Assessing Performance Budgeting at OMB: The Influence of Politics, Performance, and Program Size

This article investigates the impact of performance, as measuredby the OMB performance budgeting initiative called PerformanceAssessment Rating Tool (PART), on recommendations in the President'sbudget. In a multivariate analysis using data from the FY 2005budget, with appropriate controls for the political contentof programs, we find that the PART scores have a statisticallysignificant impact on budget decisions within OMB. We find thatPART scores have a larger impact on small and medium sized programsthan on large programs. We also find that the "results" componentof PART scores has a smaller impact on budget decisions thanthe "program purpose" component, a finding which tends to contradictthe goal of performance budgeting to redirect resources to programsthat produce results. The relative unimportance of the "results"component may be due to the lack of good outcome measures formost programs in PART.     

An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.     

An extension of the median test to analysis of variance

In a k-way analysis of variance model, the major concern is testing for main effects and for the presence of interaction between the factors. When the assumptions of normality and equal variances are satisfied, the appropriate test to use is the usual F-test for ANOVA. However, when the normality assumption is not satisfied then a robust or nonparametric test is needed to conduct the analysis. In this paper a nonparametric method based on cell counts is proposed. Each cell is divided into L subcells based on predetermined outpoints and the resulting frequencies are laid out in a contingency table. Then the Pearson x² and tne likelihood ratio tests are performed. A comparison with the classical ANOVA F-test indicates that the proposed method is preferable when the data comes from a thick-tailed highly skewed distribution.     

EVVMA and cusum control charts in the presence of correlation

The statistical properties of control charts are usually evaluated under the assumption that the observations from the process are independent. For many processes however, observations which are closely spaced in time will be correlated. This paper considers EWMA and CUSUM control charts for the process mean when the observations are from an AR(1) process with additional random error. This simple model may be a reasonable model for many processes encountered in practice. The ARL and steady state ARL of the EWMA and CUSUM charts are evaluated numerically using an integral equation approach and a Markov chain approach. The numerical results show that correlation can have a significant effect on the properties of these charts. Tables are given to aid in the design of these charts when the observations follow the assumed model.     

An exploration of social & emotional factors impacting well-being in women who have sex with women (WSW)

Abstract

“Women who have sex with women” or WSW have unique perspectives within the LBGTQ spectrum. The purpose of this study included documenting perceptions, feelings, and experiences among WSW. Thirty-seven women participated in six focus groups, ages ranged from 20–64 years. Responses were transcribed and thematically coded. Four themes emerged as influencing factors: shame and fear, community, gender roles, and normalcy. Each major theme produced minor themes providing a context within major themes. Results offer valuable insights that can assist community members, health professionals, and scientists to better appraise the disparities and stigma impacting social and emotional well-being among WSW.     

Erratum to: Mapping the Regional Divide in Europe: A Measure for Assessing Quality of Government in 206 European Regions

Social Indicators Research -     

The Practical Significance of Measurement Error in Pulmonary Function Testing Conducted in Research Settings

Conventional spirometry produces measurement error by using repeatability criteria (RC) to discard acceptable data and terminating tests early when RC are met. These practices also implicitly assume that there is no variation across maneuvers within each test. This has implications for air pollution regulations that rely on pulmonary function tests to determine adverse effects or set standards. We perform a Monte Carlo simulation of 20,902 tests of forced expiratory volume in 1 second (FEV₁), each with eight maneuvers, for an individual with empirically obtained, plausibly normal pulmonary function. Default coefficients of variation for inter‐ and intratest variability (3% and 6%, respectively) are employed. Measurement error is defined as the difference between results from the conventional protocol and an unconstrained, eight‐maneuver alternative. In the default model, average measurement error is shown to be ～5%. The minimum difference necessary for statistical significance at p < 0.05 for a before/after comparison is shown to be 16%. Meanwhile, the U.S. Environmental Protection Agency has deemed single‐digit percentage decrements in FEV₁ sufficient to justify more stringent national ambient air quality standards. Sensitivity analysis reveals that results are insensitive to intertest variability but highly sensitive to intratest variability. Halving the latter to 3% reduces measurement error by 55%. Increasing it to 9% or 12% increases measurement error by 65% or 125%, respectively. Within‐day FEV₁ differences ≤5% among normal subjects are believed to be clinically insignificant. Therefore, many differences reported as statistically significant are likely to be artifactual. Reliable data are needed to estimate intratest variability for the general population, subpopulations of interest, and research samples. Sensitive subpopulations (e.g., chronic obstructive pulmonary disease or COPD patients, asthmatics, children) are likely to have higher intratest variability, making it more difficult to derive valid statistical inferences about differences observed after treatment or exposure.     

PRODUCT DIVERSITY,DEMAND STRUCTURES,AND OPTIMAL TAXATION

This article studies optimal taxation in a general equilibrium macroeconomic model with endogenous entry. We compare the constant elasticity of substitution (CES) model to three alternative demand structures: oligopolistic competition in prices, oligopolistic competition in quantities, and translog preferences. Our economy is characterized by two distortions: a labor distortion due to the misalignment of markups on goods and leisure, and an entry distortion due to the misalignment of the consumer surplus effect and the profit destruction effect of entry. The two distortions interact in determining the wedge between the market‐driven and optimal level of product diversity. We show how optimal labor and entry taxes depend on the prevailing demand structure, the nature and size of entry costs, and the degree of substitutability between goods. (JEL E22, E61, E62)