The Levels and Trends in Deep and Extreme Poverty in the U.S., 1993-2016

Abstract

Recently, there has been tremendous interest in deep and extreme poverty in the U.S. We advance beyond prior research by using higher-quality data, improving measurement, and following leading standards in international income research. We estimate deep (measured as less than 20% of medians) and extreme (measured as less than 10% of medians) poverty in the U.S. from 1993-2016. Using the Current Population Survey, we match the income definition of the Luxembourg Income Study and adjust for underreporting using Urban Institute’s TRIM3 model. In 2016, we estimate 5.2 to 7.2 million Americans (1.6-2.2%) were deeply poor and 2.6 to 3.7 million (.8-1.2%) were extremely poor. While deep and extreme poverty fluctuated over time, including declines from 1993-1995 and 2007-2010, we find significant increases from lows in 1995 to peaks in 2016 in both deep (increases of 48-93%) and extreme poverty (increases of 54-111%). We even find significant increases with thresholds anchored at 1993 medians. Adding homelessness, deep poverty would be 7-8% higher and extreme poverty would be 19-23% higher in 2016, which suggests 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.

Do Networks Help People To Manage Poverty? Perspectives from the Field

“…Seventeen million of them experienced deep or extreme poverty, defined as a household income below 50 percent of the household’s poverty threshold (Semega et al 2019). Extreme poverty has almost doubled between 1995 and 2016 (Brady and Parolin 2019). Given the magnitude of the problem both in the United States and worldwide, it is hardly surprising that poverty has had a steady place on the agenda of the social sciences.…”

Income and Wealth as Salient Gradational Aspects of Stratification

“…In the 1990s, the United Nations (2011) convened "The Canberra Group," which led to an international consensus on best practices in income measurement (Atkinson 2015;Brady and Parolin 2020;Duncan and Peterson 2001;Rainwater and Smeeding 2004). This consensus was then institutionalized by the world's leader in income data -the Luxembourg Income Study (Gornick and Smeeding 2018).…”

“…These criteria realistically recognize that individuals share resources within HHs and only have "disposable" income after taxes and transfers (Brady and Bostic 2015;Guillaud et al 2020;Rainwater and Smeeding 2004). While, of course, income and wealth are not always equally shared between HH members (Lersch 2017b), living in HH's and accessing transfers are principal ways individuals smooth their incomes, manage volatility and maximize well-being (Brady et al 2018;Brady and Parolin 2020). For example, welfare transfers to a household improve children's well-being and have lasting benefits into adulthood (Hoynes et al 2016).…”

The Long Arm of Prospective Childhood Income for Mature Adult Health in the U.S.

“…For these reasons, we propose the long arm literature can benefit from advances in international income research. Following the United Nations' (2011) "Canberra Group," a consensus emerged on the criteria for optimal income measurement Brady et al 2018;Brady and Parolin 2020;Daly et al 2002;Duncan et al 2001;Rainwater and Smeeding 2004;Smeeding and Weinberg 2003). Measures of income should: (1) include all sources from all household (HH) members; (2) incorporate taxes and transfers (i.e.…”

“…For example, Hoynes and colleagues (2016) show that the Supplemental Nutritional Assistance Program (SNAP) improves children's well-being and has lasting benefits into adulthood. As well, tax credits and transfers reduce poverty and inequality (Brady and Parolin 2020;Rainwater and Smeeding 2005). Given the volatility of income, the reliability of income measurement increases significantly with multiple observations (Brady et al 2018;Fox et al 2016;Mazumder 2016).…”