Bayesian Sampling Algorithms for the Sample Selection and Two-Part Models

This paper considers two models to deal with an outcome variable that contains a large fraction of zeros, such as individual expenditures on health care: a sample-selection model and a two-part model. The sample-selection model uses two possibly correlated processes to determine the outcome: a decision process and an outcome process; conditional on a favorable decision, the outcome is observed. The two-part model comprises uncorrelated decision and outcome processes. The paper addresses the issue of selecting between these two models. With a Gaussian specification of the likelihood, the models are nested and inference can focus on the correlation coefficient. Using a fully parametric Bayesian approach, I present sampling algorithms for the model parameters that are based on data augmentation. In addition to the sampler output of the correlation coefficient, a Bayes factor can be computed to distinguish between the models. The paper illustrates the methods and their potential pitfalls using simulated data setsSample Selection, Data Augmentation, Gibbs Sampling

Troubled in School : Does Maternal Involvement Matter for Adolescents?

We estimate the causal effect of mother's involvement on the amount of trouble an adolescent experiences in school. We use multiple measures of school-trouble and factor analysis to construct a composite and then link this composite with noncognitive skills. Our measure of mother's involvement encompasses discussing school-related matters and providing help with school projects. Using an instrumental variable constructed from a suitably chosen peer group, our main finding is that an increase in maternal involvement leads to a significant decrease in school trouble. We find this result to be robust across a large number of sensitivity tests designed to account for possible selection effects, shocks at the peer group level, and further potential violations of the exclusion restriction. Additionally, we present evidence suggesting that the effect of maternal involvement may operate through its effect on adolescents' college aspirations, mental health, and the perception of parental warmth

Bayesian moment-based inference in a regression model with misclassification error

We present a Bayesian analysis of a regression model with a binary covariate that may have classification (measurement) error. Prior research demonstrates that the regression coefficient is only partially identified. We take a Bayesian approach which adds assumptions in the form of priors on the unknown misclassification probabilities. The approach is intermediate between the frequentist bounds of previous literature and strong assumptions which achieve point identification, and thus preferable in many settings. We present two simple algorithms to sample from the posterior distribution when the likelihood function is not fully parametric but only satisfies a set of moment restrictions. We focus on how varying amounts of information contained in a prior distribution on the misclassification probabilities change the posterior of the parameters of interest. While the priors add information to the model, they do not necessarily tighten the identified set. However, the information is sufficient to tighten Bayesian inferences. We also consider the case where the mismeasured binary regressor is endogenous. We illustrate the use of our Bayesian approach in a simulated data set and an empirical application investigating the association between narcotic pain reliever use and earnings

Community Health Center Efficiency: The Role of Grant Revenues in Health Center Efficiency

Objective: To test the relationship between external environments, organizational characteristics, and technical efficiency in federally qualified health centers (FQHCs). We tested the relationship between grant revenue and technical efficiency in FQHCs. Data Sources/Study Design: Secondary data were collected in each year from the Uniform Data System (UDS) on 644 eligible U.S.-based FQHCs between 2005 and 2007. The study employs a retrospective longitudinal cohort design with instrumental variables. Principal Findings: Increased grant revenues did not increase the probability that a health center would be on the efficiency frontier. However, increased grant revenues had a negative association with technical efficiency for health centers that were not fully efficient. Conclusion: If all health centers were operating efficiently, anywhere from 39 to 45 million patient encounters could have been delivered instead of the actual total of 29 million in 2007. Policy makers should consider tying grant revenues to performance indicators, and future work is needed to understand the mechanisms through which diseconomies of scale are present in FQHCs

Binary Misclassification and Identification in Regression Models

We study a regression model with a binary explanatory variable that is subject to misclassification errors. The regression coefficient is then only partially identified. We derive several results that relate different assumptions about the misclassification probabilities and the conditional variances to the size of the identified set

Many-instruments Asymptotic Approximations Under Nonnormal Error Distributions

In this paper we derive an alternative asymptotic approximation to the sampling distribution of the limited information maximum likelihood estimator and a bias corrected version of the two-stage least squares estimator. The approximation is obtained by allowing the number of instruments and the concentration parameter to grow at the same rate as the sample size. More specifically, we allow for potentially nonnormal error distributions and obtain the conventional asymptotic distribution and the results of Bekker (1994, Econometrica 62, 657–681) and Bekker and Van der Ploeg (2005, Statistica Neerlandica 59, 139–267) as special cases. The results show that when the error distribution is not normal, in general both the properties of the instruments and the third and fourth moments of the errors affect the asymptotic variance. We compare our findings with those in the recent literature on many and weak instruments

Time Series Analysis of Sexual Assault Case Characteristics and the 2007-2008 Period of Post-Election Violence in Kenya

Background: Following the declaration that President Mwai Kibaki was the winner of the Kenyan presidential election held on December 27, 2007, a period of post-election violence (PEV) took place. In this study, we aimed to identify whether the period of PEV in Kenya was associated with systematic changes in sexual assault case characteristics.Methods and Findings: Medical records of 1,615 patients diagnosed with sexual assault between 2007 and 2011 at healthcare facilities in Eldoret (n = 569), Naivasha (n = 534), and Nakuru (n = 512) were retrospectively reviewed to examine characteristics of sexual assault cases over time. Time series and linear regression were used to examine temporal variation in case characteristics relative to the period of post-election violence in Kenya. Key informant interviews with healthcare workers at the sites were employed to triangulate findings. The time series of sexual assault case characteristics at these facilities were examined, with a specific focus on the December 2007–February 2008 period of post-election violence. PraisWinsten estimates indicated that the three-month period of post-election violence was associated with a 22 percentage point increase in cases where survivors did not know the perpetrator, a 20 percentage-point increase in cases with more than one perpetrator, and a 4 percentage-point increase in cases that had evidence of abdominal injury. The post-election violence period was also associated with an 18 percentage-point increase in survivors waiting .1 month to report to a healthcare facility. Sensitivity analyses confirmed that these characteristics were specific to the post-election violence time period.Conclusion: These results demonstrate systematic patterns in sexual assault characteristics during the PEV period in Kenya

The relation between tobacco taxes and youth and young adult smoking: What happened following the 2009 U.S. federal tax increase on cigarettes?

BackgroundOn April 1, 2009, the federal government raised cigarette taxes from $0.39 to$1.01 per pack. This study examines the impact of this increase on a range of smoking behaviors among youth aged 12 to 17 and young adults aged 18 to 25.MethodsData from the 2002–2011 National Survey on Drug Use and Health (NSDUH) were used to estimate the impact of the tax increase on five smoking outcomes: (1) past year smoking initiation, (2) past-month smoking, (3) past year smoking cessation, (4) number of days cigarettes were smoked during the past month, and (5) average number of cigarettes smoked per day. Each model included individual and state-level covariates and other tobacco control policies that coincided with the tax increase. We examined the impact overall and by race and gender.ResultsThe odds of smoking initiation decreased for youth after the tax increase (odds ratio (OR) = 0.83, p < 0.0001). The odds of past-month smoking also decreased (youth: OR = 0.83, p < 0.0001; young adults: OR = 0.92, p < 0.0001), but the odds of smoking cessation remained unchanged. Current smokers smoked on fewer days (youth: coefficient = - 0.97, p = 0.0001; young adults: coefficient = - 0.84, p < 0.0001) and smoked fewer cigarettes per day after the tax increase (youth: coefficient = - 1.02, p = 0.0011; young adults: coefficient = - 0.92, p < 0.0001).ConclusionsThe 2009 federal cigarette tax increase was associated with a substantial reduction in smoking among youths and young adults. The impact of the tax increase varied across male, female, white and black subpopulations

Total Cost of Care Lower among Medicare Fee-for-Service Beneficiaries Receiving Care from Patient-Centered Medical Homes

Objective. To compare health care utilization and payments between NCQA-recognized patient-centered medical home (PCMH) practices and practices without such recognition.Data Sources. Medicare Part A and B claims files from July 1, 2007 to June 30, 2010, 2009 Census, 2007 Health Resources and Services Administration and CMS Utilization file, Medicare’s Enrollment Data Base, and the 2005 American Medical Association Physician Workforce file.Study Design. This study used a longitudinal, nonexperimental design. Three annual observations (July 1, 2008–June 30, 2010) were available for each practice. We compared selected outcomes between practices with and those without NCQA PCMH recognition.Data Collection Methods. Individual Medicare fee-for-service (FFS) beneficiaries and their claims and utilization data were assigned to PCMH or comparison practices based on where they received the plurality of evaluation and management services between July 1, 2007 and June 30, 2008.Principal Findings. Relative to the comparison group, total Medicare payments, acute care payments, and the number of emergency room visits declined after practices received NCQA PCMH recognition. The decline was larger for practices with sicker than average patients, primary care practices, and solo practices.Conclusions. This study provides additional evidence about the potential of the PCMH model for reducing health care utilization and the cost of care

A Bayesian analysis of binary misclassification

We consider Bayesian inference about the mean of a binary variable that is subject to misclassification error. If the error probabilities are not known, or cannot be estimated, the parameter is only partially identified. For several reasonable and intuitive prior distributions of the misclassification probabilities, we derive new analytical expressions for the posterior distribution. Our results circumvent the need for Markov chain Monte Carlo simulation. The priors we use lead to regions in the identified set that are a posteriori more likely than others