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Neighborhood Effects On The Propensity Score Matching


  • Marusca De Castris

    (Roma Tre, University of Rome)

  • Guido Pellegrini

    (Sapienza, University of Rome)


The focus of our paper is the identification of the regional effects of industrial subsidies when the presence of subsidized firms is spatially correlated. In this case the stable unit treatment value assumption (SUTVA) in the Rubin model is not valid and some econometric methods should be used in order to detect the consistent policy impact in presence of spatial dependence. We propose a new methodology for estimating the unbiased “net” effect of the subsidy, based on novel “spatial propensity score matching” technique that compare treated and not treated units affected by similar spillover effects due to treatment. We offer different econometrical approaches, where the “spatial” propensity score is estimated by standard or spatial probit models. Some robustness tests are also implemented, using different instrumental variable spatial models applied to a probit model. We test the model using an empirical application, based on a dataset that incorporates information on incentives to private capital accumulation by Law 488/92, mainly devoted to SME, and Planning Contracts, created for large projects, in Italy. The analysis is carried out on a disaggregated territorial level, using the grid of the local labour system. The results show a direct effect of subsidies on subsidized firms. The sign of the impact is generally positive, the output effect outweighing the substitution effect. Confronting the standard and the “spatial” estimation, we observed a positive but small crowding out effect across firms in the same area and across neighbouring areas, mostly in the labour market. However, due to the small sample, the difference in impacts estimated by the standard and the “spatial” effect of subsidies is not statistically significant.

Suggested Citation

  • Marusca De Castris & Guido Pellegrini, 2015. "Neighborhood Effects On The Propensity Score Matching," Working Papers 0515, CREI Università degli Studi Roma Tre, revised 2015.
  • Handle: RePEc:rcr:wpaper:05_15

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    References listed on IDEAS

    1. BAUMONT, Catherine & ERTUR, Cem & LE GALLO, Julie, 2001. "A Spatial Econometric Analysis of Geographic Spillovers and Growth for European Regions, 1980-1995," LATEC - Document de travail - Economie (1991-2003) 2001-04, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    3. Luc Anselin, 2003. "Spatial Externalities, Spatial Multipliers, And Spatial Econometrics," International Regional Science Review, , vol. 26(2), pages 153-166, April.
    4. Marusca de Castris & Guido Pellegrini, 2012. "Evaluation of Spatial Effects of Capital Subsidies in the South of Italy," Regional Studies, Taylor & Francis Journals, vol. 46(4), pages 525-538, June.
    5. Bernini, Cristina & Pellegrini, Guido, 2011. "How are growth and productivity in private firms affected by public subsidy? Evidence from a regional policy," Regional Science and Urban Economics, Elsevier, vol. 41(3), pages 253-265, May.
    6. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
    7. André Luis Squarize Chagas & Rudinei Toneto & Carlos Roberto Azzoni, 2012. "A Spatial Propensity Score Matching Evaluation of the Social Impacts of Sugarcane Growing on Municipalities in Brazil," International Regional Science Review, , vol. 35(1), pages 48-69, January.
    8. Adams, Renée & Almeida, Heitor & Ferreira, Daniel, 2009. "Understanding the relationship between founder-CEOs and firm performance," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 136-150, January.
    9. Augusto Cerqua & Guido Pellegrini, 2013. "Beyond the SUTVA: how industrial policy evaluations change when we allow for interaction among firms," ERSA conference papers ersa13p340, European Regional Science Association.
    10. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
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    Cited by:

    1. Di Gennaro, Daniele & Pellegrini, Guido, 2016. "Evaluating direct and indirect treatment effects in Italian R&D expenditures," MPRA Paper 76467, University Library of Munich, Germany, revised 28 Jan 2017.
    2. Daniele Di Gennaro & Guido Pellegrini, 2016. "Policy Evaluation In Presence Of Interferences: A Spatial Multilevel Did Approach," Working Papers 0416, CREI Università degli Studi Roma Tre, revised 2016.

    More about this item


    spatial propensity score; policy evaluation; propensity score matching; spatial analysis;

    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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