Impact evaluation of grants for SME modernization in the ...
Transcript of Impact evaluation of grants for SME modernization in the ...
National Development Agency
Impact evaluation of grants for SME modernization in
the framework of National Development Plan 2004-
2006
Attila Béres 01/2009
Table of Contents 1. Introduction............................................................................................. 3 2. The description of the assistance ................................................................ 5 3. Methodology ............................................................................................ 6
3.1 Data preparation ................................................................................. 6 3.2 Models without matching .................................................................... 10 3.3 Models with (Propensity Score) Matching .............................................. 13
4. Results.................................................................................................. 15 4.1 Results of the models without matching ................................................ 15 4.2 Results of the models with matching .................................................... 16
5. Conclusions ........................................................................................... 20 References ................................................................................................ 22 Annexes – The results of the regressions ....................................................... 23
I. Annex - The difference in differences models..................................... 24 II. Annex - The results of the propensity score matching...................... 29
2
1. Introduction1
Hungary received a considerable amount of EU funds for the 2004-2006 period in
the framework of the National Development Plan (NDP). One of the most important
objectives of the NDP was the assistance of the domestic small and medium sized
enterprises. SMEs received support mostly in the form of non repayable grants from
the Economic Competitiveness Operational Programme (ECOP). The most popular
component of the ECOP (in terms of the number of applicants) was the 2.1.1
Development of the technical and technological background of small and medium-
sized enterprises. Through this component of the ECOP altogether almost 42 billion
HUF of grant was approved to around 3500 SMEs. The objective of this component
was the modernization of the SME sector through “assisting the purchase of new
machines, equipment and construction, enlargement of buildings related to
production, (…) the introduction of high technology and the spreading and
application of innovative procedures”2.
In this analysis the impact of the ECOP 2.1.1 component on the investments and
the growth of SMEs which applied for grant were examined.
The focus of the analysis was twofold:
1. to test the impact evaluation tools on the EU co-financed supports,
2. to evaluate the performance of SMEs receiving (or applying for) grants in
comparison to those not receiving (nor applying for) grants in the framework
of the programme.
The evaluation of financial assistance provided for SMEs has been introduced very
recently in Hungary thus there is not much literature on the topic yet. The mid-
term evaluation of the Economic Competitiveness Operational Programme3 is one of
the most quoted studies, which states that approximately 80-90% of SMEs
supported would have implemented the investment even without public resources.
This suggests that only considerably small part of the available funds was
1 I would like to thank the Hungarian Ministry of Justice and Law Enforcement for providing the necessary firm level data for the analysis. I would also like to thank to dr. Tamás Tétényi (National Development Agency), Gábor Kézdi (Central European University), Gábor Balás and Ákos Szalai (kormanyzas.hu), the Managing Authority of the Economic Development OperationalProgramme (MAEDOP), András Kaszap (KPMG) and Zoltán Bendó (former head of unit of the MAEDOP) for their contributions to this work. 2 Ministry of Economy and Transport (2003), p. 82. 3 KPMG-PWC-Fitzpatrick (2006), p. 6.
3
successful in the sense that it provided for additional investment and growth
possibilities to the small and medium-sized enterprises.
Some economists argue that this kind of assistance does not contribute to faster
growth and better performance of enterprises; on the contrary, they believe that
SMEs will rather concentrate on acquiring grants instead of elaborating and
implementing a development strategy for performing better on the market4.
To examine the effectiveness of the assistance in the short run5, two variables were
selected from the financial statements and the balance sheets of the SMEs, as
follows:
1) tangible assets and
2) net sales.
Two methods were applied to carry out the analysis:
difference in differences (DiD) without matching and
DiD with (propensity score) matching.
Based on both of the methods a significant positive effect of grants on providing
extra investments is observed, though the degree was different, but neither
methods could verify that this extra investment contributed to the generation of
extra growth of SMEs. This phenomenon can be the result of the relatively short
time period of the available data.
Through the analysis it has also been found that many of those SMEs which applied
for but did not receive grants also implemented their intended investments. This
result supports the findings of the mid-term evaluation of ECOP and suggests that
SMEs having received grant from the NDP would probably have implemented some
extra investments without public resources as well.
4 See e.g. Váradi, Balázs (2006) 5 As the programme started in 2004, there is not enough experience available to examine the long run effects.
4
2. The description of the assistance
The analysis focused on the Economic Competitiveness Operational Programme
(ECOP), component 2.1.1 (Development of the technical and technological
background of small and medium-sized enterprises) for the 2004-2006 period.
Through the assistance, SMEs could finance purchasing different kinds of assets
(either tangible or intangible) or building premises. Most of the SMEs bought
machinery to expand their production.
ECOP 211 in figures:6
− maximum grant: 25 million HUF (around 100 000 €)
− private contribution: min. 50%
− more than 9000 applicants
− more than 3500 beneficiaries
− total assistance approved: 42 billion HUF (around 170 million €)
− average assistance per project: 12 million HUF (around 50 thousand €)
− average project cost: 32 million HUF (around 130 thousand €)
6 Data from 2004-2006.
5
3. Methodology
Two different methods were applied to carry out the analysis:
1. difference in differences (DiD) without matching and
2. DiD with (propensity score) matching.
3.1 Data preparation
Two datasets formed the basis of the analysis:
1) dataset of the Ministry for Justice and Law Enforcement: annual financial
statements and balance sheets of the double-entry book-keeping Hungarian
enterprises (2002-2006),
2) dataset of the National Development Agency (NDA): grants approved to
SMEs (2004-2006).
The first 8 digit of the taxation number was used as the identifier of enterprises.
While one of the most important objectives of this analysis was to test the
methodology, to keep the analysis as simple as possible the following enterprises
were removed from the database:
− that had false taxation number;
− that did not prepared their reports for a whole calendar year (from 1st
January to 31st December);
− that had no data on the relevant variables in any year from 2002-2006;
− that prepared their financial statement and balance sheet in a foreign
currency (other than HUF);
− that were not eligible for the funding based on their 4 digit branch code
(TEÁOR 2003);
− that had negative net sales or tangible assets in any year from 2002-2006;
− that had net sales over 12.5 billion Ft (around 50 million €) which was set as
an eligibility criteria in the call for applications.7
From the treated dataset those SMEs were removed which applied for the
assistance more than once from the relevant component (ECOP 2.1.1).8 The control
7 Law on Small and Medium Sized Enterprises (2004. évi XXXIV. törvény a kis- és középvállalkozásokról, fejlődésük támogatásáról) determines the upper threshold to be an SME in 50 million EUR, which is around 12.5 billion HUF.
6
group included only those SMEs which did not apply for grants from the
component.9
Seven subgroups were created for both analyses:10
1-3.: those SMEs which applied for the grants but did not win the assistance (three
different subgroups for the three years: 2004-2006),
4-6.: those SMEs which received assistance in one of the three years (three
different subgroups for each year),
7.: those SMEs which did not participate in the application process (one control
group).
In these 7 subgroups there were altogether around 65 thousand SMEs, which is a
relatively large sample representing about 8% of all the Hungarian SMEs. However
the results should be interpreted carefully as only the double entry book-keeping
firms were in the sample.11
Table 1 – Number of SMEs in the database after the selection
Based on the balance sheets and the financial statements of these SMEs from
2002-2006, the impact of the assistance on the investments through the changes in
the tangible assets12, and the impact on the growth through the changes in the net
sales was analyzed.
In the next figure one can find the average values of variables of SMEs in the
dataset. It is clear, that enterprises that participated in the programme were larger
8 It was assumed that the application for funds had some impact on even those enterprises which applied for but did not receive grants, not to loose all the efforts made to the preparation for investments (making financial plans and calculating costs and benefits of the investment, and choosing suitable equipments, etc.) 9 Determining the control group raised some difficulties, because there are many other types of SME supports schemes existing in Hungary (other forms of grants, microcredits for investments, current assets credit, etc.). As data were not available for enterprises being applicants in those support schemes, they could not be excluded from the control group. 10 There was not overlap between the different subgroups: one SME belonged only in one subgroup. 11 The Hungarian law on accountancy ordered most of the enterprises to use double entry book keeping from 2004, but before this date, more enterprises used the simple method. 12 Though the assisted SMEs could have purchased intangible assets, according to the experience of the managing authority it was not typical.
7
8
and grew faster even before programme participation. The participating SMEs were
3-4 times larger in terms of the net sales and the tangible assets. This means that
there existed a selection bias in the tendering process towards the bigger and more
successful companies.
Figure 1 – Averages of the variables of interest in the basic dataset
3.2 Models without matching
The difference in differences is a very powerful method in evaluating public policy
programmes13, while comparing target variables of the same firm in different time
periods, there is no need to have control for variables constant through time (at
least in the short run), like geographical location, sector, size, etc.
To examine the impact of the grants on the input side (tangible assets), the
following model was applied:
ititititit upartDabsvalabsvalgrowth ,,32
,12,110, _)( ++++= −− ββββ
Where
growth: is the growth in absolute value in the relevant variable (tangible assets);
absval: is the absolute value of the relevant variable (tangible assets) one year
before the grant was approved;
D_part: is a dummy variable, which is 1 if the SME applied for grant (either
receiving or not receiving it) and 0 otherwise;
u: is the error term;
i: represents the ith SME and
t: represents the year of the approval.
There was some advance payments to projects, but generally grants are paid to
SMEs after the whole project or a certain part of the project is realized and
payment application is approved. In many cases enterprises launched the project
right after the approval and did not wait for contracting. The reason behind this was
that for banks (in case there is need for SMEs to take out a loan for implementing
the project) the documentation proving approval is enough for starting necessary
financing. While the assisted enterprises purchased mostly machinery and
equipment for 30-40 million Ft, it was assumed that investments were realized (the
growth in tangible assets) in the year of approval (for those applicants, who were
assisted in November and December, the data for the following year was applied).
The next assumption was that larger enterprises invested more (positive beta1
coefficient).
13 See e.g. Wooldridge (2006) p. 467-470.
The quadratic of absval represented the assumption that the marginal effect of the
size is decreasing (negative beta2 coefficient).
The impact of the grant on the investments is represented by beta3. This coefficient
means that the applicants invested this amount more than they would have done
without the assistance.
To examine the effect of the grants on the growth (in terms of net sales) of the
SMEs, the following model was used:
ititititit upartDabsvalabsvalgrowth ,,32
,2,10,1 _)( ++++=+ ββββ
Where
growth: represents the growth in absolute value in the relevant variable (net
sales);
absval: is the absolute value of the relevant variable (net sales) in the year of
project approval;
D_part: is a dummy variable, which is 1 if the SME applied for grant (either
receiving or not receiving it) and 0 otherwise;
u: is the error term;
i: represents the ith SME and
t: represents the year of the approval.
Assumptions:
1. the effect of the assistance was realized one year after the approval of the
project by the managing authority;
2. larger enterprises have better possibilities for growth, at least in absolute
terms (the beta1 coefficient is positive);
3. these growth possibilities decrease on the margin for larger SMEs (a
quadratic form of absval was applied).
In the regression beta3 (the coefficient of the participation dummy) represents the
impact of the assistance.
As absolute values were applied, heteroskedasticity (larger SMEs grew more in
absolute terms) and serial correlation had to be handled in both cases. Therefore,
11
Newey-West heteroskedasticity and autocorrelation consistent standard errors and
covariances were applied.
12
3.3 Models with (Propensity Score) Matching
Propensity score matching (PSM) based models are rather new methods in
programme evaluation.14 The basic idea behind PSM is finding suitable pairs for
entities being treated (enterprise applying for grant, enterprise receiving grant)
from a control group. The pairs should be chosen in a way that the two entities are
as similar in terms of different kinds of variables to each other at the beginning of
the programme as possible. The comparison of the performance of pairs is made
after the treatment (application for grant, receiving grant). The average of the
differences gives the average treatment effect (ATE).
Figure 2 – An example of the average treatment effect (ATE)
The performance of the impact assessment depends strongly on how successful was
the selection of the pairs.
The initial dataset was the same as for the models without matching. A logistic
regression was run on the participation dummy with the values of the tangible
assets and net sales before the programme participation: 14 On propensity score based methods se e.g. Wooldridge (2002), p. 614-621.
13
Table 2 – Control variables for the propensity score
Independent variable: participation dummy.
Note: the regressions for the 2004 applicants, who did not received assistance is different from the 2004
assisted enterprises.
For example if an SME applied for but did or did not receive grants in 2004, the net
sales in 2004 and the tangible assets in 2003 were chosen as dependent variables.
For a specific SME, the substitution of the values of the explanatory variables, the
result of the regression provides the probability of the application or the assistance.
The treated and the control pairs have been chosen based on this probability. A
certain threshold (2*10-7) in the difference in variables was applied in order to
identify suitable pairs of SMEs. Another necessary condition for creating the pairs
was that enterprises had the same main branch code. As a treated SME often
matched with many controls, the pairs for the treated entities were created as the
averages of the variables of the relevant controls.15
For the matched pairs the difference of the growth difference between the treated
and the control entities’ variables was regressed against a constant and the
difference of the absolute values (for the net sales in the year of project approval
and for the tangible assets the previous year).16
15 Thus the pairs were formed from treated and fictitious entities. 16 This is a combination of the PSM and the DiD to control for the size of the companies. See Annex II.
14
4. Results
4.1 Results of the models without matching
The difference in differences model without matching showed significant positive
effects of the grants in the growth of tangible assets, both for enterprises receiving
and applied for but not receiving grants. For the net sales, the regression showed
significant differences only at the enterprises receiving grants and those applying
for but not receiving grants in 2005 (at 10% and at 5% level respectively).
Table 3 - Average differences in the growth of the variables and the
support data
Planned/Approved Net Sales Tangible Assets Project Cost Grants received
Not won 2004 15 261 039 Ft 27 894 098 Ft 25 199 315 Ft - Won 2004 21 380 714 Ft 30 670 581 Ft 28 484 871 Ft 12 694 308 Ft Not won 2005 18 708 300 Ft 16 254 427 Ft 30 579 028 Ft - Won 2005 44 401 979 Ft 21 762 181 Ft 34 573 444 Ft 12 752 685 Ft Not won 2006 - 13 683 806 Ft 25 478 564 Ft - Won 2005 - 29 639 433 Ft 37 880 486 Ft 12 531 055 Ft significant at 5% level significant at 10% level
These results indicate that:
1. grant were successful in the sense that it generated extra investments;
2. those SMEs, which applied for grants, but did not receive any, implemented
some of their planned investments anyway (though the amount was smaller
than the planned project cost);
3. the effect of the grants on growth (of net sales) is not straightforward; at
5% the difference in growth was only significant for the 2005 winners (the
2006 participation could not be examined because of the lag in data
availability).
Comparing the total project cost and the approved assistance for the assisted
SMEs, 1 HUF of assistance should have had to generate 2.63 HUF of extra
investment. But comparing the difference in the investments of the treated and the
non-treated, 1 HUF of assistance generated only 2.16 HUF of extra investment. This
means that the impact on the investment level is somewhat smaller than the
assisted companies reported.
15
According to the tendering data, the assisted enterprises’ own contribution to the
projects was around 20.2 billion HUF altogether, so the companies invested this
amount from their own financial resources. However the analysis showed that the
difference in the investments between the treated and the control SMEs was only
14.3 billion. This means that according to the non-matched model the so called
“dead weight loss” is around 29%.17
4.2 Results of the models with matching
The database was somewhat smaller for the matching as for the previous method
while only the most suitable control SMEs were used, and because some treated
SMEs did not have a pair:
Table 4 – The number of SMEs in the treated and control groups (PSM)
Number of SMEs in the treated group
Number of SMEs in the control group
Not won 2004 143 7 794 Won 2004 208 6 077 Not won 2005 233 6 960 Won 2005 197 3 800 Not won 2006 248 5 070 Won 2005 156 3 052
As for some treated SMEs many control pairs were suitable for matching, the
weighted averages of these were applied as the controls.
17 The European Commission defines the dead weight loss as follows: „Change observed among direct beneficiaries following the public intervention, or reported by direct addressees as a consequence of the public intervention, that would have occurred, even without the intervention. http://ec.europa.eu/regional_policy/sources/docgener/evaluation/evalsed/glossary/glossary_d_en.htm
16
The results of the matching were the following:
Figure 3 - – Averages of the variables of interest in the matching model
17
In the previous figures it can be seen that for the tangible assets the difference
between the control and the treated SMEs, in the year of the assistance becomes
larger. This result is in line with the theory (compare with figure 2).
After the matching, a difference in differences model was applied to calculate the
difference in the growth of relevant variables of the control and the treatment
groups (for tangible assets in the year of assistance, and for the growth in net sales
the following year). The explanatory variables were the difference in the absolute
values of the relevant variables (for tangible assets in the year of assistance, and
for the growth in net sales the following year). The impact of the assistance was
represented by the constant of the regression (see Appendix II). Besides the
coefficients and the constant, the average project costs and the average assistance
are also represented in the next table. (Note: the planned and the approved project
costs and the average assistance differ from the number in table 3, while some
applied and assisted SMEs did not have a suitable pair, so those were left out of the
analysis.)
Table 5 – Average differences in the growth of the variables and the
support data (in thousand HUF)
Planned/Approved Net Sales Tangible Assets Project Cost Grants received
Not won 2004 -9 765 14 152 23 566 - Won 2004 12 517 17 108 25 992 11 721 Not won 2005 -5 074 4 463 25 663 - Won 2005 5 486 18 044 31 352 11 576 Not won 2006 - 14 415 23 123 - Won 2005 - 18 814 34 893 11 682
significant at 5% level significant at 10% level
The results show a similar picture as the model without matching (though the
differences between the variables of the control and treated SMEs were much
smaller):
1. grants were successful in the sense that it generated extra investments;
2. those SMEs, which applied for grants, but did not receive any, implemented
some of their planned investments anyway (though the amount was smaller
than the planned project cost);
3. the model did not find significant impact on growth (of net sales).
18
Comparing the differences in the tangible assets of the assisted and the control
groups, the analysis showed that 1 HUF of assistance generated 1.54 HUF of extra
investments on average. This impact is much smaller than the impact shown in the
project documents, where 1 HUF of assistance generated 2.6 HUF of extra
investment (total project cost) on average.
According to the data of the project documents, the assisted enterprises’ own
contribution to the projects was around 10.5 billion HUF altogether. (Note: this
number is much smaller than for the non-matched model, while some SMEs had to
be left out from the analysis, while some treated companies did not have a control
pair.) The results of the analysis showed that the real impact was much smaller;
the extra investment less the assistance was only around 3.5 billion HUF, which
indicates a 67% dead weight loss. (This result is in line with the results of the mid-
term evaluation of the ECOP, which stated that around 80% of the assisted SMEs
would have implemented their projects even without the assistance.)
The different outcomes of the two models implicate that different approaches can
result in very different implications. According to the literature, the matching
models provide smaller, though more reliable results,18 while if one can find proper
pairs, these models can catch the changing characteristics of the economic entities.
18 See e.g. Lalonde (1986), Dehejia & Wahba (1999), or Smith & Todd (2004). These studies based upon the comparison of randomized trails with DiD and matching.
19
5. Conclusions
In the analysis the impact of an EU co-funded component of the Economic
Competitiveness Operative Programme for SMEs grants for modernization
implemented in the framework of National Development Plan in Hungary for the
2004-2006 period was examined with two different approaches:
1. difference in differences models without matching and
2. diff-in-diffs with (propensity score) matching.
The results showed that:
− these impact evaluation techniques can be successfully applied for the EU
co-financed programmes,
− the necessary data for the analysis is available from the monitoring system
of the ECOP, and from other governmental databases and
− without these techniques the impacts of the assistance can not be estimated
precisely.
The analysis had two main conclusions regarding the impacts of the assistance:
1. grants generated extra investments for the assisted SMEs,
2. these extra investments did not result in a faster growth (at least in the
short run19).
The two models resulted in different impacts on the investments:
− the non-matched model resulted that the assisted SMEs would have
implemented around 30% of their investments even without the assistance,
− the matched diff-in-diffs model resulted that assisted SMEs would have
implemented around 70% of their investments without the assistance.
The related literature concluded that the matching models are more precise in
estimating the impacts.
The analysis has found two more important results:
− those SMEs, which applied for the grants were bigger and grew faster even
before the assistance (or the application),
19 While it is possible that the impact of the assistance on growth can be observed only in the long run, the analysis has to be repeated with longer time series.
20
− the call for tenders generated extra investments at those SMEs which
applied for but did not receive any assistance.
21
References
Dehejia, R., Wahba, S. (1999): Causal effects in nonexperimental studies:
reevaluating the evaluation of training programs. Journal of the American
Statistical Association 94, 1053–1062.
Ministry of Economy and Transport (2003): Economic Competitiveness
Operational Programme, http://www.nfu.hu/nft_i_operativ_programok .
Ministry of Economy and Transport (2007): J/4724. számú JELENTÉS a kis- és
középvállalkozások 2005-2006. évi helyzetéről, gazdálkodási
feltételrendszeréről, valamint a kis- és középvállalkozások részére nyújtott
támogatásokról, http://www.parlament.hu/irom38/04724/04724.pdf .
Kállay, László (2002): Paradigmaváltás a kisvállalkozás-fejlesztésben,
Közgadasági Szemle, XLIX. évf., 2002. július-augusztus (557 - 573. o.).
KPMG-PricewaterhouseCoopers- Fitzpatrick Associates (2006): A Gazdasági
Versenyképesség Operatív Program (GVOP) közbenső értékelése, 9. KÖTET
PROJEKTZÁRÓ DOKUMENTUM V2.0.
Lalonde, R. (1986): Evaluating the econometric evaluations of training programs.
American Economic Review 76, 604–620.
OECD (2004): Evaluation of SME Policies and Programmes, összefoglaló: 2nd
OECD Conference of Ministers Responsible for Small and Medium-Sized
Enterprises (SMEs), Istanbul, Turkey 3-5 June 2004.
Smith, J., Todd, P. (2004): Does matching overcome Lalonde’s critique of
nonexperimental estimators. Journal of Econometrics.
Váradi, Balázs (2006): Miért folyik a csata? Avagy a 8000 milliárd átka, Élet és
Irodalom 2006/44.
Wooldridge, Jeffrey M. (2002): Econometric Analysis of Cross Section and Panel
Data, 2002, MIT
Wooldridge, Jeffrey M. (2006): Introductory Econometrics, 3rd Edition
22
Annexes – The results of the regressions
23
I. Annex - The difference in differences models
Applied for grants in 2004 but did not receive any:
Dependent Variable: NET SALES05-NET SALES04
Method: Least Squares
Date: 08/09/08 Time: 19:25
Sample: 1 63210
Included observations: 63210
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT -6924622. 3698089. -1.872487 0.0611
NET SALES04 0.156470 0.040152 3.896920 0.0001
NET SALES04^2 -3.35E-11 8.55E-12 -3.914876 0.0001
PART_DUM 15261039 14574589 1.047099 0.2951
R-squared 0.141806 Mean dependent var 7058844.
Adjusted R-squared 0.141765 S.D. dependent var 1.98E+08
S.E. of regression 1.83E+08 Akaike info criterion 40.88783
Sum squared resid 2.12E+21 Schwarz criterion 40.88840
Log likelihood -1292256. F-statistic 3481.329
Durbin-Watson stat 1.986519 Prob(F-statistic) 0.000000
Dependent Variable: TANGIBLE ASSETS04-TANGIBLE ASSETS03
Method: Least Squares
Date: 08/09/08 Time: 19:34
Sample: 1 63210
Included observations: 63210
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 1622184. 349268.0 4.644526 0.0000
TANGIBLE ASSETS03 0.050122 0.012124 4.134270 0.0000
TANGIBLE ASSETS03^2 -6.41E-12 1.80E-12 -3.553596 0.0004
PART_DUM 27894098 7844611. 3.555829 0.0004
R-squared 0.015615 Mean dependent var 3671269.
Adjusted R-squared 0.015568 S.D. dependent var 61630502
S.E. of regression 61148887 Akaike info criterion 38.69558
Sum squared resid 2.36E+20 Schwarz criterion 38.69616
Log likelihood -1222970. F-statistic 334.2013
Durbin-Watson stat 2.003400 Prob(F-statistic) 0.000000
24
Received grants in 2004:
Dependent Variable: NET SALES05-NET SALES04
Method: Least Squares
Date: 08/09/08 Time: 19:26
Sample: 1 63317
Included observations: 63317
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT -7106658. 3706401. -1.917401 0.0552
NET SALES04 0.157665 0.040179 3.924035 0.0001
NET SALES04^2 -3.35E-11 8.55E-12 -3.918406 0.0001
PART_DUM 21380714 22987755 0.930091 0.3523
R-squared 0.139544 Mean dependent var 7216052.
Adjusted R-squared 0.139503 S.D. dependent var 1.99E+08
S.E. of regression 1.85E+08 Akaike info criterion 40.90541
Sum squared resid 2.16E+21 Schwarz criterion 40.90599
Log likelihood -1295000. F-statistic 3422.589
Durbin-Watson stat 1.987716 Prob(F-statistic) 0.000000
Dependent Variable: TANGIBLE ASSETS04-TANGIBLE ASSETS03
Method: Least Squares
Date: 08/09/08 Time: 19:35
Sample: 1 63317
Included observations: 63317
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 1594802. 350472.5 4.550435 0.0000
TANGIBLE ASSETS03 0.050820 0.012148 4.183402 0.0000
TANGIBLE ASSETS03^2 -6.49E-12 1.81E-12 -3.589422 0.0003
PART_DUM 30670581 4401694. 6.967903 0.0000
R-squared 0.016866 Mean dependent var 3740850.
Adjusted R-squared 0.016820 S.D. dependent var 61359159
S.E. of regression 60840946 Akaike info criterion 38.68549
Sum squared resid 2.34E+20 Schwarz criterion 38.68606
Log likelihood -1224721. F-statistic 362.0612
Durbin-Watson stat 2.002308 Prob(F-statistic) 0.000000
25
Applied for grants in 2005 but did not receive any:
Dependent Variable: NET SALES06-NET SALES05
Method: Least Squares
Date: 08/09/08 Time: 19:38
Sample: 1 63368
Included observations: 63368
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT -1185354. 1221232. -0.970622 0.3317
NET SALES05 0.132710 0.014216 9.335450 0.0000
NET SALES05^2 -1.26E-11 3.47E-12 -3.618690 0.0003
PART_DUM 18708300 11073787 1.689422 0.0911
R-squared 0.043713 Mean dependent var 16446292
Adjusted R-squared 0.043668 S.D. dependent var 2.00E+08
S.E. of regression 1.95E+08 Akaike info criterion 41.01731
Sum squared resid 2.42E+21 Schwarz criterion 41.01788
Log likelihood -1299588. F-statistic 965.4834
Durbin-Watson stat 2.012070 Prob(F-statistic) 0.000000
Dependent Variable: TANGIBLE ASSETS05-TANGIBLE ASSETS04
Method: Least Squares
Date: 08/09/08 Time: 19:38
Sample: 1 63368
Included observations: 63368
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 913662.9 807402.2 1.131608 0.2578
TANGIBLE ASSETS04 0.061974 0.025337 2.446001 0.0144
TANGIBLE ASSETS04^2 -1.31E-11 6.06E-12 -2.163035 0.0305
PART_DUM 16254427 6995595. 2.323523 0.0202
R-squared 0.036113 Mean dependent var 3173793.
Adjusted R-squared 0.036068 S.D. dependent var 68179788
S.E. of regression 66938957 Akaike info criterion 38.87652
Sum squared resid 2.84E+20 Schwarz criterion 38.87710
Log likelihood -1231760. F-statistic 791.3366
Durbin-Watson stat 1.991166 Prob(F-statistic) 0.000000
26
Received grants in 2005:
Dependent Variable: NET SALES06-NET SALES05
Method: Least Squares
Date: 08/09/08 Time: 19:26
Sample: 1 63293
Included observations: 63293
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT -1387269. 1230848. -1.127084 0.2597
NET SALES05 0.134129 0.014284 9.390044 0.0000
NET SALES05^2 -1.26E-11 3.50E-12 -3.617104 0.0003
PART_DUM 44401979 15746618 2.819779 0.0048
R-squared 0.045105 Mean dependent var 16597992
Adjusted R-squared 0.045060 S.D. dependent var 2.01E+08
S.E. of regression 1.96E+08 Akaike info criterion 41.02553
Sum squared resid 2.43E+21 Schwarz criterion 41.02610
Log likelihood -1298310. F-statistic 996.5072
Durbin-Watson stat 2.012650 Prob(F-statistic) 0.000000
Dependent Variable: TANGIBLE ASSETS05-TANGIBLE ASSETS04
Method: Least Squares
Date: 08/09/08 Time: 19:41
Sample: 1 63293
Included observations: 63293
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT -1649754. 416937.0 -3.956842 0.0001
TANGIBLE ASSETS04 0.102237 0.013073 7.820710 0.0000
TANGIBLE ASSETS04^2 -1.03E-11 3.14E-12 -3.287076 0.0010
PART_DUM 21762181 3938959. 5.524856 0.0000
R-squared 0.066520 Mean dependent var 3213251.
Adjusted R-squared 0.066476 S.D. dependent var 67660649
S.E. of regression 65373078 Akaike info criterion 38.82918
Sum squared resid 2.70E+20 Schwarz criterion 38.82975
Log likelihood -1228804. F-statistic 1503.333
Durbin-Watson stat 1.991695 Prob(F-statistic) 0.000000
27
Applied for grants in 2006 but did not receive any:
Dependent Variable: TANGIBLE ASSETS06-TANGIBLE ASSETS05
Method: Least Squares
Date: 08/09/08 Time: 19:44
Sample: 1 63415
Included observations: 63415
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 777349.1 531494.2 1.462573 0.1436
TANGIBLE ASSETS05 0.061562 0.015645 3.935049 0.0001
TANGIBLE ASSETS05^2 -7.89E-12 2.23E-12 -3.541829 0.0004
PART_DUM 13683806 4303595. 3.179622 0.0015
R-squared 0.013224 Mean dependent var 3592517.
Adjusted R-squared 0.013177 S.D. dependent var 84749324
S.E. of regression 84189109 Akaike info criterion 39.33509
Sum squared resid 4.49E+20 Schwarz criterion 39.33566
Log likelihood -1247213. F-statistic 283.2508
Durbin-Watson stat 1.999687 Prob(F-statistic) 0.000000
Received grants in 2006:
Dependent Variable: TANGIBLE ASSETS06-TANGIBLE ASSETS05
Method: Least Squares
Date: 08/09/08 Time: 19:30
Sample: 1 63230
Included observations: 63230
Newey-West HAC Standard Errors & Covariance (lag truncation=16)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 791966.4 532679.6 1.486759 0.1371
TANGIBLE ASSETS05 0.061247 0.015684 3.904953 0.0001
TANGIBLE ASSETS05^2 -7.86E-12 2.23E-12 -3.529042 0.0004
PART_DUM 29639433 4726693. 6.270650 0.0000
R-squared 0.013554 Mean dependent var 3621125.
Adjusted R-squared 0.013507 S.D. dependent var 84717140
S.E. of regression 84143055 Akaike info criterion 39.33400
Sum squared resid 4.48E+20 Schwarz criterion 39.33457
Log likelihood -1243540. F-statistic 289.5775
Durbin-Watson stat 1.999912 Prob(F-statistic) 0.000000
28
II. Annex - The results of the propensity score
matching
Applied for grants in 2004 but did not receive any:
Dependent Variable: (NS05-NS04)-(NS05_C-NS04_C)
Method: Least Squares
Date: 08/24/08 Time: 14:06
Sample: 1 143
Included observations: 143
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT -9765460. 11605941 -0.841419 0.4015
NS04-NS04_C 1.545708 1.270848 1.216281 0.2259
R-squared 0.044191 Mean dependent var -14291833
Adjusted R-squared 0.037412 S.D. dependent var 1.86E+08
S.E. of regression 1.82E+08 Akaike info criterion 40.89094
Sum squared resid 4.67E+18 Schwarz criterion 40.93238
Log likelihood -2921.702 F-statistic 6.519014
Durbin-Watson stat 2.360032 Prob(F-statistic) 0.011736
Dependent Variable: (TA04-TA03)-(TA04_C-TA03_C)
Method: Least Squares
Date: 08/24/08 Time: 14:11
Sample: 1 143
Included observations: 143
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 14151695 4358759. 3.246726 0.0015
TA03-TA03_C 0.138971 0.098577 1.409771 0.1608
R-squared 0.119799 Mean dependent var 16715433
Adjusted R-squared 0.113556 S.D. dependent var 66578428
S.E. of regression 62684356 Akaike info criterion 38.75901
Sum squared resid 5.54E+17 Schwarz criterion 38.80045
Log likelihood -2769.269 F-statistic 19.19062
Durbin-Watson stat 1.919892 Prob(F-statistic) 0.000023
Note: NS05 stands for net sales of the treated group in 2005, NS05_C stands for the control group in 2005, TA04 stands for tangible assets of the treated group in 2004 and TA04_C stands for the tangible assets of the control group.
29
Received grants in 2004:
Dependent Variable: (NS05-NS04)-(NS05_C-NS04_C)
Method: Least Squares
Date: 08/24/08 Time: 15:57
Sample: 1 208
Included observations: 208
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 12516964 7644054. 1.637477 0.1031
NS04-NS04_C -0.895246 0.457579 -1.956484 0.0518
R-squared 0.121822 Mean dependent var 12999930
Adjusted R-squared 0.117559 S.D. dependent var 1.29E+08
S.E. of regression 1.21E+08 Akaike info criterion 40.06647
Sum squared resid 3.01E+18 Schwarz criterion 40.09857
Log likelihood -4164.913 F-statistic 28.57660
Durbin-Watson stat 2.095343 Prob(F-statistic) 0.000000
Dependent Variable: (TA04-TA03)-(TA04_C-TA03_C)
Method: Least Squares
Date: 08/24/08 Time: 15:58
Sample: 1 208
Included observations: 208
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 17108470 2758427. 6.202257 0.0000
TA03-TA03_C -0.000758 0.023778 -0.031898 0.9746
R-squared 0.000003 Mean dependent var 17094981
Adjusted R-squared -0.004851 S.D. dependent var 41043269
S.E. of regression 41142699 Akaike info criterion 37.91256
Sum squared resid 3.49E+17 Schwarz criterion 37.94465
Log likelihood -3940.906 F-statistic 0.000690
Durbin-Watson stat 1.911437 Prob(F-statistic) 0.979064
Note: NS05 stands for net sales of the treated group in 2005, NS05_C stands for the control group in 2005, TA04 stands for tangible assets of the treated group in 2004 and TA04_C stands for the tangible assets of the control group.
30
Applied for grants in 2005 but did not receive any:
Dependent Variable: (NS06-NS05)-(NS06_C-NS05_C)
Method: Least Squares
Date: 08/24/08 Time: 16:50
Sample: 1 233
Included observations: 233
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT -5074380. 10118838 -0.501479 0.6165
NS05-NS05_C -1.062929 0.622342 -1.707948 0.0890
R-squared 0.091573 Mean dependent var -1310414.
Adjusted R-squared 0.087641 S.D. dependent var 1.85E+08
S.E. of regression 1.77E+08 Akaike info criterion 40.82613
Sum squared resid 7.21E+18 Schwarz criterion 40.85575
Log likelihood -4754.244 F-statistic 23.28579
Durbin-Watson stat 1.979176 Prob(F-statistic) 0.000003
Dependent Variable: (TA05-TA04)-(TA05_C-TA04_C)
Method: Least Squares
Date: 08/24/08 Time: 16:51
Sample: 1 233
Included observations: 233
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 4463430. 6229511. 0.716498 0.4744
TA04-TA04_C 0.076111 0.047657 1.597072 0.1116
R-squared 0.005895 Mean dependent var 4845676.
Adjusted R-squared 0.001592 S.D. dependent var 93358973
S.E. of regression 93284652 Akaike info criterion 39.54876
Sum squared resid 2.01E+18 Schwarz criterion 39.57838
Log likelihood -4605.430 F-statistic 1.369821
Durbin-Watson stat 1.972085 Prob(F-statistic) 0.243049
Note: NS05 stands for net sales of the treated group in 2005, NS05_C stands for the control group in 2005, TA04 stands for tangible assets of the treated group in 2004 and TA04_C stands for the tangible assets of the control group.
31
Received grants in 2005:
Dependent Variable: (NS06-NS05)-(NS06_C-NS05_C)
Method: Least Squares
Date: 08/24/08 Time: 16:54
Sample: 1 197
Included observations: 197
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 5486371. 9076453. 0.604462 0.5462
NS05-NS05_C -0.461904 0.112071 -4.121544 0.0001
R-squared 0.412186 Mean dependent var 9008679.
Adjusted R-squared 0.409171 S.D. dependent var 1.70E+08
S.E. of regression 1.31E+08 Akaike info criterion 40.22493
Sum squared resid 3.33E+18 Schwarz criterion 40.25827
Log likelihood -3960.156 F-statistic 136.7374
Durbin-Watson stat 2.025904 Prob(F-statistic) 0.000000
Dependent Variable: (TA05-TA04)-(TA05_C-TA04_C)
Method: Least Squares
Date: 08/24/08 Time: 16:56
Sample: 1 197
Included observations: 197
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 18044461 3493057. 5.165807 0.0000
TA04-TA04_C -0.159925 0.041346 -3.867953 0.0001
R-squared 0.305792 Mean dependent var 17059826
Adjusted R-squared 0.302232 S.D. dependent var 54456148
S.E. of regression 45488587 Akaike info criterion 38.11392
Sum squared resid 4.03E+17 Schwarz criterion 38.14725
Log likelihood -3752.221 F-statistic 85.89566
Durbin-Watson stat 2.013844 Prob(F-statistic) 0.000000
Note: NS05 stands for net sales of the treated group in 2005, NS05_C stands for the control group in 2005, TA04 stands for tangible assets of the treated group in 2004 and TA04_C stands for the tangible assets of the control group.
32
Applied for grants in 2006 but did not receive any:
Dependent Variable: (TA06-TA05)-(TA06_C-TA05_C)
Method: Least Squares
Date: 08/24/08 Time: 17:00
Sample: 1 248
Included observations: 248
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 14415077 5506749. 2.617711 0.0094
TA05-TA05_C -0.006963 0.118212 -0.058902 0.9531
R-squared 0.000260 Mean dependent var 14396516
Adjusted R-squared -0.003804 S.D. dependent var 92798625
S.E. of regression 92974963 Akaike info criterion 39.54159
Sum squared resid 2.13E+18 Schwarz criterion 39.56992
Log likelihood -4901.157 F-statistic 0.063959
Durbin-Watson stat 2.046587 Prob(F-statistic) 0.800556
Received grants in 2006:
Dependent Variable: (TA06-TA05)-(TA06_C-TA05_C)
Method: Least Squares
Date: 08/24/08 Time: 17:02
Sample: 1 156
Included observations: 156
Newey-West HAC Standard Errors & Covariance (lag truncation=4)
Variable Coefficient Std. Error t-Statistic Prob.
CONSTANT 18813554 4145545. 4.538258 0.0000
TA05-TA05_C 0.056538 0.054970 1.028532 0.3053
R-squared 0.037242 Mean dependent var 19748042
Adjusted R-squared 0.030991 S.D. dependent var 42772092
S.E. of regression 42104107 Akaike info criterion 37.96193
Sum squared resid 2.73E+17 Schwarz criterion 38.00103
Log likelihood -2959.030 F-statistic 5.957186
Durbin-Watson stat 1.572399 Prob(F-statistic) 0.015791
33