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Counterfactual Spatial Distributions

Author

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  • Paul E. Carrillo

    (Department of Economics/Institute for International Economic Policy, George Washington University)

  • Jonathan Rothbaum

    (Development Research Group, The World Bank)

Abstract

The influential contributions of DiNardo, Fortin, and Lemieux (1996), Firpo, Fortin, and Lemieux (2009), Machado and Mata (2005), and Donald, Green, and Paarsch (2000) provide researchers with a useful toolbox to estimate counterfactual distributions of scalar random variables. These techniques have been widely applied in the literature. Typically, the dependent variable of interest has been a scalar and little consideration has been given to spatial factors. In this paper we propose a simple method to construct the counterfactual distribution of the location of a variable across space. We apply the spatial counterfactual technique to assess 1) how much changes in individual characteristics of Hispanics in the Washington, DC, area account for changes in the distribution of their residential location choices, and 2) how changes in the average characteristics of shareholders account for changes in the spatial distribution of new firms in Quito, Ecuador.

Suggested Citation

  • Paul E. Carrillo & Jonathan Rothbaum, 2014. "Counterfactual Spatial Distributions," Working Papers 2014-05, The George Washington University, Institute for International Economic Policy.
    • Handle: RePEc:gwi:wpaper:2014-05
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    References listed on IDEAS

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    Cited by:

    1. Luis Ayala & Javier Martín‐Román & Juan Vicente, 2020. "The contribution of the spatial dimension to inequality: A counterfactual analysis for OECD countries," Papers in Regional Science, Wiley Blackwell, vol. 99(3), pages 447-477, June.
    2. Kristian Behrens, 2016. "Agglomeration and clusters: Tools and insights from coagglomeration patterns," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(4), pages 1293-1339, November.
    3. Baum-Snow, Nathaniel & Hartley, Daniel, 2020. "Accounting for central neighborhood change, 1980–2010," Journal of Urban Economics, Elsevier, vol. 117(C).
    4. Paul E. Carrillo & Jonathan L. Rothbaum, 2022. "Counterfactual dissimilarity: Can changes in demographics and income explain increased racial integration in US cities?," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 21-56, January.
    5. Priscila Espinosa & Daniel Aparicio-Pérez & Emili Tortosa-Ausina, 2023. "On the Impact of Next Generation EU Funds: A Regional Synthetic Control Method Approach," Working Papers 2023/07, Economics Department, Universitat Jaume I, Castellón (Spain).
    6. Kristian Behrens & Brahim Boualam & Julien Martin & Florian Mayneris, 2024. "Gentrification and Pioneer Businesses," The Review of Economics and Statistics, MIT Press, vol. 106(1), pages 119-132, January.
    7. Renan Xavier Cortes & Sergio Rey & Elijah Knaap & Levi John Wolf, 2020. "An open-source framework for non-spatial and spatial segregation measures: the PySAL segregation module," Journal of Computational Social Science, Springer, vol. 3(1), pages 135-166, April.

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    More about this item

    Keywords

    Decomposition; Non-parametric Estimation;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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