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In recent years, a growing number of studies have applied regression discontinuity designs (RDDs) to the analysis of prominent regional economic development and business incentive policies. As a result, impact evaluations of these policy interventions are being increasingly commissioned under the assumption that local linear regressions have superior impact identification properties in the cases in which an eligibility boundary, geographical border or application cutoff is identifiable as a source of treatment assignment. This paper shows that in a number of frequently encountered cases, however, economic development and business incentive programs pose peculiar impact identification conditions in terms of violations of main assumptions for the validity of RDDs and of running variables that have little or no influence of the outcome variable. Under these circumstances, that bear generalizable relevance also for the analysis of other public programs at large, no adequate testing to support the internal validity of the applied discontinuity design is often performed in the literature and the use of local linear/polynomial regressions, or cross-border comparisons, becomes an inadequate choice. When these conditions apply, adopting instead other quasi-experimental methods to achieve an actual adequate treatment-control balance of the relevant baseline characteristic will often be a better approach.JEL classification; O1; R5; C23

This article proposes an empirical method to evaluate decentralized economic development programmes with heterogeneous characteristics implemented in different regions or states. The evaluation design developed in the article is a comparative analysis that operationalizes differences in regional policy features and controls for exogenous factors that may affect outcomes independently from the programme intervention. The proposed method is illustrated and tested through the evaluation of the US Enterprise Zone programmes. The results of the analysis show that the proposed evaluation design is an effective tool for turning the heterogeneity of decentralized economic development programmes from a threat to the validity of the analysis into a great opportunity for testing the effectiveness of a variety of region- (or state-)specific policy implementation features.

While the importance of enterprise support policies in the EU continues to grow, there remains only limited empirical evidence examining the effects of the policies on socially relevant outcomes such as employment. This paper shows how to exploit firm-level data, formed by merging longitudinal employment and firm demographic information with the firm-level archives of the incentive payments, to offer robust counterfactual impact evaluation evidence on the employment effects of the coexisting European Regional Development Fund (ERDF) co-sponsored, national and regional programs commonly operated in many EU regions. The analysis uses data from a large northern Italian region and yields employment impacts of the policies under plausible identification assumptions, disentangling the impacts of different values of both the economic intensities of the program assistance and different forms of assistance (the latter distinguishing between capital grants and below-market interest rate/revolving loans). The paper finds that the absolute per-firm employment effects of the programs are increasingly larger the higher the economic value of the incentives awarded to the assisted firms. The incentives with the highest per-firm economic value, however, yield employment impacts with a much higher cost per each additional new job than incentives with a lower economic value. The results of the analysis also show that the absolute per-firm employment effects of soft loans are similar to those of capital grants. However, taking into consideration that soft loans bear a much lower cost in terms of public money devoted to the subsidies than capital grants, the impact estimates retrieved from analysis indicates that soft loans possess an higher employment effectiveness than capital grants.

Public support to firm-level investments in innovation is one of the main mechanisms through which the European Union promotes socioeconomic convergence among regions and the creation of quality jobs is considered a necessary condition for the convergence of disadvantaged regional economies. This paper exploits the availability of natural experiment conditions and linked employer-employee microdata in Portugal to offer empirical evidence on the impact on relevant job-quality outcomes of a large EU-cohesion-policy program to support SMEs' innovation investments. The analysis is implemented by means of stratification/coarsened exact matching model, combined with a difference in difference scheme, suitable to the specific impact identification conditions. Our results indicate that the policy intervention in Portugal had a positive impact on job-quality outcomes, with each supported firm generating an average of 4.9 additional standard-working-time jobs, +2.9 skilled jobs, and +2.0 permanent-contract jobs, compared to a counterfactual scenario of no public support. These impacts were at a cost of about 16,100€, 27,100€ and 39,400€ in public subsidies per additional job, respectively. We also estimate that the program impact was responsible for a 2.20€ (+17.8%) increase of the per-hour remuneration. These findings are robust to sensitivity analysis, in terms of alternative matching procedures and comparison groups, and they highlight the fact that increasing job-quality is a policy goal that can be pursued, at a reasonable cost, also by means of cohesion-policy support to innovation aimed at enhancing the competitiveness of SMEs. JEL classification: O1; R5; C23