The equivalence of three latent class models and ML estimators

The purpose of this letter is to show the equivalence of three latent class models; the switching regression model with endogenous switching and a latent outcome (the binary Roy model), the probit model with a systematically misclassified dependent variable, and a trivariate probit model with partial observability. The probit model with measurement error is an enhanced version of existing models which allows for the potential correlation between error terms. Establishing this connection, we hope, will help a researcher working on one of these classes of estimators to benefit from the literature and software related to other families

The Pot Calling the Kettle Black? A Comparison of Measures of Current Tobacco Use

Researchers often use the discrepancy between self-reported and biochemically assessed active smoking status to argue that self-reported smoking status is not reliable, ignoring the limitations of biochemically assessed measures and treating it as the gold standard in their comparisons. Here, we employ econometric techniques to compare the accuracy of self-reported and biochemically assessed current tobacco use, taking into account measurement errors with both methods. Our approach allows estimating and comparing the sensitivity and specificity of each measure without directly observing true smoking status. The results, robust to several alternative specifications, suggest that there is no clear reason to think that one measure dominates the other in accuracy

Measurement Invariance and Response Bias: A Stochastic Frontier Approach

The goals of the present paper were to assess measurement invariance using a common econometric method and to illustrate the approach with self-reported measures of parenting behaviors before and after a family intervention. Most recent literature on measurement invariance (MI) in psychological research 1) explores the use of structural equation modeling (SEM) and confirmatory factor analysis to identify measurement invariance, and 2) tests for measurement invariance across groups rather than across time. We use method, Stochastic Frontier Estimation, or SFE, to identify response bias and covariates of response bias both across individuals at a single point in time and across two measurement occasions (before and after participation in a family intervention). We examined the effects of participant demographics (N = 1437) on response bias; gender and race/ethnicity were related to magnitude of bias and to changes in bias across time, and bias was lower at posttest than at pretest. We discuss analytic advantages and disadvantages of SFE relative to SEM approaches and note that the technique may be particularly useful in addressing the problem of “response shift bias” or “recalibration” in program evaluation -- that is, a shift in metric from before to after an intervention which is caused by the intervention itself and may lead to underestimates of program effects.Measurement invariance, measurement equivalence, response bias, response-shift bias, stochastic frontier analysis

Dirty hands on troubled waters: Sanitation, access to water and child health in Ethiopia

In this paper, we investigate the impact of access to drinking water sources and sanitation facilities on the incidence of diarrheal diseases among children below 5 years of age in Ethiopia using the propensity score matching technique with a polychotomous treatment variable. We find that among the water sources traditionally considered as improved, only water piped into dwelling, yard or plot leads to a large percentage point reduction in diarrhea incidence. The other water sources, generally believed as clean, are not effective in reducing diarrhea even compared with some of the unimproved water sources. We also find that some unimproved water sources and sanitation facilities are less inferior than they are believed to be. These results suggest that the traditional way of categorizing different types of improved and unimproved water sources and sanitation facilities into a dichotomous variable, “improved” or “unimproved”, could be misleading as it masks the heterogeneous effects of the water sources and the sanitation facilities

Estimating treatment effectiveness with sample selection

We consider a situation where treatment outcome is observed after two stages of selection; first of participation into the treatment, then in completion of the treatment. Estimates were obtained using two methods. First, three different binary response selection models were estimated sequentially in multiple steps. Second, all three equations were estimated jointly. All methods produce similar parameter estimates. We find evidence of selection effects from completion to outcome that could bias parameter estimates of the outcome equation, but not from participation to outcome, indicating that correcting only for participation may be insufficient to avoid biased estimates in the outcome equation.selection bias, trivariate probit, bivariate probit, treatment effects

Estimation of a selectivity model with misclassified selection

Despite the great interest in models of self-selection and models with misclassification, there have been few studies combining the two. Notable exceptions are given by McCarthy, Millimet, and Roy and Shiu. None of these models have been developed in a contingent valuation setting that we are interested in. The goal of this note is to add to this literature by presenting a model for estimating willingness to pay using data collected through a contingent valuation survey. We examine the case of a selectivity model in which the outcome equation is interval censored but the decision indicator is not observed

Adversity in Infancy and Childhood Cognitive Development: Evidence From Four Developing Countries

Objectives: We investigated whether adverse experiences at age 1 (AE-1) affect the level of and change in cognition during childhood using harmonized data from four developing countries.Methods: Data included children born in 2001/2002 and were followed longitudinally in 2006/2007 and in 2009/2010 by Young Lives study in Ethiopia, India, Peru, and Vietnam. Childhood cognition was measured using the Peabody Picture Vocabulary Test (PPVT) at ages 5 (PPVT-5) and 8 (PPVT-8). We also examined the effect on a change in cognition between age 5–8 (PPVT-Change). The AE-1 scores were constructed using survey responses at age 1. The ordinary least squares regression was used for estimation.Results: We found that children with higher adversities as infants had lower cognition scores at ages 5 and 8. The change in cognition between the two ages was also generally smaller for those with severe adversities at infancy. The negative association between adversities and childhood cognition was strongest for India.Conclusion: The results provide policy relevant information for mitigation of undesirable consequences of early life adversities through timely interventions

Counting unreported abortions: A binomial-thinned zero-inflated Poisson model

Background: Self-reported counts of intentional abortions in demographic surveys are significantly lower than the actual counts. To estimate the extent of misreporting, previous research has required either a gold standard or a validation sample. However, in most cases, a gold standard or a validation sample is not available. Objective: Our main intention here is to show that a researcher has an alternative tool to estimate the extent of underreporting in a given dataset, particularly when neither a valid gold standard nor a validation sample is available. Methods: We adopt a binomial-thinned zero-inflated Poisson model and apply it to a sample dataset, the National Survey of Family Growth (NSFG), for which an alternative estimate of the average reporting rate (38Š) is available. We show how this model could be used to estimate the reporting probabilities of intentional abortions by each individual in addition to the overall average reporting rate. Results: Our model estimates the average reporting rate in the NSFG during 2006‒2013 as 35.3Š (SE 8.2Š). Individual reporting probabilities vary significantly. Conclusions: Our estimate of the average reporting rate of the dataset used is qualitatively and statistically similar to the available alternative estimate. Contribution: The model we propose can be used to predict the reporting probability of abortions of each individual, which in turn can be used to correct the bias due to underreporting in any model in which the number of abortions is used as the dependent variable or as one of the covariates

Topics in Health Econometrics

This dissertation discusses three topics in health econometrics focusing on misclassification in binary data, sources and the nature of bias, the impact of using misclassified data in econometric estimations and methods to identify and correct the bias. The first chapter proposes an econometric estimator to estimate correct inferences when the dependent variable of the binary choice model is endogenously misclassified. The approach is validated using a simulation study and applied to the analysis of a treatment program designed to improve family dynamics. The second chapter analyses the `HIV-virgin puzzle' where a number of adolescents in Africa who were found HIV positive report as never having sex, which indicates either the dominance of non-sexual modes of HIV transmission or systematic misreporting of sexual behavior. A method is proposed to estimate the extent of misreporting and the contribution of sexual mode for HIV transmission in Africa after accounting for misreporting. The third chapter employs econometric techniques to compare self-reported and objectively measured smoking data taking into account errors with both methods and cautions that objective measures may not always be more reliable than self-reported data

Behold, a Virgin is with HIV!: Misreporting Sexual Behaviour Among Infected Adolescents

In four Southern African countries where the HIV prevalence rate is among the highest in the world, 46% of female and 68% of male adolescents infected with HIV report having never engaged in sex. This finding indicates either the dominance of non-sexual modes of HIV transmission or systematic misreporting of sexual behavior in these countries. We use a structural model to estimate the extent of misreporting and find that the true percentages of virgins among the HIV infected adolescent females and males are 23% and 62% respectively. Despite misreporting, sexual modes are not the dominant mode of HIV transmission.misclassification, premarital sex, HIV transmission mode, partial observability, sub Saharan Africa