IDEAS home Printed from https://ideas.repec.org/a/taf/specan/v16y2021i3p313-332.html
   My bibliography  Save this article

Inference for the neighbourhood inequality index

Author

Listed:
  • Francesco Andreoli
  • Eugenio Peluso

Abstract

The neighborhood inequality (NI) index measures aspects of spatial inequality in the distribution of incomes within a city. It is 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 neighbours located within a certain distance range. The approach overcomes the modifiable areal units problem affecting local inequality measures. This paper provides minimum bounds for the NI index standard error and shows that unbiased estimators can be identified under fairly common hypothesis in spatial statistics. Results from a Monte Carlo study support the relevance of the approximations. Rich income data are then used to infer about trends of NI in Chicago, IL, over the last 35 years.

Suggested Citation

  • Francesco Andreoli & Eugenio Peluso, 2021. "Inference for the neighbourhood inequality index," Spatial Economic Analysis, Taylor & Francis Journals, vol. 16(3), pages 313-332, July.
  • Handle: RePEc:taf:specan:v:16:y:2021:i:3:p:313-332
    DOI: 10.1080/17421772.2020.1800071
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/17421772.2020.1800071
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/17421772.2020.1800071?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Nathaniel Baum-Snow & Ronni Pavan, 2013. "Inequality and City Size," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1535-1548, December.
    2. Raj Chetty & Nathaniel Hendren, 2018. "The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(3), pages 1107-1162.
    3. Enrico Moretti, 2013. "Real Wage Inequality," American Economic Journal: Applied Economics, American Economic Association, vol. 5(1), pages 65-103, January.
    4. Jens Ludwig & Greg J. Duncan & Lisa A. Gennetian & Lawrence F. Katz & Ronald C. Kessler & Jeffrey R. Kling & Lisa Sanbonmatsu, 2013. "Long-Term Neighborhood Effects on Low-Income Families: Evidence from Moving to Opportunity," American Economic Review, American Economic Association, vol. 103(3), pages 226-231, May.
    5. Valentino Dardanoni & Antonio Forcina, 1999. "Inference for Lorenz curve orderings," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 49-75.
    6. Kuan Xu, 2007. "U-Statistics and Their Asymptotic Results for Some Inequality and Poverty Measures," Econometric Reviews, Taylor & Francis Journals, vol. 26(5), pages 567-577.
    7. Ludwig, Jens & Duncan, Greg J. & Katz, Lawrence F. & Kessler, Ronald & Kling, Jeffrey R. & Gennetian, Lisa A. & Sanbonmatsu, Lisa, 2012. "Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults," Scholarly Articles 11870359, Harvard University Department of Economics.
    8. Hardman, Anna & Ioannides, Yannis M., 2004. "Neighbors' income distribution: economic segregation and mixing in US urban neighborhoods," Journal of Housing Economics, Elsevier, vol. 13(4), pages 368-382, December.
    9. Bishop, John A & Chakraborti, S & Thistle, Paul D, 1989. "Asymptotically Distribution-Free Statistical Inference for Generalized Lorenz Curves," The Review of Economics and Statistics, MIT Press, vol. 71(4), pages 725-727, November.
    10. Tara Watson, 2009. "Inequality And The Measurement Of Residential Segregation By Income In American Neighborhoods," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(3), pages 820-844, September.
    11. George Galster, 2001. "On the Nature of Neighbourhood," Urban Studies, Urban Studies Journal Limited, vol. 38(12), pages 2111-2124, November.
    12. Francesco Andreoli & Eugenio Peluso, 2018. "So close yet so unequal: Neighborhood inequality in American cities," Working Papers 477, ECINEQ, Society for the Study of Economic Inequality.
    13. Anthony Shorrocks & Guanghua Wan, 2005. "Spatial decomposition of inequality," Journal of Economic Geography, Oxford University Press, vol. 5(1), pages 59-81, January.
    14. Davidson, Russell, 2009. "Reliable inference for the Gini index," Journal of Econometrics, Elsevier, vol. 150(1), pages 30-40, May.
    15. Pyatt, Graham, 1976. "On the Interpretation and Disaggregation of Gini Coefficients," Economic Journal, Royal Economic Society, vol. 86(342), pages 243-255, June.
    16. Francesco Andreoli, 2018. "Robust Inference for Inverse Stochastic Dominance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 146-159, January.
    17. Muliere, Pietro & Scarsini, Marco, 1989. "A note on stochastic dominance and inequality measures," Journal of Economic Theory, Elsevier, vol. 49(2), pages 314-323, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesco Andreoli & Eugenio Peluso, 2016. "So close yet so unequal: Reconsidering spatial inequality in U.S. cities," Working Papers 21/2016, University of Verona, Department of Economics.
    2. ANDREOLI Francesco & PELUSO Eugenio, 2017. "So close yet so unequal: Spatial inequality in American cities," LISER Working Paper Series 2017-11, Luxembourg Institute of Socio-Economic Research (LISER).
    3. Francesco Andreoli & Mauro Mussini & Vincenzo Prete & Claudio Zoli, 2021. "Urban poverty: Measurement theory and evidence from American cities," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 599-642, December.
    4. ANDREOLI Francesco & MUSSINI Mauro & PRETE Vincenzo, 2019. "Urban poverty: Theory and evidence from American cities," LISER Working Paper Series 2019-07, Luxembourg Institute of Socio-Economic Research (LISER).
    5. Hongyi Jiang & Zhenting Sun & Shiyun Hu, 2023. "A Nonparametric Test of $m$th-degree Inverse Stochastic Dominance," Papers 2306.12271, arXiv.org, revised Jul 2023.
    6. Francesco Andreoli & Arnaud Mertens & Mauro Mussini & Vincenzo Prete, 2022. "Understanding trends and drivers of urban poverty in American cities," Empirical Economics, Springer, vol. 63(3), pages 1663-1705, September.
    7. Pascaline Vincent & Frédéric Chantreuil & Benoït Tarroux, 2012. "Appraising the breakdown of unequal individuals in large French cities," Economics Working Paper Archive (University of Rennes & University of Caen) 201220, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    8. Mathias Moser & Matthias Schnetzer, 2014. "The Geography of Average Income and Inequality: Spatial Evidence from Austria," Department of Economics Working Papers wuwp191, Vienna University of Economics and Business, Department of Economics.
    9. Philipp Ehrl, 2014. "A breakdown of residual wage inequality in Germany," Working Papers 150, Bavarian Graduate Program in Economics (BGPE).
    10. Francesco Andreoli & Tarjei Havnes & Arnaud Lefranc, 2019. "Robust Inequality of Opportunity Comparisons: Theory and Application to Early Childhood Policy Evaluation," The Review of Economics and Statistics, MIT Press, vol. 101(2), pages 355-369, May.
    11. Russell Davidson, 2010. "Innis Lecture: Inference on income distributions," Canadian Journal of Economics, Canadian Economics Association, vol. 43(4), pages 1122-1148, November.
    12. Francesco Andreoli, 2018. "Robust Inference for Inverse Stochastic Dominance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 146-159, January.
    13. Rabensteiner, Thomas & Guschanski, Alexander, 2022. "Autonomy and wage divergence: evidence from European survey data," Greenwich Papers in Political Economy 37925, University of Greenwich, Greenwich Political Economy Research Centre.
    14. Rey, Sergio, 2015. "Bells in Space: The Spatial Dynamics of US Interpersonal and Interregional Income Inequality," MPRA Paper 69482, University Library of Munich, Germany.
    15. Farrokhi, Farid & Jinkins, David, 2019. "Wage inequality and the location of cities," Journal of Urban Economics, Elsevier, vol. 111(C), pages 76-92.
    16. Carlino, Gerald & Kerr, William R., 2015. "Agglomeration and Innovation," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 349-404, Elsevier.
    17. John Gathergood & Fabian Gunzinger & Benedict Guttman-Kenney & Edika Quispe-Torreblanca & Neil Stewart, 2020. "Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending," Papers 2012.09336, arXiv.org, revised Dec 2020.
    18. Sophie van Huellen & Duo Qin, 2019. "Compulsory Schooling and Returns to Education: A Re-Examination," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
    19. Sharon Barnhardt & Erica Field & Rohini Pande, 2017. "Moving to Opportunity or Isolation? Network Effects of a Randomized Housing Lottery in Urban India," American Economic Journal: Applied Economics, American Economic Association, vol. 9(1), pages 1-32, January.
    20. Luis Bauluz & Sebastien Breau & Pawel Bukowski & Mark Fransham & Annie Seong Lee & Neil Lee & Margarita Lopez Forero & Clement Malgouyres & Filip Novokmet & Moritz Schularick & Gregory Verdugo, 2023. "Spatial wage inequality in North America and Western Europe: changes between and within local labour markets 1975-2019," CEP Discussion Papers dp1941, Centre for Economic Performance, LSE.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:specan:v:16:y:2021:i:3:p:313-332. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RSEA20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.