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Erschienen in: Social Indicators Research 1/2022

06.03.2022 | Original Research

Spatial and Temporal Trends in Multidimensional Poverty in the United States over the Last Decade

verfasst von: Shatakshee Dhongde, Robert Haveman

Erschienen in: Social Indicators Research | Ausgabe 1/2022

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Abstract

This paper undertakes a comprehensive analysis of multidimensional poverty in the United States over the last decade. It provides estimates of multidimensional poverty over more than a decade, from 2008 to 2019, which covers the Great Recession and the recovery following the recession when major policy changes such as the Affordable Care Act were implemented. For the first time, spatial trends in estimates of multidimensional poverty are also provided. We measure annual poverty levels in 4 regions, 50 states and examine the relation between multidimensional poverty and neighborhood characteristics. We find that on average, 13 percent of the United States population was multidimensional poor. Poverty rates were high in the South and the West and among young adults, immigrants and Hispanics. Alternative indices of multidimensional poverty show consistent trends; multidimensional poverty in the United States rose between 2008 and 2010 and then gradually declined. However, more than a quarter of individuals with incomes above the poverty threshold remained multidimensional poor. This underscores the fact that income does not always capture deprivation experienced by individuals. Policies geared towards affordable housing, health insurance and higher education will help reduce multidimensional poverty in the United States.

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2
For examples, see Martinez Jr. and Peralez (2017) for Australia, Yang and Mukhopadhaya (2019) for China, D’Ambrosio et al. (2014) for Japan, Nowak and Scheicher (2017), Suppa (2018) for Germany, Sevinc (2020) for the United Kingdom and Weziak-Bialowolska (2016) for the European Union.
 
3
See Dhongde and Haveman (2017), Dhongde et al. (2019), Glassman (2019, 2021), Mitra and Brucker (2019), Dhongde (2020) and Dhongde and Dong (2022).
 
4
Previous studies used U.S. data to demonstrate applications of multidimensional poverty but were limited to estimates from a single year (e.g., Alkire and Foster, 2011; Mitra and Brucker, 2016).
 
5
Glassman (2019, 2021) provide values of multidimensional poverty estimates in 2016–2017 and 2018–2019 respectively though Fig. 2 in both the working papers shows a trend line since 2009–2010.
 
6
See Dhongde and Dong (2022) for application of alternative indices to measure multidimensional poverty in the United States.
 
7
Glassman (2019) does not provide statewide estimates but provides changes in state official poverty rates and multidimensional poverty rates between 2016 and 2017. Glassman (2021) provides statewide poverty rates for 2019.
 
8
Glassman (2021) used the Area Deprivation Index (ADI) as an indicator of multidimensional poverty. The ADI is based on economic indicators at a block level such as percent of poor, unemployment rate, and so on and does not include any neighborhood indicators we consider in Sect. 6.
 
9
About 5 percent of the sample in the ACS lives in group quarters (GQs). GQs include such places as college residence halls, residential treatment centers, skilled nursing facilities, group homes, military barracks, correctional facilities, and workers’ dormitories. Survey values for GQs are often imputed.
 
10
See Dhongde and Haveman, (2017), Mitra and Brucker, (2019), Dhongde et al. (2019). For other reports on well-being, see the Federal Reserve Bank’s Report on the Economic Well-Being of U.S. Households (2016), the Gallup-Healthways Report in the U.S. and the OECD report by Stiglitz et al., (2018) which built upon the previous work of the Stiglitz-Sen-Fitoussi Commission.
 
11
Mitra and Brucker (2019) and Dhongde and Dong (2022) use data on unemployment in the CPS as an indicator for personal activities. Unlike in the CPS, in the ACS, unemployed individuals are identified as those who were actively looking for work in the last 4 weeks. Hence, in the ACS unemployment can be a result in the short term and may not always indicate deprivation. Suppa (2021) proposes a multidimensional index to measure deprivation in social participation.
 
12
See Nolan and Whelan (2010) and Alkire and Apablaza (2017) for applications with EU-SILC data.
 
13
Housing burden categories are: (i) No housing burden when less than 30 percent of household income is spent on housing costs, (ii) Moderate burden when 30 to 49.9 percent of income is spent on housing costs, and, (iii) severe burden when housing costs are 50 percent or above (Schwartz and Wilson, 2007).
 
14
The Redundancy Measure shows the percentage of individuals deprived in any two indicators as a proportion of the minimum of the two marginal deprivations (in percentage) in each indicator. A high value of the measure indicates that one of the two indicators may be empirically redundant in the analysis. See Klasen and Villalobos (2020) for an application of the redundancy measure.
 
15
The MPI is also referred to as the multidimensional deprivation index (MDI) by Dhongde and Haveman (2017), Glassman (2019, 2021) or simply as the headcount ratio by Mitra and Brucker (2016, 2019).
 
16
Note that the official estimates of OPM and SPM are based on CPS data so are not directly comparable with the MPI which is based on ACS data. Fox et al. (2020) provide estimates of OPM and SPM based on ACS data from 2014 to 2017.
 
17
Following the literature, we assigned children years of schooling of the head of the household. Disability data were missing for a majority of children; so we assigned the highest disability score among adults in the same household. For all other indicators, children and adults belonging to the same household were assigned the same values.
 
18
See, for example, Dhongde and Haveman (2017), Mitra and Brucker, (2016, 2019), Glassman (2019, 2021); Dhongde and Dong (2022) conduct an in-depth analysis of multidimensional poverty by race and ethnicity.
 
19
See Alkire and Santos (2014), Santos and Villatorro (2018) among others for different types of sensitivity tests.
 
20
PUMAs are non-overlapping contiguous areas and do not cross state boundaries. There is no territory within a state that is not assigned to a PUMA. ACS 2008 to 2011 uses PUMA codes based on 2000 census whereas ACS 2012 to 2017 uses PUMA codes based on 2010 census. We use the cross-walk between PUMA and county provided by the Missouri Census Data Center http://​mcdc.​missouri.​edu/​geography/​PUMAs.​html.
 
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Metadaten
Titel
Spatial and Temporal Trends in Multidimensional Poverty in the United States over the Last Decade
verfasst von
Shatakshee Dhongde
Robert Haveman
Publikationsdatum
06.03.2022
Verlag
Springer Netherlands
Erschienen in
Social Indicators Research / Ausgabe 1/2022
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-022-02902-z

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