IDEAS home Printed from https://ideas.repec.org/p/cii/cepidt/2017-10.html
   My bibliography  Save this paper

Spatial Differencing: Estimation and Inference

Author

Listed:
  • Federico Belotti
  • Edoardo Di Porto
  • Gianluca Santoni

Abstract

Spatial differencing is a spatial data transformation pioneered by Holmes (1998) increasingly used to estimate causal effects with non-experimental data. Recently, this transformation has been widely used to deal with omitted variable bias generated by local or site-specific unobservables in a "boundary-discontinuity" design setting. However, as well known in this literature, spatial differencing makes inference problematic. Indeed, given a specific distance threshold, a sample unit may be the neighbor of a number of units on the opposite side of a specific boundary inducing correlation between all differenced observations that share a common sample unit. By recognizing that the spatial differencing transformation produces a special form of dyadic data, we show that the dyadic-robust variance matrix estimator proposed by Cameron and Miller (2014) is, in general, a better solution compared to the most commonly used estimators.

Suggested Citation

  • Federico Belotti & Edoardo Di Porto & Gianluca Santoni, 2017. "Spatial Differencing: Estimation and Inference," Working Papers 2017-10, CEPII research center.
  • Handle: RePEc:cii:cepidt:2017-10
    as

    Download full text from publisher

    File URL: http://www.cepii.fr/PDF_PUB/wp/2017/wp2017-10.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sandra E. Black, 1999. "Do Better Schools Matter? Parental Valuation of Elementary Education," The Quarterly Journal of Economics, Oxford University Press, vol. 114(2), pages 577-599.
    2. Gibbons, Stephen & Machin, Stephen & Silva, Olmo, 2013. "Valuing school quality using boundary discontinuity," LSE Research Online Documents on Economics 45246, London School of Economics and Political Science, LSE Library.
    3. Gilles Duranton & Laurent Gobillon & Henry G. Overman, 2011. "Assessing the Effects of Local Taxation using Microgeographic Data," Economic Journal, Royal Economic Society, vol. 121(555), pages 1017-1046, September.
    4. Fack, Gabrielle & Grenet, Julien, 2010. "When do better schools raise housing prices? Evidence from Paris public and private schools," Journal of Public Economics, Elsevier, vol. 94(1-2), pages 59-77, February.
    5. Gibbons, Stephen & Machin, Stephen & Silva, Olmo, 2013. "Valuing school quality using boundary discontinuities," Journal of Urban Economics, Elsevier, vol. 75(C), pages 15-28.
    6. Chirinko, Robert S. & Wilson, Daniel J., 2008. "State investment tax incentives: A zero-sum game?," Journal of Public Economics, Elsevier, vol. 92(12), pages 2362-2384, December.
    7. Oskari Harjunen & Mika Kortelainen & Tuukka Saarimaa, 2018. "Best Education Money Can Buy? Capitalization of School Quality in Finland," CESifo Economic Studies, CESifo, vol. 64(2), pages 150-175.
    8. Thomas J. Holmes, 1998. "The Effect of State Policies on the Location of Manufacturing: Evidence from State Borders," Journal of Political Economy, University of Chicago Press, vol. 106(4), pages 667-705, August.
    9. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    10. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    11. Belotti, Federico & Di Porto, Edoardo & Santoni, Gianluca, 2016. "The effect of local taxes on firm performance: evidence from geo-referenced data," Working Paper Series 2016:3, Uppsala University, Department of Economics.
    12. Kahn, Matthew E., 2004. "Domestic pollution havens: evidence from cancer deaths in border counties," Journal of Urban Economics, Elsevier, vol. 56(1), pages 51-69, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cerqua, Augusto & Pellegrini, Guido, 2018. "Local multipliers at work," MPRA Paper 85326, University Library of Munich, Germany.

    More about this item

    Keywords

    Spatial Differencing; Boundary Discontinuity; Robust Inference; Dyadic Data;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cii:cepidt:2017-10. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://edirc.repec.org/data/cepiifr.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.