivity. The second part of the paper draws on the tenets of Critical Race Theory (CRT) to set out some principles to guide the future use and analysis of quantitative data. These ‘QuantCrit’ ideas concern (1) the centrality of racism as a complex and deeply-rooted aspect of society that is not readily amenable to quantification; (2) numbers are not neutral and should be interrogated for their role in promoting deficit analyses that serve White racial interests; (3) categories are neither ‘natural’ nor given and so the units and forms of analysis must be critically evaluated; (4) voice and insight are vital: data cannot ‘speak for itself’ and critical analyses should be informed by the experiential knowledge of marginalized groups; (5) statistical analyses have no inherent value but can play a role in struggles for social justice

Maintaining (locus of) control? : Assessing the impact of locus of control on education decisions and wages

This paper establishes that individuals with an internal locus of control, i.e., who believe that reinforcement in life comes from their own actions instead of being determined by luck or destiny, earn higher wages. However, this positive effect only translates into labor income via the channel of education. Factor structure models are implemented on an augmented data set coming from two different samples. By so doing, we are able to correct for potential biases that arise due to reverse causality and spurious correlation, and to investigate the impact of premarket locus of control on later outcomes