The Levels and Trends in Deep and Extreme Poverty in the United States, 1993–2016

Abstract

Recently, there has been tremendous interest in deep and extreme poverty in the United States. We advance beyond prior research by using higher-quality data, improving measurement, and following leading standards in international income research. We estimate deep (less than 20% of medians) and extreme (less than 10% of medians) poverty in the United States from 1993 to 2016. Using the Current Population Survey, we match the income definition of the Luxembourg Income Study and adjust for underreporting using the Urban Institute’s TRIM3 model. In 2016, we estimate that 5.2 to 7.2 million Americans (1.6% to 2.2%) were deeply poor and 2.6 to 3.7 million (0.8% to 1.2%) were extremely poor. Although deep and extreme poverty fluctuated over time, including declines from 1993 to 1995 and 2007 to 2010, we find significant increases from lows in 1995 to peaks in 2016 in both deep (increases of 48% to 93%) and extreme poverty (increases of 54% to 111%). We even find significant increases with thresholds anchored at 1993 medians. With homelessness added, deep poverty would be 7% to 8% higher and extreme poverty 19% to 23% higher in 2016, which suggests that our estimates are probably lower bounds. The rise of deep/extreme poverty is concentrated among childless households. Among households with children, the expansion of SNAP benefits has led to declines in deep/extreme poverty. Ultimately, we demonstrate that estimates of deep/extreme poverty depend critically on the quality of income measurement.

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Data Availability

CPS data are publicly available. The analytic code for this paper is in the online appendix. The code for constructing variables is available at https://bradydave.files.wordpress.com/2018/11/replicationpackage_extremechildpoverty.pdf. See also Parolin and Brady (2019).

Notes

  1. 1.

    Shaefer and Edin’s (2013) counts of the number of extreme poor HHs could increase simply because of population growth. Without standardizing by population, it is unclear how trends in raw counts should inform our understanding of trends in extreme poverty.

  2. 2.

    Section 6 of the online appendix also examines the potential underreporting of earnings.

  3. 3.

    Although Shaefer and Edin (2013:257) claimed that the underreporting of income and welfare transfers is smaller in the SIPP than the CPS, the problem is still present in the SIPP.

  4. 4.

    We improve on the LIS protocol by including state EITCs, which are not included by the LIS. However, we acknowledge that the CPS assumes full take-up of the EITC and ACTC, whereas the actual take-up rate is estimated approximately 80% of eligible earners (Jones and Ziliak 2019). Housing allowances are measured in the CPS as the value of federal housing assistance received by members of a family as estimated using matched administrative data.

  5. 5.

    The Census Bureau tax simulation appears to overcorrect at times. In 1993–1994, the simulation recodes some households with high gross income into those with low incomes. In 1993, for example, 415 individuals in the CPS have zero disposable income (i.e., their tax liability exceeds their gross incomes) but have gross income of $100,000+; this compares with only 10 such individuals in the 1995 sample. Although 415 is a small share of the 1993 sample of 150,943, this could bias the very low estimates of extreme poverty. Therefore, we impose a decision rule that if gross income is above the median, we do not code these households as deeply or extremely poor regardless of the tax simulation.

  6. 6.

    Our results also hold when we remove workers with imputed earnings. See section 6 of the online appendix.

  7. 7.

    In sensitivity checks, we reestimate all poverty rates using the modified OECD equivalence scale, and the results are quite similar. The direction of the trends are unchanged, although in some years, the levels of extreme poverty are slightly lower when measured with the modified OECD equivalence scale rather than the square root scale.

  8. 8.

    To the best of our knowledge, the World Bank never justified this measure’s zero economies of scale. Indeed, there was never much scientific basis for the $2/day threshold even in developing countries (Smeeding 2016). It appears to have always been a politically constructed measure that was not based on any scientific absolute measure of deprivation.

  9. 9.

    Trends in our anchored poverty measures are comparable if we apply the Current Price Index for Urban Consumers (CPI-U) or Current Price Index research series (CPI-U-RS) deflators rather than the PCE. We use the PCE in our primary analysis because it is the most conservative. Thus, if anchored poverty increases with the PCE, it will (and does) also increase with the other two deflators. There are concerns that the CPI-U overstates inflation relative to the PCE (Winship 2016), but that dispute remains unsettled given that the consumption patterns of low-income households may not be well reflected in the PCE indicator. Our relative measures of poverty are, of course, not affected by choice of income deflator.

  10. 10.

    All our estimates are based on headcount measures of the percentage below the threshold. Unlike intensity or ordinal measures, headcount measures neglect the depth of poverty below the threshold (Brady 2009). However, given the few cases at the very bottom of the distribution and the low poverty thresholds used in this study, we would be cautious with analyses of the depth of extreme poverty. Headcount measures require confidence that a given HH’s income is below a threshold, but it requires a higher level of confidence in income data to utilize the exact values of HH income for those below the threshold.

  11. 11.

    That said, we would be cautious about setting the threshold lower than we do because the sample sizes (even in the CPS, which are much larger than the SIPP) become very small, making it difficult to discern trends.

  12. 12.

    All estimates are based on the World Bank (2018) estimates of the U.S. population.

  13. 13.

    Because all measures of deep/extreme poverty show a peak in 2014, we scrutinized the 2014 data but found no major problems. SNAP and TANF benefit levels declined in 2014, which explains part of the trend. One factor is the end of the Emergency Unemployment Compensation program on January 1, 2014. UI benefits saw a large drop from 2013 to 2014. Estimates without UI show no increase from 2013 to 2014.

  14. 14.

    Recently, Shaefer and Edin (2018) used the CPS and TRIM3 to estimate $2/day poverty in 1995–2012 for children. However, they did not equivalize income for household size and continued to use cash income by omitting SNAP and other aspects of disposable income. They found that 1.2 million children (1.6%) were poor in 2012. They also found more than a 300% increase in the raw count of children in extreme poverty in 1995–2012. Even using the CPS and TRIM3, their estimates of the level and trend were much higher than ours. They wrote, “When we control for underreporting, we find that the downward spiral since 1995 is even more dramatic than previously reported” (p. 26).

  15. 15.

    We encourage some caution about the over-time increase in $2/day poverty. The upper-bound confidence interval in 1993 and the lower-bound confidence interval in 2016 are both .41.

  16. 16.

    Of course, our estimates could be undercounts in other ways. For example, the poor consume a much higher share of their income than the nonpoor, and therefore sales taxes exert a greater cost on the poor. It would be very difficult to estimate what share of the poor’s income is subject to sales tax and subtract state- and local-specific sales tax rates from that share of income. One would also need to apply such corrections to median HH income because this would affect the thresholds. Nevertheless, it seems reasonable to suggest that sales taxes disproportionally lower the poor’s income relative to the median HH.

  17. 17.

    In 1993–1995, 67.1% of the extremely poor and 54.6% of the deeply poor were households without children. From 2014 to 2016, 81.8% of the extremely poor and 64.5% of the deeply poor were households without children. The concentration of deep/extreme poverty on nonchildren would likely be even clearer if we include the homeless. In recent national point-in-time reports, the number of individuals without families has increased to be more than two-thirds of the homeless.

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Acknowledgments

The authors are listed alphabetically, and each contributed equally. We especially appreciate extensive discussions with Bruce Meyer and Luke Shaefer. We thank Liana Fox and Bruce Meyer for sharing their unpublished work. We are grateful for comments from Demography reviewers, Thomas Biegert, Ryan Finnigan, Shawn Fremstad, Marco Giesselmann, and Scott Winship.

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Correspondence to David Brady.

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Brady, D., Parolin, Z. The Levels and Trends in Deep and Extreme Poverty in the United States, 1993–2016. Demography (2020). https://doi.org/10.1007/s13524-020-00924-1

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Keywords

  • Poverty
  • Income
  • Measurement
  • Extreme poverty
  • Deep poverty