Poverty Measurement: Methods for Incorporating Benefits
(Poverty and Income Policy)
*Names in bold indicate Presenter
Increasingly, United States anti-poverty programs take the form of in-kind benefits, such as health insurance and housing assistance, and tax credits. Poverty measures that do not include those benefits in resources available to meet needs will not accurately measure poverty. The official poverty measure (OPM) counts as resources only pre-tax cash income and is therefore incapable of showing the impact of in-kind benefits and tax credits. The supplemental poverty measure (SPM) counts as resources tax credits and in-kind benefits, other than health insurance, and is widely used to show the anti-poverty impacts of those benefits. However, the SPM does not include health insurance in resources and therefore cannot show the direct or full impact of health insurance benefits, such as Medicaid, on poverty. Instead, the SPM addresses health care and insurance needs by subtracting from resources all out-of-pocket spending on health care and insurance. The SPM is also a measure of relative, rather than absolute, poverty, because it updates its thresholds using a five-year moving-average of consumer spending. Finally, both the OPM and SPM are based on self-reports of benefit receipt in the Current Population Survey (CPS), with potentially substantial systematic reporting errors.
The three papers in this session address these and other issues in incorporating benefits in poverty measurement, which have gotten increasing attention from policymakers.
Burkhauser, Corinth, Elwell and Larrimore anchor their Full-Income Poverty Measure to President Johnson’s baseline as operationalized by the OPM for 1963 and show how subsequent poverty rates are affected by its improved measures of inflation and income that accounts for taxes and in-kind benefits, especially the value of health insurance.
Meyer and Wu focus on obtaining accurate measures of in-kind benefits and taxes, as well as cash income. They use groundbreaking data created by linking administrative tax and benefit data to CPS and other survey data to show how accurate benefit measures affect poverty measures incorporating in-kind benefits.
Korenman, Remler and Hyson focus on assessing the various methods for incorporating health insurance benefits, and health care needs, into poverty measurement, which has long proven difficult and controversial. They analyze the various approaches taken in the past, contrasting those methods with their health inclusive poverty measure.