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Evaluating locally-based policies in the presence of neighbourhood effects: The case of touristic accommodation in the Garda district of Trentino


  • Giovanni Cerulli


  • Roberto Gabriele
  • Enrico Tundis


This paper presents a counter-factual model identifying Average Treatment Effects (ATEs) by Conditional Mean Independence when externality (or neighbourhood) effects are incorporated within the traditional potential outcome model. As such, it tries to generalize the usual approach, widely used in program evaluation, when SUTVA (i.e. Stable Unit Treatment Value Assumption) is relaxed. This new approach is applied to a locally-based policy. More specifically, we focus on the Garda lake area that is one of the 14 touristic districts in Trentino ? an Alpine province in north-east Italy. In the time window under scrutiny ? 2002-2006 ? Trentino had in place a subsidy policy for hotels ? 3-digit sector 55.1: hotels and similar accommodation, as defined in the NACE Rev.2 ? within the Provincial Law 6/99. There is no confounding effects coming from other policy measures given that Trentino hotels can only have access to subsidies related to Provincial Law 6/99. We rely on a database built relying on different sources and contains administrative information, structural characteristics of hotels and exhaustive information about the subsidies they received during the period. The sample size is 415 and consists of the Garda lake hotels active from 2002 to 2006.

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  • Giovanni Cerulli & Roberto Gabriele & Enrico Tundis, 2014. "Evaluating locally-based policies in the presence of neighbourhood effects: The case of touristic accommodation in the Garda district of Trentino," ERSA conference papers ersa14p715, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa14p715

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    References listed on IDEAS

    1. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    2. Sobel, Michael E., 2006. "What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1398-1407, December.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, September.
    4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    5. Hudgens, Michael G. & Halloran, M. Elizabeth, 2008. "Toward Causal Inference With Interference," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 832-842, June.
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    7. Wooldridge, Jeffrey M., 1997. "On two stage least squares estimation of the average treatment effect in a random coefficient model," Economics Letters, Elsevier, vol. 56(2), pages 129-133, October.
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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.

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    More about this item


    ATEs; causal model; SUTVA; neighbourhood effects; locally-based policy;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software


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