Partially Identifying the Prevalence of Health Insurance Given Contaminated Sampling Response Error

This paper derives simple closed-form identification regions for the U.S. nonelderly population's prevalence of health insurance coverage in the presence of household reporting errors. The methods extend Horowitz and Manski's (1995) nonparametric analysis of contaminated samples for the case that the outcome is binary. In this case, draws from the alternative distribution (i.e., not the distribution of interest) might naturally be defined as response errors. The derived identification regions can dramatically reduce the degree of uncertainty about the outcome distribution compared with the contaminated sampling bounds. These regions are estimated using data from the Medical Expenditure Panel Survey (MEPS) combined with health insurance validation data available for a nonrandom portion of the sample.partial identification; nonparametric bounds; contaminated sampling; classification error

Aluminum oxide barriers in MCrAlY superalloy systems

An investigation was made of sputtered aluminum oxide diffusion barriers to protect gas turbine engine blade and vane alloys from their coatings. MAR M200 + Hf coated with sputtered NiCoCrAlY and MAR M509 coated with sputtered FeCrAlY were obtained both with and without 1 and 2 micron sputtered Al2O3 barrier layers. Electron dispersive X-ray analysis was used to determine the concentration profiles of as-received and heat treated samples

Partially Identifying the Prevalence of Health Insurance Given Contaminated Sampling Response Error

This paper derives simple closed-form identification regions for the U.S. nonelderly population\u27s prevalence of health insurance coverage in the presence of household reporting errors. The methods extend Horowitz and Manski\u27s (1995) nonparametric analysis of contaminated samples for the case that the outcome is binary. In this case, draws from the alternative distribution (i.e., not the distribution of interest) might naturally be defined as response errors. The derived identification regions can dramatically reduce the degree of uncertainty about the outcome distribution compared with the contaminated sampling bounds. These regions are estimated using data from the Medical Expenditure Panel Survey (MEPS) combined with health insurance validation data available for a nonrandom portion of the sample

Food Stamps and Food Insecurity: What Can Be Learned in the Presence of Non-Classical Measurement Error?

Policymakers have been puzzled to observe that food stamp households appear more likely to be food insecure than observationally similar eligible nonparticipating households. We reexamine this issue allowing for nonclassical reporting errors in food stamp participation and food insecurity. Extending the literature on partially identified parameters, we introduce a nonparametric framework that makes transparent what can be known about conditional probabilities when a binary outcome and conditioning variable are both subject to nonclassical measurement error. We find that the food insecurity paradox hinges on strong assumptions about the reliability of the data that are not supported by the previous food stamp participation literature.

Regression Coefficient Identification Decay in The Presence of Infrequent Classification Errors

Recent evidence from Bound, Brown, and Mathiowetz (2001) and Black, Sanders, and Taylor (2003) suggests that reporting errors in survey data routinely violate all of the classical measurement error assumptions. The econometrics literature has not considered the consequences of fully arbitrary measurement error for identification of regression coefficients. This paper highlights the severity of the identification problem given the presence of even infrequent arbitrary errors in a binary regressor. In the empirical component, health insurance misclassification rates of less than 1.3% generate double-digit percentage point ranges of uncertainty about the variable\u27s true marginal effect on the use of health services

Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean Independence

We consider the problem of identifying a mean outcome in corrupt sampling where the observed outcome is a mixture of the distribution of interest and some other distribution. We make two contributions to this literature. First, the statistical independence assumption maintained under contaminated sampling is relaxed to the weaker assumption that the outcome is mean independent of the mixing process. We then generalize this restriction to allow the two conditional means to differ by a known or bounded factor of proportionality. Second, in the special case of a binary outcome, we consider the possibility that draws from the alternative distribution are known to be erroneous, as might be the case in a mixture model of response error. We illustrate how these assumptions can be used to inform researchers about the population's use of illicit drugs in the presence of nonrandom reporting errors. In this application, we find that a response error model with multiplicative mean independence is easy to motivate and can have substantial identifying power.

Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors

Measurement error in health and disability status has been widely accepted as a central problem for social science research. Long-standing debates about the prevalence of disability, the role of health in labor market outcomes, and the influence of federal disability policy on declining employment rates have all emphasized issues regarding the reliability of self-reported disability. In addition to random error, inaccuracy in survey datasets may be produced by a host of economic, social, and psychological factors that can lead respondents to misreport work capacity. We develop a nonparametric foundation for assessing how assumptions on the reporting error process affect inferences on the employment gap between the disabled and nondisabled. Rather than imposing the strong assumptions required to obtain point identification, we derive sets of bounds that formalize the identifying power of primitive nonparametric assumptions that appear to share broad consensus in the literature. Within this framework, we introduce a finite-sample correction for the analog estimator of the monotone instrumental variable (MIV) bound. Our empirical results suggest that conclusions derived from conventional latent variable reporting error models may be driven largely by ad hoc distributional and functional form restrictions. Under relatively weak nonparametric assumptions, nonworkers appear to systematically overreport disability.

Inferring Disability Status from Corrupt Data

In light of widespread concerns about the reliability of self-reported disability, we investigate what can be learned about the prevalence of work disability under various assumptions on the reporting error process. Developing a nonparametric bounding framework, we provide tight inferences under our strongest assumptions but then find that identification deteriorates rapidly as the assumptions are relaxed. For example, we find that inferences are highly sensitive to how one models potential inconsistencies between subjective self-assessments of work limitation and more objective measures of functional limitation. These two indicators appear to measure markedly different aspects of health status.