Are We Underestimating Food Insecurity? Partial Identification with a Bayesian 4-Parameter IRT Model

Christian A. Gregory ()
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Christian A. Gregory: Economic Research Service

Journal of Classification, 2020, vol. 37, issue 3, No 7, 632-655

Abstract: Abstract This paper addresses measurement error in food security in the USA. In particular, it uses a Bayesian 4-parameter IRT model to look at the likelihood of over- or under-reporting of the conditions that comprise the food security module (FSM), the data collection administered in many US surveys to assess and monitor food insecurity. While this model’s parameters are only partially identified, we learn about the likely values of these parameters by using a Bayesian framework. My results suggest significant under-reporting of more severe food security items, particularly those in the child module. I find no evidence of over-reporting of food hardships. I show that, under conservative assumptions, this model predicts food insecurity prevalence between 1 and 3 percentage points higher than current estimates, or roughly 4 to 15 percent of prevalence, for the years 2007–2015. Results suggest much larger increases—on the order of 50 percent of prevalence—for very low food security among households that were screened into the food security module.

Keywords: Food security; Measurement; 4-Parameter model; Bayesian methods (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s00357-019-09344-2

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