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.

The value-added of primary schools: what is it really measuring?

This paper compares the official value-added scores in 2005 for all primary schools in three adjacent LEAs in England with the raw-score Key Stage 2 results for the same schools. The correlation coefficient for the raw- and value-added scores of these 457 schools is around +0.75. Scatterplots show that there are no low attaining schools with average or higher value-added, and no high attaining schools with below average value-added. At least some of the remaining scatter is explained by the small size of some schools. Although some relationship between these measures is to be expected – so that schools adding considerable value would tend to have high examination outcome scores – the relationship shown is too strong for this explanation to be considered sufficient. Value-added analysis is intended to remove the link between a schools’ intake scores and their raw-score outcomes at KS2. It should lead to an estimate of the differential progress made by pupils, assessed between schools. In fact, however, the relationship between value-added and raw scores is of the same size as the original relationship between intake scores and raw-scores that the value-added is intended to overcome. Therefore, however appealing the calculation of value-added figures is, their development is still at the stage where they are not ready to move from being a research tool to an instrument of judgement on schools. Such figures may mislead parents, governors and teachers and, even more importantly, they are being used in England by OFSTED to pre-determine the results of school inspections

Utility of patient-derived lymphoblastoid cell lines as an ex vivo capecitabine sensitivity prediction model for breast cancer patients.

Capecitabine is commonly used in treating breast cancer; however, therapeutic response varies among patients and there is no clinically validated model to predict individual outcomes. Here, we investigated whether drug sensitivity quantified in ex vivo patients' blood-derived cell lines can predict response to capecitabine in vivo. Lymphoblastoid cell lines (LCLs) were established from a cohort of metastatic breast cancer patients (n = 53) who were prospectively monitored during treatment with single agent capecitabine at 2000 mg/m2/day. LCLs were treated with increasing concentrations of 5'-DFUR, a major capecitabine metabolite, to assess patients' ex vivo sensitivity to this drug. Subsequently, ex vivo phenotype was compared to observed patient disease response and drug induced-toxicities. We acquired an independent cohort of breast cancer cell lines and LCLs derived from the same donors from ATCC, compared their sensitivity to 5'-DFUR. As seen in the patient population, we observed large inter-individual variability in response to 5'-DFUR treatment in patient-derived LCLs. Patients whose LCLs were more sensitive to 5'-DFUR had a significantly longer median progression free survival (9-month vs 6-month, log rank p-value = 0.017). In addition, this significant positive correlation for 5'-DFUR sensitivity was replicated in an independent cohort of 8 breast cancer cell lines and LCLs derived from the same donor. Our data suggests that at least a portion of the individual sensitivity to capecitabine is shared between germline tissue and tumor tissue. It also supports the utility of patient-derived LCLs as a predictive model for capecitabine treatment efficacy in breast cancer patients

Profitable failure: antidepressant drugs and the triumph of flawed experiments

Drawing on an analysis of Irving Kirsch and colleagues? controversial 2008 article in PLoS [Public Library of Science] Medicine on the efficacy of SSRI antidepressant drugs such as Prozac, I examine flaws within the methodologies of randomized controlled trials (RCTs) that have made it difficult for regulators, clinicians and patients to determine the therapeutic value of this class of drug. I then argue, drawing analogies to work by Pierre Bourdieu and Michael Power, that it is the very limitations of RCTs ? their inadequacies in producing reliable evidence of clinical effects ? that help to strengthen assumptions of their superiority as methodological tools. Finally, I suggest that the case of RCTs helps to explore the question of why failure is often useful in consolidating the authority of those who have presided over that failure, and why systems widely recognized to be ineffective tend to assume greater authority at the very moment when people speak of their malfunction

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

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

Golden Rule of Forecasting: Be Conservative

This article proposes a unifying theory, or the Golden Rule, or forecasting. The Golden Rule of Forecasting is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek out and use all knowledge relevant to the problem, including knowledge of methods validated for the situation. Twenty-eight guidelines are logically deduced from the Golden Rule. A review of evidence identified 105 papers with experimental comparisons; 102 support the guidelines. Ignoring a single guideline increased forecast error by more than two-fifths on average. Ignoring the Golden Rule is likely to harm accuracy most when the situation is uncertain and complex, and when bias is likely. Non-experts who use the Golden Rule can identify dubious forecasts quickly and inexpensively. To date, ignorance of research findings, bias, sophisticated statistical procedures, and the proliferation of big data, have led forecasters to violate the Golden Rule. As a result, despite major advances in evidence-based forecasting methods, forecasting practice in many fields has failed to improve over the past half-century

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