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Targeting and Effectiveness of Location-Based Policies

Author

Listed:
  • Carrieri, Vincenzo

    (University of Calabria)

  • de Blasio, G.

    (International Monetary Fund)

  • Ferrara, Antonella Rita

    (University of Calabria)

  • Nisticò, Rosanna

    (University of Calabria)

Abstract

This paper provides insights into the design of effective location-based policies. In the context of European regional policy, we use algorithms to predict regions that are likely to underutilize funding and identify the key determinants of their low absorptive capacity. We then use a regression discontinuity design (RDD) to document that EU funds are ineffective in recipients predicted to have low absorptive capacity while increasing output and employment in high-capacity regions. Our approach allows early identification and targeting of interventions to increase regional spending capacity based on publicly available data and standard algorithms, thereby facilitating implementation by policymakers.

Suggested Citation

  • Carrieri, Vincenzo & de Blasio, G. & Ferrara, Antonella Rita & Nisticò, Rosanna, 2024. "Targeting and Effectiveness of Location-Based Policies," IZA Discussion Papers 17308, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp17308
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    References listed on IDEAS

    as
    1. Maximilian v. Ehrlich & Henry G. Overman, 2020. "Place-Based Policies and Spatial Disparities across European Cities," Journal of Economic Perspectives, American Economic Association, vol. 34(3), pages 128-149, Summer.
    2. Sascha O. Becker & Peter H. Egger & Maximilian von Ehrlich, 2013. "Absorptive Capacity and the Growth and Investment Effects of Regional Transfers: A Regression Discontinuity Design with Heterogeneous Treatment Effects," American Economic Journal: Economic Policy, American Economic Association, vol. 5(4), pages 29-77, November.
    3. Benoit Dicharry, 2023. "Regional growth and absorption speed of EU funds: when time isn’t money," Regional Studies, Taylor & Francis Journals, vol. 57(3), pages 511-524, March.
    4. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    5. Nicholas Charron & Lewis Dijkstra & Victor Lapuente, 2015. "Mapping the Regional Divide in Europe: A Measure for Assessing Quality of Government in 206 European Regions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 122(2), pages 315-346, June.
    6. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    7. Nicholas Charron & Lewis Dijkstra & Victor Lapuente, 2015. "Erratum to: Mapping the Regional Divide in Europe: A Measure for Assessing Quality of Government in 206 European Regions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(3), pages 1059-1059, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    machine learning; location-based policies; program design;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • F35 - International Economics - - International Finance - - - Foreign Aid
    • H77 - Public Economics - - State and Local Government; Intergovernmental Relations - - - Intergovernmental Relations; Federalism
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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