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

http://ec.europa.eu/regional_policy/sources/docgener/evaluation/evalsed/glossary/glossary_d_en.htm

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

http://www.nfu.hu/nft_i_operativ_programok

http://www.parlament.hu/irom38/04724/04724.pdf

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


Date: 08/09/08 Time: 19:34

Sample: 1 63210




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


Adjusted R-squared 0.015568 S.D. dependent var 61630502

S.E. of regression 61148887 Akaike info criterion 38.69558




24

Received grants in 2004:



Date: 08/09/08 Time: 19:26

Sample: 1 63317




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









Date: 08/09/08 Time: 19:35

Sample: 1 63317




CONSTANT 1594802. 350472.5 4.550435 0.0000

TANGIBLE ASSETS03 0.050820 0.012148 4.183402 0.0000


PART_DUM 30670581 4401694. 6.967903 0.0000







25




Date: 08/09/08 Time: 19:38

Sample: 1 63368




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








Date: 08/09/08 Time: 19:38

Sample: 1 63368




CONSTANT 913662.9 807402.2 1.131608 0.2578

TANGIBLE ASSETS04 0.061974 0.025337 2.446001 0.0144


PART_DUM 16254427 6995595. 2.323523 0.0202







26




Date: 08/09/08 Time: 19:26

Sample: 1 63293




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









Date: 08/09/08 Time: 19:41

Sample: 1 63293




CONSTANT -1649754. 416937.0 -3.956842 0.0001

TANGIBLE ASSETS04 0.102237 0.013073 7.820710 0.0000


PART_DUM 21762181 3938959. 5.524856 0.0000







27




Date: 08/09/08 Time: 19:44

Sample: 1 63415




CONSTANT 777349.1 531494.2 1.462573 0.1436

TANGIBLE ASSETS05 0.061562 0.015645 3.935049 0.0001


PART_DUM 13683806 4303595. 3.179622 0.0015










Date: 08/09/08 Time: 19:30

Sample: 1 63230




CONSTANT 791966.4 532679.6 1.486759 0.1371

TANGIBLE ASSETS05 0.061247 0.015684 3.904953 0.0001


PART_DUM 29639433 4726693. 6.270650 0.0000







28

II. Annex - The results of the propensity score

matching


Dependent Variable: (NS05-NS04)-(NS05_C-NS04_C)


Date: 08/24/08 Time: 14:06

Sample: 1 143




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




Log likelihood -2921.702 F-statistic 6.519014


Dependent Variable: (TA04-TA03)-(TA04_C-TA03_C)


Date: 08/24/08 Time: 14:11

Sample: 1 143




CONSTANT 14151695 4358759. 3.246726 0.0015

TA03-TA03_C 0.138971 0.098577 1.409771 0.1608







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.

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Date: 08/24/08 Time: 15:57

Sample: 1 208




CONSTANT 12516964 7644054. 1.637477 0.1031

NS04-NS04_C -0.895246 0.457579 -1.956484 0.0518









Date: 08/24/08 Time: 15:58

Sample: 1 208




CONSTANT 17108470 2758427. 6.202257 0.0000

TA03-TA03_C -0.000758 0.023778 -0.031898 0.9746


Adjusted R-squared -0.004851 S.D. dependent var 41043269






30




Date: 08/24/08 Time: 16:50

Sample: 1 233




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.








Date: 08/24/08 Time: 16:51

Sample: 1 233




CONSTANT 4463430. 6229511. 0.716498 0.4744

TA04-TA04_C 0.076111 0.047657 1.597072 0.1116








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Date: 08/24/08 Time: 16:54

Sample: 1 197




CONSTANT 5486371. 9076453. 0.604462 0.5462

NS05-NS05_C -0.461904 0.112071 -4.121544 0.0001









Date: 08/24/08 Time: 16:56

Sample: 1 197




CONSTANT 18044461 3493057. 5.165807 0.0000

TA04-TA04_C -0.159925 0.041346 -3.867953 0.0001








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Date: 08/24/08 Time: 17:00

Sample: 1 248




CONSTANT 14415077 5506749. 2.617711 0.0094

TA05-TA05_C -0.006963 0.118212 -0.058902 0.9531


Adjusted R-squared -0.003804 S.D. dependent var 92798625








Date: 08/24/08 Time: 17:02

Sample: 1 156




CONSTANT 18813554 4145545. 4.538258 0.0000

TA05-TA05_C 0.056538 0.054970 1.028532 0.3053







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