Multi-Dimensional Deprivation in the U.S.


This paper presents a comprehensive analysis of multidimensional deprivation in the U.S. since the Great Recession, from 2008 to 2013. We estimate a Multidimensional Deprivation Index by compiling individual level data on several well-being dimensions from the American Community Survey. Our results indicate that the proportion of the population that is multidimensional deprived averages about 15 percent, which exceeds the prevalence of official income poverty. Lack of education, severe housing burden and lack of health insurance were some of the dimensions in which Americans were most deprived in. Though deprivation increased during the recession, it trended towards a decline between 2010 and 2013. Unlike the official and the supplemental poverty measure which did not show any decline, the deprivation index better reflects the economic recovery since the recession. Overall, the prevalence of deprivation was higher in the southern and the western states and among the Asian and the Hispanic population. Importantly, there was not much overlap between individuals who were income poor and those who were multidimensional deprived. In fact, almost 30 % of individuals with incomes slightly above the poverty threshold experienced multiple deprivations. Our analysis underscores the need to look beyond income based poverty statistics in order to fully realize the impact of the recession on individuals’ well-being.

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  1. 1.

    United Nations Sustainable Development Goals Summit held in September 2015 adopted 17 Sustainable Development Goals which will build upon the previous Millennium Development Goals. See

  2. 2.

    Wagale (2009) used fuzzy sets and factor analysis to measure deprivation using data from the U.S. National Opinion Research Center. We do not review literature on material hardship measures (e.g. Beverly 2001; Carle et al. 2009), since material hardship measures are able to capture deprivation only in basic consumer durables (e.g., refrigerators, telephones); these measures fail to reflect a variety of non-material capabilities such as health outcomes, employment status and level of education.

  3. 3.

    Other methodologies in the literature including latent variables analysis, factor analysis, fuzzy set and information theory have also been used to formulate multidimensional deprivation measures (see Kakwani and Silber 2008, for a summary).

  4. 4.

    ACS data in 2008 and 2009 are controlled to population estimates based on Census 2000 counts; data from 2010 onwards are controlled to estimates based on Census 2010 counts. We do not use data from previous rounds (2005, 2006 and 2007) since there were changes made to the ACS questionnaire for several subject areas in 2008. PUMS is a sample of population and housing unit records from the ACS; the 1-year ACS PUMS file represents about 1 % of the total U.S. population.

  5. 5.

    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.

  6. 6.

    Not all dimensions in the commission’s report are included. For instance, we are not able to find data in the ACS to measure deprivation as lack of political voice and governance and personal activities.

  7. 7.

    The disability/difficulty distribution was approximately: 9 %-ambulatory, 6 %-difficulty in independent living, 5 %-hearing or cognitive, and 3 %-vision or difficulty in self-care.

  8. 8.

    The housing burden categories are: No housing burden (under 30 % of income spent on housing costs), moderate burden (between 30 and 49.9 %), and severe burden (over 50 %), (Schwartz and Wilson 2007).

  9. 9.

    See the report “Measuring Overcrowding in Housing” published by the U.S. Department of Housing and Urban Development (2007). The ACS reports data on housing facilities such as plumbing (e.g., hot and cold running water, a flush toilet, a bathtub or shower) and kitchen facilities (e.g., a sink with a faucet, a stove, range, or a refrigerator). We do not include these indicators since less than 1 percent of the sample lived in households without kitchen or plumbing facilities.

  10. 10.

    Although the recession officially lasted from December 2007 through June 2009, monthly unemployment rates remained above 9 % for more than 2 years after the official start of economic recovery (Danziger et al 2012).

  11. 11.

    Though historically there is some evidence suggesting that growth of GDP since about 1980 seems to have a smaller antipoverty effect than in the 1960 s and 1970 s, see Haveman and Schwabish (2000).

  12. 12.

    Although the Affordable Care Act has lowered the proportion of people without health insurance, there are reports that many low-income workers still find the coverage unaffordable; thus the indicator will remain relevant in the future. A report in the New York Times reports that for those workers trying to get by on near-minimum wages, a plan that qualifies as “affordable” can still seem far out of reach. Workers making $15,000 to $20,000 a year buy employer-sponsored individual insurance when it is offered only 37 % of the time.

  13. 13.

    The limited overlap between income poor and multidimensional deprived is not driven by the exclusion of children and elderly from the sample. Using the entire population, we re-estimated the values and found them to be very similar. Values in the entire population were: 15.6 % as income poor, 15.5 % as multidimensional deprived and 7 percent overlap.

  14. 14.

    Compared to the non-elderly adult population, these two population groups have a skewed percent of individuals deprived in the chosen indicators. The percent of elderly with no health insurance stood at less than 1 percent as compared to 21 % among the non-elderly, in 2011. On the other hand, more than 20 percent of the elderly had more than one disabling condition compared to less than 5 % among the non-elderly, whereas data on disabilities was missing for 82 % of children. While 18.5 % of elderly lacked a high school degree, only 11 % of the non-elderly lacked this degree. In order to construct a meaningful measure of deprivation among children and the elderly population, it will be helpful to use datasets other than the ACS which are more relevant to these groups.

  15. 15.

    Given the benchmark for education of high school completion, for children below 18 years of age we assigned the average years of schooling of all adults in the same household. Data on most disability variables was missing for a majority of children; so we assigned the highest disability score among adults in the same household. Unlike schooling or disabilities, data on the remaining 4 indicators is available at the household level; so children and adults belonging to the same household were assigned the same values.

  16. 16.

    Mitra and Brucker (2014) too found about 15 % individuals to be deprived in two or more indicators. However there are important differences between the two studies; their estimates are based on 1 year (2013) CPS data and on slightly different indicators.

  17. 17.

    The income-poverty ratio in the ACS is estimated by comparing the person’s total family income in the last 12 months with the poverty threshold appropriate for that person's family size and composition, according to the standards specified by the Office of Management and Budget in Statistical Policy Directive 14. If the total income of that person's family is less than the threshold appropriate for that family, then the person is considered “below the poverty level,” together with every member of his or her family. The appropriate poverty thresholds are determined by multiplying the base-year poverty thresholds (1982) by the average of the monthly inflation factors for the 12 months preceding the data collection.

  18. 18.

    See Klasen (2000), Roche (2008) for PCA and Asselin (2009), Ezzarari and Verme (2012) for MCA applications.


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We are grateful to an anonymous referee for providing useful comments on the previous version. We also thank Gordon Anderson, Sarah Bruch, James Foster, Thesia Garner, David Grusky, Julie Hotchkiss, Stephan Klasen, Timothy Smeeding, Barbara Wolfe, Madeline Zavodny, and seminar participants at the Bureau of Labor Statistics, Washington D.C., Gordon College, Boston, Institute for Research on Poverty at the University of Wisconsin-Madison, and the Stanford Center on Poverty and Inequality, Palo Alto, where versions of this paper were presented.

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Correspondence to Shatakshee Dhongde.



See Tables 7 and 8.

Table 7 Standard errors of Multidimensional Deprivation Indices over time
Table 8 Multidimensional Deprivation Index by regions and states

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Dhongde, S., Haveman, R. Multi-Dimensional Deprivation in the U.S.. Soc Indic Res 133, 477–500 (2017).

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  • American community survey
  • Capabilities approach
  • Measurement
  • Multidimensional
  • Poverty
  • Recession
  • United States

JEL Classification

  • D6
  • I32
  • O15