Unlocking growth? : EU investment programmes and firm performance.
Date: 2025-08-18
Institutions: De Sanctis, Alessandro, European Central Bank, Wojciechowski, Robert
Subjects: EU financing, Structural Funds, economic and social cohesion, fiscal policy, investment policy, small and medium-sized enterprises, work productivity

Working Paper Series 

Alessandro De Sanctis, Daniel Kapp, 
Francesca Vinci, Robert Wojciechowski 

Unlocking Growth? EU investment 
programmes and firm performance 

No 3099 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
Abstract

This study evaluates the effectiveness of EU Cohesion Policy as an investment programme,
employing a novel dataset that links firm-level data from Orbis with project-level information
It focuses on two key questions: (1) Which firms receive EU
from the Kohesio database.
funding? (2) How does receiving EU funding affect firm performance? By applying a logit
model and a local projection difference-in-differences approach, we provide new insights into the
allocation mechanisms of EU Cohesion Policy funds and their firm-level impact. Our findings
show that funding tends to be allocated to firms that already perform relatively well, and
that firms receiving EU funding experience a persistent productivity increase of approximately
3% after 4 years, with smaller and more financially constrained firms experiencing relatively
greater improvements. Moreover, funding targeting “SME investment” tends to enhance firm
performance disproportionately more than other categories, whereas projects directed the “green
transition” appear comparatively less beneficial.

Keywords: European Structural and Investment Funds, Productivity, Corporate Investment,
Fiscal Policy, Place-based Policy.

JEL classification: E22, D24, H54, O38, O52

ECB Working Paper Series No 3099

1

ECB 1Non-technical summary

Productivity is a key driver of long-term economic growth and living standards. In recent decades,

however, Europe has experienced a marked and persistent slowdown in productivity growth, raising

concerns among policymakers and bringing the topic to the centre of the policy debate. Against

this backdrop, EU Cohesion Policy, delivered primarily through the European Structural and

Investment Funds (ESIF), represent one of the EU’s main instruments for promoting economic

development, productivity, and regional convergence.

This paper evaluates the impact of the 2014–2020 ESIF on firm-level outcomes across the EU.

Using a novel dataset that links firm-level information from the Orbis database with project-level

data from the European Commission’s Kohesio dataset, the study examines (1) which firms receive

EU funding and (2) how receiving funding affects their investment and productivity.

The analysis shows that EU funding is typically directed toward relatively well-performing

firms, that are less capital-intensive and face some degree of financial constraints. Firms receiving

funding increase their capital by around 15% within one year and experience a gradual productivity

growth, reaching approximately 3% after four years. These effects are more pronounced for small

and medium-sized enterprises (SMEs) and for firms facing financing constraint, suggesting that EU

funding helps to unlock investment by easing access to capital.

Moreover, the study finds that not all categories of funding yield the same outcomes. Projects

aimed at supporting “SME investment” have stronger positive effects on capital accumulation and

productivity growth, while projects targeting green transition objectives show more limited gains.

The findings highlight the important role of EU programmes in enhancing firm-level invest-

ment and productivity. They also suggest that targeted allocation—especially towards SMEs and

financially constrained firms—can improve the policy’s effectiveness. These insights are especially

relevant as the EU continues to channel investments during the ongoing Cohesion Policy program-

ming period—and the Recovery and Resilience Facility—, while also preparing to revamp the EU

budget for the 2028–2034 cycle.

ECB Working Paper Series No 309921 Introduction

Productivity is a fundamental driver of long-term economic growth and improvements in living

standards.1 While productivity performance has long been a priority for policymakers, it has re-

cently taken centre stage in the EU policy debate amid the pronounced and persistent slowdown

observed in Europe over the past three decades (Draghi, 2024). In this context, assessing the effec-

tiveness of existing policy instruments aimed at fostering investment and productivity is essential,

not only to evaluate their impact, but also to inform future policy design by identifying what works,

for whom, and under what conditions.

An example of such instruments is the EU Cohesion Policy. While the EU’s fiscal capacity

remains limited, the Cohesion Policy—operationalized through the European Structural and In-

vestment Funds (ESIF)—stands as one of the Union’s most significant instruments for promoting

economic development. Over the past 25 years, the EU has invested approximately one trillion eu-

ros through these funds, with the objective of reducing regional disparities and promoting economic

and social cohesion. For the current programming period (2021-2027), the EU has allocated around

€392 billion for Cohesion Policy initiatives, confirming its commitment to promoting convergence

and growth through this instrument.2

This paper contributes to the growing literature on the effectiveness of place-based policy by

evaluating the impact of EU structural funds on firm-level outcomes, focusing in particular on

firms’ investment and productivity. To do so, we construct a novel dataset that links firm-level

information from the Orbis database with project-level data from the European Commission’s

Kohesio dataset, covering the entire EU.

We address two main questions: (1) What are the characteristics of firms that receive EU

funding? (2) What are the effects of receiving EU funding on firm performance? To address the first

question, we use a logistic regression model to identify the firm characteristics that most influence

the likelihood of receiving funding. In addition, to prevent over-fitting and isolate the most relevant

predictors, we also apply lasso penalization to select a key subset of firm-level variables. This

allows us to uncover structural differences between treated and never-treated firms, and informs our

subsequent identification strategy. For the second question, we employ a local projection difference-

in-differences (LP-DiD) approach following Dube et al. (2023). Our identification strategy relies

on including covariates and restricting the sample only to firms that eventually received EU funds,

1As Paul Krugman wrote, in a now famous quote, “productivity isn’t everything, but in the long run it is almost
everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to
raise its output per worker” (Krugman, 1990).

2For more details on the 2021-2027 programming period see the European Commission website.

ECB Working Paper Series No 30993leveraging variation in the timing of treatment across firms to pin down the effects of receiving

funding. In other words, we exploit the fact that firms first receive funding in different years to use

not-yet (but eventually) treated firms as controls for already treated firms.

Our results indicate that EU funding is typically granted to firms that are growing, less capital

intensive, and financially constrained. Following the receipt of funds, these firms increase invest-

ment sharply — as capital rises by 15% within one year — and expand their leverage, suggesting

improved access to external finance. The effects on productivity materialize more gradually, with

total factor productivity (TFP) increasing cumulatively by up to 3% four years after the interven-

tion. We also find modest but statistically significant increases in intangible intensity, pointing to

a shift in capital composition.

Importantly, the benefits are concentrated among smaller firms and those with greater financial

constraints, underscoring the value of targeted support for small and medium-sized enterprises

(SMEs). Moreover, we explore heterogeneity by policy priority and find that funding allocated to

“SME investment” yields more robust firm-level gains, while funding directed towards the “Green

transition” improves firm outcomes to a lesser extent.

Overall, this paper provides new empirical evidence that EU Cohesion Policy funds enhance

firm performance and productivity, with significant differences by firm size, financial conditions,

and funding category. These findings highlight the importance of designing targeted funding

mechanisms—that balance support for high-performing firms with interventions that alleviate fi-

nancing constraints—to maximize the impact of EU investment programmes.

The remainder of the paper is structured as follows: section 2 reviews the relevant literature

and outlines the paper’s contribution, section 3 presents the data, section 4 describes the research

design and discusses the results, and section 5 concludes.

2 Literature review

Our work relates to the growing literature aiming to measure the impact of public policies by

employing micro data. Our findings largely corroborate prior research, which consistently showed

that EU funds help firms invest and grow. Our first contribution relates to the novelty of the

dataset, which allows us to study productivity at the firm-level and in all EU countries. Previous

studies tend to find mixed results on firms’ productivity. For example, Bachtrögler et al. (2019)

analyse the effects of Cohesion Policy funding for the 2007-2013 programming period on a sample

of manufacturing firms, finding positive employment and capital impacts but limited productivity

ECB Working Paper Series No 30994improvements. Single-country studies reveal similar patterns. Beņkovskis, Tkačevs and Yashiro

(2019) show that Latvian firms receiving funds from The European Regional Development Fund

(ERDF) increase employment and turnover immediately, but achieve productivity gains only after

two years. Cabral and Manuel Campos (2023) find positive and persistent effects across multiple

performance indicators, including productivity, for Portuguese firms using dynamic difference-in-

differences methodology. Bernini and Pellegrini (2011) analyse Italian regional policy, finding higher

growth in output and employment but negative long-term productivity impacts.

We believe that our positive results on productivity are partly due to our large sample of firms,

as well as our methodological choices, which allow time for the productivity effects to emerge, by

studying firm outcomes up to four years after receiving funds. Lastly, the empirical methodology we

employ addresses the issues highlighted by the new Difference-in-Differences literature (de Chaise-

martin and D’Haultfœuille, 2020; Goodman-Bacon, 2021; Callaway and Sant’Anna, 2021). These

concerns have put into question the findings of applied papers—including some prior work on

the effects of EU funds on firm performance—that used the now discredited two-way fixed effects

regressions in settings with staggered treatment and heterogeneous, dynamic treatment effects.

Moreover, our findings that smaller firms benefit disproportionately from public support are

largely consistent with prior evidence. Criscuolo et al. (2019) show that productivity enhancing

effects of subsidies exist solely for small firms, while large companies do not display improvements.

Alexandre, Chaves and Portela (2025) also find that only micro- and small-sized firms benefit from

single grants. However, Beņkovskis, Tkačevs and Yashiro (2019) show that initially less productive,

larger, and more leveraged ERDF beneficiaries achieve larger productivity gains.

We find that Cohesion funds are allocated to financially constrained firms, helping them increase

leverage and capital. This can explain why we see larger productivity improvements for financially

constrained firms. These findings are broadly consistent with literature that examines the role of

publicly funded banks and development institution in mitigating firms’ financing constraints. Ak-

cigit et al. (2024) analyse Turkey’s large credit guarantee program and find that supported firms

preserved 17% percent more employment and achieved 70% percent higher sales. However, the

program did not spur productivity-enhancing investment, such as R&D, and resulted in increased

firm indebtedness. The gains were most pronounced for medium-sized firms and labour-intensive

sectors. Zecchini and Ventura (2009) find causal relationships between Italian public credit guar-

antees and higher debt leverage with lower costs for SMEs. An impact evaluation of European

Investment Bank (EIB) supported credit in the Western Balkans (EIB, 2023) finds EIB-funded

firms achieved about 15% higher employment growth than comparable non-beneficiaries, translat-

ECB Working Paper Series No 30995ing to roughly 15 additional jobs per €1 million of EIB loans. Beneficiary firms also substantially

increased their capital stock – on average total assets grew 20% and fixed assets 35% relative to

control firms – suggesting that EIB credit enabled firms to undertake investments they otherwise

could not finance.

Our paper is also related to studies that use regional data to estimate the multipliers of EU

structural funds. Canova and Pappa (2021) find that the ERDF has positive short-term effects

on regional variables, though gains dissipate within three years. Coelho (2019) estimates regional

multipliers of 1.8 contemporaneously and 4.1 three years after shocks, while Durand and Espinoza

(2021) find short-term multipliers of 1.2-1.8 percent with some evidence of private investment

crowding-in. De Santis and Vinci (2025) find strong crowding-in effects. Gabriel, Klein and Pessoa

(2021) estimate a government spending multiplier of around 2 for output and 1.4 for employment

in the eurozone. Fiuratti et al. (2023) provide a more cautious assessment, finding little evidence

of large GDP multipliers despite strong regional investment responses.

3 Data

Our empirical analysis is based on three main datasets: 1) Orbis by Bureau van Dijk, which contains

firm-level balance sheet information at annual frequency; 2) Kohesio dataset from the European

Commission, which provide project-level data on Cohesion Policy for the programming period

2014-2020 for the 27 EU member states; 3) Eurostat, from which we obtain aggregate variables at

sectoral (NACE) and regional (NUTS) level.

3.1 Orbis

Our Orbis sample runs from 2010-2022 for the 27 member states of the European Union. The raw

data requires intensive cleaning to deal with data-quality issues and to construct a nationally rep-

resentative dataset. The cleaning steps we implement follow closely Díez, Fan and Villegas-Sánchez

(2021) and Şebnem Kalemli-Özcan et al. (2024). The data cleaning process begins by removing

observations with basic reporting errors (e.g. negative sales or missing industry information). It

also includes checks based on ratios of key balance-sheet variables, and excludes firms reporting

implausible annual growth rates in sales, revenues, or number of employees. After cleaning the

data, we deflate the nominal variables using country level GDP deflators from Eurostat, so that all

monetary variables are expressed in real terms (with base year 2015).

ECB Working Paper Series No 30996Figure 1: Average Gross Output coverage over the period 2010-2022 in our cleaned Orbis sample.
Coverage is computed by summing for each country in each year the operating revenue of all the
firms and then dividing the sum by the country’s gross output data from Eurostat. We then average
the coverage for each country across the years from 2010 to 2022.

The map in figure 1 shows the average coverage for the EU27 countries for the period we use in

our analysis (2010-2022). Coverage varies considerably across the countries ranging from around

20% to 80% of total gross output.

Figure 2 shows the evolution of average coverage across EU27 countries in our cleaned Orbis

sample. We report the coverage for the period 2000-2022, but in our analysis we only use data from

2010 onwards. In the period of interest (2010-2022), firms in our sample cover on average roughly

50% of total gross output and 25% of employment.

ECB Working Paper Series No 30997Figure 2: Average coverage for the EU27 countries in the cleaned Orbis sample. Coverage is first
computed for each country by year, followed by an unweighted average across countries for each year
to construct the time series. The denominators for nominal output and employment are sourced
from Eurostat.

3.2 EU Cohesion Policy

.

The EU Cohesion Policy aims at reducing regional disparities and foster job creation, business

competitiveness, economic growth, sustainable development and improvements to citizens’ quality

of life across the European Union. 3

Concretely, the EU Cohesion Policy is delivered through specific funds, whose names, composi-

tion, amounts and objectives can change between programming periods. In the programming period

2014-2020, funding was delivered through the European Structural and Investment Funds (ESIF),

which comprises the following funds: the European Regional Development Fund (ERDF), the Eu-

ropean Agricultural Fund for Rural Development (EAFRD), the European Social Fund (ESF), the

Cohesion Fund (CF), the Youth Employment Initiative (YEI) and the European Maritime and

Fisheries Fund (EMFF). Table 1 displays the breakdown of ESIF by sub-funds, highlighting the

relative size of each of them, as well as the amount of the EU and national contribution.

Figure 3 display the regional distribution of ESIF per capita by NUTS2 regions, showing that

less developed regions receive relatively more funding per capita, in line with the policy’s objectives.

3For more details see the European Commission website.

ECB Working Paper Series No 30998Fund
ERDF
230,005,563,849
EAFRD 136,042,437,812
104,402,027,966
ESF
61,434,784,152
CF
8,960,645,385
YEI
5,616,815,235
EMFF
546,462,274,399
Total

EU Amount % of Total National Amount Total Amount
308,122,924,696
192,947,387,339
140,382,915,509
72,380,951,517
10,465,423,879
7,785,803,910
732,085,406,850

78,117,360,847
56,904,949,527
35,980,887,543
10,946,167,365
1,504,778,494
2,168,988,675
185,623,132,451

42%
25%
19%
11%
2%
1%
100%

Table 1: Planned financing under the different EU Cohesion Policy funds. Columns represent: (1)
the fund name, (2) EU amount of planned financing under each fund, (3) percentage share of each
fund in the total EU contribution, (4) national co-financing amount, and (5) the total amount of
planned financing. Source: European Commission.

Figure 3: Map showing the total Cohesion Policy per capita spending in the 2014-2020 programming
period. The numbers are based on a regional dataset provided by the European Commission.

The mechanism by which ESI funds are allocated is relatively complex and involves multiple

stages and authorities. From a firm’s perspective, the process starts by identifying funding oppor-

tunities, which are often advertised on platforms such as the EU Funding & Tenders Portal4 or

4For more information see the EU Funding & Tenders Portal website.

ECB Working Paper Series No 30999on national and regional portals. Once a suitable opportunity is identified, firms must check their

eligibility. This involves ensuring that their project aligns with EU priorities and verifying their

capacity to co-finance the project. Interested firms need to compile a comprehensive set of docu-

ments, including a project proposal, budget, impact assessments, and necessary legal and financial

documents.5 Submitted applications are subject to evaluation and decision-making processes con-

ducted by Managing Authorities and independent experts. If the application is successful, the firm

moves into the implementation and reporting phase, during which it must adhere to EU reporting

standards and undergo regular audits.

Key stakeholders in this process include the European Commission, which sets priorities and

approves programs, and Managing Authorities, which are national or regional bodies that oversee

project selection, for example, Poland’s Ministry of Development Funds and Regional Policy or

regional governments in Spain. Independent experts evaluate the technical and financial viability

of projects, while civil society participates in consultations and monitoring.

Decision-makers and evaluation criteria vary depending on the funding source. For EU pro-

grams, decisions are made by European Commission panels and external experts. For regional

funds, national or regional Managing Authorities are responsible. Evaluation criteria focus on the

project’s alignment with EU priorities like climate action and digitalization, as well as factors such

as innovation, feasibility, scalability, economic, social, or environmental impact, cost-effectiveness,

and sustainability.

Data on Cohesion Policy projects for the 27 EU member states are sourced from the Kohesio

dataset6 provided by the European Commission.7 For the 2014-2020 programming period, the

Kohesio dataset contains detailed information at project level, 8 including the total expenditure,

co-financing rates, project duration, and strategic priority addressed.9 Crucially, for each project

the dataset provides the name of the beneficiary, which we use to identify the corresponding firm

in Orbis.

The Kohesio dataset also has some limitations. First, the data contains some missing values

5Applicants are required to meet specific criteria and provide detailed documentation. A clear project proposal de-
tailing objectives, timelines, and deliverables is essential. The budget breakdown should include proof of co-financing
and detailed cost estimates. Impact assessments are necessary to quantify the anticipated social, environmental, or
economic benefits of the project. Additionally, legal and financial documents such as company registration, financial
statements, and partnership agreements, if applicable, must be submitted.

6The data is available on the European Commission website.
7We focus on the 2014-2020 programming period because the current one (2021-2027) is not yet completed and,

to our knowledge, data on previous programming periods are not publicly available at project level.

8The data were updated to the end of 2022 at the time of download.
9The Cohesion Policy framework entails a grace period of up to 3 years resulting in projects being completed
beyond the end of the programming period. For more details see A modernised Cohesion Policy: The mid-term
review.

ECB Working Paper Series No 309910mean

p25

p50

p75

sd

Total funds awarded 232,790
329,251
Total project size
2.18
Project duration
0.72
Cofinance rate

1,767
2,306
1.00
0.50

7,570
10,967
2.00
0.75

58,094
91,737
3.00
0.85

3,741,000
4,588,000
1.22
0.21

Table 2: Summary statistics for the Cohesion Policy projects for the programming period 2014-2020
for which project level data is available. Monetary values are in 2015 EUR and are deflated using
the value of the deflator at the start year of the project. p25 refers to the 25th percentile, p50 is
the median and p75 is the 75th percentile, sd is the standard deviation.

and occasional reporting errors. Second, the dataset only provides information on the start dates

and the completion dates of projects, without the full time profile of the expenditures. To address

this issue, we distribute the total amount of each project evenly across its duration.10 Figure 4

compares the aggregate regional expenditure data provided by the European Commission with the

time series we construct aggregating project level information from the Kohesio dataset. The two

lines display the similar patterns and strong co-movement. Our measure of total annual spending

shows a somewhat more front-loaded expenditure profile than the regional level data, where a

higher share of spending takes place towards the end of the programming cycle. Moreover, the

total spending in the aggregate regional expenditure data is larger than the project level Kohesio

data, partly as a result of the missing data in the project-level dataset.

Figure 4: The orange line represents aggregate regional ESIF expenditure, as reported by the
European Commission, while the green line shows the aggregate expenditure from the Kohesio
dataset, calculated by distributing each project’s total amount evenly over its duration.

10This is the same approach used, for example, in Auerbach, Gorodnichenko and Murphy (2020).

ECB Working Paper Series No 3099113.3 Eurostat

We obtain country and sector level indicators on output, investments, employment and price levels

from Eurostat. These indicators are used to deflate Orbis variables and to assess coverage against

aggregate data.

3.4 Merging Orbis firm-level data with Cohesion funds data

A key challenge in linking the project-level Cohesion Funds dataset with the firm-level Orbis dataset

is the absence of a common identifier. To overcome this limitation, we match firm names, which are

available in both datasets. To reduce the likelihood of false positive matches that would compromise

the treatment group, the matching procedure is designed with a conservative approach. As only

the beneficiary name and country are available, matches are restricted to entities located within

the same country. The matching algorithm proceeds in four steps:

1. names are cleaned of spaces, punctuation, accents, all letters are capitalized and common

acronyms, such as “LLC” for “Limited Liability Company”, are harmonized using a dictionary

of acronyms specific for each country; 11

2. the cleaned names are used as if they were IDs and an exact matching is performed;

3. firms that were part of one-to-one matches are considered valid treatment group firms, while

firms that were part of many-to-one or one-to-many matches are flagged and are excluded

from further analysis;

4. finally, firms for which the total amount of project exceeds their total assets are also flagged

and excluded.

Table 3 presents the number of beneficiaries with available project data by country (first col-

umn), alongside the number of firms included in valid matches (second column) and those excluded

(third column). It is important to note that not all beneficiaries are firms; many are public or non-

profit entities such as schools, hospitals, or charities. In addition, several data quality issues affect

both the coverage and the success rate of the matching algorithm. For instance, in the Cohesion

dataset, beneficiary names are occasionally missing, making it impossible to match certain projects

to firms in Orbis. In some cases, information on project funding amounts is also absent. Firms

that are matched only to projects with missing funding data are therefore included in the excluded

11The dictionary of acronyms has been developed in-house and is available upon request.

ECB Working Paper Series No 309912Country
Austria
Belgium
Bulgaria
Cyprus
Czechia
Germany
Denmark
Estonia
Spain
Finland
France
Greece
Croatia
Hungary
Ireland
Italy
Lithuania
Luxembourg
Latvia
Malta
Netherlands
Poland
Portugal
Romania
Sweden
Slovenia
Slovakia
EU27 Total 733,379

All Beneficiaries Treatment Group Excluded Group
2,557
2,215
35,179
4,055
36,600
117,289
193
9,484
72,072
5,503
20,209
36,379
7,741
24,592
310
129,872
9,677
60
897
2,192
13,447
57,419
124,265
10,256
1,158
4,543
5,215

203
-
-
-
2,043
7,835
1
5
2,816
696
2,525
-
426
160
4
35,179
634
-
6
123
519
1,263
13,289
3,086
34
9
63
70,919

367
-
-
-
2,153
8,492
5
7
16,268
2,241
721
9
861
1,506
-
10,216
2,924
-
45
-
393
1,760
38,174
2,811
61
46
198
89,258

Table 3: The table shows the numbers of unique Cohesion Beneficiaries for the 2014-2020 program-
ming period by country. All Beneficiaries is counting the number of unique beneficiaries that we
attempt to merge with the firms in the Orbis dataset. The treatment group column shows the
number of firms that were valid matches with Cohesion projects. The excluded group shows the
number of firms that were part of non-valid matches (mostly due to being part of one-to-many or
many-to-one matches).

group. Importantly, the treatment group used in the regression analysis is smaller than the number

of valid matches reported in table 3, as some firms are dropped from the regressions due to missing

values in one or more explanatory variables or insuﬀicient coverage over time.

3.5 Productivity estimation

One of the key variables we analyse is firm level total factor productivity (TFP). Since TFP is

not directly observable we need to estimate it from the firm level data. We follow the approach of

Levinsohn and Petrin (2003) and use Orbis data to estimate the production functions separately

for each country and two digit NACE sector pair. Further details are provided in Appendix A.2.

ECB Working Paper Series No 3099134 Empirical Analysis

4.1 Summary Statistics

We match approximately 90,000 firms in Orbis to projects data from the Cohesion Policy. The

total EU funding allocated to the firms in our dataset accounts for approximately 16 billion euros,

which accounts for 34% of the total of 47 billion euros directly allocated to firms under the Cohesion

Policy for the 2014-2020 programming period12.

Based on current Commission reporting, 47 billion had been allocated to firms by 2022, entirely

under the ERDF, accounting for roughly 22% of the fund’s total allocation. Hence, while our

dataset covers a substantial share of the funds directly matched to firms, our analysis focuses on a

specific component of the overall Cohesion Policy.

Consistently with reporting by the Commission, most of the firms matched (approximately

81%) and assigned to our treatment group receive funding from the European Regional Develop-

ment Fund. However, we also find that 17% of firms receive funding from the European Social

Fund and under 2% from the Youth Employment Initiative. This discrepancy is not unexpected,

as Commission reporting is subject to ongoing updates and has not yet been finalised. The policy

objectives under which firms receive funding are: Smarter Europe – 75% of firms, Social Europe

– 19%, and Greener carbon-free Europe – 6%. The most common thematic objectives are: Com-

petitiveness of SMEs – 63% of firms, Sustainable and quality employment – 15%, Research and

innovation – 11%, and Low-carbon economy – 5%.

Figure 5 illustrates a word cloud derived from the descriptions of funded projects involving firms

in our treatment group. The visualization prominently features key terms such as “development,”

“business,” “knowhow,” “permanent,” “new,” “structural,” “competitiveness,” “implementation,”

“overcome,” and “diﬀiculties.” These words indicate a strong focus on promoting sustainable and

enduring business growth, emphasizing structural changes, and enhancing firms’ competitiveness.

Moreover, the frequent appearance of terms such as “innovation,” “internationalisation,” “ex-

pansion,” “modernisation,” and references to “SMEs” underline the intent of funding transformative

investments. These terms collectively suggest EU Cohesion funding is primarily aimed at addressing

structural and operational challenges, facilitating innovation, and boosting long-term productivity

and competitiveness.

12The share of funds directly matched to firms comprises FIRMS: Private match grant aid, FIRMS: Private match
non-grant, and RTDI: Private match investment, as reported in the ESIF 2014-2020 Achievement Details time-series
dataset provided by the Commission. The latter shows that funds directly matched to firms mostly pertain to the
ERDF fund, as confirmed by our matching exercise.

ECB Working Paper Series No 309914Figure 5: Word cloud of projects descriptions - matched firms.

For an average firm that we match to Cohesion projects, we see non-missing project funding

for just over 2 years. Some firms receive more than one project in a given year in which case we

sum the project amounts to keep our final dataset at firm-year level. Comparing tables 2 with 4

we can see that the projects matched to firms have similar co-financing rates and duration but are

smaller in size. Table 4 also allows us to contextualize the size of the funding received by firms by

comparing the project amounts with firms’ total assets or annual sales figures. A median firm that

receives funding under Cohesion Policy receives total funding of approximately 4% of total assets

or 5% of annual sales.

mean

p25

p50

p75

sd

Total funds awarded
Total project size
Co-finance rate
Funds awarded to assets ratio
Funds awarded to sales ratio
Project size to assets ratio
Project size to sales ratio
Active projects
Years with projects data

176,924
319,805
0.75
0.11
0.44
0.17
0.65
1.25
2.13

3,750
6,545
0.50
0.01
0.01
0.01
0.01
1
1

12,499
18,190
0.80
0.04
0.04
0.05
0.05
1
2

60,375
103,306
1.00
0.13
0.12
0.18
0.16
1.125
3

2,399,000
3,195,000
0.23
0.21
10.88
0.33
16.01
7.39
1.53

Table 4: Summary statistics relating to the Cohesion Projects that were successfully matched to the
firm-level panel (i.e. our treatment group). p25 refers to the 25th percentile, p50 is the median and
p75 is the 75th percentile, sd is the standard deviation. Monetary values are in 2015 euros. Total
funds awarded and Total project size are at firm level (summing over the years the total amounts
for each firm). Active projects is the number of projects under which a firm is receiving funding
in a given year. Years with projects data is counting the number of non-missing observations of
total funds per firm. The ratios of totals to assets and sales are computed using the total amounts
pertaining to projects divided by firms’ average total assets and average annual sales, Co-finance
rate is the ratio of total funds awarded to total project size.

ECB Working Paper Series No 309915Table 5 reports summary statistics for key firm-level variables separately for the firms that were

matched to Cohesion projects and those that were not matched. We can see that the firms that

received funding under the Cohesion Policy are generally larger, growing faster, more leveraged, and

more reliant on debt financing. These differences could be explained by composition effects (if for

example firms receiving funding operate predominantly in sectors with larger, faster-growing, more

leveraged firms). Moreover, as the means are based on the entire period (pre and post treatment)

we cannot ascertain to what extent the differences are due to selection versus effects of the policy.

Revenue
Sales
Assets
Capital to labour ratio
Employment
Producitivity growth
Employment growth
Sales growth
Gross profit margin
Capital growth
Debt to equity ratio
Sales to assets ratio
Leverage ratio
Intangible intensity
Equity to assets ratio
Current ratio

Unmatched firms
obs
median
27,280,366
306,186
26,758,172
297,390
27,280,366
281,042
26,109,180
7,527
27,280,366
4
13,112,474
0.1%
19,240,478
0.0%
19,504,156
1.0%
12,292,197
57.6%
18,906,352
-7.2%
19,940,642
0.00
26,758,172
1.25
19,940,804
0.00
27,028,142
0.00
27,257,884
0.43
26,923,680
1.7

mean
5,953,601
5,657,518
8,201,400
90,655
25.4
0.7%
1.6%
1.4%
33.4%
-0.9%
0.11
1.68
0.11
0.03
0.45
122.7

Matched firms

mean
13,221,530
12,650,531
17,770,130
53,421
60.3
1%
4%
5%
53%
5%
0.18
1.43
0.18
0.03
0.43
11.4

median
771,970
734,950
694,215
14,227
9
1%
0%
4%
54%
-5%
0.11
1.13
0.11
0.00
0.40
1.7

obs
740,843
736,961
740,843
738,464
740,843
473,910
616,499
614,524
493,446
603,926
573,387
736,961
573,389
739,376
740,839
732,842

Table 5: Summary statistics reported for the firms that were not matched to any Cohesionproject
(unmatched firms) and firms that were matched to a project (matched firms). The “mean” and
“median” are based on the entire 2010-2022 period, “obs” counts the number of non-missing ob-
servations for each variable. All monetary variables are expressed in 2015 euros. Growth rates are
annual log growth rates, i.e. ln(Yt) − ln(Yt−1).

4.2 Which firms receive funding?

There is no single easy criterion to answer the question of which firms receive funding under the

Cohesion Policy programmes. In this paper, we study the selection based on firm characteristics,

and model this choice using a binary outcome model. In particular, we average firm level variables

over the four year pre-treatment period (2010-2013) and use them to predict whether a firm received

Cohesion Policy funding at any point between 2014-2022. The estimation equation is:

logit[P (Yi = 1 | ¯X ′

i)] = β0 +

X

j∈J

βj ¯Xi,j +

X

s∈S

γsnacei,s +

X

r∈R

δrregioni,r

ECB Working Paper Series No 309916where Yi takes value of 1 if firm i received funding and 0 otherwise, ¯Xi,j are the firm-level variables,

nacei,s is a dummy that takes value 1 is firm i is in sector s and 0 otherwise, regioni,r is a dummy

that takes value 1 if firm i is in country r and 0 otherwise, logit[ ] is the logistic function.

Our most parsimonious specification includes in ¯Xi only a limited set of firm-level variables:

total assets, capital to labour ratio, sales growth, and firm age. We also consider a more extended

set of variables, including the leverage ratio, current ratio, sales to assets ratio, TFP growth, growth

in employment, growth in capital, and intangible intensity.

We run three versions of each specification with varying levels of granularity of the sector level

dummies and report the results in table 6. All specifications include region dummies (at NUTS2

level), and we control for: NACE letter, 2 digit or 3 digit sectors. We Z-Score normalize all the

continuous variables so that the Odds Ratios reported in the regression table can be reported as

the effects of increasing the variable by one standard deviation. The reported standard errors are

clustered at the NUTS2 region level, as the selection of Cohesion Policy beneficiaries is conducted

at the NUTS2 administrative level.

(1)

(2)

Logit: Odds Ratio
(4)

(5)

(3)

(6)

(7)

(8)

(9)

1.65***
0.76***
1.19***
1.05*

1.65***
0.77***
1.19***
1.05*

1.67***
0.77***
1.19***
1.04*

1.65***
0.64***
1.20***
1.05**
1.29***
0.53

1.64***
0.65***
1.19***
1.05**
1.29***
0.55

1.66***
0.65***
1.19***
1.05**
1.29***
0.55

Assets
Capital/Labour
Sales growth
Age
Leverage ratio
Current ratio
Sales/Assets
TFP growth
Employment growth
Capital growth
Intangible intensity

1.65***
0.50***
1.15***
1.04*
1.27***
0.44
0.80***
1.02***
1.08***
1.07***
0.68***

1.64***
0.52***
1.15***
1.03*
1.27***
0.45
0.78***
1.02**
1.08***
1.08***
0.67***

1.66***
0.52***
1.15***
1.03*
1.27***
0.46
0.79***
1.02**
1.08***
1.08***
0.66***

Observations
Sector dummies
NUTS2 dummies

1,568,454
Letter
YES

1,568,228
2 digit
YES

1,565,966
3 digit
YES

1,379,406
Letter
YES
*** p<0.01, ** p<0.05, * p<0.1

1,379,230
2 digit
YES

1,377,074
3 digit
YES

1,379,108
Letter
YES

1,378,932
2 digit
YES

1,376,776
3 digit
YES

Table 6: Logit regressions for whether firms receive funding in the 2014-2020 programming period
based on 2010-2013 average firm characteristics. The dependent variable takes value 1 if a firm
received Cohesionfunding in the 2014-2020 programming period and 0 otherwise. The explanatory
variables are Z-score normalized. The coeﬀicients are odds ratios (values above 1 mean positive
effect, values below 1 negative effect on probability of receiving funds), Sector dummies are at
either NACE Letter level, NACE 2 digit, or NACE 3 digit sector level. The standard errors used
to compute statistical significance were clustered by NUTS2 region.

The results in table 6 show that firms with higher total assets have higher odds of receiving

funding, while firms with higher capital-to-labour ratios are less likely to receive funding. Fast-

growing firms show higher probabilities of selection, while a firm’s age has a modest positive effect

on receiving funding. The extended specifications reveal that more leveraged firms are more likely to

ECB Working Paper Series No 309917receive funding, while firms with higher intangible intensity are less likely to be selected. Together,

this suggests that larger, growing, more leveraged, less capital-intensive firms are favoured.

In addition to the model described above, we employ a data-driven approach to guide the se-

lection of the most relevant predictors. We begin by estimating a lasso-penalized linear probability

model for whether a firm is part of the treatment group. The model includes a wide range of firm-

level predictors Xi,j, but not all of them are selected. Since lasso is a penalized regression we are

not worried about over-fitting and can include many regressors knowing that only those with the

most predictive ability will be selected. In other words, we can be agnostic about what variables

are important in predicting if a firm receives funding and “let the data speak” about what is the

relevant set of predictor variables. The variables we include that are subject to selection are: region

dummies, sector dummies, total assets, employment, capital, value added, materials expenditure,

productivity growth, employment growth, capital growth, value added growth, sales growth, debt

to equity ratio, sales to assets ratio, leverage ratio, equity to assets ratio, capital to labour ratio,

current ratio, intangible intensity, age, dummies for whether a firm is under 5 and under 10 years

old, and a number of dummies capturing whether the firm is relatively productive/leveraged/capital

intensive/liquid compared to peers within its’ NACE 2-digit sector country year group. The “fi-

nancially constrained” dummy takes the value of 1 if a firm is less than 15 years old and has above

median leverage compared to other firms in its country and NACE 2-digit sector, in the spirit of

Cloyne et al. (2023), Durante, Ferrando and Vermeulen (2022) and Anaya Longaric et al. (2025).

The “>p90 TFP” dummy takes a value of 1 if a firms is in the top 10% of the TFP distribution in

its country and NACE 2-digit sector, following the definition of a firm at the productivity frontier

by Andrews, Criscuolo and Gal (2015).

The lasso regression minimizes the following objective function:

Objective =

0

@Yi − β0 −

1
2n

nX

i=1

X

j∈J

βj ¯Xi,j −

X

s∈S

βsnacei,s −

X

r∈R

βrregioni,r

1
2
A

+ λ

pX

j=1

|βj|,

where n is the number of observations, p is the number of predictors considered for selection,

λ is the penalty parameter controlling the strength of the regularization. The ℓ1-penalty term
|βj|) induces sparsity by shrinking some coeﬀicients exactly to zero, effectively excluding

P

(λ

p
j=1

the corresponding predictors from the model. A higher λ value increases the penalty, leading to

more coeﬀicients being set to zero. For a given λ, lasso minimizes the objective function, yielding

a model with a subset S ⊂ J of predictors included.

We use the Bayesian Information Criterion (BIC) to select the optimal penalty parameter (λ)

ECB Working Paper Series No 309918that balances goodness-of-fit and model complexity. The BIC is defined as:

BIC = −2 ln( ˆL) + p ln(n),

where ˆL is the maximum likelihood of the model, p is the number of non-zero model parameters,

and n is the number of observations.

Our lasso procedure evaluates models across a grid of one hundred potential λ values, calculating

the BIC for each model. The selected λ and its corresponding model is the one that minimizes

the BIC. To compare the results from this procedure with the results from our Logit model, we

estimate the Logit model as before but with the set S ⊂ J of predictors determined by the lasso

procedure just described. For interpretability we report the coeﬀicients as Odds Ratios and we

Z-score normalize all continuous variables.

The lasso logistic regression results in Table 7 reveal that allocation is concentrated among

firms exhibiting larger size, faster growth, higher productivity, and moderate financial risk. Larger

firms—measured by both employment and capital stock—are more likely to receive aid. At the same

time, firms with less than 50 employees are more likely to receive funding, suggesting that funding is

primarily allocated to relatively larger yet still small enterprises. Growth indicators are among the

most robust drivers of selection. Firms with higher growth in productivity, employment, capital and

value added enjoy higher odds of receiving funding. Productivity itself also matters. Firms above

the 10th, 50th and 90th percentiles of sector–country TFP exhibit higher odds. Financial risk profiles

exhibit a non-linear selection function. Firms just above the 10th percentile of leverage ratio and

current ratio are disproportionately supported, whereas those at or above the median thresholds

see lower odds. Continuous measures of sales‐to‐assets and intangible intensity carry significant

negative effects, while higher equity‐to‐assets ratios have a positive effect. In combination, these

results indicate that the policy is directing funds toward firms that are financially stretched—highly

leveraged and relatively illiquid—yet not so overextended as to be deemed non-viable. Age plays

virtually no role once other factors are controlled for. Higher capital-to-labour ratio is inversely

related with the probability of receiving funding, suggesting that funding is allocated to less capital-

intensive firms. Together, we see that funding is primarily allocated to larger, growing, less capital-

intensive, and productive SMEs under moderate financial pressure.

ECB Working Paper Series No 309919(2)
1.44***
1.15**
0.97

1.04***
1.09***
1.07***
1.19***
0.69
0.66***
2.38
0.72***
0.79***
1.18***

Labour
Capital
Value Added
Materials
TFP growth
Employment growth
Capital growth
Value Added growth
Debt/Equity
Sales/Assets
Leverage ratio
Intangible intensity
Capital/Labour
Equity/Assets
Current ratio
Age
<50 Employees
Financially constrained
>p10 TFP
>p10 Leverage ratio
>p10 Current ratio
>p50 TFP
>p50 Leverage ratio
>p50 Capital/Labour
>p50 Current ratio
>p90 TFP
>p90 Leverage ratio
>p90 Current ratio
>p90 Capital/Labour
Less than 5 years old
Less than 10 years old
Observations
Sector dummies
NUTS2 dummies

(1)
1.35***
1.27***
1.09
1.06
1.05***
1.09***
1.07***
1.20***
0.60
0.63***
2.64
0.73***
0.76***
1.18***
0.73
1.00
1.33***
0.96**
1.49***
2.63***
1.25***
1.35***
0.50***
0.85***
0.93*
0.93**
1.10***
1.08**
0.42***
0.45***
0.70***
0.71***
0.76***
0.78***
0.96**
0.96*
1.01
1.01
1,382,114
1,382,291
2 digit
Letter
YES
YES
*** p<0.01, ** p<0.05, * p<0.1

0.99
1.39***
0.96***
1.52***
2.78***
1.27***
1.37***
0.47***

(3)
1.47***
1.16**
0.97

1.04***
1.09***
1.07***
1.19***
1.53
0.67***
1.08
0.72***
0.78***
1.19***

0.99
1.43***
0.96
1.52***
2.70***
1.25***
1.35***
0.48***

0.93**
1.10***
0.43***
0.70***
0.77***
0.97

1,380,609
3 digit
YES

Table 7: Logit regressions results for whether firms receive funding in the 2014-2020 programming
period based on 2010-2013 average firm characteristics. The dependent variable takes value 1 if
a firm received Cohesion funding in the 2014-2020 programming period and 0 otherwise. The set
of explanatory variables was selected by the lasso regression using the optimal penalty parameter
according to BIC. Continuous explanatory variables were Z-score normalized. The coeﬀicients are
odds ratios (values above 1 mean positive effect, values below 1 negative effect on probability of
receiving funds), region and sector dummies are not reported. The sector dummies were at: NACE
Letter level, NACE 2 digit, or NACE 3 digit sector level. The standard errors used to compute
statistical significance were clustered by NUTS2 region.

4.3 How does EU funding affect firm performance?

To answer how funding affects firm performance, we rely on the Difference-in-Differences framework.

However, as our setting differs from the canonical 2 groups in 2 periods design of Heckman, Ichimura

and Todd (1998), we rely on novel estimators designed for settings with many periods and staggered

ECB Working Paper Series No 309920treatment adaption. In particular, most of our analysis is based on the Local Projections Difference-

in-Differences framework of Dube et al. (2023), but we also reproduce our main results using

the Callaway and Sant’Anna (2021) Difference-in-Differences with multiple periods estimator (see

Appendix A.3). We choose these estimators as they allow for heterogeneous and dynamic treatment

effects, and for the “parallel trends assumption” to hold potentially only after conditioning on

observed covariates.

Following the exposition in Dube et al. (2023), we define: outcome Yit as observed for i =

1, . . . , N firms over t = 1, . . . , T years. Firms can receive a binary treatment (i.e.

receive EU

funds or not) denoted by the indicator variable Dit ∈ {0, 1}. We assume treatment is permanent13,
therefore we have Dis ≤ Dit for s < t. We let pi denote the year in which firm i receives funding
for the first time, with pi = ∞ if firm i never receives funding.

We define the treatment groups (cohorts) g ∈ {0, 1, . . . , G} as firms that first received funding

in the same year, and we define group g = 0 as the never treated group (i.e. firms that never receive

EU funding throughout the sample period). We denote the year in which firm g enters treatment

as pg.

Using the potential outcomes framework, we let Yi,t(0) denote the potential outcome that firm

i would experience in year t if they were to remain untreated throughout the whole sample period.

We let Yi,t(p) denote the outcome for firm i in year t, if firm i were to enter treatment at time
p ̸= ∞. Define the (unit- and time-specific) treatment effect at time t for a firm which enters

treatment at year pi as:

τit = Yi,t(pi) − Yi,t(0).

As we do not observe the counterfactual no-treatment path of outcomes for the treated firms, the

above is not observable. This is the fundamental problem of causal inference. As such, we turn our

attention to the average effect on the treated (ATT). We define the (group-specific and dynamic)

ATT at horizon h for group g which enters treatment at time p as:

τg(h) = E [Yi,p+h(p) − Yi,p+h(0) | pi = p] .

τg(h) represents the average dynamic effect, h periods after entering treatment, for all firms be-

longing to a group g that enter treatment at time p.

We allow for heterogeneous treatment effects across different cohorts, i.e. τg(h) ̸= τg′(h) for

13Although the projects firms undertake after receiving funds have a finite horizon we assume permanent treatment

effects as we believe the effects of the projects on the firm persist beyond the completion of a project.

ECB Working Paper Series No 309921some horizons h and some pairs of groups g′ ̸= g. We also include covariates and region and sector

fixed effects to allow for the parallel trends assumption to potentially hold only conditionally on
W ′

i,t−1 are lagged time-varying firm characteristics, γs and δr are

i,t−1, γs, δr}, where X ′

i,t−1 = {X ′

sector and region dummies. To recover the ATT we make the following assumptions:

Assumption 1. Conditional no anticipation

(cid:2)

E

Yit(p) − Yit(0) | W ′

i,t−1

(cid:3)

= 0,

for all p and t such that t < p.

Assumption 2. Conditional parallel trends

(cid:2)

E

Yi,t+h(0) − Yi,t−1(0) | W ′

i,t−1, pi = p

(cid:3)

(cid:2)

= E

Yi,t+h(0) − Yi,t−1(0) | W ′

i,t−1

(cid:3)

,

for all t ∈ {2, . . . , T }, all h ∈ {0, . . . , T − 1}, and all p ∈ {1, . . . , T, ∞}.

Assumption 3. Linear conditional expectation function

(cid:2)

E

Yi,t+h(0) − Yi,t−1(0) | W ′

i,t−1

(cid:3)

= δh

t + W ′

i,t−1θh

Assumption 4. Treatment effects are independent of covariates14

Finally, we can define our main object of interest as the dynamic ATT at horizon h conditional on

covariates as:

ATTh(x) = E

(cid:2)

Yi,pi+h(pi) − Yi,pi+h(0) | pi ̸= ∞, w = W ′

i,t−1

(cid:3)

.

Our main identification challenge in recovering the ATT is that firm characteristics predict

receiving funding, as documented in the previous section. As such, the assumption of no treatment

anticipation is unlikely to hold unconditionally. Moreover, as we only see the projects that got

accepted, we cannot distinguish between selection into treatment due to firms’ decision to apply

versus the policy-makers decision of which applications to accept. We address this challenge by

including covariates (including region and sector fixed effects) in our model and by restricting our

sample to only the firms that did eventually receive funding. In other words, we use the not-yet

(but eventually) treated firms as the control group for the already treated firms. Formally, for each

h = 0, 1, 2, 3, 4 estimate:

Yi,t+h − Yi,t−1 = βh∆Di,t + X ′

i,t−1γh + τ h

t + γh

s + δh

r + eh
i,t,

(1)

14We drop this assumption by implementing the Regression Adjustment LP DiD estimator (see section A.3).

ECB Working Paper Series No 309922restricting at each h the sample only to firms that are either:

• newly treated at time t, i.e. ∆Di,t = 1,

• not-yet (but eventually) treated, i.e. Di,t+h = 0 and Di,T = 1.

s and δh

t , γh
τ h
use region and sector fixed effects to absorb common sectoral and common regional variation in the

r are year, sector (NACE letter) and region (NUTS2) fixed effects respectively. We

growth rates of the dependent variables. In Appendix A.3 we verify that our results are robust to

not including region and sector fixed effects and to including higher-dimensional region by sector

fixed effects.

Under assumptions 1 - 4, the LP-DiD equation 1 consistently estimates the variance-weighted

ATT:

E( ˆβh) =

X

g̸=0

ωg,h τ h
g .

The weights (ωg,h) assigned to each group-specific effect are given by:

ωg,h =

P

NCCSg,hng,h(1 − ng,h)
g̸=0 NCCSg,hng,h(1 − ng,h)

.

where CCSg,h is the clean control sample for cohort g at time horizon h (all firms that are

newly treated or not-yet treated), NCCSg,h is the number of observations in CCSg,h, and ng,h =

Ng/NCCSg,h is the share of treated units in CCSg,h. This variance-weighted ATT aggregates the ef-

fects over the treatment cohorts and summarizes the dynamic treatment effect as a single weighted

average per horizon (with the horizon being in relative time to treatment administration).

Furthermore, we control for the lagged values of: total assets, sales growth, current ratio,

capital to labour ratio, sales to assets ratio, and age. We chose these variables based on the Logit

model results from section 4.2. Conditioning on covariates that predicted receiving EU funds adds

credibility to the conditional parallel trends assumptions we are relying on for identification. In the

Appendix A.3, we report results without covariates and with a richer set of covariates. To test for

treatment anticipation effects we estimate equation 1 for the pre-treatment horizons h = −2, −3, −4.
Checking that ˆβh = 0 for pre-treatment horizons (h < −1) ensures that there are no systematic

differences in the pre-treatment growth rates of the outcome variable between the cohort of firms
treated at t and the cohorts treated later. Rejecting the hypothesis that ˆβh = 0 when h < −1

would indicate a violation of the assumption of no treatment anticipation and call into question

our estimates of the treatment effects. To verify the no treatment anticipation assumption we plot

ECB Working Paper Series No 309923these pre-treatment coeﬀicients when reporting our results. Lastly, to ensure that our results are

not affected by entry and exit of firms into the sample we only study firms that have non-missing

productivity data for the whole sample period. 15

Due to the additional restrictions to our sample, the results presented in this section are based

on a smaller number of treated firms compared to the one reported in table 3 for the full treatment

group. The number of firms that are treated over the period considered is 14,538, with the majority

located in Portugal (7,246 firms), Spain (4,334 firms), and Italy (1,786 firms). These three countries

together account for over 90% of the firms in our sample. This over-representation is partly due to

the significant amount of Cohesion Policy funding directed to these countries, but it also reflects

the relatively high coverage and data quality available for them in the Orbis dataset compared to

other EU member states.

Our main variables of interest in the empirical analysis are: total factor productivity, leverage

ratio, intangible intensity and capital. Our baseline results are plotted on figure 6, we also report

these results in table format in the appendix section A.5. We find that firms that receive funding

see their productivity increase gradually with a cumulative effect of 3% after 4 years (our maximum

horizon). This effect is both statistically significant (at the 99% confidence level) and economically

important. It implies that a firm that received funding after 4 years can produce almost 3% more

value added with the same capital and labour inputs. We see no immediate effect on productivity

at horizon 0 while capital and leverage ratio rise immediately. This is to be expected if capital

investments take time to become productive. The fact that firms increase their leverage ratio after

receiving EU funding suggests that they are able to attract additional financing after receiving the

funds. In fact, they are likely borrowing in order to finance their project. 16 Firms quickly increase

their capital by 1.5% by horizon 1. This shape and timing of the response suggests that firms invest

immediately after receiving the funding, but do not appear to have higher investment rates past

the 2 years horizon.

15Our results are robust to removing this restriction and keeping all firms with at least one year of data, see

Appendix section A.3.

16While projects are co-financed by the EU, firms only receive a reimbursement of costs ex post, up to 85% of the

total project amount.

ECB Working Paper Series No 309924(a) TFP

(b) Capital

(c) Leverage Ratio

(d) Intangible Intensity

Figure 6: Cumulative effects of receiving Cohesion Policy funding on firms outcomes, plotted with
99% confidence intervals (robust standard errors clustered at firm-level). Coeﬀicient shows the
effect size, for panels (a) and (b) a coeﬀicients of 0.1 means 10% cumulative growth effect. For
panel (c) and (d) the effect is the change in the leverage ratio and the ratio of intangible capital in
total assets respectively. Event time is in years.

4.4 Heterogeneity Analysis

To better understand the mechanisms underlying our baseline results, we investigate potential

heterogeneity in the effects of EU funding across firms. First, in section 4.4.1 we look at the role of

firms’ characteristics. Secondly, in section 4.4.2 we examine the role played by the funding intended

purpose. To this end, we extend our baseline LP-DiD specification by interacting the treatment

variable with a binary indicator Bi that captures the relevant dimension of interest. This approach

follows the state-dependent local projections methodology in Cloyne, Jordà and Taylor (2023),

allowing us to assess how treatment effects vary conditional on firm-specific conditions and funding

ECB Working Paper Series No 309925characteristics. Our regression equation becomes:

Yi,t+h − Yi,t−1 = βh

0 + βh

1 Bi + βh

2 ∆Di,t + βh

3 ∆Di,t × Bi + X ′

i,t−1γh

1 + τ h

t + γh

s + δh

r + eh
i,t,

(2)

where now βh

2 is the effect on firms with Bi = 0 and βh

2 + βh

3 is the effect on firms with Bi = 1.

We focus on dummies Bi, which define either the category of EU funding received or specific firm

characteristics.

4.4.1 Heterogeneity by firm characteristics

We study the differential impact of receiving funding on: 1) small and medium sized firms (< 50

employees) versus the rest and 2) financially constrained firms versus non-financially constrained

firms. Financially constrained are defined as young firms (i.e. which are less than 15 years old) and

with a leverage above median compared to other firms in their country and NACE 2-digit sector,

in the spirit of Cloyne et al. (2023) Durante, Ferrando and Vermeulen (2022) and Anaya Longaric

et al. (2025). We build dummies based on pre-2014 data, to rule out possible treatment effects on

the state of the firm.

Figures 7, 8, 9, 10 show the effect of receiving EU funding for different dependent variables,

namely capital, intangible intensity, TFP and leverage ratio. The effect is generally larger for

smaller and for financially constrained firms (blue lines). The heterogeneity is less pronounced in

the responses of TFP and intangible intensity. Financially constrained firms see a larger effect on

their leverage ratio in response to receiving funding as compared with non-financially constrained

firms. This suggests that receiving EU funding helps constrained firms access other forms of

financing, possibly by serving as a good signal to banks or potential investors. Larger firms tend

to increase their leverage only temporarily while SMEs see a permanent increase in their leverage

ratio. SMEs and financially constrained firms see a larger growth in their capital stock.

ECB Working Paper Series No 309926(a) Size

(b) Financial Constraint

Figure 7: Cumulative effect on firm Capital with 99% Confidence Interval (SEs clustered at firm
level). Coeﬀicient of 0.01 means 1% growth effect. Time in years relative to when firm first
received funding. Left panel: solid blue line plots effect for small firms, dashed red line for large
firms. Right panel: solid blue line plots effect for financially constrained firms, dashed red line for
non-constrained firms.

(a) Size

(b) Financial Constraint

Figure 8: Cumulative effect on firm Intangible Intensity with 99% Confidence Interval (SEs
clustered at firm level). Time in years relative to when firm first received funding. Left panel: solid
blue line plots effect for small firms, dashed red line for large firms. Right panel: solid blue line
plots effect for financially constrained firms, dashed red line for non-constrained firms.

ECB Working Paper Series No 309927(a) Size

(b) Financial Constraint

Figure 9: Cumulative effect on firm TFP with 99% Confidence Interval (SEs clustered at firm
level). Coeﬀicient of 0.01 means 1% growth effect. Time in years relative to when firm first
received funding. Left panel: solid blue line plots effect for small firms, dashed red line for large
firms. Right panel: solid blue line plots effect for financially constrained firms, dashed red line for
non-constrained firms.

(a) Size

(b) Financial Constraint

Figure 10: Cumulative effect on firm Leverage Ratio with 99% Confidence Interval (SEs clustered
at firm level). Time in years relative to when firm first received funding. Left panel: solid blue line
plots effect for small firms, dashed red line for large firms. Right panel: solid blue line plots effect
for financially constrained firms, dashed red line for non-constrained firms.

4.4.2 Heterogeneity by funding category

Our data contains rich details on the projects for which firms received funding, extending beyond

just the amounts and disbursement dates. In particular, we have project level data on the thematic

category, policy priority and even a short project description. Leveraging this information, we

categorize the funding based on the policy objective of the project. In particular, we group together

ECB Working Paper Series No 309928funding for the following five broad categories: “Small and Medium Enterprise (SME) investment”,

“SME support, Research & Development”, “Green transition”, “Social and territorial cohesion”.17

We find that while all funding categories support firm outcomes, the categories “SME invest-

ment” and “Green transition” offer the most interesting insights.18 Projects categorized under

“SME investment” (Figure 11a) prominently highlight terms such as “equipment”, “production”,

“capacity”, “investment,” “acquisition,” “modernisation”, and “innovation”. These keywords in-

dicate a strong emphasis on capital expenditure, with funding directed towards tangible assets

and production-enhancing investments aimed at improving the competitiveness, productivity, and

innovation capabilities of SMEs.

On the other hand, the “Green transition” category (Figure 11b) features frequent references

to “energy”, “eﬀiciency”, “saving”, “renewable”, “replacement,” “installation” and “management”.

These terms reflect a clear focus on sustainability, including measures to enhance energy eﬀiciency,

implement renewable energy solutions, and promote sustainable management practices within firms.

(a) SME investement

(b) Green transition

Figure 11: Word Clouds for projects pertaining to “SME investment” and “Green transition”

Results suggest that funding targeted specifically towards “SME investment” is more effective

in facilitating firm investment and enhancing total factor productivity (TFP). Consistently, this

targeted funding leads to a more pronounced increase in firms’ leverage ratios.

In contrast, funding received for “Green transition” projects has a more modest and shorter-lived

effect on increasing firm capital, with no significant effect beyond year 2, when compared to other

projects. It also has little to no effect on productivity, intangible intensity, or the leverage ratio.

This suggests that adopting green practices may not necessarily yield substantial productivity

gains in the short term — for instance, when operational high-emitting equipment is replaced

17The ‘SME investment” dummy is based on the descriptive data pertaining to the projects under which firms
received funding, while in the previous section the “SME” dummy was based purely on the size of the firm. irrespective
of the rationale for the funding it received.

18Results from the other categories are presented in the Appendix.

ECB Working Paper Series No 309929prematurely. Nevertheless, our findings indicate that green projects still positively influence firm

outcomes overall. 19

(a) SME investement

(b) Green transition

Figure 12: Cumulative effect on firm Capital with 99% Confidence Interval (SEs clustered at firm
level). Coeﬀicient of 0.01 means 1% growth effect. Time in years relative to when firm first received
funding. Left panel: solid blue line plots effect from funding for “SME investment”, dashed red
line for other funding. Right panel: solid blue line plots effect for funding for “Green transition”,
dashed red line for other funding.

(a) SME investment

(b) Green transition

Figure 13: Cumulative effect on firm Intangible Intensity with 99% Confidence Interval (SEs
clustered at firm level). Time in years relative to when firm first received funding. Left panel:
solid blue line plots effect from funding for “SME investment”, dashed red line for other funding.
Right panel: solid blue line plots effect for funding for “Green transition”, dashed red line for other
funding.

19The effectiveness of funds supporting green investments should not be evaluated solely—or even primarily—by
their impact on productivity, but rather by their contribution to emission reductions and broader environmental
outcomes. A full assessment of these effects lies beyond the scope of this study. Here, we simply note that green
investments have a positive, albeit smaller, effect on firms’ outcomes.

ECB Working Paper Series No 309930(a) SME investement

(b) Green transition

Figure 14: Cumulative effect on firm TFP with 99% Confidence Interval (SEs clustered at firm
level). Coeﬀicient of 0.01 means 1% growth effect. Time in years relative to when firm first received
funding. Left panel: solid blue line plots effect from funding for “SME investment”, dashed red
line for other funding. Right panel: solid blue line plots effect for funding for “Green transition”,
dashed red line for other funding.

(a) SME investement

(b) Green transition

Figure 15: Cumulative effect on firm Leverage Ratio with 99% Confidence Interval (SEs clustered
at firm level). Time in years relative to when firm first received funding. Left panel: solid blue line
plots effect from funding for “SME investment”, dashed red line for other funding. Right panel:
solid blue line plots effect for funding for “Green transition”, dashed red line for other funding.

5 Conclusions

This paper provides empirical evidence on the effects of the 2014-2020 EU Structural and Invest-

ment Funds on firm-level investment and productivity. By linking firm-level data from the Orbis

database with project-level information from the European Commission’s Kohesio dataset, the

study addresses two central questions: which firms receive EU funding? How does this funding

ECB Working Paper Series No 309931affect firm performance?

The analysis shows that funding tends to be allocated to firms that are already performing

relatively well, but are less capital-intensive and financially constrained. On average, firms receiving

funding experience a sharp increase in capital, which grows by 15% after one year, and a gradual

increase in total factor productivity, which goes from 1% after one year to approximately 3%

after 4 years. Notably, smaller firms experience relatively greater improvements, indicating that

EU funding plays a particularly important role in supporting productivity and capital growth

in small and medium-sized enterprises (SMEs). Moreover, financially constrained firms increase

their capital investment and leverage ratio more than non-financially constrained firms, suggesting

that EU funds contribute to alleviate financial constraints. Lastly, we find that projects aimed at

supporting “SME investment” bring the largest gains, while the gains form projects targeting the

green transition are more modest.

Overall, the findings of this paper highlight the crucial contribution of the European Structural

and Investment Funds to fostering firm growth and driving sustained improvements in productivity,

in line with the overall objectives of the EU Cohesion Policy. Moreover, our findings suggest that

crafting targeted funding strategies that balance support for high-performing firms with assistance

to financially constrained ones could enhance the effectiveness of these investments.

The present analysis opens several avenues for future research. First, investigating the role of

co-financing rates could offer insights into whether an “optimal” rate exists—one that maximizes

the effectiveness of EU funds. Such analysis could help inform the design of more eﬀicient funding

mechanisms. Second, a natural extension would be to estimate firm-level fiscal multipliers in order

to quantify the impact of each euro invested, for example, in terms of output and employment.

Finally, this research provides a foundation for evaluating EU funding instruments, and a similar

approach could be used to evaluate other programs, including the Next Generation EU.

ECB Working Paper Series No 309932References

Akcigit, Ufuk, Ünal Seven, İbrahim Yarba, and Fatih Yılmaz. 2024. “Firm-level impact of

public credit guarantees.” European Economic Review, 170: 104861.

Alexandre, Fernando, Miguel Chaves, and Miguel Portela. 2025. “Investment grants

and firms’ productivity: how effective is a grant booster shot?” Small Business Economics,

64(4): 1601–1641.

Anaya Longaric, Pablo, Vasileios Kostakis, Laura Parisi, and Francesca Vinci. 2025.

“Oil Shocks and Firm Investment on the Two Sides of the Atlantic.” Mimeo.

Andrews, Dan, Chiara Criscuolo, and Peter Gal. 2015. “Frontier Firms, Technology Diffusion

and Public Policy: Micro Evidence from OECD Countries.” OECD Productivity Working Papers,

, (2).

Auerbach, Alan, Yuriy Gorodnichenko, and Daniel Murphy. 2020. “Local Fiscal Multipliers

and Fiscal Spillovers in the USA.” IMF Economic Review, 68(1): 195–229.

Bachtrögler, Julia, Christoph Hammer, Wolf Heinrich Reuter,

and Florian

Schwendinger. 2019. “Guide to the galaxy of EU regional funds recipients: evidence from

new data.” Empirica, 46: 103–150.

Bernini, Cristina, and Guido Pellegrini. 2011. “How are growth and productivity in private

firms affected by public subsidy? Evidence from a regional policy.” Regional Science and Urban

Economics, 41(3): 253–265.

Beņkovskis, K., Oļegs Tkačevs, and Naomitsu Yashiro. 2019. “Importance of EU regional

support programmes for firm performance.” Economic Policy.

Cabral, Sónia, and Maria Manuel Campos. 2023. “European structural funds and the per-

formance of Portuguese firms.” Economic Bulletin and Financial Stability Report Articles and

Banco de Portugal Economic Studies.

Callaway, Brantly, and Pedro H.C. Sant’Anna. 2021. “Difference-in-Differences with multiple

time periods.” Journal of Econometrics, 225: 200–230.

Canova, Fabio, and Evi Pappa. 2021. “What are the likely macroeconomic effects of the EU

Recovery Plan?” CEPR Working Paper.

ECB Working Paper Series No 309933Cloyne, James, Clodomiro Ferreira, Maren Froemel, and Paolo Surico. 2023. “Mone-

tary policy, corporate finance, and investment.” Journal of the European Economic Association,

21(6): 2586–2634.

Cloyne, James, Òscar Jordà, and Alan M Taylor. 2023. “State-dependent local projections:

Understanding impulse response heterogeneity.” National Bureau of Economic Research.

Coelho, Maria. 2019. “Fiscal Stimulus in a Monetary Union: Evidence from Eurozone Regions.”

IMF Economic Review, 67: 573–617.

Criscuolo, Chiara, Ralf Martin, Henry G. Overman, and John Van Reenen. 2019. “Some

Causal Effects of an Industrial Policy.” American Economic Review, 109(1): 48–85.

de Chaisemartin, Clément, and Xavier D’Haultfœuille. 2020. “Two-Way Fixed Effects

Estimators with Heterogeneous Treatment Effects.” American Economic Review, 110(9): 2964–

96.

De Santis, Roberto, and Francesca Vinci. 2025. “Private Investment, R&D and European

Structural and Investment Funds: Crowding-in or Crowding-out?” Mimeo.

Draghi, Mario. 2024. “The Future of European Competitiveness.” European Commission.

Dube, Arindrajit, Daniele Girardi, Oscar Jorda, and Alan M Taylor. 2023. “A local

projections approach to difference-in-differences event studies.” National Bureau of Economic

Research Cambridge, Massachusetts.

Durand, Luigi, and Raphael Espinoza. 2021. “The Fiscal Multiplier of European Structural

Investment Funds: Aggregate and Sectoral Effects with an Application to Slovenia, WP/21/118,

April 2021.” IMF Working Paper.

Durante, Elena, Annalisa Ferrando, and Philip Vermeulen. 2022. “Monetary policy, in-

vestment and firm heterogeneity.” European Economic Review, 148: 104251.

Díez, Federico J., Jiayue Fan, and Carolina Villegas-Sánchez. 2021. “Global declining

competition?” Journal of International Economics, 132.

EIB. 2023. “Impact of EIB operations in the Western Balkans.” European Investment Bank Eval-

uation Report, Available from the EIB website.

ECB Working Paper Series No 309934Fiuratti, Federico Ivan, Desislava Enikova Nikolova, Steven Michael Pennings, and

Marc Tobias Schiffbauer. 2023. “Are Regional Fiscal Multipliers on EU Structural and In-

vestment Fund Spending Large? A Reassessment of the Evidence.” Policy Research Working

Paper Series, WPS 10658.

Gabriel, Ricardo Duque, Mathias Klein, and Ana Sofia Pessoa. 2021. “The Effects of

Government Spending in the Eurozone.” SSRN Electronic Journal.

Goodman-Bacon, Andrew. 2021. “Difference-in-differences with variation in treatment timing.”

Journal of Econometrics, 225(2): 254–277. Themed Issue: Treatment Effect 1.

Heckman, James J, Hidehiko Ichimura, and Petra Todd. 1998. “Matching as an econometric

evaluation estimator.” The review of economic studies, 65(2): 261–294.

Krugman, Paul R. 1990. The age of diminished expectations: US economic policy in the 1990s.

MIT press.

Levinsohn, James, and Amil Petrin. 2003. “Estimating Production Functions Using Inputs to

Control for Unobservables.” The Review of Economic Studies, 70(2): 317–341.

Rovigatti, Gabriele, and Vincenzo Mollisi. 2016. “PRODEST: Stata module for production

function estimation based on the control function approach.” Statistical Software Components,

Boston College Department of Economics.

Zecchini, Salvatore, and Marco Ventura. 2009. “The impact of public guarantees on credit

to SMEs.” Small Business Economics, 32(2): 191–206.

Şebnem Kalemli-Özcan, Bent E. Sørensen, Carolina Villegas-Sanchez, Vadym

Volosovych, and Sevcan Yeşiltaş. 2024. “How to Construct Nationally Representative Firm-

Level Data from the Orbis Global Database: New Facts on SMEs and Aggregate Implications

for Industry Concentration.” American Economic Journal: Macroeconomics, 16: 353–374.

ECB Working Paper Series No 309935A Appendix

A.1 Orbis cleaning steps

Following Díez, Fan and Villegas-Sánchez (2021) our steps to clean the Orbis dataset prior to

producitivty function estimation and subsequent empirical analysis are:

1. Basic variables and initial cleaning:

1.1. Drop duplicate observations

1.2. If available keep the unconsolidated (U1 or U2) company accounts, if not keep the

consolidated (C1) account.

1.3. Drop firm-year observations with missing information on total assets, operating revenues,

sales, and employment

1.4. Drop firms in all years if: a) Total assets are negative in any year, b) Employment is

negative or greater than 2 million in any year, c) Sales are negative in any year, d)

Tangible fixed assets are negative in any year

1.5. Drop firm-year observations with missing, zero, or negative values for materials, operat-

ing revenue, and total assets

1.6. Drop firm-year observations with missing industry information

1.7. Keep only firm-year observations with 12-month reporting periods

1.8. Filter outliers based on the following ratios, dropping firm-year observations outside the

0.1st to 99.9th percentile range: a) Employment per million of assets, b) Employment

per million of revenue, c) Revenue to assets ratio.

1.9. Drop firm-year observations with negative age.

1.10. Drop firms in all years if calculated age is negative.

2. Further quality checks:

2.1. Drop firm-year observations where the ratio of total shareholder funds and liabilities to

total assets is outside the range of 0.9 to 1.1.

2.2. Drop firm-year observations with negative or zero liabilities.

2.3. Drop firm-year observations where the ratio of liabilities to current liabilities plus non-

current liabilities is outside the range of 0.9 to 1.1.

ECB Working Paper Series No 3099362.4. Drop firm-year observations with zero or missing fixed assets.

2.5. Drop firm-year observations with missing or zero costs of employees.

2.6. Drop firm-year observations with negative values for various financial variables (e.g.,

current liabilities, non-current liabilities, current assets, loans, creditors, etc.

2.7. Drop firm-year observations where the ratio of short-term bank liabilities to total liabil-

ities is greater than 1.1.

2.8. Drop firm-year observations where the ratio of long-term bank liabilities to total liabili-

ties is greater than 1.1.

2.9. Drop firm-year observations with negative depreciation and amortization.

2.10. Drop firms in all years if the ratio of employee costs to capital is: a) greater than 1000,

b) less than 0.1st percentile or greater than 99.9th percentile of the distribution.

2.11. Drop firm-year observations where the ratio of tangible fixed assets to total assets is

greater than 1.

2.12. Drop firm-year observations with negative shareholder funds.

2.13. Filter outliers based on the ratio of other shareholder funds to total assets (dropping

firm-year observations

2.14. Drop firm-year observations with negative value added.

2.15. Filter outliers based on the ratio of employee costs to value added: a) Drop if ratio

greater than 1.1, b) Drop if ratio less than 1st percentile or greater than 99th percentile

of the distribution, c) Drop if ratio less than 0.1 and number of employees is missing.

2.16. Filter outliers based on leverage ratios, dropping firm-year observations outside the 0.1st

to 99.9th percentile range for: a) Capital to shareholder funds ratio, b) Total assets to

shareholder funds ratio.

3. Growth-based filtering:

3.1. Filter outliers based on growth rates, dropping firms in all years if: a) For firms with

0-10 employees: growth rate ≥ 1000% (10 times), b) For firms with 11-20 employees:

growth rate ≥ 500% (5 times), c) For firms with 21-50 employees: growth rate ≥ 300% (3

times), d) For firms with 51-100 employees: growth rate ≥ 200% (2 times), e) For firms

with 101+ employees: growth rate ≥ 100% (1 time), f) For firms with missing lagged

employment: growth rate ≥ 2000% (20 times). These conditions are applied separately

for: Employment growth, Sales growth, Operating revenue growth.

ECB Working Paper Series No 3099374. Deflate nominal variables using country-specific GDP deflators from the World Bank World

Development Indicators database (we use 2015 as base year so that all the nominal values

are expressed in constant 2015 EUR).

A.2 Estimating firm-level productivity

We follow the approach of Levinsohn and Petrin (2003) to estimate firm level total factor produc-

tivity (TFP) as implemented in STATA by the prodest function written by Rovigatti and Mollisi

(2016). We estimate the production functions separately for each country and two-digit NACE

sector pair. We exclude NACE sector A (Agriculture, forestry and fishing) as for this sector the

production function without land as a production input is misspecified.

We begin with a 3 inputs log-linear Cobb-Douglas production function:

yit = β0 + βk kit + βl lit + βm mit + ωit + εit,

where yit is the log of output for firm i at time t, kit is capital, lit is labour, mit is materials (or

other intermediate input), ωit represents the firm’s unobserved productivity (TFP), εit is an i.i.d.

measurement error.

The key econometric challenge is that ωit is observed by the firm when making input decisions

but not by the econometrician, potentially leading to endogeneity problems.

The estimation is carried out in two stages, under the following assumptions:

1. Monotonicity and Invertibility: It is assumed that the material input mit is chosen as a

function of capital and the unobserved productivity shock. Formally, we assume

mit = f (kit, ωit) ,

where f is strictly monotonic in ωit. This ensures that the function can be inverted so that

productivity can be written as

ωit = h (kit, mit) .

2. Free Variables: Firms are assumed to adjust their material and labour inputs flexibly after

observing the productivity shock. These inputs are non-dynamic in the sense that they are

decided within the period and do not affect future profits.

3. State Variable: Capital evolves according to the investment policy function i(), which is

ECB Working Paper Series No 309938decided at time t − 1.

4. Markovian Productivity Process: The unobserved productivity shock follows a first-order

Markov process:

ωit = g (ωit−1) + ξit,

where ξit is an innovation term that is assumed to be uncorrelated with the inputs.

Stage 1:

In this first step, the idea is to remove the endogeneity induced by the unobserved productivity

shock ωit. Using the invertibility assumption, we replace ωit with the control function g(kit, mit).

The production function is re-written as:

yit = β0 + βk kit + βl lit + βm mit + h(kit, mit) + εit.

Letting

We can rewrite

ϕ(kit, mit) = β0 + βk kit + βm mit + h(kit, mit)

yit = βl lit + ϕ(kit, mit) + εit.

which can be estimated non-parametrically (we use a third-order polynomial in mit and kit for

ϕ(kit, mit)). This identifies βl

Stage 2:

In the second stage, we estimate the remaining parameters βm and βk. To identify βm and βk, we

use two moment conditions:

E[(ξit + εit)kit] = E[ξitkit] = 0,

E[(ξit + εit)mit−1] = E[ξitmit−1] = 0.

The first moment condition identifies βk by assuming that capital is a state variable and does

not respond to ξit. The second condition exploits the assumption that last period’s material choice

is uncorrelated with ξit, allowing us to identify βm.

ECB Working Paper Series No 309939The estimate of the residual comes from the following relationship:

d

ξit + εit(β∗) = yit − ˆβllit − β∗

mmit − β∗

kkit − d

E[ωit|ωit−1].

Noting that the residual is a function of the two candidate parameters β∗ = (β∗

k), and that
the estimates of ωit are obtained from the first stage results combined (i.e. the estimate ˆβl) with
the candidate values (β∗

m, β∗

m, β∗
k).

A.3 Robustness of the empirical results

A.3.1 Using Difference-in-Differences with multiple time periods estimator.

Here we report our main result, but using the Callaway and Sant’Anna (2021) estimator instead of

Dube et al. (2023) estimator. We replicate our main results showing the increase in firm productivity

from receiving EU funds using the Callaway and Sant’Anna (2021) Difference-in-Differences with

multiple periods estimator. We present the results both using the same set of controls as in the

baseline specification and without any controls.

(a) TFP effect (no controls)

(b) TFP (baseline)

Figure 16: Cumulative effects of receiving Cohesion Policy funding on firms total factor produci-
tivity, plotted with 99% confidence intervals (robust standard errors clustered at firm-level). An
ATT coeﬀicient of 0.1 means 10% cumulative growth effect. Period to treatment are in years. Both
panels are estimated using the estimator of Callaway and Sant’Anna (2021). The left panel is a
model without controls. The right panel has the same controls as the baseline LP DiD specification.

A.3.2 Changing the fixed effects specification

We present the results of estimating the LP DiD model in equation 1 except without including

region and sector fixed effects or with the inclusion of higher dimensional sector (NACE letter) by

ECB Working Paper Series No 309940region (NUTS2) fixed effects.

(a) TFP effect (no region or sector FEs)

(b) TFP (region by sector FEs)

Figure 17: Cumulative effects of receiving Cohesion Policy funding on firms total factor produc-
tivity, plotted with 99% confidence intervals (robust standard errors clustered at firm-level). A
coeﬀicient of 0.1 means 10% cumulative TFP growth effect. Event time is in years. Both panels
are estimated using the LP DiD estimator in equation 1, but the left panel removes region and
sector fixed effects (γh
r ), while the right panel replaces them with region by sector fixed effects
(γh
s,r)

r becomes ξh

s + δh

s + δh

A.3.3 Adding more time-varying controls

We present the results of estimating the LP DiD model in equation 1 but with a richer set of time
varying firm level covariates in X ′

t−1. In particular we include the first three lags of: capital growth,

value added growth, sales growth, employment growth, (log) employment, (log) total assets, (log)

capital, (log) materials, (log) value added,intangible intensity, sales to assets ratio, capital to labour

ratio and age.

ECB Working Paper Series No 309941Figure 18: Cumulative effects of receiving Cohesion Policy funding on firms total factor produc-
tivity, plotted with 99% confidence intervals (robust standard errors clustered at firm-level). A
coeﬀicient of 0.1 means 10% cumulative TFP growth effect. Event time is in years. LP DiD model
with a richer set of time varying firm level covariates.

A.3.4 Removing the coverage restriction on firms

We replicate the baseline results without the restriction that firms must have TFP data for the

whole duration of the sample. Instead, we consider all eventually treated firms in the regression,

no matter how many years of data coverage we have for them.

Figure 19: Cumulative effects of receiving Cohesion Policy funding on firms total factor produc-
tivity, plotted with 99% confidence intervals (robust standard errors clustered at firm-level). A
coeﬀicient of 0.1 means 10% cumulative TFP growth effect. Event time is in years. LP DiD model
on a sample of all eventually treated firms (without a minimum coverage restriction, i.e. unbal-
anced panel).

ECB Working Paper Series No 309942A.4 Heterogeneity analysis - additional funding categories

(a) R&D

(b) Social cohesion

(c) SME support

Figure 20: Cumulative effect on firm Capital with 99% Confidence Interval (SEs clustered at firm
level). Coeﬀicient of 0.01 means 1% growth effect. Time in years relative to when firm first received
funding.

ECB Working Paper Series No 309943(a) R&D

(b) Social cohesion

Figure 21: Cumulative effect on firm Intangible Intensity with 99% Confidence Interval (SEs
clustered at firm level). Time in years relative to when firm first received funding.

(c) SME support

ECB Working Paper Series No 309944(a) R&D

(b) Social cohesion

(c) SME support

Figure 22: Cumulative effect on firm TFP with 99% Confidence Interval (SEs clustered at firm
level). Coeﬀicient of 0.01 means 1% growth effect. Time in years relative to when firm first received
funding.

ECB Working Paper Series No 309945(a) R&D

(b) Social cohesion

Figure 23: Cumulative effect on firm Leverage Ratio with 99% Confidence Interval (SEs clustered
at firm level). Time in years relative to when firm first received funding.

(c) SME support

A.5 Baseline results in table format

For completeness we present the main results presented in figure 6 from our LP-DiD regressions in

table format.

ECB Working Paper Series No 309946Event-time estimates Coeﬀicient S.E.
pre4
pre3
pre2
pre1
impact
post1
post2
post3
post4

-0.0002
0.0006
-0.0006
0.0000
-0.0025
0.0105
0.0224
0.0270
0.0326

0.0014
0.0011
0.0007
.
0.0010
0.0015
0.0020
0.0027
0.0037

|t|
-0.11
0.48
-0.82
.
-2.44
6.87
11.02
9.88
8.94

P >|t|
0.916
0.630
0.413
.
0.015
0.000
0.000
0.000
0.000

99 % C.I.

-0.004
-0.002
-0.002
.
-0.005
0.007
0.017
0.020
0.023

0.004
0.003
0.001
.
0.000
0.014
0.028
0.034
0.042

Observations
89,033
103,559
118,068
.
118,068
103,261
87,365
68,400
53,452

Pooled estimates
Pre
Post

Coeﬀicient S.E.
-0.0004
0.0183

0.0009
0.0025

|t|
-0.43
7.44

P >|t|
0.667
0.000

99 % C.I.

-0.003
0.012

0.002
0.025

Observations
89,033
53,452

Table 8: LP-DiD regressions results for the effect of receiving EU funding on firm’s total factor
productivity. A coeﬀicient of 0.1 means a 10% growth effect. Event time is in years. Pooled
estimates report average estimates for the pre and post treatment periods. Robust standard errors
are clustered at firm level.

Event-time estimates Coeﬀicient S.E.
pre4
pre3
pre2
pre1
impact
post1
post2
post3
post4

-0.0215
-0.0091
-0.0031
0.0000
0.0684
0.1457
0.1743
0.1746
0.1731

0.0081
0.0067
0.0047
.
0.0051
0.0086
0.0117
0.0153
0.0199

Pooled estimates
Pre
Post

Coeﬀicient S.E.
-0.0091
0.1487

0.0058
0.0138

|t|
-2.64
-1.36
-0.67
.
13.31
17.01
14.95
11.38
8.7

|t|
-1.56
10.79

P >|t|
0.008
0.173
0.501
.
0.000
0.000
0.000
0.000
0.000

99 % C.I.

-0.042
-0.026
-0.015
.
0.055
0.124
0.144
0.135
0.122

-0.001
0.008
0.009
.
0.082
0.168
0.204
0.214
0.224

Observations
89,033
103,559
118,068
.
118,068
103,261
87,365
68,400
53,452

P >|t|
0.118
0.000

99 % C.I.

-0.024
0.113

0.006
0.184

Observations
89,033
53,452

Table 9: LP-DiD regressions results for the effect of receiving EU funding on firm’s capital. A
coeﬀicient of 0.1 means a 10% growth effect. Event time is in years. Pooled estimates report
average estimates for the pre and post treatment periods. Robust standard errors are clustered at
firm level.

ECB Working Paper Series No 309947Event-time estimates Coeﬀicient S.E.
pre4
pre3
pre2
pre1
impact
post1
post2
post3
post4

-0.0005
-0.0009
-0.0021
0.0000
0.0071
0.0136
0.0140
0.0113
0.0094

0.0015
0.0013
0.0009
.
0.0010
0.0015
0.0020
0.0026
0.0033

Pooled estimates
Pre
Post

Coeﬀicient S.E.
-0.0014
0.0124

0.0011
0.0023

|t|
-0.33
-0.68
-2.23
.
7.11
9.13
6.98
4.3
2.83

|t|
-1.24
5.37

P >|t|
0.739
0.496
0.026
.
0.000
0.000
0.000
0.000
0.005

99 % C.I.

-0.004
-0.004
-0.004
.
0.005
0.010
0.009
0.005
0.001

0.003
0.002
0.000
.
0.010
0.017
0.019
0.018
0.018

Observations
64,728
76,984
90,052
.
91,969
78,556
65,238
50,429
38,786

P >|t|
0.217
0.000

99 % C.I.

-0.004
0.006

0.001
0.018

Observations
60,534
35,768

Table 10: LP-DiD regressions results for the effect of receiving EU funding on firm’s leverage
ratio (defined as the share of long term debt in total assets). Event time is in years. Pooled
estimates report average estimates for the pre and post treatment periods. Robust standard errors
are clustered at firm level.

Event-time estimates Coeﬀicient S.E.
pre4
pre3
pre2
pre1
impact
post1
post2
post3
post4

0.0002
0.0003
0.0000
0.0000
0.0005
0.0024
0.0026
0.0028
0.0026

0.0004
0.0003
0.0002
.
0.0003
0.0005
0.0006
0.0008
0.0010

Pooled estimates
Pre
Post

Coeﬀicient S.E.
0.0001
0.0022

0.0003
0.0006

|t|
0.6
0.8
0.03
.
1.79
5.21
4.17
3.33
2.5

|t|
0.32
3.36

P >|t|
0.551
0.427
0.975
.
0.073
0.000
0.000
0.001
0.012

99 % C.I.

-0.001
-0.001
-0.001
.
0.000
0.001
0.001
0.001
0.000

0.001
0.001
0.001
.
0.001
0.004
0.004
0.005
0.005

Observations
89,016
103,536
118,043
.
118,049
103,247
87,351
68,389
53,445

P >|t|
0.749
0.001

99 % C.I.

-0.001
0.001

0.001
0.004

Observations
89,000
53,438

Table 11: LP-DiD regressions results for the effect of receiving EU funding on firm’s intangible
intensity (defined as a share of intangible fixed assets in total assets). Event time is in years. Pooled
estimates report average estimates for the pre and post treatment periods. Robust standard errors
are clustered at firm level.

ECB Working Paper Series No 309948Acknowledgements 

We thank Andrea Caggese, Massimo Ferrari Minesso, Arnaud Mehl, and David Sondermann for their helpful comments, as well as all 
participants to ECB internal seminars. 
The views in this paper are those of the authors and do not necessarily reflect the views of the European Central Bank or the 
Eurosystem. 

Alessandro De Sanctis 
European Central Bank, Frankfurt am Main, Germany; email: alessandro.de_sanctis@ecb.europa.eu 

Daniel Kapp 
European Central Bank, Frankfurt am Main, Germany; email: daniel.kapp@ecb.europa.eu 

Francesca Vinci 
European Central Bank, Frankfurt am Main, Germany; email: francesca_romana.vinci@ecb.europa.eu 

Robert Wojciechowski 
Universitat Pompeu Fabra, Barcelona, Spain; Barcelona School of Economics, Barcelona, Spain; email: robert.wojciechowski@upf.edu 

© European Central Bank, 2025 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

+49 69 1344 0 
www.ecb.europa.eu 

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

PDF 

ISBN 978-92-899-7410-3 

ISSN 1725-2806 

doi:10.2866/2770359 

QB-01-25-183-EN-N