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Effects of grants from EU funds on business performance of non-financial corporations in Croatia*
Matej Bule**
Matej Bule
Affiliation: Croatian National Bank, Zagreb, Croatia
Correspondence
matej.bule@hnb.hr
Article | Year: 2021 | Pages: 177 - 207 | Volume: 45 | Issue: 2 Received: June 1, 2020 | Accepted: February 10, 2021 | Published online: June 6, 2021
|
FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
LICENCING
PDF
Indicator
|
Basic pattern
|
Extended pattern
|
Borderline effects
|
Standard error
|
Borderline effects
|
Standard error
|
Labour
productivity
|
0.076
|
(0.074)
|
0.044
|
(0.032)
|
TFP
|
0.009
|
(0.065)
|
0.017
|
(0.030)
|
Enterprise age
|
0.013***
|
(0.003)
|
0.004***
|
(0.001)
|
Employment rate
|
0.221***
|
(0.028)
|
0.186***
|
(0.014)
|
Capital
intensity
|
0.024**
|
(0.011)
|
0.022**
|
(0.003)
|
Indebtedness
rate
|
0.046***
|
(0.016)
|
0.045***
|
(0.007)
|
Average salary
|
0.093*
|
(0.057)
|
0.011
|
(0.016)
|
Profitability
|
0.078***
|
(0.022)
|
0.052***
|
(0.009)
|
Export
intensity
|
0.047***
|
(0.016)
|
0.023***
|
(0.007)
|
Import
intensity
|
-0.002
|
(0.019)
|
0.033***
|
(0.008)
|
Control
variables
year
|
Yes
|
Yes
|
NACE sector
|
Yes
|
Yes
|
region
|
Yes
|
Yes
|
Number of
corporate beneficiaries
|
227
|
1,643
|
Number of
observations
|
476,682
|
482,503
|
McFadden Pseudo
R2
|
0.3251
|
0.2665
|
Note: *, ** and *** mark statistical significance levels of 10%, 5% and 1% respectively. The basic pattern pertains to 227 corporate beneficiaries that received their first EU grant no later than 2016. These enterprises were used throughout the analysis. On the other hand, the extended pattern pertains to all corporate beneficiaries after sample adjustments, including those that received their first grant after 2016. Source: Authors’ own calculation based on data by the Ministry of Finance, Financial Agency and Court Registry.
Indicator
|
Period
|
Number of nearest neighbours
|
No caliper
|
Caliper (0.05)
|
1
|
5
|
1
|
5
|
Operating
income
|
t
|
0.213***
|
0.163**
|
0.319**
|
0.138***
|
t+1
|
0.173*
|
0.257**
|
0.290***
|
0.270***
|
t+2
|
0.286***
|
0.295***
|
0.236*
|
0.317***
|
Added value
|
t
|
0.167***
|
0.126***
|
0.141**
|
0.102***
|
t+1
|
0.213***
|
0.240***
|
0.311***
|
0.244***
|
t+2
|
0.332***
|
0.353***
|
0.388***
|
0.321***
|
Fixed assets
|
t
|
0.513***
|
0.502***
|
0.488***
|
0.516***
|
t+1
|
0.610***
|
0.659***
|
0.721***
|
0.679***
|
t+2
|
0.580***
|
0.658***
|
0.868***
|
0.636***
|
Employment rate
|
t
|
0.076***
|
0.069***
|
0.103***
|
0.080***
|
t+1
|
0.106***
|
0.129***
|
0.235***
|
0.167***
|
t+2
|
0.141***
|
0.180***
|
0.270***
|
0.201***
|
Labour
productivity
|
t
|
0.080*
|
0.050
|
0.031
|
0.014
|
t+1
|
0.094*
|
0.100**
|
0.054
|
0.061
|
t+2
|
0.175***
|
0.158***
|
0.098
|
0.103**
|
TFP
|
t
|
0.062
|
0.033
|
0.018
|
-0.002
|
t+1
|
0.059
|
0.084
|
0.092
|
0.072
|
t+2
|
0.160**
|
0.171***
|
0.161**
|
0.139***
|
Number of
treated observations
|
226
|
226
|
219
|
215
|
Number of
control observations
|
218
|
985
|
212
|
933
|
Note: *, ** and *** mark statistical reliability levels of 10%, 5% and 1% respectively. Statistical reliability was determined by means of a bootstrapping procedure with 500 repetitions. Source: authors’ own calculation based on data by the Ministry of Finance, Financial Agency and Court Registry.
Indicator
|
Period
|
Number of nearest neighbours
|
No caliper
|
Caliper (0.05)
|
1
|
5
|
1
|
5
|
Indebtedness
rate
|
t
|
0.156*
|
0.176***
|
0.179*
|
0.171**
|
t+1
|
0.267**
|
0.274***
|
0.324***
|
0.264***
|
t+2
|
0.334**
|
0.294***
|
0.300**
|
0.296***
|
Average salary
|
t
|
0.005
|
-0.008
|
0.008
|
-0.006
|
t+1
|
-0.026
|
-0.007
|
-0.022
|
-0.003
|
t+2
|
0.035
|
0.021
|
0.044*
|
0.029
|
Capital
intensity
|
t
|
0.539***
|
0.503***
|
0.444**
|
0.510***
|
t+1
|
0.567***
|
0.577***
|
0.547**
|
0.551***
|
t+2
|
0.476**
|
0.507***
|
0.657***
|
0.453**
|
Profitability
|
t
|
-0.034
|
-0.057
|
-0.081
|
-0.098
|
t+1
|
-0.143
|
-0.115
|
-0.197**
|
-0.214***
|
t+2
|
-0.136
|
-0.169*
|
-0.320***
|
-0.255***
|
Export
intensity
|
t
|
-0.084
|
-0.011
|
0.015
|
-0.004
|
t+1
|
-0.166*
|
-0.083
|
-0.037
|
-0.045
|
t+2
|
-0.161
|
-0.058
|
0.027
|
-0.007
|
Import
intensity
|
t
|
0.021
|
0.005
|
0.113
|
0.066
|
t+1
|
0.005
|
-0.003
|
0.168
|
0.074
|
t+2
|
0.094
|
0.131
|
0.191
|
0.159*
|
Number of
treated observations
|
226
|
226
|
219
|
215
|
Number of
control observations
|
218
|
985
|
212
|
933
|
Note: *, ** and *** mark statistical reliability levels of 10%, 5% and 1% respectively. Statistical reliability was determined by means of a bootstrapping procedure with 500 repetitions. Additional calculations with control samples comprising 2 and 10 nearest neighbours are provided in Appendix 4 below. Source: authors’ own calculation based on data by the Ministry of Finance, Financial Agency and Court Registry.
Note: *, ** and *** mark statistical reliability levels of 10%, 5% and 1% respectively. Statistical reliability was determined by means of a bootstrapping procedure with 500 repetitions. Additional calculations with control samples comprising 2 and 10 nearest neighbours are provided in Appendix 4 below. Source: authors’ own calculation based on data by the Ministry of Finance, Financial Agency and Court Registry.
Note: The solid black curve depicts the conditional expectation of growth rates of the indicator under observation with the provided relative intensity of treatment and estimated generalised propensity score. The shadowed sections represent the ceiling and floor values of the 95% confidence interval calculated through the bootstrapping method with 500 repetitions. Source: Authors’ own calculation based on data by the Ministry of Finance, Financial Agency and Court Registry.
Note: The solid black curve depicts the conditional expectation of growth rates of the indicator under observation with the provided relative intensity of treatment and estimated generalised propensity score. The shadowed sections represent the ceiling and floor values of the 95% confidence interval calculated through the bootstrapping method with 500 repetitions. Source: Authors’ own calculation based on data by the Ministry of Finance, Financial Agency and Court Registry.
Variable
name
|
Description
|
Note
|
Revenue
|
Real value of
operating income
|
Deflated by GDP
deflator
|
Employment rate
|
Total number of
employees based on hours of labour
|
-
|
Fixed assets
|
Real value of
fixed assets
|
Deflated by GDP
deflator
|
Age
|
Number of years
passed since the establishment of the enterprise
|
-
|
Added value
|
Difference
between operating income and value of intermediary inputs and other costs of
sold products
|
Deflated by
implicit added value deflators to the second digit level of the National
Classification of Activities (NKD) Energy costs have been deflated by the
implicit added value deflator for the electricity, gas, steam and air
conditioning supply sector.
|
Labour
productivity
|
Ratio between
added value and number of employees
|
-
|
Total factor
productivity
|
Residual of the
Cobb-Douglas production function
|
See Appendix 2
for more details on calculation methodology.
|
Capital
intensity
|
Ratio between
fixed assets and number of employees
|
-
|
Profitability
|
Ratio between
period profit and total assets
|
-
|
Indebtedness
rate
|
Ratio between
non-current liabilities and total commitments
|
-
|
Average salary
|
Ratio between
total gross employee costs and number of employees
|
Deflated by
implicit added value deflators to the second digit level of the National
Classification of Activities (NKD)
|
Export
intensity
|
Ratio between
revenue from sales abroad and operating income
|
-
|
Import
intensity
|
Ratio between
import value and operating income
|
-
|
Regional
affiliation
|
Divided into
five regions: Eastern Croatia, Central Croatia, Northern Croatia, Adriatic
Croatia and the City of Zagreb.
|
An enterprise’s
geographic affiliation is classified into regions, which have been defined on
the basis of the first version of the new NUTS-2 classification in Croatia
(Institute for Development and International Relations, 2018). This
classification is used here solely for analytical purposes.
|
uijzuh
Indicator
|
Before adjustments
|
Before merger
|
Number of nearest neighbours
|
No caliper
|
Caliper (0.05)
|
1
|
2
|
5
|
10
|
1
|
2
|
5
|
10
|
Labour
productivity
|
0.580***
|
0.729***
|
-0.042
|
0.000
|
-0.032
|
-0.036
|
-0.032
|
0.047
|
0.014
|
-0.010
|
Enterprise age
|
4.431***
|
6.573***
|
0.430
|
0.560
|
0.459
|
0.295
|
1.157
|
0.513
|
0.783
|
1.147*
|
Employment rate
|
1.530***
|
1.791***
|
-0.145
|
-0.075
|
-0.020
|
0.040
|
0.023
|
-0.050
|
0.084
|
0.209**
|
TFP
|
1.066***
|
1.168***
|
-0.053
|
-0.005
|
-0.030
|
-0.023
|
-0.130
|
-0.027
|
0.008
|
0.032
|
Capital
intensity
|
3.022***
|
3.790***
|
-0.123
|
0.005
|
-0.013
|
-0.016
|
-0.086
|
0.017
|
-0.019
|
0.032
|
Export
intensity
|
0.970***
|
1.515***
|
0.091
|
0.093
|
0.116
|
0.126
|
-0.101
|
0.174
|
0.154
|
0.220*
|
Indebtedness
rate
|
0.648***
|
0.955***
|
-0.063
|
-0.027
|
-0.044
|
-0.018
|
0.007
|
-0.095
|
-0.068
|
0.036
|
Number of
treated observations
|
1.921
|
226
|
226
|
226
|
226
|
226
|
219
|
219
|
215
|
213
|
Number of
control observations
|
772,528
|
441,846
|
218
|
417
|
985
|
1,787
|
212
|
411
|
933
|
1,694
|
Note: *, ** and *** mark statistical reliability levels of 10%, 5% and 1% respectively. Source: authors’ own calculation based on data by the Ministry of Finance, Financial Agency and Court Registry.
Indicator
|
Period
|
Number of nearest neighbours
|
No caliper
|
Caliper (0.05)
|
2
|
10
|
2
|
10
|
Operating
income
|
t
|
0.200***
|
0.182***
|
0.219**
|
0.210**
|
t+1
|
0.300**
|
0.309***
|
0.288***
|
0.292***
|
t+2
|
0.328***
|
0.324***
|
0.308***
|
0.346***
|
Added value
|
t
|
0.148***
|
0.134***
|
0.125**
|
0.129***
|
t+1
|
0.248***
|
0.256***
|
0.227***
|
0.256***
|
t+2
|
0.328***
|
0.375***
|
0.326***
|
0.364***
|
Fixed assets
|
t
|
0.506***
|
0.482***
|
0.470***
|
0.465***
|
t+1
|
0.632***
|
0.695***
|
0.735***
|
0.748***
|
t+2
|
0.582***
|
0.709***
|
0.709***
|
0.722***
|
Employment rate
|
t
|
0.073***
|
0.071***
|
0.080***
|
0.081***
|
t+1
|
0.123***
|
0.142***
|
0.166***
|
0.160***
|
t+2
|
0.187***
|
0.184***
|
0.220***
|
0.202***
|
Labour
productivity
|
t
|
0.067
|
0.055
|
0.032
|
0.041
|
t+1
|
0.113*
|
0.100**
|
0.045
|
0.083*
|
t+2
|
0.123**
|
0.175***
|
0.087*
|
0.149***
|
TFP
|
t
|
0.057
|
0.039
|
0.026
|
0.024
|
t+1
|
0.109*
|
0.089*
|
0.046
|
0.081*
|
t+2
|
0.147***
|
0.189***
|
0.112**
|
0.169***
|
Indebtedness
rate
|
t
|
0.189**
|
0.200***
|
0.165**
|
0.200***
|
t+1
|
0.348***
|
0.287***
|
0.317***
|
0.301***
|
t+2
|
0.336***
|
0.289***
|
0.277**
|
0.297***
|
Average salary
|
t
|
-0.003
|
0.005
|
-0.002
|
0.009
|
t+1
|
0.016
|
-0.003
|
0.019
|
0.001
|
t+2
|
0.014
|
0.029
|
0.023
|
0.042
|
Capital
intensity
|
t
|
0.511***
|
0.474***
|
0.432***
|
0.457***
|
t+1
|
0.557***
|
0.603***
|
0.620***
|
0.660***
|
t+2
|
0.416***
|
0.571***
|
0.541***
|
0.585***
|
Profitability
|
t
|
-0.028
|
-0.052
|
-0.083
|
-0.105
|
t+1
|
-0.101
|
-0.130*
|
-0.145*
|
-0.183**
|
t+2
|
-0.178*
|
-0.192**
|
-0.214**
|
-0.259***
|
Export
intensity
|
t
|
-0.024
|
-0.010
|
-0.018
|
0.013
|
t+1
|
-0.079
|
-0.079
|
-0.096
|
-0.055
|
t+2
|
-0.102
|
-0.071
|
-0.089
|
-0.001
|
Import
intensity
|
t
|
0.025
|
0.026
|
0.052
|
0.022
|
t+1
|
0.021
|
0.052
|
-0.001
|
0.034
|
t+2
|
0.163
|
0.121
|
0.075
|
0.128
|
Number of
treated observations
|
226
|
226
|
219
|
213
|
Number of
control observations
|
417
|
1,787
|
411
|
1,694
|
Note: *, ** and *** mark statistical reliability levels of 10%, 5% and 1% respectively. Statistical reliability was determined by means of a bootstrapping procedure with 500 repetitions. Source: authors’ own calculation based on data by the Ministry of Finance, Financial Agency and Court Registry.
Indicator
|
Coefficient
|
Standard error
|
Employment rate
|
-0.495***
|
0.085
|
Capital
intensity
|
-0.074**
|
0.031
|
Labour
productivity
|
-0.191**
|
0.085
|
TFP
|
7.69E-10
|
5.38E-10
|
Age
|
0.095
|
0.121
|
Export
intensity
|
0.016
|
0.054
|
Indebtedness
rate
|
0.148***
|
0.052
|
Number of
observations
|
217
|
Note: *, ** and *** mark statistical reliability levels of 10%, 5% and 1% respectively. Statistical reliability was determined by means of a bootstrapping procedure with 500 repetitions. Source: authors’ own calculation based on data by the Ministry of Finance, Financial Agency and Court Registry.
Ackerberg, D., Caves, K. and Garth, F., 2006. Structural identification of production functions. MPRA Paper, 38349.
Bachtrogler, J. and Hammer, C., 2018. Who are the beneficiaries of the structural funds and the cohesion fund and how does the cohesion policy impact firm-level performance? OECD Economics Department Working Papers, No. 1499 [ CrossRef]
Bartoluci, M. [et al.], 2018. Sredstva EU fondova u funkciji razvoja ruralnog turizma u Hrvatskoj. Acta Economica Et Turistica, 4(1), pp. 63-78 [ CrossRef]
Becker, B., Vanino, E. and Roper, S., 2019. Knowledge to money: assessing the business performance effects of publicly-funded R&D grants. Research policy, 48(7), pp. 1714-1737 [ CrossRef]
Bouayad-Agha, S., Turpinn, N. and Vedrine, L., 2011. Fostering the development of European regions: A spatial dynamic panel data analysis of the impact of cohesion policy. Regional Studies, 47(9), pp. 1573-1593 [ CrossRef]
Breidenbach, P., Mitze, T. and Schmidt, C. M., 2016. EU Structural Funds and Regional Income Convergence - A sobering experience. CEPR Discussion Paper, No. 11210 [ CrossRef]
Cappelen, A. [et al.], 2003. The impact of EU regional support on growth and convergence in the European Union. Journal of Common Market Studies, 41(4), pp. 621-644 [ CrossRef]
Crescenzi, R. and Giua, M., 2017. Different approaches to the analysis of EU cohesion policy. In: J. Bachtler [et al.], eds. EU cohesion policy. Reassessing performance and direction. London: Routledge [ CrossRef]
Dai, M., Li, X. and Lu, Y., 2017. How Urbanization Economies Impact TPF of R&D Performers: Evidence from China. Sustainability, 9(10), pp. 1-17 [ CrossRef]
Dai, X. and Cheng, L., 2015. The effect of public subsidies on corporate R&D investment: An application of the generalized propensity score. T echnological Forecasting & Social Change, 90(Part B), pp. 410-419 [ CrossRef]
Dall’Erba, S. and Le Gallo, J., 2007. Cohesion policy, the convergence process and employment in the European Union. Czech Journal of Economics and Finance, 57, pp. 324-340.
Dall’Erba, S. and Le Gallo, J., 2008. Regional convergence and the impact of European structural funds over 1989–1999: A spatial econometric analysis. Papers in Regional Science, 87(2), pp. 219-244 [ CrossRef]
De Zwaan, M. and Merlevede, B., 2013. Regional policy and firm productivity. European Trade Study Group (ETSG) Working Paper, No. 377.
Ederveen, S., De Groot, H. L. F. and Nahuis, R., 2006. Fertile soil for structural funds? A panel data analysis of the conditional effectiveness of European cohesion policy. Kyklos, 59(1), pp. 17-42 [ CrossRef]
Esposti, R. and Bussoletti, S., 2008. Impact of Objective 1 funds on regional growth convergence in the European Union: A panel data approach. Regional Studies, 42(2), pp. 159-173 [ CrossRef]
Fagerberg, J. and Verspagen, B., 1996. Heading for divergence? Regional growth in Europe reconsidered. JCMS: Journal of Common Market Studies, 34(3), pp. 431-448 [ CrossRef]
Fattorini, L., Ghodsi, M. and Rungi, A., 2018. Cohesion Policy Meets Heterogeneous Firms. WIIW Working Paper, No. 142 [ CrossRef]
Ferrara, A. R. [et al.], 2016. Assessing the impacts of cohesion policy on EU regions: A non-parametric analysis on interventions promoting research and innovation and transport accessibility. Papers in Regional Science, 96(4), pp. 817-841 [ CrossRef]
Fryges, H. and Wagner, J., 2008. Exports and Productivity Growth: First Evidence from a Continuous Treatment Approach. Review of World Economics, 144, pp. 695-722 [ CrossRef]
Hartsenko, J. and Sauga, A., 2013. The role of financial support in SME and economic development in Estonia. Business and Economic Horizons, 9(2), pp. 10-22 [ CrossRef]
Hirano, K. and Imbens, G. W., 2004. The propensity score with continuous treatments. In: Gelman, A. and MengX., eds. Applied Bayesian modeling and causal inference from incomplete-data perspectives, pp. 73-84 [ CrossRef]
Kersan Škabić, I. and Tijanić, L., 2017. Regional abrorption capacity of EU funds. Economic research - Ekonomska istraživanja, 30(1), pp. 1192-1208 [ CrossRef]
Kotarski, V., 2016. European funding – Impact on Research Capacity in Croatia. Review of Innovation and Competitiveness: A Journal of Economic and Social Research, 2(3), pp. 47-64 [ CrossRef]
Levinsohn, J. and Petrin, A., 2003. Estimating Production Functions Using Inputs to Control for Unobservables. Review of Economic Studies, 70(2), pp. 317-341 [ CrossRef]
Martinis, A. and Ljubaj, I., 2017. Prekomjerni dug poduzeća u Hrvatskoj: mikroprocjena i makroimplikacije. HNB Istraživanja, I-52.
Medić, M., Hadrović Zekić, B. and Sabljo, V., 2017. Impact of EU Funds on Regional Development. In: U. Bacher [et al.], eds. Interdisciplinary Management Research XIII, pp. 1060-1077.
Olley, G. S. and Pakes, A., 1996. The dynamics of productivity in the telecommunications equipment industry. Econometrica, 64(6), pp. 1263-1297 [ CrossRef]
Ott, K., Bronić, M. and Stanić, B., 2018. EU grants to Croatian counties, cities and municipalities 2015-2016. Newsletter : an occasional publication of the Institute of Public Finance, No. 114 [ CrossRef]
Rodriguez-Pose, A. and Fratesi, U., 2004. Between development and social policies: The impact of European structural funds in Objective 1 regions. Regional Studies, 38(1), pp. 97-113 [ CrossRef]
Rosenbaum, P. R. and Rubin, D., 1983. The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70(1), pp. 41-55 [ CrossRef]
Sikirić, S., Sikirić, A. and Vašiček, D., 2015. Lokalni proračun i programsko planiranje u funkciji financiranja projekata iz EU fondova. Business Consultant / Poslovni Konsultant, 7(51), pp. 31-42.
Šostar, M. and Marukić, A., 2017. Challenges of public procurement in Eu funded projects. Journal of Contemporary Management Issues, 22(2), pp. 99-113 [ CrossRef]
Valdec, M. and Zrnc, J., 2015. The direction of causality between exports and firm performance: microeconomic evidence from Croatia using the matching approach. Financial Theory and Practice, 39(1), pp. 1-30 [ CrossRef]
Valdec, M. and Zrnc, J., 2019. Karakteristike hrvatskih izvoznika iz prerađivačkog sektora i oporavak izvoza tijekom velike recesije – rezultati istraživanja modula za trgovinu Istraživačke mreže za konkurentnost (CompNet). HNB Pregledi, P-42.
Viskovic, J. and Udovičić, M., 2017. Awareness of SMEs on the EU Funds Financing Possibilities: The Case of Split-Dalmatia County. KnE Social Sciences, 1(2), pp. 319-332 [ CrossRef]
Wooldridge, J. M., 2009. On estimating firm-level production functions using proxy variables to control for unobservables. Economics Letters, 104(3), pp. 112-114 [ CrossRef]
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June, 2021 II/2021 |