Philipp Grunau (p), Florian Hoffmann, Thomas Lemieux, Mirko Titze

Giulio Pedrini

We study the wage and employment effects of a German place-based policy using a research design that exploits conditionally exogenous EU-wide rules governing the program parameters at the regional level. The place-based program subsidizes investments to create jobs with a subsidy rate that varies across labor market regions. The analysis uses matched data on the universe of establishments and their employees, establishment-level panel data on program participation, and regional scores that generate spatial discontinuities in program eligibility and generosity. These rich data enable us to study the incidence of the place-based program on different groups of individuals. We find that the program helps establishments create jobs that disproportionately benefit younger and less-educated workers. Funded establishment increase their wages to attract new workers, but unlike employment, wage gains do not persist in the long run. Employment effects estimated at the local area level are slightly larger than establishment-level estimates, suggesting limited spillover effects. Using subsidy rates as an instrumental variable for actual subsidies indicates that it costs approximately EUR 25,000 to create a new job in the economically disadvantaged areas targeted by the program.

Eva Dettmann (p), Elena Ragazzi (p), Lisa Sella

Philipp Grunau

The ISI calls are a policy offering incentives to firms to invest in occupational safety and health. One measure of this policy is devoted to the adoption of systems to manage occupational risk (risk management systems, RMS now on). These are adaptive sets of actions undertaken by a firm to improve its preparedness to manage the emergencies and to reduce risks. In this paper we will show the results of new estimates on the impact of ISI incentives on the firms’ accident profile.
In previous papers based on this research, some impact of the incentives was detected, but the results were very volatile and not reliable. There are many possible explanations for this lack of robustness:
-Choice of the unit of observation (local unit vs whole firm)
-Sample size (even though our sample is not very small, accidents are very rare events, so large samples are required to detect the impact)
-The problem of non-compliance to assigned treatment (attrition and firms investing even without the incentive), which affects the credibility of the natural experiment evaluation setting
-The role of non-observables as factors conditioning the impact in OSH
-The role of heterogeneity among yearly calls (in pooled estimations)
In this more advanced version of our research we tackle those problems by adopting a new combination of panel matching and difference-in-difference that considers time varying treatments and including the accident profile of firms prior to participation in the program as a proxy for motivation of management and workers.

Sarah Fritz (p), Catherine van der List

Eva Dettmann

EU Cohesion Policy - the European Union's place-based policy amounting to one-third of its budget - is designed to equalize outcomes across and within countries in the EU. Despite five periods of funding for EU cohesion policy, spatial disparities are large and the long-run efficacy of the policy is still poorly understood. Recent empirical work has documented the positive effects of this investment policy on growth in less developed regions, but these effects may not be long-lived. In order to understand the medium- to long-term effects of this place-based policy, we study its impact on reallocation, including firm entry and exit in local economic areas in Italy. New firms are a key driver of local economic growth, and subsidies may drive entry through improved provision in public goods and/ or more skilled labor supply. Alternatively, the policy could suppress entry if the subsidies were to keep non-viable firms in declining industries in business.

Giulio Pedrini (p), Gianluca Monturano (p), Raffaele Scuderi

Sarah Fritz

This paper provides a comprehensive evaluation of the impacts of cohesion fund projects on local tourism development within Italian municipalities. It applies an ex-post counterfactual methodology alongside advanced machine learning techniques to assess the effectiveness of these projects in enhancing the tourism sector. By using granular data on cohesion policies, the study compares municipalities that received funding for tourism projects from the 2007-2013 and 2014-2020 programming cycles with those that did not. Then the diff-in-diff estimate identifies whether different types of tourism projects had a positive outcome on tourism supply, controlling for a wide set of demographic, socio-economic, and institutional variables. Furthermore, predictive analysis using machine learning offers insights into future tourism trends and the potential impacts of ongoing and future cohesion projects. The findings reveal that targeted investment in tourism infrastructure, cultural heritage, and sustainable tourism practices can significantly boost local economies. However, the effectiveness of such investments varies based on regional specifics, suggesting the need for tailored approaches in policy planning and implementation. The study underscores the importance of integrating technological tools and data analytics in policy evaluation and development, paving the way for more informed and strategic decision-making in the tourism sector. Future developments in the field should focus on refining predictive models and exploring the long-term sustainability of tourism-related projects, ensuring they contribute to equitable and inclusive growth across regions.