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sustainability
Article
Effects of Soft Loans and Credit Guarantees on
Performance of Supported Firms: Evidence from the
Czech Public Programme START
Ondˇrej DvouletýID
Department of Entrepreneurship, University of Economics in Prague, W. Churchill Sq. 1938/4, 130 67 Prague 3,
Czech Republic; ondrej.dvoulety@vse.cz; Tel.: +420-728-430-027
Received: 15 November 2017; Accepted: 8 December 2017; Published: 10 December 2017
Abstract:
The purpose of this article was to conduct an empirical evaluation of the Czech
public programme START, which was funded from the European Regional Development Fund.
The programme lasted from 2007–2011, and supported new entrepreneurs through the zero interest
soft loans and credit guarantees. The counterfactual analysis (using three matching techniques:
propensity score, nearest neighbour, and kernel) was conducted on the firm level and investigated
the changes in financial performance (net profits, return on assets (ROA), return on equity (ROE),
sales, assets turnover, and debt ratio) of the supported firms four years after the end of intervention.
The obtained findings could not support the hypothesis assuming a positive impact of the programme
on the firm’s performance. On the contrary, supported companies reported on average lower
sales and lower return on assets, when compared to the control group. The remaining variables
could not prove any statistically significant impact of the programme. Indicators measuring firm’s
profitability (net profit, return on assets, and return on equity) suggested a negative influence of the
programme and the variable representing debt ratio further indicated that firms that were supported
by the programme reported on average higher debt ratio in comparison with the control group.
Several policy implications are discussed in the study.
Keywords:
Entrepreneurship Policy Evaluation; Public Start-up Programme; Soft Loans; Credit
Guarantees; Counterfactual Analysis; the Czech Republic
1. Introduction
1.1. Background
The idea of supporting entrepreneurial activity through the system of public policies originated
in the scientific empirical evidence, indicating a positive influence of entrepreneurship on economic
growth and job creation, e.g., [
1
–
7
]. Policymakers mainly turn their attention towards the support
of small and medium-sized enterprises (SMEs), which are considered as those, bearing innovation
and increasing regional competitiveness, through the various entrepreneurship policies and public
interventions, e.g., [
8
–
12
]. To better understand entrepreneurship policies, one can recall a definition
by Stevenson and Lundström [
13
], who explain entrepreneurship policies as “policy measures taken
to stimulate entrepreneurship that are aimed at the pre-start, the start-up and post-start-up phases
of the entrepreneurial process” [
13
] (p. 23). Policymakers often use a variety of tools to stimulate
entrepreneurial activity, such as soft loans, credit guarantees, payable and non-repayable capital grants,
investment incentives, tax deductions, and different forms of entrepreneurial education and trainings to
achieve higher economic growth and increased employment by “picking-up winners”, e.g., [
9
,
14
–
17
].
Foreman-Peck [
18
] and others, e.g., [
19
–
22
], report the positive effects of the participation in the
governmental programmes, boosting entrepreneurship on the firm level. However, outcomes of
Sustainability 2017,9, 2293; doi:10.3390/su9122293 www.mdpi.com/journal/sustainability
Sustainability 2017,9, 2293 2 of 17
entrepreneurship policies do not have to be always as positive, as they would be expected by the
policymakers. There are researchers, e.g., [
5
,
23
,
24
], who point out that the usage of public policies
promoting entrepreneurship should be reduced since impacts of policies may be ambiguous and even
could have zero impact on the supported individuals and companies. To shed more light onto this
issue, one needs to dive into the empirical field and to investigate the outcomes of concrete programs
and policies, e.g., [
25
]. Recently published reviews of empirical literature by Grimm and Paffhausen [
1
]
and by Cho and Honorati [
26
] show that it is important to establish access to capital for the new
entrepreneurs, nevertheless the authors also point out that the entrepreneurial education and business
training programmes have larger positive impacts on supported entrepreneurs when compared to the
capital grants and other policies. Both of the studies also indicate that entrepreneurship policies have
more significant impacts when it comes to the support of founding enterprises/new start-ups than in
expanding of employment of already established companies. Their main conclusion is that there is an
overall lack of empirical studies conducted on the firm level, and especially those that aim to assess
the long-term impacts of the governmental programmes.
One way to allocate the financial capital to the new entrepreneurs and to help them with the
establishment of their own business is through the system of soft loans and credit guarantees that
are provided by the public sector. Soft loans aim to “finance businesses or projects over a period of
time and at an agreed rate of return, typically on the basis of the quality of cash flow and strength
of the underlying assets on subsidized terms” [
27
] (p. 108), based on Michie and Wishlade [
28
].
Credit guarantees aim to “provide security for firms that are unable to obtain financing otherwise;
may cover all or part of the capital. May take the form of guarantees on bank loans, micro-credit or
equity. May involve a fee or higher interest rate for the borrower” [
27
] (p. 108) based on Michie and
Wishlade [28].
The reasoning behind this form of public support, which has become an interest in this article,
is to facilitate an access to the financial capital and to remove the financial barriers of high interest rates,
collateral requests and other disadvantageous conditions offered by the regular market based financial
institutions. This “financial gap”, known as the problem of information asymmetry, originates in the
market imperfections. Financial institutions are not capable to obtain all of the relevant information
about the loan applicants, therefore, they over-qualify applicant’s risk of bankruptcy, especially for
new projects/start-ups and as a result, entrepreneurs aiming to deliver innovation to the market,
do not get enough of financial resources. Because of information asymmetries and high transaction
costs, financial institutions are reluctant to lend to entrepreneurs, or they lend at very high interest
rates, and, therefore, public intervention through soft loans and credit guarantees might be an option
as to how to alleviate some of the market imperfections, e.g., [29–34].
For the supported SMEs is gained capital a chance to grow, to offer new jobs and to expand
their business activities, since they receive a competitive advantage through the public intervention.
Accordingly, their support may lead to an increased economic growth and reduction of unemployment.
When compared to the capital grants and to other non-repayable forms of entrepreneurship support,
in the case of soft loans and credit guarantees, all of the resources that are allocated by the state do
not have to necessarily imply a negative cash flow for the state, because not all supported individuals
result in bankruptcy, e.g., [9,35–39].
On the other hand, there are still high monitoring costs for the loan provider. If the supported
business owners lose their entrepreneurial alertness or start acting riskier, then the borrowed credit
might be lost. Stiglitz and Weiss [
40
] described this behaviour as a moral hazard. In addition
to moral hazard, the success of public intervention might be endangered by the adverse selection
problem. Therefore, the public authorities need to be cautious when entering the market as financial
providers, e.g., [
41
–
44
]. Overall, the effects of public interventions might differ (across countries or
over time), and therefore it is important to evaluate the specific programme with respect to the local
conditions, e.g., [17].
Sustainability 2017,9, 2293 3 of 17
Evaluations of the financial forms of entrepreneurship support in Europe on the firm level were
in the past years mostly investigated by the scholars from the Southern European countries, such as
Italy or Spain, e.g., [
7
,
20
,
30
,
45
–
48
]. However, the empirical evidence investigating the outcomes of the
soft loans and credit guarantees and generally questioning the outcomes of entrepreneurship policies
in the Central and especially Eastern European countries have not deserved much research attention
so far, e.g., [
27
,
49
–
59
]. This increasing research gap attracts researchers, who strive to form policy
recommendations that are based on the empirical evidence, and those who are willing to be trained as
evaluators since a lot of programmes need to be assessed.
Most of the recent studies in the Central and Eastern European region have been focused on the
evaluation of cohesion R&D policies and those aimed at the elimination of the regional disparities,
e.g., [
60
–
70
], policies facilitating trainings and education [
71
,
72
] and effectivity of the self-employment
programmes for the unemployed [
73
–
75
]. However, based on a search in the databases of previously
published academic articles and research reports, no study has been focused on the evaluation of
entrepreneurship support through the financial instruments yet. Therefore, the main aim of this
study is to contribute to the regional knowledge, by the assessment of the outcomes of the Czech
public programme START, funded from the European Regional Development Fund [
76
], which was
providing credit guarantees and soft loans to the newly established entrepreneurs during the period
of years 2007–2011 in the Czech Republic [
77
]. The empirical analysis is conducted on the firm level
(from micro-econometric perspective) and it investigates the changes in the financial performance of the
supported firms based on the application of the counterfactual impact analysis (quantitative approach).
The article is structured as follows, in the Section 1.2, the findings of the previously published
empirical studies are presented. In the following part (Section 1.3) of the article, the programme
START is described and analysed from the economic perspective and the regional allocation of the
financial resources is depicted. The second section (Section 2) describes the collected dataset of firms,
analysed outcome variables (net profits, return on assets (ROA), return on equity (ROE), assets turnover
and debt ratio), and applied empirical strategy. In the third part (Section 3), micro-econometric analysis
of the data, employing the framework of counterfactual analysis is conducted. Obtained results are
discussed with respect to the previous empirical findings in the fourth part (Section 4). The last part
of the article (Section 5) is focussed on the policy recommendations and it provides suggestions for
future research.
1.2. Review of Empirical Studies
This section presents the findings of the past studies that are focused on the analysis of the
outcomes of the public policies facilitating financial instruments on the firm level. The methodology
of the previous scholars is mostly quantitative and it is based on the implementation of econometric
methods. Maggioni et al. [
78
] analysed the outcomes of the Italian programme supporting new ventures
through loans with reduced interest rates. They were unable to prove any significant influence of
the programme on the sales, their growth, or on a number of employees. Honjo and Harada [
79
]
investigated the impact of the Japanese Creative Business Promotion Law (CBPL) on the growth of sales,
assets, and employment of the supported businesses. Their results confirm a positive influence of the
programme on the growth of assets. Kang and Heshmati [
80
] studied the effects of the credit guarantee
policy in Korea on the productivity, sales, and employees of the supported enterprises. They found no
impact of the policy on the employment, however, positive influence was observed for the variables
that were measuring firm performance (productivity and sales). Zecchini and Ventura [
81
] investigated
the effects of the Italian credit guarantees scheme on the assets, sales, number of employees, and
debt-ratio of the supported enterprises. Obtained empirical results confirm the positive influence of
the scheme on the sales and assets of the supported companies. However, no influence was found on
a number of employees. Furthermore, the authors of the study observed a higher debt ratio for the
supported firms in comparison with the control group. Oh et al. [
82
] analysed the outcomes of the
Korean programme facilitating credit guarantees. Investigated variables accounted for survival rates,
Sustainability 2017,9, 2293 4 of 17
growth rates of productivity, employment, sales, R&D status, and investment intensity. The authors
conclude that the programme positively influenced a firm’s survival rate, but no effect was observed
for R&D activity and productivity. Kösters [
83
] studied the effects of the Eastern German programme
supporting firms with soft loans and credit guarantees. She finds no statistically significant impact
of the programme on the survival rates and growth in employment of the supported companies.
On the contrary, Garcia-Tabuenca and Crespo-Espert [
48
], found a positive influence of the Spanish
programme facilitating financial instruments on the productivity, sales, value added, and profitability
of the supported firms. Gubert and Roubaud [
84
] investigated the outcomes of the micro-finance loan
schemes in Madagascar. They conclude that the programme had a positive influence on the turnover,
productivity, value added, profit, and number of employees of the supported enterprises. One of the
more recent studies was conducted by Cowling and Siepel [
85
] who analysed the outcomes of the Loan
Guarantee Scheme (SFLG) in the United Kingdom. They report positive effects of the programme on
the sales, exports, and job creation of the supported enterprises.
Based on the presented outcomes of the previously published studies, it is very difficult to
derive any conclusions regarding the outcomes of the programmes facilitating financial instruments.
Public schemes could have both positive and negative effects on the firm’s profitability, performance,
and amount of employees. Previous studies serve as an inspiration for the analysed outcome variables
and empirical approach towards the programme evaluation. The presented study contributes to this
research debate by the assessment of the Czech public programme START, which is described in the
following section.
1.3. Public Programme START
The programme START took place in the Czech Republic in the two subsequent calls, during the
period of years 2007–2011. As requested for the European Union (EU) public support, the programme
was following the principle de minimis. The programme was a part of the Operational Programme
Enterprises and Innovation (OPEI) funded from the European Regional and Development Fund (ERDF)
during the EU programming period of years 2007–2013 [
76
,
86
]. The main organizing institutions,
which were responsible for the programme, were the Czech-Moravian Guarantee and Development
Bank [
87
] and the Ministry of Industry and Trade [
77
]. The programme that START intended to support
completely new entrepreneurs or those who were starting a business activity seven years after they
ended their previous one. The programme’s objective was to increase the competitiveness of the Czech
Republic. The idea behind the programme was to facilitate access to financial capital for new business
ideas through the system of zero interest soft loans and credit guarantees. Applicants had to meet
several criteria to obtain public support. Business activity of applicants could not be focused on the
sector of agriculture (with exceptions) and they could not have any liabilities against the Czech public
authorities. Entrepreneurs aiming to start a business in the Capital Prague were also excluded from
the application process. In the first step of the selection process, a risk profile, business authorization
and financial history of the clients have been assessed. Applications were assessed by external
evaluators, who made decisions about the acceptance of the business proposals (criteria included
preparedness of the project, its feasibility, and cost structure). Each of the evaluators had to provide a
brief summary of the strengths and weaknesses of the proposal, together with his/her recommendation.
Finally, the approved requests were executed by the Czech-Moravian Guarantee and Development
Bank [77,87,88].
Applicants could choose between the two forms of support from the programme START. The first
one offered them a zero interest soft loan, covering up to 90% of the project costs, up to 0.75 mil.
CZK (29,528 EUR) in the case of a solo entrepreneur, or up to 1.5 mil. CZK (59,055 EUR) in the case
of an entrepreneurial team. Please note, that all of the financial amounts were converted into Euros
by the average exchange rate for the analysed period of years 2007–2014. Data were collected from
Eurostat [89] and the calculated average exchange rate was 27.54 EUR/CZK. The maximum possible
maturity was set up to be seven years. The second scheme offered applicants a credit guarantee
Sustainability 2017,9, 2293 5 of 17
covering up to 80% of the loan. The maximum amount of the loan was 1.5 mil. CZK (59,055 EUR) and
the maturity had to be longer than three years. The recipient of the guarantee had to pay
0.1% p. a.
of
the guarantee for the service, however, at the same time, he/she received a public support that
was worth 3% of the guarantee. If the guarantee was not applied, then the project was implemented
successfully within two years, and the supported entrepreneur(s) was/were repaying the loan, then the
recipient received an extra bonus making 15% of the loan [77,88].
The total amount of funds that was allocated to the projects from public resources was 79.7 mil.
CZK (3.1 mil. EUR); however, the projects got financial capital worth 170.3 mil. CZK (6.7 mil. EUR)
in total, since the loans and guarantees were organized within the partnerships with other market
based financial institutions. 88 projects were supported by the credit guarantees and 100 projects got
zero interest soft loans. The highest share of the projects was supported in 2008 and the last projects
were supported in 2010. The majority of the supported were self-employed/freelances (107), and the
rest of them represented a business company. From the projects supported by the credit guarantees,
the most frequent project was the construction of the photovoltaic power plants, and out of the projects
that were supported by the zero interest soft loans, it was the foundation of a store. When it comes
to the number of employees, the majority of the supported businessmen (112) reported that they
have fewer than five employees. Regional allocation of the projects across the Czech NUTS three
regions is depicted in Figure 1. The highest amounts of public resources have been allocated to the
regions Ústecký, Pardubický, and Stˇredoˇceský. On average, each of the projects received 0.9 mil.
CZK (35,433 EUR), out of which 0.4 mil. CZK (15,748 EUR) was obtained from public funds [88].
Sustainability 2017, 9, 2293 5 of 17
the guarantee for the service, however, at the same time, he/she received a public support that was
worth 3% of the guarantee. If the guarantee was not applied, then the project was implemented
successfully within two years, and the supported entrepreneur(s) was/were repaying the loan, then
the recipient received an extra bonus making 15% of the loan [77,88].
The total amount of funds that was allocated to the projects from public resources was 79.7 mil.
CZK (3.1 mil. EUR); however, the projects got financial capital worth 170.3 mil. CZK (6.7 mil. EUR)
in total, since the loans and guarantees were organized within the partnerships with other market
based financial institutions. 88 projects were supported by the credit guarantees and 100 projects got
zero interest soft loans. The highest share of the projects was supported in 2008 and the last projects
were supported in 2010. The majority of the supported were self-employed/freelances (107), and the
rest of them represented a business company. From the projects supported by the credit guarantees,
the most frequent project was the construction of the photovoltaic power plants, and out of the
projects that were supported by the zero interest soft loans, it was the foundation of a store. When it
comes to the number of employees, the majority of the supported businessmen (112) reported that
they have fewer than five employees. Regional allocation of the projects across the Czech NUTS three
regions is depicted in Figure 1. The highest amounts of public resources have been allocated to the
regions Ú stecký, Pardubický, and Středočeský. On average, each of the projects received 0.9 mil. CZK
(35,433 EUR), out of which 0.4 mil. CZK (15,748 EUR) was obtained from public funds [88].
Figure 1. Allocation of Resources from the Program START across the Czech NUTS 3 regions in CZK
(Legend from the top: number of projects, name of the region, public support, total amount of
resources). Source: [88].
To see whether this remarkable allocation of financial capital had any impact on the financial
performance of the supported enterprises, I run the following counterfactual analysis, applying the
methodology of previous scholars, e.g., [19,20,83,90], assuming a quasi-experimental research
framework. The following key performance indicators were selected based on the previously
published studies and based on the data availability. My research hypothesis is formally stated as
follows:
Hypotheses 1 (H1): Firms participated in the programme START reported better financial performance in
terms of higher profits, higher return on assets (ROA), higher return on equity (ROE), higher sales and higher
assets turnover, in comparison with the control group.
Figure 1.
Allocation of Resources from the Program START across the Czech NUTS 3 regions in
CZK (Legend from the top: number of projects, name of the region, public support, total amount of
resources). Source: [88].
To see whether this remarkable allocation of financial capital had any impact on the financial
performance of the supported enterprises, I run the following counterfactual analysis, applying the
methodology of previous scholars, e.g., [
19
,
20
,
83
,
90
], assuming a quasi-experimental research
framework. The following key performance indicators were selected based on the previously published
studies and based on the data availability. My research hypothesis is formally stated as follows:
Sustainability 2017,9, 2293 6 of 17
Hypotheses 1 (H1):
Firms participated in the programme START reported better financial performance in
terms of higher profits, higher return on assets (ROA), higher return on equity (ROE), higher sales and higher
assets turnover, in comparison with the control group.
An additional variable of interest might be a debt ratio, which is often studied in the previously
published studies, e.g., [
81
], however it is difficult to make any assumptions about the relationship
between financial performance of the firm and its indebtedness, e.g., [91].
2. Materials and Methods
The empirical analysis is based on the firm level data, covering the period of years 2006
−
2014.
Based on the list of supported projects [
88
], 188 supported companies were identified (for sectoral
distribution, see Table A1). As a second step, the database Albertina [
92
] was used to collect the data.
To ensure that the selection of companies in the control group would not affect the results, a control
group of 18,499 firms was selected randomly from the population of active enterprises and their data
were collected from the database. Collected variables are depicted in Table 1. After the data collection,
the descriptive statistics have been inspected and several adjustments have been made. All entities
that are not considered as business units (e.g., schools, foundations, or associations) were removed.
To make sure that the extreme values (outliers) would not affect the results, the main financial outcome
variables of the control group (return on assets, assets turnover, and debt ratio) have been restricted by
the interval (
−
400; 400). This aimed to achieve that “value leaders and losers” were excluded from the
sample as suggested by the previous researchers, e.g., [
93
]. Unfortunately, the dataset suffers from
an extreme amount of missing values. No data could be obtained for the self-employed/freelancers
(107) participating in the programme, which do not have any obligation to report their financial
records. Out of the 81 remaining business companies, I was able to collect data for 57 firms, having in
total data for 30% of all supported enterprises and for 70% of the supported business companies.
Therefore, I am able to evaluate the programme only with respect to the supported business companies.
Control sample finally consisted out of 10,681 firms, having complete data.
Table 1. List of Variables.
Variable Definition
Treated Dummy variable indicating, whether the particular firm participated in the
program START (188 participating enterprises).
Net Profit Outcome variable, calculated as an average of net profits of the firm during the
years 2011–2014.
Return on Assets (ROA) Outcome variable, calculated as an average percentage share of net profits of the
firm and its assets during the years 2011–2014.
Return on Equity (ROE) Outcome variable, calculated as an average percentage share of net profits of the
firm and its own capital during the years 2011–2014.
Sales Outcome variable, calculated as an average sales for own products and services
during the years 2011–2014.
Assets Turnover Outcome variable, calculated as an average ratio of sales/turnover and assets
during the years 2011–2014.
Debt Ratio Outcome variable, calculated as an average percentage share of liabilities of the
firm and its assets during the years 2011–2014.
Year of Registration Control variable, referring to a year when the company was officially established.
Company Size
Control variable, dividing firms into the four dummy categories, according to a
number of employees reported: Micro (less than 10 employees), Small (10–49
employees), Medium (50–249 employees) and Large (more than 250 employees).
Sector
Control variable, dividing firms into the 21 NACE dummy categories according to
their business activity.
Region
Control variable, dividing firms into the 14 NUTS3 dummy categories according to
the Czech region, where they operate.
Source: [88,92], own elaboration.
Sustainability 2017,9, 2293 7 of 17
To evaluate the impact of the participation in the programme on the performance of the supported
firms, I apply a quasi-experimental approach and perform a counterfactual analysis based on the
established research methodology. The idea behind the counterfactual impact evaluation is to match
two hypothetically identical individuals/companies, one with the treatment (support) and one
without it and to observe the effect of the particular intervention. Because we do not have two
identical individuals/companies, we use the matching procedures to make pairs with the most similar
characteristics. The estimated effect of the participation in the programme START (Average Treatment
Effect on the Treated—ATET) is thus quantified after the application of the matching procedures
(kernel matching, propensity score matching and nearest neighbour matching). Matching procedures
are implemented to connect with each of the supported firm (Treated) a partner non-supported firm
(Control) with the most possible similar characteristics that were based on the estimated propensity
score. The propensity score is quantified based on the results of the logistic regression estimating
the probability of the participation in the programme. Each pair is then matched based on the
characteristics before the programme started, and the average treatment effect on the treated (ATET)
is quantified after the programme ended, as a difference between the Treated firms and the Control
group. Implementation of the three different matching techniques serves as an additional robustness
check, since there are slight differences between the three matching techniques. Especially nearest
neighbour matching (matching firm that is closest in terms of the propensity score) is different from
the two previous techniques (based on weighted averages of nearly all of the available firms) and it is
usually employed to help in reducing bias [38,94–97].
3. Results
My empirical approach begins with the estimation of the logistic regression needed for a
calculation of the propensity score, then the different matching procedures are applied and finally the
results are interpreted in the line of existing research [
98
]. All of the calculations were made in the
software STATA 14. Estimated logistic regression is presented in Table 2. The dependent (outcome)
variable in the model was the probability of the participation (Treated) in the programme START, and as
explanatory variables (covariates) were used Year of Registration, Company Size, Sector and Region
applied commonly in the previous empirical studies, e.g., [
9
]. The model fit is quite good, the Pseudo
R-Squared informs us that the model was able to explain 36% of the variability of the dependent
variable. Despite that all coefficients were not found to be statistically significant, the covariates are
kept in the model in order to calculate the most appropriate propensity score [
98
]. The model supported
the previously presented descriptive evidence. Likelihood of the participation in the programme
START is higher for younger companies, enterprises doing business in Pardubickýregion, and when it
comes to a sector, the higher probability was observed for firms in services, manufacturing or motor
vehicles sectors.
Table 2. Robust Logistic Regression Used for Calculation of the Propensity Score.
Variable Coefficient Std. Error P > z
Year of Registration 0.18 *** 0.01 0.00
Region Praha (omitted)
Region Jihomoravský−1.03 *** 0.39 0.01
Region Jihoˇceský0.28 0.41 0.48
Region Karlovarský−0.25 0.69 0.71
Region Královéhradecký−0.18 0.41 0.66
Region Liberecký−0.30 0.59 0.71
Region Moravskoslezský−0.60 0.40 0.14
Region Olomoucký0.09 0.44 0.84
Region Pardubický0.74 * 0.40 0.07
Region Plzeˇnský−0.34 0.49 0.49
*** stat. significance of 1%, ** on 5%, * 10%., (omitted) refers to a reference category or to a category with
no observations.
Sustainability 2017,9, 2293 8 of 17
Table 2. Cont.
Variable Coefficient Std. Error P > z
Region Stˇredoˇceský0.24 0.41 0.56
Region Vysoˇcina 0.15 0.48 0.75
Region Zlínský0.53 0.41 0.20
Region Ústecký(omitted)
Micro −2.34 *** 0.24 0.00
Small −5.14 *** 0.38 0.00
Medium (omitted)
Large (omitted)
Administrative and Support Service Activities −0.77 0.95 0.41
Transportation and Storage −0.66 0.76 0.39
Information and Communication 0.06 0.73 0.94
Arts, Entertainment and Recreation (omitted)
Other Service Activities 1.81 *** 0.58 0.00
Financial and Insurance Activities 0.57 0.97 0.56
Professional, Scientific and Technical Activities 1.53 *** 0.59 0.01
Construction 0.95 * 0.58 0.10
Mining and Quarrying (omitted)
Accommodation and Food Service Activities 1.11 * 0.60 0.07
Wholesale and Retail Trade, Repair of Motor Vehicles 1.37 *** 0.53 0.01
Public Administration and Defence (omitted)
Education −0.39 1.56 0.80
Electricity, Gas, Steam and Air Conditioning Supply 0.90 0.65 0.16
Human Health and Social Work Activities 1.58 *** 0.63 0.01
Agriculture, Forestry and Fishing 0.81 0.69 0.24
Manufacturing 1.50 *** 0.53 0.01
Water Supply, Sewerage, Waste management −0.52 0.84 0.53
Real Estate Activities (omitted)
Constant −362.99 *** 26.72 0.00
Wald chi2(32) 458.51 Number of Obs. 4715
Prob > chi2 0.0000 Pseudo R2 0.364
L. P. Likelihood −477.98
Given the fact that the selected covariates were found to be statistically significant when it comes
to the participation in the public programme START, I proceed with the estimation of the ATET with
the usage of the three matching techniques—propensity score matching (PSM), kernel matching,
and nearest neighbour matching. However, before I present the results after the matching, it is worth
having a look at the raw results as they occur in the sample. The outcomes are analysed as four-year
averages after the intervention was over (averages for years 2011–2014), allowing for me to discuss
particular effects of the programme. Initially, it looks like the supported firms reported a lower net
profit, lower return on assets (ROA), lower return on equity (ROE), lower sales, lower assets turnover,
and higher debt ratio, as can be seen in Table 3.
Table 3. Average Outcomes over the Years 2011–2014 before the Matching Procedures.
Outcome Net Profit Return on Assets Return on Equity
Group Control Treated Control Treated Control Treated
N 10,681 57 10,681 57 10,681 57
mean 6182.45 −112.497 1.48 −27.67 12.97 −70.87
min −5,613,094 −11,200 −264.48 −1624.55 −398.90 −3701.49
max 2,583,422 3008.75 256.67 33.56 387.37 499.41
Outcome Sales Assets Turnover Debt Ratio
Group Control Treated Control Treated Control Treated
N 10,681 57 10,681 57 10,681 57
mean 124,803.8 3715.8 0.82 0.43 67.60 743.85
min −2577 0 −0.05 0.00 −290.04 3.72
max 6.15 ×10758,995.5 132.79 4.22 399.47 37,250.63
Sustainability 2017,9, 2293 9 of 17
Obviously, the former results depicted in Table 3suffer from the large heterogeneity, and therefore
it is very useful to implement matching techniques to reduce the bias. Following the methodology of the
previous researchers, e.g., [
38
,
94
–
97
]. I used the three matching techniques mentioned above to achieve
the lowest possible bias between the Treated and Control groups. After the estimation of the propensity
score, I have checked the mean and median bias and I conclude that the matching procedures
substantially reduced the bias (for standardized percentage bias across covariates, see Figure A1
in Appendix A). Therefore, I am allowed to proceed with the interpretation of the estimated ATETs,
which are reported in Table 4. Out of the six indicators, the variable representing sales (and a natural
logarithm of sales—Log(Sales)—as a robustness check) was found to be the most statistically significant,
proving that when compared to the control group, firms that participated in the programme START
(Treated) reported on average lower sales during the period of four years after the participation in
the programme (2011–2014). The second statistically significant outcome variable, representing assets
turnover, confirmed that firms that participated in the programme START (Treated) reported on
average lower assets turnover during the period of four years after the participation in the programme
(2011–2014). The remaining indicators could not prove any statistically significant impact of the
programme. Noteworthy is that all three variables measuring a firm’s profitability (net profit, return on
assets, and return on equity) suggested a negative influence of the programme, regardless of the
applied matching technique. The variable representing debt ratio further suggested that firms that
were supported by the programme reported on average higher debt ratio in comparison with the
control group.
Table 4. Estimated Average Treatment Effect on the Treated (ATET) over the Years 2011–2014.
Outcome Variable Matching ATET Std. Error P > abs. Z N
Net Profit Nearest Neighbour (1) −525.137 342.190 0.125 9238
Net Profit PSM −665.338 510.174 0.192 4595
Net Profit Kernel −466.281 330.221 0.136 4595
Return on Assets Nearest Neighbour (1) −28.715 29.078 0.323 9238
Return on Assets PSM −29.068 22.731 0.201 4595
Return on Assets Kernel −20.666 36.692 0.573 4595
Return on Equity Nearest Neighbour (1) −84.544 69.570 0.224 9238
Return on Equity PSM −92.416 64.924 0.155 4595
Return on Equity Kernel −87.592 71.600 0.221 4595
Sales Nearest Neighbour (1) −7816.04 *** 3168.15 0.014 9238
Sales PSM −12,807.15 ** 6021.29 0.033 4595
Sales Kernel −16,402.37 *** 4168.82 0.000 4595
Log(Sales) Nearest Neighbour (1) −0.807 *** 0.080 0.000 9213
Log(Sales) PSM −0.883 *** 0.602 0.001 4573
Log(Sales) Kernel −0.982 ** 0.427 0.022 4573
Assets Turnover Nearest Neighbour (1) −0.770 *** 0.277 0.005 9238
Assets Turnover PSM −0.645 0.427 0.131 4595
Assets Turnover Kernel −0.512 *** 0.145 0.000 4595
Debt Ratio Nearest Neighbour (1) 677.685 657.62 0.303 9238
Debt Ratio PSM 675.783 717.82 0.346 4595
Debt Ratio Kernel 709.764 689.15 0.303 4595
Note: *** stat. significance of 1%, ** on 5%, * 10%., besides NN matching, bootstrapped standard errors with
100 replications were used.
4. Discussion
As I have already mentioned in the introduction, such an analysis has not been conducted in
the Czech Republic so far, and therefore the only remaining option is to compare the results with
the findings of scholars from abroad. For instance, Gubert and Roubaud [
84
] and Cowling and
Siepel [
85
] report a statistically significant positive impact of the participation in the programme on
the performance of the supported enterprises, which was not a case in this study. Presented results are
Sustainability 2017,9, 2293 10 of 17
also against the findings of Honjo and Harada [
79
], Zecchini and Ventura [
81
], or Garcia-Tabuenca and
Crespo-Espert [
48
], who are in favour of the positive impact of interventions on sales and profitability of
the supported companies. Obtained results can be compared with the findings of
Maggioni et al. [78]
or
Kösters [
83
], who were also unable to find positive outcomes on the performance of the supported
companies by the public policies. Concretely, Maggioni et al. [
78
] were unable to prove any impact of
the programme on sales and they also report a higher debt ratio for the treated firms, in comparison
with the control group. Furthermore, Kösters [
83
] report even higher failure rates for the group of
supported firms. Stated hypothesis (
H1
), assuming a better financial performance of the supported
(Treated) firms, in comparison with the control group, in terms of higher net profits, higher return on
assets (ROA), higher return on equity (ROE), higher sales and higher assets turnover, based on the
obtained results cannot be supported.
Despite the fact that there is a theoretical justification of the policies facilitating access to
the financial capital, based on the existence of information asymmetry and market imperfections,
the presented results fit more to the point of view of scholars, who are sceptical about the public
support of entrepreneurship, e.g., [
23
,
99
]. Companies that are supported by the programme START
do not seem to be new unicorns or high-growth enterprises [
100
] increasing the competitiveness of
the Czech economy. They even underperform the regular (non-supported) companies. Therefore,
the public policy does not seem to be fulfilling its main objective. Perhaps, the supported projects
would not have been normally supported by the regular-market based financial institutions due to
their higher risk of default or in-sufficient profitability, which cannot be appropriately assessed by the
public evaluators and representatives. The owners of companies might tend to use this opportunity
more than necessary and get even a higher amount of financial capital than they would have originally
needed, because of the favourable conditions that are provided by the government. This may decrease
their entrepreneurial alertness and it may lead to an increase in their risk acceptance rates. The more
risk-taking that firms are, the higher probability that there is a default. Decades ago, Stiglitz and
Weiss [
40
] described this behaviour as a moral hazard. In addition to the moral hazard, this might
also be a case of adverse selection problem, e.g., [
43
,
44
]. Therefore, the supported enterprises may
report even higher rates of the default and worse financial results when compared to the regular
non-supported companies, which is of course not favourable for the taxpayers who have to pay the
costs of the programme, e.g., [30,33,81,82].
5. Conclusions
The recent empirical evidence suggests that the facilitation of financial capital towards the new
entrepreneurs as a way to achieve higher entrepreneurial activity, higher economic growth, and higher
employment rates may work. This approach is theoretically justified by an existence of the “finance
gap”, which is caused by the presence of information asymmetries on the financial markets for small
and medium-sized enterprises (SMEs), which are unable to gain enough of financial capital for their
business activities, e.g., [
31
,
33
,
34
]. Allocation of the financial resources is often mediated through the
system of zero interest soft loans and credit guarantees provided by the governmental institutions.
However, the researchers also point out that it is necessary to evaluate the concrete programmes
implemented in the particular regions with respect to the local conditions. A well-designed programme
might boost the supported companies, nevertheless, the programme that is dominated by a moral
hazard and adverse selection problems might lead to a waste of public resources on the burden of
taxpayers. The outcomes of the programmes may, therefore, vary over the time and across the regions,
e.g., [9,20,30,39,90,101].
Evaluations of the entrepreneurship policies in the Central and Eastern European countries
have not received much research attention so far and therefore there is a substantial research gap,
which needs to be fulfilled, e.g., [
54
,
55
,
59
,
102
–
105
]. The main purpose of this article was to contribute
to this perceived research gap by the performance of an empirical evaluation of the programme
START, funded from the European Regional Development Fund (ERDF), which was providing credit
Sustainability 2017,9, 2293 11 of 17
guarantees and zero interest soft loans to the newly established entrepreneurs during the period of
years 2007–2011 in the Czech Republic. The total amount of funds allocated to the projects from the
public resources was 79.7 mil. CZK (3.1 mil. EUR), however, the projects received financial capital
worth 170.3 mil. CZK (6.7 mil. EUR) in total, since the loans and guarantees have been organized
within the partnerships with other market based financial institutions. Obtained results from the
evaluation may be used as a support material for the future policy adjustments, and as a retrospective
feedback for the local policy representatives [
106
–
108
]. The empirical analysis was conducted on the
firm level and investigated changes in the financial performance of the supported firms in comparison
with other non-supported firms. As for the methods, the counterfactual analysis was implemented.
The obtained findings could not support the hypothesis assuming a better financial performance
of the supported (Treated) firms, in comparison with the control group, in terms of higher profits,
higher return on assets (ROA), higher return on equity (ROE), higher sales, and higher assets turnover
four years after the end of programme. Supported companies reported on average lower sales and
lower return on assets, when compared to the control group. The remaining variables could not
prove any statistically significant impact of the programme. Indicators measuring firm’s profitability
(net profit, return on assets, and return on equity) suggested a negative influence of the programme
and the variable representing debt ratio further indicated that firms that were supported by the
programme reported on average a higher debt ratio in comparison with the control group. Moreover,
four years after the end of the programme 9% of the supported firms have already been listed as
economically inactive.
Nevertheless, the conducted analysis suffers from at least two limitations that need to be stated.
Firstly, the outcomes of the programme could have been analysed only on the smaller share of all
the supported firms due to the missing data. Since only 70% of the supported business companies
had available data for the evaluation, the presented findings might be also influenced by the sample
bias, which could be mitigated by having complete data for the supported companies. Secondly,
the supported companies were not matched with the rejected participants, who applied for the same
programme and who would have been the best control group for the analysis. Having rejected
participants as a second control group would have definitely increased the reliability of the empirical
analysis, e.g., [108,109].
Based on this empirical experience, there are many requests that need to be transferred to
policymakers. It looks like public authorities and external evaluators of the programme START failed
to support projects with a growth potential. From a theoretical perspective, adverse selection and
moral hazard have dominated the programme START. Therefore the public authorities should more
carefully inspect the cost structures of the projects and judge whether the amounts of requested funds
are adequate for the business intentions and to filter out requests that only aim to collect as much
funding as possible for the given favourable conditions [
40
,
43
,
44
,
110
]. Furthermore, Kaplan and
Strömberg [
111
] or Denis [
34
] argue, that entrepreneurs would less likely abandon businesses with
a high potential/value. Therefore, the sustainability and potential growth of the project proposals
should be more carefully assessed by the evaluation team. In order to increase the quality of the
evaluation process and due diligence of the project proposals, additional training of the evaluation
team might be useful. Nevertheless, there are researchers, e.g., [
42
], who doubt that public sector
employees could better or equally correctly screen the project proposals and to minimize information
asymmetries, when compared to the banks and regular market based financial institutions.
For future evaluations of the public programmes in Central and Eastern Europe, researchers need
to have reliable data, which may result from the narrow cooperation between the research community
and public authorities, e.g., [
16
]. Such cooperation would help policymakers to establish a set of
outcome indicators and pathways to their evaluation. Presented experience revealed that it is very
difficult to assess the data, which are not available. It is therefore impossible to assess employment
outcomes of the programme. Thus, it is necessary for policymakers, setting up the rules of the
programme, to include a reporting duty on the supported companies on a number of employees,
Sustainability 2017,9, 2293 12 of 17
or to extract the data from the social security system. Reporting duty also needs to be imposed on
financial variables and on all forms of entrepreneurship (e.g., self-employed/freelances), otherwise,
their data cannot be included in the evaluation, serving as a remedy to mitigate the threat of moral
hazard and adverse selection. Additionally, the list of rejected participants should be available for
research purposes as well. It would be excellent if the public authorities (in the case of this particular
study the Czech Ministry of Industry and Trade), would collect the key financial indicators (from the
balance sheets, profit and loss statements, cash flow, and employees reports) by themselves and based
on a mutual confidential agreement would facilitate the data to the particular research teams.
Follow-up research could investigate the potential effects of the public programmes on other outcome
variables. Interesting variables might be growth in employment, productivity, investment intensity,
and assets. Another suggestion might be to investigate outcomes of the programme with respect to
sectors to see, which sectors mostly benefit from the public interventions. Future research should also
concentrate on the collection of the best policy practices, in order to improve quality of the programmes,
facilitating financial instruments and access to financial capital.
Acknowledgments:
Author thanks to Ilan Alon, Ivana Blažková, Jan ˇ
Cadil, Rangamohan Eunni, Martin Lukeš
and to the three anonymous referees for their contributions to paper development. This research was funded
under the EU collaborative research project CUPESSE (Cultural Pathways to Economic Self-Sufficiency and
Entrepreneurship; Grant Agreement No. 613257).
Conflicts of Interest: The author declares no conflict of interest.
Appendix A
Table A1. Sectoral distribution of the supported companies (21 NACE categories, N= 176).
Sector Frequency (%)
Administrative and Support Service Activities 1.14
Transportation and Storage 1.70
Information and Communication 2.70
Arts, Entertainment and Recreation 0.00
Other Service Activities 10.23
Financial and Insurance Activities 1.14
Professional, Scientific and Technical Activities 6.25
Construction 10.23
Mining and Quarrying 0.00
Accommodation and Food Service Activities 5.68
Wholesale and Retail Trade, Repair of Motor Vehicles 19.32
Public Administration and Defence 0.00
Education 0.57
Electricity, Gas, Steam and Air Conditioning Supply 7.39
Human Health and Social Work Activities 5.68
Agriculture, Forestry and Fishing 3.41
Manufacturing 21.02
Water Supply, Sewerage, Waste management 1.70
Real Estate Activities 2.27
Sustainability 2017,9, 2293 13 of 17
Sustainability 2017, 9, 2293 13 of 17
Figure A1. Standardized percentage bias across covariates.
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