This study examines historical trends in poverty using an anchored version of the U.S. Census Bureau’s recently developed Research Supplemental Poverty Measure (SPM) estimated back to 1967. Although the SPM is estimated each year using a quasi-relative poverty threshold that varies over time with changes in families’ expenditures on a core basket of goods and services, this study explores trends in poverty using an absolute, or anchored, SPM threshold. We believe the anchored measure offers two advantages. First, setting the threshold at the SPM’s 2012 levels and estimating it back to 1967, adjusted only for changes in prices, is more directly comparable to the approach taken in official poverty statistics. Second, it allows for a better accounting of the roles that social policy, the labor market, and changing demographics play in trends in poverty rates over time, given that changes in the threshold are held constant. Results indicate that unlike official statistics that have shown poverty rates to be fairly flat since the 1960s, poverty rates have dropped by 40 % when measured using a historical anchored SPM over the same period. Results obtained from comparing poverty rates using a pretax/pretransfer measure of resources versus a post-tax/post-transfer measure of resources further show that government policies, not market incomes, are driving the declines observed over time.
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See Blank and Greenberg (2008), Citro and Michael (1995), and Hutto et al. (2011) for discussions of the adequacy of the official poverty measure and improved income-poverty measures. Some scholars prefer a consumption-based to an income-based measure of poverty, arguing that consumption provides a better measure of overall economic well-being than income (Meyer and Sullivan 2012a, b, c). We do not take up that debate here, but note that trends using consumption and income poverty, when properly measured, line up fairly well (Bavier 2008; Council of Economic Advisers 2014).
Official poverty thresholds are based on an entirely different concept and were based on food costs as a percentage of total family budgets in the 1950s and 1960s.
Contemporary SPM thresholds will include food spending derived from SNAP benefits. One might prefer a threshold that doesn’t include such spending if one wanted a fully complete accounting of the total effect of the role of government policies and programs in reducing the poverty rate.
Official poverty thresholds are updated annually using the Consumer Price Index for All Urban Consumers (CPI-U) and historically were updated first using changes in food prices and then using the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W) (Fisher 1998). The CPI-U-RS is thought by many economists to be superior to the CPI-U in that it corrects for many known biases in early years. Some economists, however, have argued that even the CPI-U-RS does not go far enough to correct such biases (for a discussion, see Meyer and Sullivan 2012b). Note that the shelter component of the CPI-U-RS is based on rental equivalence of housing rather than shelter spending per se: an inflation index based on spending alone might be preferable for consistency’s sake. However, despite these limitations, we believe the CPI-U-RS to be the best measure for consistently adjusting prices over the period.
Details on the imputation procedures and sensitivity thereof can be found in Fox et al. (2015: technical appendix). Among those items imputed, medical out-of-pocket (MOOP) expenses lead to the biggest difference in poverty rates in any given year and must be imputed for the entire time series. (Actual MOOP expenses are available in the CPS starting in 2010 for calendar year 2009, but we use our imputation procedure for the entire time series for consistency’s sake.) However, findings are robust to whether MOOP is considered at all. For other programs, such as SNAP and WIC, we impute only in the early years of our study period, when these programs were still fairly small. Mean imputed benefits constituted approximately 9 % of total estimated government benefits in our sample in years prior to 1979.
See Fox et al. (2015) for the derivation of the 1967 threshold. This threshold is then adjusted annually for inflation using the CPI-U-RS.
These counterfactual estimates indicate, in an accounting sense, how much taking government taxes and transfers into account alters our estimates of poverty. Because we do not model potential behavioral responses to the programs, these estimates cannot reveal what actual poverty rates would be in the absence of the programs. However, because research suggests that these behavioral effects are small, the estimates are useful first approximations (Ben-Shalom et al. 2011).
Detailed results are available upon request. Meyer and Sullivan (2012c) conducted a similar analysis for taxes and noncash benefits, finding a large effect of the tax system but a small role for noncash transfers. One source of difference is that we study the effect of all transfers—cash and noncash—whereas Meyer and Sullivan compared poverty estimates based on money income, which would include cash transfers, with poverty estimates based on money income plus taxes and in-kind transfers. Meyer and Sullivan also adjusted for the fungible value of Medicare and Medicaid, employer health benefits, and the net return on housing equity. We do not make these adjustments. Our results for the decline in poverty over time are substantively similar if we make all other changes except for the exclusion of MOOP from resources. Ignoring MOOP, we would find that poverty (measured against 2012 anchored thresholds) declined from 22.8 % to 12.5 %, or about a 45 % decline.
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We are grateful for funding support from the Annie E. Casey Foundation and from the National Institute of Child Health and Human Development (NICHD) through grant R24 HD058486-03 to the Columbia Population Research Center (CPRC). We benefited from research assistance from Madeleine Gelblum, Nathan Hutto, JaeHyun Nam, and Ethan Raker. We are also grateful to seminar participants at CPRC and Russell Sage Foundation, as well as many colleagues who provided helpful insights and advice, in particular, Jodie Allen, Ajay Chaudry, Sheldon Danziger, Daniel Feenberg, Gordon Fisher, Jason Furman, Thesia Garner, Charles Hokayem, David Johnson, Mark Levitan, Jordan Matsudaira, Laryssa Mykyta, Trudi Renwick, Kathy Short, Tim Smeeding, and Betsy Stevenson.
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Wimer, C., Fox, L., Garfinkel, I. et al. Progress on Poverty? New Estimates of Historical Trends Using an Anchored Supplemental Poverty Measure. Demography 53, 1207–1218 (2016). https://doi.org/10.1007/s13524-016-0485-7
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