Guide to the galaxy of EU regional funds recipients: evidence from new data

2.1 The distribution of EU regional funds

The EU’s cohesion policy works under the principle of shared management (refer to Article 14 and 15 in European Commission 2006). That is why multiple European and national (as well as sub-national) institutions are involved in the allocation process of regional funds.

For each MFF, the European Council prepares a document with strategic guidelines on reducing economic, social and territorial disparities. These guidelines for 2007–2013 encompass three priorities (European Council 2006a): (1) improving transport infrastructure, environmental and energy issues, (2) creating more and better jobs, and (3) a focus on knowledge transfer and innovation. Regarding the latter, special emphasis is put on supporting small and medium-sized enterprises (SME) which “often represent the highest source of employment at the regional level” (European Council 2006a, p. 19). The strategic guidelines on cohesion serve as a basis for the so-called national strategic reference framework that needs to be provided by each member state. The national strategic reference framework provides an overview of fields for intervention of cohesion policy in the particular country and undergoes a review process by the European Commission. Besides a proposal for the annual allocation of regional funds across the period, the member states need to generate a list of operational programmes (per objective and fund) for the objectives Convergence and Regional Competitiveness and Employment. Each operational programme is prepared and implemented by a managing authority, a private or public body appointed by the member state (Article 59 of European Council 2006b). In the most cases (in the case of a national OP always) it has a specified thematic target, e.g., improving regional human capital or social infrastructure. In most cases, they refer to particular NUTS-2 or NUTS-1 regions, however, there are also national, NUTS-0, programmes (see Appendix A.1 and Title III in European Council 2006b). The OPs must incorporate reasons for focusing on specific priority axes proposed by the Commission (see Annex IV of European Council 2006b).⁹ From these priority axes, the European Commission derives fifteen so-called (priority) themes.

In the next step, the OPs’ managing authorities select appropriate projects that are carried out by firms, i.e., the beneficiaries.¹⁰ According to Article 2 of Council Regulation (EC) No. 1083/2006, a beneficiary is defined as “an operator, body or firm, whether public or private, responsible for initiating and implementing operations”. An operation is referred to as “a project or groups of projects selected by the managing authority of the operational programme [...] allowing achievement of the goals of the priority axis to which it relates” (European Council 2006b). The amount of EU co-funding for each project depends on the eligible expenditure and the designated co-financing rate.¹¹

The structure of the new database builds on these regulations. The dataset includes the OP to which each observation (project) is assigned, the corresponding fund, objective as well as the theme. Thus, we are able to check the validity of our data by comparing it in different dimensions with official numbers on a more aggregate level by DG REGIO (see Sect. 3.3).

While the next section covers the literature that evaluates the effects of cohesion policy on regions or firms, there is also a literature that discusses the allocation process and the factors that potentially influence this bargaining. Bachtler and Mendez (2007) discuss the history of the allocation process up to the beginning of the MFF 2007–2013. They narratively highlight the negotiation process between member states and the EU Commission as well as the ongoing spatial concentration of the majority of funds. A more quantitative line of research (e.g., Bouvet and Dall’Erba 2010; Dellmuth 2011; Dellmuth and Stoffel 2012; Tosun 2014) finds that, indeed, factors such as the political situation, the type of governance, previous success of implementation or even the degree of eurosceptisism drive part of the allocation process.

2.2 Literature review

Most studies that analyse cohesion policy in a pan-European setting focus on the regional (in many cases NUTS-2) level (Hagen and Mohl 2009; Pienkowski and Berkowitz 2016). Most recently, Dall’Erba and Fang (2017) provide a meta-analysis of econometric studies on the evaluation of cohesion policy effects.

Generally speaking, there is no consensus in the literature regarding the outcome of cohesion policy. The effectiveness is measured, e.g., as a positive effect on gross domestic product (GDP) per capita growth (e.g., Pellegrini et al. 2013), investments per capita (Becker et al. 2013) or regional research and development activity (Ferrara et al. 2016). Most studies find a conditional positive effect of regional funds assignment (e.g., Becker et al. 2013; Cappelen et al. 2003; Ferrara et al. 2016), while others provide results that even suggest a negative impact (Breidenbach et al. 2016). In recent years, the potential reasons for heterogeneous (conditional) cohesion policy effects have gained major attention.

First, Rodríguez-Pose and Fratesi (2004) move the focus to expenditure categories of the main funding instruments. They find that investments in infrastructure or agriculture do not have sustainable effects on regional growth (see also Puga 2002; Dall’Erba and Le Gallo 2008), though, projects that foster human capital lead to sustainable positive effects on economic cohesion. This and other studies use data on the distribution of expenditure across NUTS-2 regions and themes (e.g., Dall’Erba and Le Gallo 2007; Percoco 2013; Ferrara et al. 2016).

Second, regional heterogeneity as a determinant of policy effectiveness has become a topic of interest and is often modelled by a region’s capacity to take advantage of regional funds. Becker et al. (2013) indicate that human capital and institutional quality matter for the effectiveness of Objective 1 funds in terms of their effect on GDP per capita growth and investment. Institutions are confirmed as influencing factor for the success of cohesion policy by other authors as well (e.g., Cappelen et al. 2003; Bachtler et al. 2014; Rodríguez-Pose 2013). Recently, Gagliardi and Percoco (2017) show that European cohesion policy is most effective in rural regions that are located close to cities.

Third, Becker et al. (2012) take the amount of regional funds expenditure spent in a region into account (instead of treatment dummies) and conclude that there is a maximum efficient level of funds and paying more does not increase the effectiveness any more (see also Kyriacou and Roca-Sagalés 2012; Rodríguez-Pose and Garcilazo 2015).

Fourth, Becker et al. (2018) and Bachtrögler (2016) analyse the effects of structural funds (on income growth) in lagging regions over time and in the context of the economic and financial crisis starting in 2007. The latter finds that the effectiveness of cohesion policy in terms of increasing GDP per capita growth appears to decrease in the crisis compared to former periods when controlling for regional structural characteristics. Barone et al. (2016) also take the time dimension into account and show that cohesion policy effects are not persistent over time.

Finally, spatial heterogeneity and spillovers are considered to play a role for cohesion policy effectiveness (Le Gallo et al. 2011; Breidenbach et al. 2016; Maynou et al. 2016). This strand of the literature widely confirms some small positive effects on regional growth or convergence for a number of regions but no general overall effect.

The majority of studies named above are based on data at the regional or local level to study growth or convergence effects. One major drawback of this type of analysis is the potential endogeneity of structural funds. As they are especially granted to lower-income regions, cohesion policy is most likely not exogenous with respect to regional growth. Attempts to overcome this statistical problem include the use of time lags (Rodríguez-Pose and Fratesi 2004), different instruments (Dall’Erba and Le Gallo 2007, 2008), generalised method of moments (GMM) estimators (Breidenbach et al. 2016) or a computable general equilibrium approach (Horridge and Rokicki 2018). Another way to possibly identify causal effects is the use of microeconometric methods with regional or micro data.

Turning to the beneficiaries as unit of observation, De Zwaan and Merlevede (2013) evaluate the effects of Objective 1 and Objective 2 payments in the programming period 2000–2006 in 25 EU member states on productivity and employment growth of firms in treated and non-treated regions. However, they do not use actual recipients of regional funds but compare all manufacturing firms (available in the ORBIS database) located in treated regions with the manufacturing firms in non-treated regions. Additional firm-level analyses are available for sub-national geographical units (Bernini and Pellegrini 2011) and certain types of regional funds (Hartsenko and Sauga 2012).

As we overcome this lack of data with the new database, this paper contributes to the literature by giving the first detailed insights on actual beneficiaries (and projects) of cohesion policy in 25 EU member states between 2007 and 2013. A combined analysis of the projects’ theme, firm-level characteristics of corresponding beneficiaries and the size of the projects’ expenditure may help to explain heterogeneous effects of regional funds allocation found in the literature, and thereby, lead to important policy implications. Moreover, it allows to identify regional funds allocation patterns across regions, e.g., less developed and others as well as urban and rural regions, and countries (see Sects. 4 and 5).

3.1 Content of the dataset

The European Commission’s Directorate-General for Regional and Urban Policy (DG REGIO) provides a collection of links to national or regional websites that make lists of beneficiaries available.¹² Unfortunately, the degree of detail of these lists’ content as well as their structure vary significantly across countries, regions and even operational programmes.¹³ Moreover, most documents are provided in national languages, different data formats and using non-standardised definitions. Besides collecting and processing all information, we extend the data on beneficiaries by matching it with the ORBIS business database by Bureau van Dijk.

The resulting set of variables can be grouped into three blocks, namely, (1) project information, (2) funding (co-financing) information, and (3) business characteristics of the beneficiary retrieved from ORBIS. First, the project information includes the country and NUTS region in which the project is carried out according to the OP and list of beneficiaries, respectively.¹⁴ Moreover, it covers the co-financing fund, the objective and corresponding OP to which the project is assigned. As already noted, the dataset includes projects co-funded by the ERDF, the ESF and the CF, under the objectives of Convergence and Regional Competitiveness and Employment. Next, a project name or description, the start and end date as well as the theme of the project are specified. The theme is not reported by all managing authorities, which is why we classify the remaining projects according to available project information using supervised text classification (see Sect. 3.2 for a detailed description). The fifteen themes are: (1) Capacity Building, (2) Culture, Heritage and Tourism, (3) Energy, (4) Environment, (5) Human Capital, (6) Innovation & Research and Technological Development (RTD), (7) IT Services and Infrastructure, (8) Labour Market, (9) Other SME and Business Support, (10) Other Transport, (11) Rail, (12) Road, (13) Social Inclusion, (14) Social Infrastructure, and (15) Urban and Territorial Dimension.

Furthermore, the declaration date refers to the time of reporting of the respective list of beneficiaries. In case it is not noted, we use the date of download.¹⁷

The third information block relates to the beneficiary. This data is produced by a matching exercise (using the name of the firm and its home country) with the ORBIS business database. We are aware of several shortcomings of this database (see, e.g., Kalemli-Ozcan et al. 2015), however, it represents the most comprehensive and accessible international business database.¹⁸ The resulting dataset contains the firm’s name in ORBIS and its location, its founding year and information on the industry in which it operates (NACE Rev. 2 industry and four-digits code), the firms’ number of employees and sales volume. Moreover, there is a size classification by ORBIS that is based on at least one of the following variables: the firms’ number of employees, total assets, operating revenue and whether it is listed at the stock exchange.¹⁹ Furthermore, the database contains information on whether a firm belongs to a corporate group and, if so, on the number of entities in this group.

Table 1 shows all variables and their coverage in the database, i.e., the share of all observations for which it is available. The OP, its location, the funding instrument, the objective as well as the name of the project and the beneficiary is available for each project. Moreover, the dataset provides at least a total project value for each observation. 39% of the observations could be matched with ORBIS.

3.2 Missing themes

As described in Sect. 2, the projects can be categorised into fifteen themes as defined by DG REGIO. Since not all managing authorities publish these themes in their lists of beneficiaries but most of them provide a project description and a project name, we employ supervised text classification to predict the missing project themes.

In order to train the classification algorithm (classifier) we use theme labels reported by some managing authorities, augmented with manually assigned theme labels. Since some of the project descriptions are given in a language other than English, we first use Google Cloud Translation API to translate the project descriptions and project names into English. Although we cannot quantify how many errors have been introduced during the translation process, we report the overall accuracy of the classification, where some part of error is attributed to translation errors. The records which cannot be translated are left unchanged. Then we remove those observations where the project name and description together have fewer than 30 characters. Thus, we overall use 1,698,191 projects (82.62% of all observations) and 588,713 labeled projects (28.64% of all observations) to train and evaluate the classifier.

Choosing an appropriate classifier is a non-trivial task. We find the recently published fastText²⁰ library (Joulin et al. 2016) to perform well on our dataset in terms of the performance metrics precision, recall and accuracy.²¹ In text classification, it is often desirable not to use the words of a text directly for estimation but to first map the text into a vector space with a much lower dimension. The fastText library which uses a single hidden layer neural network can be applied for text classification and to learn the vector representations of words.

The basic idea of the model is to proceed in two steps: first, the data is mapped into a low dimensional vector space (i.e., each sentence is mapped into a numeric vector) in such a way that similar texts have similar vector representations. Second, multinomial logistic regression is used to predict the labels.

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In the confusion matrix, we see that, given the true theme Other Transport, in 105 cases the model is able to predict the true label and in seven cases the model predicts the theme Road. Overall, we obtain an average classification accuracy of 0.94. However, for completeness we note that there are duplicates in the trainings and test set. Accounting for this, the average accuracy without duplicates is 0.90. In order to make the classification results easily reproducible, we assemble the R (R Core Team 2016) package fastTextR that contains an interface to the fastText library and is available at CRAN.²²

3.3 Comparison of the dataset with official data

We assess the validity of the assembled data by checking for outliers and plausibility and comparing its dimension with official data on regional funds assignment (equivalent to \(\text {C}\_\text {EU}_i\) in Eq. 1) published by DG REGIO. Table 2 shows the sum of total project values in the database (\(\text {Total value}_i\) in Eq. 1) per country and objective, excluding projects which cannot be assigned to a specific objective. The total values in the database in general do not only consist of committed values by the EU. Multiplying the total project values with the maximum co-financing rate per country and objective (see Sect. 2.1) results in the highest amount the EU should provide. If the official committed value, given in the last column of Table 2, is lower or equal to the maximum EU’s co-funding, we expect the sum of total project values in our database to be plausible. The latter is true for the large majority of member states. For Bulgaria, lists of beneficiaries are available for only less than a third of their operational programmes. The Estonian source is an online database which might not yet contain all projects. For Denmark, the gap may arise due to the total project value summing up paid-out and not committed amounts in the database.

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Next, we compare the distribution of regional funds among co-financing instruments and themes as reported in the database and by DG REGIO. First, 55% of the sum of total project values correspond to projects co-funded by the ERDF (unequivocal classification) while according to data by DG REGIO around the same amount (58%) of structural funds and the Cohesion Fund is transferred via the ERDF. For ESF, the share of the total project values amounts to 22% which is exactly the same one as in official data. As several operational programmes are co-funded by the ERDF and the CF and there is no more detailed information reported in the lists of beneficiaries, we are able to attribute only about 4% of total project values in the database to the Cohesion Fund. For about 20%, we cannot clearly say which one of the funds is the supporting one. Following DG REGIO data, about 20% of structural and Cohesion funds commitments are settled via the Cohesion Fund, i.e., it is likely that the major part of the not uniquely assigned total values in the lists of beneficiaries can be attributed to the Cohesion Fund.

Regarding the distribution of funds and total project values across the fifteen themes (project categories), our database proves to be consistent with official data (Fig. 2). The highest project expenditure is dedicated to Innovation & RTD and Environment according to the dataset and as also reported by DG REGIO.

4.1 Stylised facts: the intraregional distribution of regional funds

In cohesion policy regulations, the EU institutions specify priority themes to be targeted by operational programmes in a programming period but do not preset any detailed requirements regarding the size or other characteristics of the projects or beneficiaries to be selected.²³ Therefore, we expect that there is no uniform strategy for distributing regional funds at the project level across European regions. The selection of projects by managing authorities is likely to depend on the accordance with an OP’s underlying priority themes and its main objective, as well as the assessment of the capability of potential beneficiaries to carry out the project (with a certain volume).

One of the features of the data that has not been analysed in previous literature is the variation in average project values shown in the top right-hand side of Fig. 3. This variable ranges between EUR 15,520 per project in Marche, Italy, and EUR 57,814,068 in Ireland. While it is comparably small in Central European countries, the average project value is relatively high in some North European regions (in the UK, Denmark or Belgium) as well as the member states that joined the EU in 2004 and later. While there are additional reasons for the regional variation in the average total values such as project and beneficiary characteristics, this suggests that regions with fewer projects, on average, have higher total values per project.

The left upper part of Fig. 3 presents the number of projects per region, which varies between 32 in South-East England, UK, and over 85,000 in Puglia, Italy. There are regions with many projects like Puglia, Italy, or North Rhine-Westphalia, Germany, which are typically characterised by a high number of projects related to the themes Labour Market or Human Capital. Both themes are associated with relatively low project amounts.²⁴ Contrarily, the regions in which the largest share of funds is allocated to Transportation Infrastructure projects (in Poland and Croatia) are characterised by relatively large project values. Also other regions, like South-East England, UK, or Vienna, Austria, have few but on average large projects. One underlying reason may be that some regions tend to report relatively many intermediate beneficiaries like public institutions on a municipal level. Those bodies apply for the funds, however, they redistribute them further or use them to carry out projects for smaller entities. In those cases, the ultimate beneficiaries are not known publicly. Finally, a part of the remaining variation in the number of projects may be largely explained by poor data availability. In Croatia, not all projects could be assigned to a NUTS-2 region given the report by the managing authority. For Bulgaria, lists of beneficiaries are only available for two out of nine operational programmes.

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The dataset consists of over two million projects granted to approximately one million individual beneficiaries. That means, on average, every beneficiary receives co-financing for two projects. Only 17% of beneficiaries carry out more than one project, only 3% have more than five and only 1% more than ten. The beneficiary with the most co-financed projects (more than 18,000) is the Spanish ICEX Espana Exportación e Inversiones, a governmental institution that promotes (foreign) investments in Spain. The second most (more than 11,000) are carried out in the city of Florence, Italy, the third most (more than 10,000) by the governmental training and orientation section of the region of Tuscany, Italy.

Besides project and beneficiary characteristics, one stylised fact arising from the data confirms the focus of EU cohesion policy of providing most financial support to less developed regions: one main objective of the EU’s cohesion policy is to support the catching-up of those regions, i.e., NUTS-2 regions with a GDP per capita below 75% of the EU-average, in economic and social terms. As one can see in Fig. 3 (left lower map), in Southern Italy, Portugal and Spain as well as Poland, Romania and Bulgaria, the Baltics or Slovenia, the total values of co-financed projects relative to the respective regional income tend to be larger than in other regions. E.g., for the Lithuania and Poland, project values account for up to eight percent of regional GDP.²⁵

Contrarily, in the so-called “Blue Banana”²⁶ the sum of total project values lies below 0.1% of regional GDP. In those regions, projects with the highest expenditure tend to be carried out by small and medium-sized firms in the education sector, in public administration as well as in professional, scientific and technical activities. Those projects are mostly aimed at Regional Competitiveness and Employment and the themes Innovation & RTD as well as Human Capital. The sum of project values in Scandinavian, French, Northern Italian, North-Eastern Spanish regions as well as Scotland and Northern Ireland, UK, ranges between 0.1% and 0.5% of regional GDP. Project values per capita follow a similar overall distribution. The amount in most regions falls below EUR 500. The region with the highest value per capita is Bratislava, Slovakia, with EUR 12,575 per inhabitant over the course of seven years.

Gagliardi and Percoco (2017) indicate that the effectiveness of EU cohesion policy varies across urban and rural (NUTS-3) regions, thus, we are also interested in potential differences in the usage of regional funds in such areas. The summary statistics of project values shown in Table 3 reveal statistically significantly higher average single project values in (predominantly) urban NUTS-3 regions. The following section provides more details on the differences in regional funds distribution within urban versus within rural regions.

4.2 The intraregional distribution of funds with respect to project and firm characteristics

Type of fund, objectives and themes

The allocation of regional funds to OPs co-financed by different types of funds and under different main objectives is closely related to previous findings regarding the development status of regions and the choice of thematic priorities.²⁷ The total values of projects subsidised by the ERDF sum to roughly EUR 270 billion compared to EUR 107 billion from the ESF and EUR 18 billion from the CF. In addition, projects accounting for EUR 100 billion are most likely funded by the CF. Typically, the Cohesion Fund (CF), for which only regions in countries with a gross national income below 90% of the EU average are eligible, is targeted at co-financing (large) infrastructure projects and the accessibility of lagging-behind regions. A major part of the European Social Fund (ESF) is allocated to projects with relatively smaller project values fostering, e.g., human capital, while the ERDF co-finances a broad spectrum of project types. These funding priorities are mirrored in the volume of corresponding projects: the median CF project value amounts to about EUR 100,000 compared to EUR 37,000 for the ERDF and EUR 3200 for the ESF.

Co-financing by different funds additionally varies across types of regions (Table 4). By construction, the CF is more important in less developed regions, while the ESF accounts for only around 10% of project volumes in those poorest regions. It is interesting that the CF plays a larger role for co-funded project volumes in (predominantly) urban regions as compared to rural ones. According to the data, this may be related to the fact that there are more relatively large Road projects funded in areas with higher population density.

The overall objective of the OP each project is part of, is closely related to the development status of the regions, as, along with several exemptions, only less developed regions are eligible for Convergence funds. While around half of the (number of) projects in our sample is aimed at Convergence, their value is three times as large as that of Regional Competitiveness and Employment projects. As expected, operational programmes in less developed regions have a focus on Convergence, while OPs in richer regions receive more to boost Regional Competitiveness and Employment.

For the appropriate targeting of regional funds, the European Commission defines fifteen priority themes (see Sect. 3.2) to classify projects. In total, the largest sums are committed to projects in categories Innovation & RTD (EUR 70 billion), Environment (EUR 65 billion) and Other SME and Business Support (EUR 46 billion), followed by Labour Market, Road and Human Capital projects. The number of observations per theme ranges from 1058 Rail projects to over 600,000 projects related to the Labour Market. There is also considerable variation of individual project values across themes. Projects related to Transportation (EUR 1–3 million), Urban and Territorial Dimension as well as Culture Heritage and Tourism (EUR 200,000 each) are the largest. Human Capital (EUR 6000), Labour Market (EUR 2200) and Energy (EUR 1250) projects are the smallest.

Additionally, the lower right part of Fig. 3 shows the theme at which the maximum sum of project values in a region is targeted. For better readability the map shows five groups of themes instead of all fifteen.²⁸ In total, the largest sums in Poland and Croatia are related to Transportation projects. Energy and Environment projects are most important in Latvia, parts of the Czech Republic, two French regions, three Spanish regions and a Polish region. Contributions to Culture, Tourism and Social Infrastructure are most pronounced in some Czech regions and East Slovakia. Scotland, South Sweden, almost all of Italy and parts of Germany and France have their largest project sums related to Human Capital, the Labour Market and Social Inclusion. The largest project sums in the rest of Europe are associated with the fifth category that includes SME and Business Services, IT services and infrastructure, Capacity Building, Innovation & RTD as well as Urban and Territorial Dimension.

Comparing all less developed to all higher-income regions, differences in the average distribution of total project values across priority themes become apparent (see Table 4). For example, Road accounts for 17% of the sum of project values in less developed regions and only for 1% in higher-income regions. Moreover, the share targeted at Labour Market projects in richer regions exceeds that in poorer ones by 11 percentage points.

Industrial structure and firm size

The matched data from ORBIS allows us to go into more detail with respect to characteristics of the beneficiaries. Figure 4 shows the distribution of funds across NACE Rev. 2 industries. The left-hand side illustrates total sums while the right-hand side shows the distribution of individual project values. The matching process and the coverage in ORBIS enable us to assign an industry to one third of observations, i.e., almost 700,000 projects.

Overall, Fig. 4 (left part) shows that projects with the highest sum of total values are carried out by firms or institutions operating in public administration, defence and social security (EUR 80 billion), transportation and manufacturing (EUR 30 billion each) and education (EUR 20 billion). The right-hand side of Fig. 4 shows that the single project volumes also vary across beneficiaries in different industries. While the median value of projects carried out by public beneficiaries (NACE Rev. 2 industry O) lies clearly below EUR 100,000, firms operating in the energy and water sector are responsible for the projects with highest median values (over EUR 200,000). Most of the latter projects have to do with electricity production, air conditioning, water supply as well as waste collection and treatment.

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Beneficiary firms or institutions also differ in their size. Table 4 shows the distribution of regional funds across four categories of firm size defined by ORBIS.²⁹ In terms of overall project sums, small firms make up the largest share (EUR 120 billion), followed by very large firms (EUR 80 billion) and medium-sized as well as large firms (EUR 50 billion each). Note that there are fifteen times more small beneficiaries of structural funds than very large ones. They are especially strongly represented in projects co-financed by the ESF. In total, 85% of recipients in the database are small or medium-sized (SME) companies, which reflects the priorities set out in the Community’s strategic guidelines on cohesion. Moreover, this may indicate that the projects, which smaller and medium-sized firms submitted in order to apply for co-funding and which were selected, are smaller in terms of single project values than the projects carried out by large firms.

Interestingly, Table 4 shows that while managing authorities in regions with an income above 75% of the EU average (developed ones) select mostly projects carried out by small firms, in less developed regions the volumes of projects carried out by very large companies is higher. Also the share of project values administered by SMEs in less developed regions falls behind the share in higher-income regions by 17 percentage points. There is a similar heterogeneity when comparing projects in rural and urban regions, and it is the share of the value of projects carried out by very large firms in urban regions which exceeds the one in rural areas by more than 30 percentage points. This pattern may be driven by the fact that cities implement relatively many large projects.

5.1 Type of fund, objective and themes

Furthermore, controlling for everything else, Table 5 shows that projects corresponding to OPs with the Convergence objective are larger than projects under the Regional Competitiveness and Employment objective (right panel of Table 5).

In general, the largest projects co-financed across all subsets are targeted at Road and Other Transportation infrastructure (and fostering the Urban and Territorial Dimension in the case of the CF). Interestingly, relatively bigger investment projects in Rail systems appear to be co-financed by structural funds (and not the CF) and implemented by private entities (not public ones). The same is true for projects with a relatively high conditional value in the fields of Social Infrastructure, Environment and Innovation & RTD.³³

Moreover, conditional differences in project values across themes do not necessarily correspond to the differences across industries. An interesting observation is that the lowest project values can be observed for the Energy theme, but the highest conditional project values for the energy industry (Sect. 5.2). The maximum project value within the Energy theme as well as the energy industry is found for the same project which amounts to EUR 906 million. However, the Energy theme includes 7715 projects with a project value of EUR 1250 each and 64% of the projects have a value below EUR 5000. Those projects below EUR 5000 are not conducted by firms in the energy industry but are observed mainly in “Real Estate activities” (59%), “Manufacturing” (11%) and “Wholesale/retail; repair vehicles” (8%).

5.2 Industry and beneficiaries’ characteristics

As expected, firm size also plays a role for the level of the total project value (Table 8). Overall, we see that the larger the beneficiary, the larger is the total value. However, the difference in project volumes is surprisingly low, as the volume of a project of a very large company is not even twice the one of a medium-sized company but the average revenue of the very large companies in our sample (EUR 653 million) is approximately 130 times the average revenue of the medium-sized companies (EUR 5 million). The pattern is similar when considering the samples split according to the co-financing type of fund and the sector the beneficiary operates in.

5.3 Residual variation in single project values

Table 10 shows regional effects based on estimating Eq. (4) for subsets related to project and beneficiary characteristics. First, the upper panel shows the ten regions in which, conditional on the other control variables, the biggest and smallest projects co-funded by structural funds on the one hand, and the Cohesion Fund on the other hand, were carried out. While the results for the first case broadly reflect the regional effects identified for the complete sample, the Cohesion Fund sample only includes a limited set of countries. From the regions located in those lagging-behind countries, projects in Estonia, the Czech Republic as well as some parts of Spain are smallest.

The lower panel of Table 10 indicates that there may be differences in selecting private and public beneficiaries across countries. While Austrian regions and Estonia form part of the Bottom 10-group only in the case of private firms, the smallest conditional project values for beneficiaries in the public sector are present in Spanish, one Italian and two German regions. Interestingly, next to British and Dutch regions, projects by public institutions are relatively big in size in Helsinki-Uusima (FI1B), Finland, and those of firms in the private sector in Malta.

The estimation of Eq. (4) controls for regional fixed effects, thus, any regional influence on the remaining differences between total project values of single projects should have been removed. Figure 5 shows the average residuals of a regression similar to Eq. (4), but excluding the regional control variables (\(R_i\)). Hence, the figure shows the (average) unexplained part of the project value when controlling for fund type, objective, theme, industry and firm size split by country. It confirms that the projects, which are similar in many dimensions but the region, with the lowest total values can be found in Austria, Estonia and Spain and the ones with the highest values in Luxembourg, the Netherlands, Denmark and the UK.

Due to the findings in Sect. 4 and as Luxembourg, the Netherlands, the UK and Denmark are relatively densely populated, one could suggest that population density determines that finding. However, the relationship is not that clear. The residual variation for Finland, which is the least densely populated country in Europe, is also relatively high, while Germany, which is relatively densely populated, shows a downward bias with regard to conditional average project volumes. Referring to Locatelli et al. (2017), the countries with the largest residuals are characterised by relatively low levels of corruption and good institutions at the regional level (Charron et al. 2015). Therefore, we suspect that the variation may be determined by historically grown funding strategies and further national institutional settings. Verifying this assumption remains for future research.

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