Session Recap: The Supplemental Poverty Measure: Four Views from Four Surveys
November 8, 2014 02:00 PM
By Yu-Ling Chang, University of Washington
Chair: Connie Citro, National Academy of Sciences
Discussant: Liana Fox, Stockholm University
This panel discussed supplemental poverty measure (SPM) and the official poverty measure (OPM) across four major national datasets. Examining how these measures worked and didn’t work helps researchers and policy makers understand how government programs supported different populations.
The first paper was The Supplemental Poverty Measure in the Survey of Income and Program Participation presented by Kathleen Shot from U.S. Census Bureau. This paper compared elements of the SPM in the Survey of Income and Program Participation (SIPP) and the Current Population Survey Annual Social and Economic Supplement (CPS ASEC). The strengths of using the SIPP include: (1) more detailed information on income and family structure; (2) collecting most of elements of the SPM such as medical out-of-pocket expenses, child care expenses, other work expenses, asset holdings; and (3) more accurate information on cash-like benefits. This paper found that the SPM poverty rates generated by using the SIPP were lower than the SPM poverty rates estimated by using the CPS. This paper also examined the relationships among income poverty and material hardship. The finding showed that the relative poverty measure performed better than the OPM and the SPM in explaining material hardship.
The second paper was How Rich Are the Elderly Poor? Examining Assets Among the Elderly Using the Supplemental Poverty Measure authored by Koji Chavez from Stanford University, Christopher Wimer from Columbia University, and David Betson from the University of Notre Dame. Using data from the Health and Retirement Study (HRS) which collected detailed information on health, income, and wealth, the authors found that including a small proportion of assets in family economic resources significantly reduced the SPM poverty rate, specifically for the Medically SPM Poor elderly who tended to have more assets than the Non-Medically SPM Poor. A major policy implication drawn from this paper was modifying the SPM to account for assets, especially for poverty estimates of the elderly
The Supplemental Poverty Measure in the American Community Survey: 2011was presented by Trudi Renwick from U.S. Census Bureau. This paper compared poverty estimates by using SPM and OPM in the American Community Survey (ACS) and the CPS ASEC. The major strength of the ACS is its capability to produce sub-national geographic estimates (i.e. state and local level poverty rates). However, the challenges of using the ACS are: (1) missing key data information needed to produce SPM estimates, including SNAP benefits, taxes, child care expenses, and medical out of pocket expendituresand (2) no relationship codes to identify unrelated subfamilies. The preliminary findings from this paper found that 2011 SPM poverty rates from the ACS were higher than those from the CPS. This difference might due to the less detailed and comprehensive income information collected in the ACS.
The fourth paper was Rates and Persistence of Poverty Using the Supplemental Poverty Measure in the Panel Study of Income Dynamics, 1998 to 2010 presented bySara Kimberlin from the Stanford University. This paper examined SPM poverty estimates by using the Panel Study of Income Dynamics (PSID), which is a long-term longitudinal dataset for studying the persistence of poverty. A major strength of using this data was its detailed income information without a lot of imputation from other datasets, while a major challenge was a lot of item-missing data. This paper found that SPM estimates from the PSID were significantly lower than the estimates form the CPS. Possible explanations for this difference included higher reported cash incomes, selective sample attrition, more complete income information, and SPM-like family units. The author suggested that future work could further explore the role of safety net programs in mitigating transient and chronic SPM poverty.