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Evaluating the Employment Impact of Business Incentive Programs in EU Disadvantaged Areas. A case from Northern Italy

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Bondonio, Daniele ()

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Abstract

Business incentive programs have been increasingly popular within the EU as regional economic development tools to promote employment growth in areas with severely distressed and/or declining socio-economic conditions. Reliable evidenced on the effective net employment impact of such initiatives are greatly needed to help refining future intervention as, to these days, European policy makersÂ’ decisions can mostly be supported only by monitoring and survey analyses. This paper proposes a method of analysis to assess the employment impact of the business incentive initiatives implemented in the EU areas with declining industrial production (EU 'Objective 2' areas). The proposed method is a comparison-group evaluation approach that uses panels of employment data aggregated by geographic areas (similarly to the evaluation approach successfully adopted by recent studies of the US Enterprise Zone Programs). Details of the proposed method of analysis are illustrated through an empirical application: the evaluation of the business incentive program co-funded by the European Regional Development Fund in the 'Objective 2' areas of the Piedmont region (Italy). Results from such application prove the effectiveness and the robustness of the proposed method for impact evaluation analyses with longitudinal data, and highlight how the PiedmontÂ’s business incentive program did not significantly affect employment in the 'Objective 2 areas'.

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Paper provided by Department of Public Policy and Public Choice - POLIS in its series P.O.L.I.S. department's Working Papers with number 27.

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Length: 28 pages
Date of creation: May 2002
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Handle: RePEc:uca:ucapdv:27

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Find related papers by JEL classification:
O1 - Economic Development, Technological Change, and Growth - - Economic Development
R5 - Urban, Rural, and Regional Economics - - Regional Government Analysis
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data

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