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Inference for the neighborhood inequality index

Francesco Andreoli ()

No 2018-19, LISER Working Paper Series from LISER

Abstract: The neighborhood inequality (NI) index measures aspects of spatial inequality in the distribution of incomes within the city. The NI index is defi ned as a population average of the normalized income gap between each individual's income (observed at a given location in the city) and the incomes of the neighbors, living within a certain distance range from that individual. This paper provides minimum bounds for the NI index standard error and shows that unbiased estimators can be identifi ed under fairly common hypothesis in spatial statistics. These estimators are shown to depend exclusively on the variogram, a measure of spatial dependence in the data. Rich income data are then used to infer about trends of neighborhood inequality in Chicago, IL over the last 35 years. Results from a Monte Carlo study support the relevance of the standard error approximations.

Keywords: income inequality; individual neighborhood; geostatistics; variogram; census; ACS; ratio measures; variance approximation; Chicago; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C12 C46 D63 R23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
Date: 2018-11
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Persistent link: https://EconPapers.repec.org/RePEc:irs:cepswp:2018-19

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