Abstract
Urban poverty arises from the uneven distribution of poor populations across neighborhoods of a city. We study the trend and drivers of urban poverty across American cities over the last 40 years. To do so, we resort to a family of urban poverty indices that account for features of incidence, distribution, and segregation of poverty across census tracts. Compared to the universally-adopted concentrated poverty index, these measures have a solid normative background. We use tract-level data to assess the extent to which demographics, housing, education, employment, and income distribution affect levels and changes in urban poverty. A decomposition study allows to single out the effect of changes in the distribution of these variables across cities from changes in their correlation with urban poverty. We find that demographics and income distribution have a substantial role in explaining urban poverty patterns, whereas the same effects remarkably differ when using the concentrated poverty indices.
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Notes
Income segregation refers to the extent at which different income groups (poor, middle class, rich, for instance) are under- or over-represented in some neighborhoods compared to the city as whole. Measures of income segregation are conceptually different from concentrated poverty measures.
There are various criteria to establish whether two spatial units are close or not; among them, a main distinction can be made between the contiguity-based criteria (e.g., two spatial units are close if they share a common border) and the distance-based ones (e.g., two spatial units are close if the distance between their centroids is less than or equal to a chosen distance).
The relative weight of the difference in poverty incidence for the pair of tracts i and j in t, \(N_{i}^{{\mathcal {A}}_{t}}N_{j}^{{\mathcal {A}}_{t}}/\left( N^{{\mathcal {A}}_{t}}\right) ^{2}\), may differ from that in \(t+1\), \(N_{i}^{{\mathcal {A}}_{t+1}}N_{j}^{{\mathcal {A}}_{t+1}}/\left( N^{{\mathcal {A}}_{t+1}}\right) ^{2}\), for effect of changes in the relative distribution of population across census tracts.
The interpretation of D is consistent with the approach suggested by O’Neill and Van Kerm (2008) to examine income convergence across countries. O’Neill and Van Kerm (2008) broke down the change in the Gini index, obtaining a two-term decomposition where a component assesses to what extent the incomes of poorer countries, initially at the bottom of the distribution, have grown proportionally more than those of richer countries at the top of the initial distribution. Such a component is therefore considered as a measure of \(\beta \)-convergence in income across countries O’Neill and Van Kerm (2008).
The overall poverty incidence, P/N, changes also when all tract poverty incidences vary in the same proportion, while both D and R are equal to 0 in that case.
c being the relative variation in overall poverty incidence, C is equal to \(1/\left( 1+c\right) \) and ranges between 0 and \(+\infty \).
Both Census 1990 and 2000 and ACS determine a family poverty threshold by multiplying the base-year poverty thresholds (1982) by the average of the monthly inflation factors for the 12 months preceding the data collection. The poverty thresholds in 1982, by size of family and number of related children under 18 years can be found on the Census Bureau web-site: https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html. For a four persons household with two underage children, the 1982 threshold is $9,783. Using the inflation factor of 2.35795 gives a poverty threshold for this family in 2013 of $23,067. If the disposable household income is below this threshold, then all four members of the household are recorded as poor in the census tract of residence, and included in the 2014 wave of ACS.
\(\beta \) here indicates the type of convergence and should not be confused with parameter \(\beta \) in Eq. 1.
For a detailed description of the variables used to construct our indicators, see Chetty et al. (2017) and Tables 6 and 10 at https://opportunityinsights.org/data/.
The unevenness dimension is captured by the dissimilarity index, measuring the proportion of poor individuals that should move to restore proportionality across the MSA tracts (about 30% on average across all MSAs), see Andreoli and Zoli (2014).
See Christafore and Leguizamon (2019) for alternative definitions of gentrification.
A spatial weights matrix representing the spatial relationships between census tracts in a MSA is needed to obtain the spatial decomposition. We specify a binary spatial weights matrix, the ij-th element of which equals 1 if tracts i and j are neighboring and 0 otherwise. A distance-based criterion is used to establish whether two tracts are neighboring Andreoli et al. (2021). More specifically, two tracts are considered close if the distance between their centroids is less than or equal to a cut-off distance, which is set equal to the minimum distance for which every tract in a MSA has at least one neighbor.
We do not report the fixed effects, which explains why the reported overall difference due to coefficients is not entirely explained by the variables shown in Table 9.
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Acknowledgements
We are grateful to two anonymous reviewers and to conference participants at RES 2018 meeting (Sussex), LAGV 2018 (Aix en Provence) and ECINEQ 2019 (Paris) for commenting on an earlier draft of the paper. The usual disclaimer applies. Replication code for this article is accessible from the authors’ web-pages. This work was supported by the Luxembourg Fonds National de la Recherche (IMCHILD grant INTER/NORFACE/16/11333934/IMCHILD and PREFER-ME CORE grant C17/SC/11715898) and by the University of Verona (Ricerca di Base grants MOBILIFE-2017-RBVR17KFHX and PREOPP-2019-RBVR19FSFA).
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Andreoli, F., Mertens, A., Mussini, M. et al. Understanding trends and drivers of urban poverty in American cities. Empir Econ 63, 1663–1705 (2022). https://doi.org/10.1007/s00181-021-02174-5
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DOI: https://doi.org/10.1007/s00181-021-02174-5
Keywords
- Concentrated poverty
- Gini index
- Oaxaca–Blinder decomposition
- Census
- American Community Survey
- Spatial inequality