Suchergebnisse

Most governments tackle the economic issues of underdeveloped areas by offering subsidies aimed at fostering economic activities and local employment. Localized policies put constraints on where businesses may locate to receive subsidies, but they generally place few restrictions on whom subsidized businesses must hire. Using administrative data on firms and workers in Italy, we adopt a multi-cutoff regression discontinuity design to empirically assess and decompose the employment effect of substantial incentives for the replacement or establishment of new capital. Our empirical strategy allows identifying the geographical origin and labor market status of new hires. The results show how the majority of recruits come from new entrants to the labor market, in particular, young people and students, while displacement effects are limited. It appears that subsidized companies tend to keep their most valuable staff and hire more qualified young people. Overall, we find only a modest spatial dispersion of the effects or a possible crowding-out of the local labor market.

In recent years, the vision of what the essential factors for growth are and therefore the role of local policies has drastically changed. The importance of aspects such as human capital, innovation, agglomeration and institutions coupled with the diversified impacts of globalization, have drawn attention to the often-neglected role of space for growth and growth policies (Barca et al. 2012). Moreover, the presence of a wide and persistent inequality in income and joblessness among local areas, regions and countries, exacerbated by the Great Recession, has suggested a more important role for spatially targeted policies. Austin et al. (2018) indicate that place-based policies should be considered in this framework, because "social problems are increasingly linked to a lack of jobs rather than a lack of income" and "subsidizing job creation may be easier at the place level than at the person level". Barca et al. (2012) argue that "space matters and shapes the potential for development not only of territories, but, through externalities, of the individuals who live in them." Therefore, the place-based approach is more appropriate than a space-neutral sectoral approach if the geographical context matters, in terms of social, cultural, and institutional characteristics. These considerations have led to a new spread of place-based policies, often accompanied by skepticism with respect to their results from a significant group of economists and politicians (see, for instance, Glaeser and Gottlieb 2008). Indeed, "a fundamental concern is that spatially targeted policies may simply shift economic activity from one locality to another, with little impact on the aggregate level of output" (Kline and Moretti 2014). It is therefore not surprising that in recent years there has been a particular effort in the development of techniques capable of evaluating the effectiveness of territorial policies. In this survey of place-based policy evaluation techniques, we have chosen to consider only methodologies and studies based on the counterfactual approach. The reason is that we are convinced that to identify the effects of a policy we need a causal model, and the counterfactual approach is the most widely used and convincing approach in this field. The counterfactual approach, typical of program evaluation literature, attempts to compare what actually happened with what would have happened in the absence of the treatment. As each unit can be exposed or not exposed to the treatment (see Holland 1986), the researcher is bound to compare treated units with distinct untreated units. This approach derives from the potential outcomes framework (see Rubin 1974) where pairs of outcomes are defined for the same unit given different levels of exposure to the treatment, with the researcher only observing the potential outcome corresponding to the level of treatment received. Models are developed for the pair of potential outcomes rather than solely for the observed outcome. The potential outcomes framework has a number of advantages over a framework based directly on realized outcomes: i) it allows one to define causal effects before specifying the assignment mechanism, and without making functional form or distributional assumptions; ii) it forces the researcher to think about scenarios under which each outcome could be observed, that is, to consider the kinds of experiments that could reveal the causal effects; iii) it allows formulation of probabilistic assumptions in terms of potentially observable variables, rather than in terms of unobserved components; iv) it separates the modeling of the potential outcomes from that of the assignment mechanism. Of particular importance in Rubin's approach is the relationship between treatment assignment and the potential outcomes (Imbens and Wooldridge 2009). The simplest case for analysis is random assignment of the treatment, which ensures that there are no systematic differences between the treatment and control groups before treatment assignment. This implies that any observed differences in outcomes following the treatment can then be attributed to the treatment itself, rather than to selection bias. Therefore, it is straightforward to obtain estimators for the average effect of the treatment. Randomized experiments have been used in some areas in economics but hardly ever in regional economics. This is why in this survey we will focus on observational studies.

Developed countries have used several place-based policies to address the socioeconomic underdevelopment of their lagging regions ranging from tax exemptions to soft loans. Among these place-based policies, investment subsidies to private firms are one of the most popular in the EU. However, the empirical evidence to date is mixed and there is no general consensus on the effectiveness of such policy. Most evaluation studies have focused on the policy impact on subsidized firms, whereas the possible spillovers on other firms have been mostly overlooked. This is due to the dependence of these analyses on the Stable Unit Treatment Value Assumption (SUTVA), i.e. they assume away any possible interaction among firms. There are several situations in which this assumption is not plausible; however, there are severe empirical difficulties in disentangling the spillover effects from more relevant confounding factors. In this paper we propose evaluation strategies capable of detecting potential positive and negative spillovers. In presence of spillovers, the use of eligible but unsubsidized firms as control group will yield biased ATT estimates. We propose to build a counterfactual scenario drawing firms from the pool of firms located in non-assisted areas as similar as possible to the eligible areas. In addition to the ATT, our approach allows to estimate spillover parameters that contrast the positive agglomeration effect with the negative cross-sectional substitution and the crowding-out effect. Econometrically we adopt a Matching difference-in-differences (DID) using the recent coarsened exact matching (CEM) that dominates commonly used existing matching methods in its ability to reduce imbalance, model dependence, and bias. Our application concerns the Italian Law 488 (L488) and contrasting positive and negative spillover effects we find positive spillovers in terms of investments and negative spillovers in terms of employment, but these estimates are not statistically significant. This demonstrates that the positive effects of the subsidies on the subsidized firms? growth in terms of investments and turnover are not engendered to the detriment of the unsubsidized firms. The main methodological contribution to the literature is our novel evaluation strategy that can be adopted in other contexts to evaluate the micro spillovers of other policy instruments.

Our empirical analysis focuses on the effect of regional policies on migration attraction factors in Europe. We employ a regression discontinuity design to assess the causal relationship between the reception of large amounts of public funds and migration flows in the EU-15 regions. In highlysubsidized regions, we find a large increase in the share of foreign citizens from less-developed countries when compared to low-subsidized regions with similar pre-treatment characteristics. The analysis shows that such an increase is due to the positive impact of the European regional policy on job market opportunities as well as the improvement of public goods supply

Our empirical analysis focuses on the effect of regional policies on migration attraction factors in Europe. We employ a regression discontinuity design to assess the causal relationship between the reception of large amounts of public funds and migration flows in the EU-15 regions. In highly-subsidised regions, we find a large increase in the share of foreign citizens from less-developed countries when compared to low-subsidised regions with similar pre-treatment characteristics. The analysis shows that such an increase is due to the positive impact of the European regional policy on job market opportunities as well as the improvement of public goods supply.

AbstractWe assess the impact of the EU Regional Policy on regional economic growth by applying a new evaluation strategy, which integrates mediation analysis with a quasi-experimental framework. Using the R&D expenditure as an indicator of innovation capability, we evaluate how much of the total effect of the EU Regional Policy is due to R&D in the poorest EU regions. Consistently with the previous literature, we found a positive impact of the overall policy on economic growth, but, among the convergence regions, those investing a higher proportion of funds in R&D have the same convergence rate as regions investing more in other priorities. These findings confirm that the EU Regional Policy played an important role in the economic recovery of the poorest regions in the aftermath of the Great Recession. However, focusing resources on R&D does not seem to provide additional economic benefits, at least in the short run.