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Estimating the Probability of Multidimensional Deprivation Incidence

In: Deprivation in America

Author

Listed:
  • Roger White

    (Whittier College)

Abstract

In this chapter, data from the 2023 American Community Survey and a logistic regression model are employed to estimate the likelihood that U.S. residents will experience multidimensional deprivation. The model incorporates 48 intersectional identity classifications, defined by race, sex, Hispanic ethnicity, and nativity, along with three broad age groups and nine Census divisions, yielding 1242 probability estimates. The results reveal strong and consistent effects of both personal characteristics and geographic context. For example, foreign-born Hispanic multiracial women aged 18–64 living in New England face multidimensional deprivation probabilities that are as much as twice the levels estimated for individuals in the same division with different identity profiles. The model also enables counterfactual simulations, illustrating how predicted deprivation outcomes change with variation in personal characteristics. These findings affirm that multidimensional deprivation is not randomly distributed but is shaped by overlapping systems of social and spatial inequality. By integrating identity, life stage, and geography into a unified empirical model, the patterned nature of disadvantages is highlighted while, simultaneously, the need for targeted, equity-oriented policy responses is stressed.

Suggested Citation

  • Roger White, 2026. "Estimating the Probability of Multidimensional Deprivation Incidence," Global Perspectives on Wealth and Distribution, in: Deprivation in America, chapter 0, pages 297-317, Palgrave Macmillan.
    • Handle: RePEc:pal:gpochp:978-3-032-19879-2_9
    DOI: 10.1007/978-3-032-19879-2_9
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