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.

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

Estimation and Inference

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.

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

Using nationally representative longitudinal survey, we examine the income mobility among rural (urban) Indian households over 1993-2004 and 2004-2011 (2004-2011) We find mobility estimates that mirror the social hierarchy: Forward Hindu Caste (FHC) households experienced the highest (lowest) upward (downward) mobility Considerable gaps between FHC households and households from the disadvantaged social groups remain in upward/downward mobility even after controlling for households characteristics We find lower conditional gaps in both upward/downward mobility in rural India for the disadvantaged groups (except for Muslims) over 2004-2011 compared to 1993-2004 For Muslims, the gaps in downward mobility increased over 2004-11 compared to 1993-2004

Household Income Mobility in India: 1993-2011

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong assumptions, and often require data from more than two periods.In this paper, we investigate what can be learned about economic mobility as measured via transition matrices while formally accounting for measurement error in a reasonably transparent manner. To do so, we develop a nonparametric partial identification approach to bound transition probabilities under various assumptions on the measurement error and mobility processes. This approach is applied to panel data from the United States to explore short-run mobility before and after the Great Recession.

Partial Identification of Economic Mobility: With an Application to the United States

We examine economic mobility in India while rigorously accounting for measurement error. Such an analysis is imperative to fully understand the welfare effects of the rise in inequality that has occurred in India over the past few decades. To proceed, we extend recently developed methods on the partial identification of transition matrices and apply this methodology to newly available panel data on household consumption. We find overall mobility has been markedly low: at least 7 out of 10 poor households remain poor or at-risk of being poor between 2005 and 2012. We also find Muslims, lower caste groups, and rural households are in a more disadvantageous position in terms of escaping poverty or transitioning into poverty compared to Hindus, upper caste groups, and urban households. These findings suggest inequality in India is likely to be chronic and also challenges the conventional wisdom that marginalized households are catching up on average.

Measuring Economic Mobility in India Using Noisy Data: A Partial Identification Approach

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity a...

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Hao Li

Papers

Partial Identification of Economic Mobility: With an Application to the United States

Measuring Economic Mobility in India Using Noisy Data: A Partial Identification Approach

Partial Identification of Economic Mobility: With an Application to the United States