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Effects of EU regional policy: 1989:2013 after agricultural assistance, the EU’s regional policy is the second-biggest line in the EU’s budget; -- countries and regions ask a justification of such budgets; -- this paper is about the effects of the Structural Funds in EU regions; Effects of EU regional policy: 1989:2013 this paper analyzes the regional effects of EU regional policy during 4 programming periods: 1989-1993, 1994-1999, 2000-2006, 2007-2013; -- the effects of Objective 1 treatment on outcomes such as growth of per-capita income and employment, and total as well as public investments; Effects of EU regional policy: 1989:2013 moreover: -- estimate the effects on economic growth and other outcomes when a region switches into and eventually loses Objective 1 status; -- this requires data from the last two programming periods 2000-06 and 2007-13; Effects of EU regional policy: 1989:2013 a region may lose Objective 1 status for two reasons: [1.] being just below the 75% threshold in the previous period, it might overtake other regions and end up above the 75% threshold; Effects of EU regional policy: 1989:2013 [2.] the expansion of the EU into Eastern Europe pulled down the 75% threshold in absolute terms; -- the same absolute level of GDP per capita which made a region eligible before no longer satisfies eligibility; main findings: -- the effects of Objective 1 status on economic growth are positive though not very long-lived; -- the effects of losing Objective 1 status on economic growth are negative; -- the effects are weaker during the Crisis than before; Effects of EU regional policy: 1989:2013 empirical approach: • the treatment is a binary Objective 1 indicator for NUTS2 region i and program period r; • Yir for economic outcomes and Xir for the forcing variable; treatment variable: • the binary Objective 1 treatment indicator variable – Dir – is determined on the basis of NUTS2 real per-capita levels in specific years before to each programming period; Effects of EU regional policy: 1989:2013 outcome variables: • average annual GDP-per-capita growth; • average annual employment growth; • average annual total investment intensity; • average annual public investment intensity; Table 2 – effects of Objective 1 treatment: 1989-2013 (all programming periods) [1.] the parameters in Table 2 are stable across the four columns, varying between 0.012 in Column (3) and 0.019 in Columns (2) and (4); -- these findings support an increase in per-capita-income growth by less than 2% due to Objective 1 treatment; [2.] this is much less the case for employment growth and (total) investment per GDP (i.e., investment intensity) in Panels B and C, respectively; -- statistically significant effects on employment growth (negative) and investment intensity (positive) only when not controlling for region specific fixed effects; -- whereas the impact of Objective 1 treatment is statistically insignificant when accounting for those effects; [3.] public investment per GDP: a significant and robust effect of about 2.2%; -- these results suggest that the increased accumulation of public capital stock crowds out some private investments; Table 3: 2000-06 and 2007–13 • Table 3 repeats the analysis of Table 2 for only the last two programming periods; Effects of EU regional policy: 1989:2013 two reasons: [1.] it allows us to understand whether effects of Objective 1 transfers are stable over time; [2.] it provides estimates for the role of Objective 1 transfers for regions in more versus less crisis-stricken countries; the findings for the effects of Objective 1 transfers in the periods 2000-06 and 2007–13 are somewhat different from the ones for the pooled period in 1989–2013; [A.] GDP growth effects are smaller than for 1989–2013; [B.] interestingly, there is a positive effect on employment growth in this sub-period; Effects of EU regional policy: 1989:2013 [C.] no effect on the total investment intensity as with pooling over all programming periods; [D.] there is no longer an effect on the public investment intensity; • the crisis induced negative effects on per-capita income and employment growth of similar magnitude; to the conditions and potential of every region; Smart specialization has been designed as a policy mechanism that can support regions to identify their growth potential by helping them identify their dynamic comparative and competitive advantages; as a new policy and new concept there is still limited knowledge about its effectiveness and impact; -- it was implemented for the first time during the programming period 2014–20 it will take some time after the end of 2020 for a concrete picture of its effectiveness to emerge; What Is Smart Specialization? -- each region should concentrate their development intervention in certain areas of specialization where there is a significant potential or competitive advantage to sustain productivity growth; -- criticism of the one-size-fits-all policy approaches (IMF); -- each region should design its development strategy with the aim of fostering specialization in knowledge-related sectors, depending on already existing assets and according to the principles of diversified specialization; S3 strategy implies identifying the economic and technological sectors in which to invest based on a number of guidelines: [A.] interventions should support regional embeddedness by identifying activities that have the greatest possibility of generating significant economic impacts; How ‘Smart’ Are Smart Specialization Strategies? [B.] they should enhance linkages across domains/sectors, prioritizing sectors that would eventually lead to related diversification: -- the development of technological activities related to existing knowledge bases following fundamental aspects of evolutionary economic geography such as related variety; How ‘Smart’ Are Smart Specialization Strategies? [B.] S3 requires experimentation and innovation in policy design, together with timely monitoring and evaluation and the constant involvement of local actors; S3 strategies assign a central role to regional government authorities; -- they are expected to perform a rigorous self-assessment of local potential, involving the key economic agents who are active in the territory; potential shortcomings of S3: [A.] it may promote a culture of picking winners, protecting already existing industrial champions; How ‘Smart’ Are Smart Specialization Strategies? [B.] if resources are misallocated towards existing industrial targets for purely political interests; -- policy priorities are not established on the basis of economic logic and are disconnected from the needs of local communities; -- more frequent if local governments are corrupt or lack the basic competences to produce effective policies; -- poor institutions and low quality of the local governments are barriers for the successful design and implementation of S3 strategies; -- bottom-up nature of S3: local actors/policy-makers hold large responsibilities in the design and implementation phases; -- poor local government quality may jeopardize the capacity to select areas of intervention in a truly effective manner; early evaluations of S3: -- little research of the effectiveness in the application of S3 strategies; -- some analyses emerge; Iacobucci and Guzzini (2016): -- the way in which S3 sectoral priorities have been defined by Italian regions; -- key concepts such as the relatedness and connectivity of technological domains have been overlooked as guiding principles behind S3; -- identifying areas of specialization has been more complicated in weaker regions; -- poor institutions were at the root of these flaws; Gianelle et al. (2020): -- the way that S3 priorities have been defined in Italian and Polish regions; -- in some cases the chosen investment activities represent suitable S3 priorities; -- in at least 11 of 39 regions the innovation areas prioritized in S3 strategies do not reflect the expected S3 criteria; -- some regions identify a large number of priorities, covering all economic areas, thus contradicting the basic S3 principle of selective intervention; descriptive features of S3: -- information on the S3 strategies adopted by European regions, as recorded by the European Commission’s Joint Research Centre (JRC) in its S3 platform; mapping S3 strategies: -- visual representation of S3 strategies across Europe for all regions that submitted S3 strategies to the European Commission for the 2014–20 period; -- in Germany S3 are being conducted at NUTS1 (the Länder) level; -- in most other countries the level chosen is NUTS2; -- in Scandinavian countries the level is NUTS3; Figure 2: -- the number of economic and scientific domains identified by each S3 regional strategy; -- economic and scientific domains are the key investment targets of S3 strategies: sectors in which the region aims to specialize; -- in many EU regions: a proliferation of both economic and scientific domains for S3 strategies; -- strong in Spanish regions: over 30 economic domains; ---- Navarra tops the S3 ranking with 88 scientific targets; -- many Belgian, Dutch, French, Italian and Polish regions are similarly ambitious in terms of economic and scientific domains; ---- Italian regions of Calabria, Campania, Marche, Emilia-Romagna and Lazio all have over 30 scientific domains in their S3 strategy; Figure 3: -- number of policy objectives of S3 strategies by region; -- the number of policy objectives also varies; -- one objective for almost all Norwegian regions; -- high numbers of objectives in many Spanish, Romanian, Italian, Polish and French regions; -- Bretagne (France) is the region with the highest number of policy targets in its S3 strategy: 50objectives; drivers of regional S3 strategies: -- how some regional characteristics are correlated with some of the features of S3 strategies: the numbers of economic and scientific domains and policy objectives; in the absence of prior theoretical knowledge about the drivers of key aspects of S3 strategies, the analysis includes many explanatory variables: -- economic, labour market, geographical, socio-demographic and institutional regional characteristics; to examine whether the economic capacity of regions exerts an influence on the characteristics of S3 strategies, they include two measures of agglomeration: [a.] population density; [b.] log-population; agglomeration is a key factor linked to productivity and consumption externalities and to greater degrees of diversification: -- expectation: higher degrees of agglomeration will also create potential for regions to plan their diversified specialisations strategically; the specification also include: -- two measures of economic performance: (a.) GDP per capita growth and (b.) unemployment; (a.) GDP per capita growth captures the economic dynamism of each territory; -- with higher rates of growth, a region can presumably afford to be more strategic in its S3 strategies for the future, thus deciding to specialize in fewer domains; How ‘Smart’ Are Smart Specialization Strategies? (b.) unemployment captures the pressures on policy, including for electoral reasons leading regional policy-makers to ‘spread their bets’, thus producing more ‘profligate’ strategies; specification also include: -- proxies for the technological capabilities and available set of skills of places measured by the log of patent applications per million inhabitants and the share of adult population with higher education; -- a measure of regional government quality; ---- the quality and characteristics of S3 strategies depend on the administrative capacities of the regions and on their overall quality of government; specification estimated using OLS: where: -- S3r is one of the four characteristics (number of axes, economic domains, scientific domains and policy objectives) of S3 strategy in region r; -- Xr is the vector of regional-level explanatory variables; -- εr is the error term. one important factor shaping the strategy is what neighbouring regions are doing: [1.] replicating or mimicking what is done elsewhere is the best way to secure funds; [2.] for considerations of territorial or yardstick competition; [3.] the rapid enactment of S3 at a European level may have led to copycat strategies [4.] the economic returns of European policies are greatly influenced by whatever strategies neighbours are pursuing; hypothesis tested by augmenting the model with the spatial lag of the dependent variable, capturing the number economic and scientific domains and policy objectives of regional neighbours; sample: EU NUTS regions and some small EU countries;