metadata: authors: 0: author: Correa,Paulo Guilherme
1: author: Iootty De Paiva Dias,Mariana
count: Europe and Central Asia
docna: 0: docna: Will the crisis affect the economic recovery
            in eastern European countries ?  evidence from firm level data
docty: Policy Research Working Paper
subtopic: Private Sector Economics,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Private Sector Development Law,Marketing,Business Cycles and Stabilization Policies,Transport Services,Financial Sector Policy
teratopic: Private Sector Development,Macroeconomics and Economic Growth,Industry,Poverty Reduction,Finance and Financial Sector Development
abstracts: cdata!: Two sources of growth are firm learning
            and innovation. Using a unique panel data for 1,686 firms in
            six countries (Bulgaria, Hungary, Latvia, Lithuania,
            Romania, and Turkey), this paper applies panel data
            estimators and Juhn-Murphy Pierce decomposition in order to
            identify  the effects of the global economic crisis on sales
            growth of innovative and young enterprises in Eastern
            European countries. The results show that innovative and
            young firms were significantly more affected by the crisis
            than non innovative and older enterprises. The authors
            interpret these results as an indication that the
            achievement of pre-crisis growth rates in those countries
            may be difficult.
repnme: repnme: Will the crisis affect the economic
            recovery in eastern European countries ?  evidence from firm
            level data
display_title: Will the crisis affect the economic
            recovery in eastern European countries evidence from firm
            level data
listing_relative_url: /research/2010/04/12142134/crisis-affect-economic-recovery-eastern-european-countries-evidence-firm-level-data
topicv3: Private Sector Development m1326060 1921,Manufacturing Sectors m1326521 1281,Transport m1326661 135,Financial Sector Development m1327681 1920,Entrepreneurship m1582903 4424
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Private Sector Development,Macroeconomics and Economic Growth,Industry,Poverty Reduction,Finance and Financial Sector Development
no_of_pages: 30
author_keys: 0: author_key: 647928
1: author_key: 1051061
model_extractions: WPS5278 Policy Research Working Paper 5278 Will the Crisis Affect the Economic Recovery in Eastern European Countries? Evidence from Firm Level Data _Paulo Correa Mariana Iootty_ The World Bank Europe and Central Asia Region Private and Financial Sector Development Sector Unit April 2010
Policy Research Working Paper 5278

# **Abstract**

Two sources of growth are firm learning and innovation. Using a unique panel data for 1,686 firms in six countries (Bulgaria, Hungary, Latvia, Lithuania, Romania, and Turkey), this paper applies panel data estimators and Juhn-Murphy Pierce decomposition in order to identify the effects of the global economic crisis on sales growth of innovative and young enterprises in Eastern European countries. The results show that innovative and young firms were significantly more affected by the crisis than non innovative and older enterprises. The authors interpret these results as an indication that the achievement of pre-crisis growth rates in those countries may be difficult.

This paper—a product of the Private and Financial Sector Development Sector Unit, Europe and Central Asia Region—is part of a larger effort in the department to study the effects of the financial crisis on the corporate sector. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pcorrea@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **WILL THE CRISIS AFFECT THE ECONOMIC RECOVERY IN EASTERN EUROPEAN COUNTRIES? EVIDENCE FROM FIRM LEVEL DATA**<sup>**1**</sup>

## **Paulo Correa**

_Lead Economist, Finance and Private Sector Development, Europe and Central Asia Region, World Bank_

## **Mariana Iootty**

_Consultant, Finance and Private Sector Development, Europe and Central Asia Region, World Bank and Federal Rural University of Rio de Janeiro_

> 1 We would like to thank to Gerardo Corrochano (Sector Director, ECSPF), Lalit Raina (Sector Manager, ECSPF) and Sophie Sirtaine (Sector Manager, ECSPF) for their guidance. Donato de Rosa, João Pedro Azevedo, Murat Seker, Peter Harrold, Ulrich Zachau and Willem van Eeghen provided valuable suggestions on earlier draft of this paper. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **1 ­ Introduction**

One question of the current international debate particularly relevant to Eastern European countries refers to the potential drivers of the economic recovery. As international trade declines and financial conditions worsen, firm learning and innovation become more relevant sources of growth in the post‐crisis period. Conditional on size and survival rate, young firms are expected to grow faster than older firms, among other reasons, due to diminishing returns to learning (Klepper and Thompson (2007); Dunne, Roberts and Samuelson (1989)). Innovative firms are also expected to provide a positive contribution to economic growth, as innovation and R&D tend to positively affect firm‐ productivity and sales performance (Aw, Roberts and Xu (2008)).<sup>2</sup> In this sense, understanding how the crisis will affect innovative and young enterprises helps comprehends Eastern European growth prospects.

In this paper, we investigate the sales growth of innovative and young firms in the region, before and after the crisis. We apply two methods. First, we use panel‐data estimators in order to assess the difference of sales growth performance between innovative (young) and non‐innovative (older) companies, over the period, when controlling for firm characteristics. Second, we use the Juhn‐Murphy‐Pierce technique to decompose the difference in sales growth performance between those groups of firms, before and after the crisis, into: i) differences in the distribution of observed characteristics (the characteristics effect); ii) differences in the “market value” of such characteristics (the returns effect); and iii) differences in unobservable attributes and “market values” (the unexplained effect). This decomposition technique not only allows us to check the econometric findings but also helps us to assess the sources of sales differentials between those firms’ cohorts.

Panel data analysis shows that sales growth rate of innovative firms have declined more than that of non‐innovative companies, a result that is robust regardless the estimator applied, and the criteria used to categorize innovation (introduction of a

> 2 The micro‐macro link between firm‐level innovation, theories of aggregate technological change and growth can be found in Klette and Kortum (2004).

2
product/process, or development of R&D activities). Our analysis also points that the decrease of sales growth rate of younger firms was more severe than older companies. The decomposition exercise confirms that the positive difference of sales growth rate in favor of innovative and in favor of young companies, before the crisis, was reversed afterwards. For innovative companies, we found evidence that the reduction in the “premium” for the ability to innovate – which we interpret as Schumpeter’s “animal spirit” – after the crisis contributed to explain the worse performance of innovative firms. Conversely, in the case of young enterprises, results suggest that their “ability to learn” contributed to mitigate the negative impact of the crisis on sales growth performance. Overall, we interpret these results as indication that the achievement of pre‐crisis growth rates in those countries may be difficult _._ The paper is organized as follows. The next section describes the data used and exposes the empirical methodology applied. Section 3 presents results on the empirical analysis, and Section 4 summarizes the main results.

# **2 – Data and Methodology**

We use a unique panel data for 1,686 firms in six countries (Bulgaria, Hungary, Latvia, Lithuania, Romania, and Turkey) covering manufacturing, retail and other service sectors.<sup>3</sup> This panel results from the matching of two datasets. The first one is the World Bank’s Financial Crisis Survey (EFCS) ‐ implemented in June/July 2009 and designed to capture the effects of the crisis on key elements of the private economy: sales, employment, finances, and expectations about the future. The second one is the World Bank’s Enterprise Survey (ES) carried out in 2008. With an original sample that is stratified by firm size, sector and region and representative of the private nonagricultural formal economy in each country, the ES 2008 provides information on firm characteristics, various performance measures and the business environment, most of them refereed to fiscal year of 2007, the pre‐crisis scenario. The merger between the two datasets was possible since the EFCS covers a subsample of firms drawn from the set of firms interviewed in the ES 2008. As the participation in EFCS was voluntary and the entire original sample of the ES, which covered

> 3Turkey is the exception, where only the original manufacturing subsample was targeted.

3
2,499 firms, was contacted to determine whether these firms were still in existence or if they had failed and/or became inactive, the sampling weights of the ES 2008 were adjusted, based in the non‐response firms. These adjusted weights were then used in order to produce the estimations that are, therefore, representative for the nonagricultural private economy within each country.

We classify the firms into groups of: i) young and older firms; and ii) innovative or non‐innovative companies. To distinguish the age status of the firm, we define a dummy ������ for firms up to five years old in before (2007) the crisis. To distinguish the innovative status of the firm, we used as a criterion the introduction of new product or process in the period 2005‐2007, according to ES 2008.<sup>4</sup> As we do not have this information for 2009, we then define a dummy ����� that equals one if the firm has introduced a product/process in 2005‐2007 period, and repeat this value for the post‐crisis period.<sup>5</sup> Alternatively, we use as a criterion to classify innovative companies the development of R&D activities in 2005‐2007 period, also according to ES 2008.<sup>6</sup> Table 1 presents the estimated proportions of innovative and non‐innovative companies across countries, following the two criteria, while Table 2 shows the estimated proportions of firms by age. Non‐innovative firms as well as older firms make the largest groups in all countries.

> 4The question used in ES 2008 was “In the last three years, has this establishment introduced new products or services?”

> 5Therefore, even though we recognize that innovation process is not static over time, we work with an innovation category that is time invariant, as we assume that innovative firms in the precrisis scenario (2004‐ 2007) are potentially innovative after the crisis (2009).

> 6The question used in ES 2008 was “In the last three years, has this establishment invested in research and development (in‐house or outsourced)?”

4
**Table 1 – Proportions of innovative/noninnovative companies, by country**

||**Noninnovative**|**Innovative**||
|---|---|---|---|
||||**N Ob**|
|**Country**|**Did not introduce new product/process**|**Introduce of new product/process**|**. s**|
|Turkey|0.60|0.40|513|
|Romania|0.51|0.49|365|
|Hungary|0.66|0.34|187|
|Latvia|0.43|0.57|226|
|Lithuania|0.34|0.66|238|
|Bulgaria|0.50|0.50|149|
|Total|0.52|0.49|1,678|
|**Country**|**Did not invest in R&D **|**Invested in R&D **|**N. Obs**|
|Turkey|0.70|0.30|513|
|Romania|0.77|0.23|359|
|Hungary|0.90|0.10|187|
|Latvia|0.84|0.16|225|
|Lithuania|0.86|0.14|239|
|Bulgaria|0.63|0.37|150|
|Total|0.76|0.24|1,673|

Source: EFCS 2009‐ES 2008

**Table 2 – Proportions of young/older companies (before the crisis), by country**

|**Country**|**Young**<br>**(< =5 years old)**|**Older**<br>**(>=6 years old)**|**N.obs**|
|---|---|---|---|
|Turkey|0.11|0.89|514|
|Romania|0.20|0.80|370|
|Hungary|0.11|0.89|187|
|Latvia|0.17|0.83|226|
|Lithuania|0.25|0.75|239|
|Bulgaria|0.11|0.89|150|
|Total|0.11|0.89|1,686|

Source: EFCS 2009‐ES 2008 Using the innovation and age groups defined above, we examine a particular measure of firm performance: growth in sales. We calculate the sales growth rates before and after the crisis (Table 3) for these categories of firms. For the pre‐crisis period, we use the sales value of 2007 fiscal year and three years before that, from ES 2008, and compute the annualized growth rate of sales in the 2004‐2007 period.<sup>7</sup> For the post‐crisis period, we use annual growth rate of sales in the 2008‐2009 period<sup>8</sup> , according to EFCS.<sup>9</sup>

> 7 Sales values for 2004 and 2007 were originally in local currencies and were converted to current US dollars using exchange rate from International Financial Statistics Database. We then controlled for outliers of sales

5
**Table 3 ­ Sales Growth Rates (in %) comparisons: before and after the crisis**

||**P**|**recrisis**<br>||**P**|**ostcrisis**<br>||
|---|---|---|---|---|---|---|
||**Average**|**95% Con **|**f. Interv. **|**Average**|**95% Con **|**f. Interv. **|
|**Innovative**|||||||
|(introduce new product/process)|36.38|_27.05_|_45.71_|‐26.48|_­29.68_|_­23.29_|
|**Noninnovative**|||||||
|(did not introduce new product/process)|23.39|_16.70_|_30.07_|‐25.34|_­28.46_|_­22.22_|
|**Innovative**|||||||
|(invest in R&D)|45.29|_27.87_|_62.71_|‐27.02|_­31.82_|_­22.21_|
|**Noninnovative**|||||||
|(didnotinvestin R&D)|24.61|_19.76_|_29.45_|‐25.55|_­28.08_|_­23.02 _|
|**Young**<br>(< = 5 years old)|45.52|_28.10_|_62.94_|‐30.13|_­36.77_|_­23.48_|
|**Older**<br>(>=6 years old)|26.90|_20.97_|_32.84_|‐25.42|_­27.78_|_­23.06_|

Source: EFCS 2009‐ ES 2008 Data shows that before the crisis, growth rates for innovative (young) firms showed to be significantly higher than non‐innovative (older) companies, at 5% level of significance.<sup>10</sup> After the crisis, however, the reduction in growth rate was higher for innovative (young) firms, though not statistically significant. These results seem to suggest that the crisis impact was higher for innovative and young companies. However, besides lacking of statistical significance for the comparisons after the crisis, these results reflect only a partial analysis. One can argue whether the difference of sales growth performance between the refereed groups of firms persist when controlling for other firm characteristics that are likely to affect sales performance.

In order to address this, we apply an empirical approach to discuss to what extent innovative (young) firms were more affected by the global downturn once firm idiosyncratic characteristics are taken into account. Two empirical methods are used.

> values for both 2004 and 2007. The criterion was to consider as an outlier the observations lying outside the interval defined as the average � three standard deviations of the refereed variable expressed in logarithm form.

> 8 June/July 2008 to June/July 2009. As the 2008/2009 growth rate ranges from ‐100% to 100%, we didn’t control for outliers.

> 9 Some World Bank reports (like the EU10 Regular Economic Report) state that the acute phase of the crisis in these countries was in the second half of 2008 (specifically September 2008), but the severity of the impact was perceived only in 2009 statistics, even though the economic activity has stabilized in the second half of 2009. As we only have data for June/July 2009, we just use this point as a postcrisis reference.

> 10 For the precrisis comparison between innovative and noninnovative companies, the results are valid regardless the criteria used to classify innovative companies.

6
it Small;, (11-50 full time employees), Medium;, <!-- Start of picture text --> Large; (=251) stand as<br><!-- End of picture text --> The omitted category is Micro;,. Export; t t As already defined, Young; i i i i we interact Inov; and Young; with a time trend variable (we do the same for Skill; Git = Bo + Bit + Binoy; + B3(t * Inov;)+Byyoung; + Bs(t * young;) + Therefore, the variable Skill;
Zit sector, as previously defined. The error term wj¢ Ujt = Aj + Eit (2) where qa; are random individual-specific effects and €;¢ a random effect model, both a; model error, uj; Let pz, = Cor(Lizljs) be the error correlation over time for individual i, the FGLS estimator sets p;, = p for all s # t so that errors are assumed to be equicorrelated. The population-averaged estimator (PFGLS) we use places no restriction on p;,
V1 = 1 Py + Uy V2 = X2 Bq + Uz (4) where 1 and 2 set the innovative and non-innovative groups of firms; y, and y, groups; x, and x, groups; fy By values” of those characteristics) for the two sub-groups; and, u, and uz Let F,(.) and F,(. )denote the cumulative distribution functions of the residuals for Pia = Fy ir |Xin) (S)

Ua —= Fypl ialXin) where F;1(.)is the inverse of the cumulative distribution function for the “innovative Next, assume that F(.) to be the reference residual distribution (e.g., the average residual distribution over both samples) and let B following variables: size (dummies of 1-10 full time employees,11-50, 51-250 and =251), firm age (a dummy
<!-- Start of picture text --> vy) = xi1B + F-1 (pix |xi1) (7)<br>Vi2 Xi2B (Pi2|Xi2) (8)<br>Vit XiBi + (Dis |Xi1) (9)<br>(B) _ F-1(n.>|x; 10<br>Vio Xi2B2 + (Di2|Xiz) (10)<br>(CC) _y. F71(p,4 1x; 11<br>Vin XiBi + Fy (pia lXi1) (11)<br>©) _y. F7-1(p.,|x; 12<br>Vin XiBi + Fy (pia lXi1) (12)<br>yO? = Vir = XinBi + Fr * (via lXi1) (13)<br>VizM_y Vi2 Xi2B2yy. + F51(p;o|x;2 (Pi2|Xi2) ( 14)<br>. So, the differential Y, — Y,<br>¥-¥% = [YO —Y] + [1 - 1) - VM -¥)) + 1 - 9) - 4 - 2”)<br>=T=C+R+U<br><!-- End of picture text -->
returns – the return (or price) effect, and U to differences in unobservable quantities and returns – the unexplained (or residual) effect.

In this paper, as we are using the JMP method to analyze the difference of sales growth performance distribution between the two groups of firms, the first component ‐ characteristics (or quantity) effect ‐ captures the part of the difference of sales growth performance between the two groups of firms that is due to differences in observable characteristics (quantities). In other words, it quantifies to what extent innovative (young) firms have a more favorable “endowment” in terms of observable characteristics as compared to non‐innovative (older) companies. For example, one firm category might have, on average, larger firms, larger use of skilled workforce and larger export orientation, and these might lead to higher growth sales performance.

The second component, the return (or price) effect, reflects the part of the gap of sales growth performance between the two groups of firms that is due differences in returns of those observable characteristics. In other words, it measures to what extent the returns of those observable characteristics on the sales performance of a firm differ between innovative (young) and non‐innovative (older) companies. For example, one firm category might get larger returns from the same observable characteristics when compared to other firm group.

The third component, the unexplained (or residual) effect, measures the part of the difference of the sales growth performance between the two groups of firms that is due to differences in unobservable characteristics and returns or to measurement error. Assuming that the OLS models that explain sales performance of the two companies’ cohorts are satisfactorily specified, any unmeasured factors that affect the performance of these two groups will be capture by this residual effect.<sup>19</sup> For the comparison between innovative and non‐innovative firms, we interpret this residual effect as a reflection of the difference between the two firm cohorts in terms of some intangible assets, such as the specialized knowledge embodied in researchers, the firms’ entrepreneurship ability or its “animal spirits”. When comparing young and older firms, this residual effect could measure

> 19 As already stated, the choice of the variables used in the OLS models that explain the sales growth rate of the two groups of firms draws on the theoretical and empirical literature of firm evolution.

11
t * Inov;) t * young;,); and ii) variable (t * Inov; 6 = [Coin — Jnonin,t) — (Gin t-1 — Inonint-1)] where gyy,t is the growth sales rate of innovative companies in the postcrisis period — 2008/2009, and gry t-1 noninnovative companies Qyonint ANd Jnonin,t-1 periods, respectively. It is important to distinguish the interpretation of (t * Inov; interpretation of Inov; (t * Inov; For instance, if the coefficient of Inov; innovative firms is larger than noninnovative companies. A negative sign for the coefficient of t « Inov;
they did grow more than noninnovative firms, on average (see result for Inov; Equation 1 including (t * Inov);, (Inov;), (Export;,) , and (t * Export;,) variables; ii) Equation 1 including (t * nov); , and (Jnov;) variables; and iii) Equation 1 including (Export;,) and (t * Export)jt (Export;,) and (t * Export;,)- do not change the fact that innovative companies have performed significantly the results of (Export;,) and (t * Export;,) have never presented statistical significance, regardless the
For the young/older comparison, the coefficient associated to the interaction variable (t * Young; é6= [(9voung,t _ Joiat) _ (Gyoung,t-1 _ Joiat-1)|

where Jyoung,t Jyoung,t-1 Similarly, goiat and Joiat-1
t * Skill; did grow more than older firms, on average (see result for Young; It must be stressed that skilled companies grew less than non skilled firms, on average (see result for Skill;
For the innovative/non‐innovative comparison, the results of the JMP decomposition are listed in Table 5. As the aim of the analyses is to explain the sales growth performance of innovative companies relative to non‐innovative firms, the distinct components that explain the sales performance gap between these firm groups (characteristics, return and unexplained effects) are considered from the point of view of innovative companies.

Table 5 shows that, for the pre‐crisis period, the total difference between sales growth performance of innovative and non‐innovative companies amounts to 13.41 percentage points (in favor of innovative). The observable characteristics effect is also positive implying that the characteristics of innovative firms were actually more favorable than those of non‐innovative companies. Turning to the composition of this characteristic effect into the explanatory variables assumed (size, use of skilled labor, export orientation, age, country and sector), we see that, among the firm characteristics, the major contributions for this positive difference came from size (see Table IV in the Annex).

**Table 5 – Decomposing the total difference of sales growth rate between innovative and noninnovative firms: JMP results before and after the crisis**<sup>#*</sup>

||**Total Difference**|**Characteristics Effect**|**Return Effect**|**Unexplained Effect**|
|---|---|---|---|---|
||13.411|1.038|12.073|0.300|
|**Before**|(5.750)|(0.388)|(0.915)|(0.152)|
||100%|7.7%|90.0%|2.2%|
||‐1.417|‐1.185|‐0.250|0.018|
|**After**|(0.679)|(0.582)|(0.103)|(0.007)|
||100%|83.6%|17.7%|‐1.3%|

# The results refer to the sample means of the sales growth rate distribution * Bootstrap standard error in parentheses

The observable return effect is also positive, revealing that the returns of those characteristics were higher for innovative companies. In other words, it means that the transformation of given “inputs” in results by the innovative firms leaded to higher sales growth rate than the transformation by non‐innovative companies, and this accounts for the largest part (90%) of the sales growth gap between the two groups of firms.

16
Finally, the unexplained components showed to be positive, suggesting that, on average, unobservable factors favor the innovative companies. As mentioned before, we interpret this as an indication of the existence of a positive premium for the entrepreneurship ability, or the “animal spirits”, of innovative companies.

In the after‐crisis period, Table 5 shows a negative gap between the sales growth performance of innovative and non‐innovative companies. This result points out a drastic change in the pattern of comparative sales performance of the two groups of firms when contrasting to the pre‐crisis period. What explains this deterioration can be identified when examining the three effects. First, the observable characteristic effect is negative, implying that the characteristics of innovative firms turned to be less favorable than those of non‐innovative companies, and this effect accounts for the largest part (83.6%) of the sales growth gap between the two types of firms after the crisis.

The observable return effect is negative as well, indicating that the returns of those characteristics turned to be smaller for innovative companies. On the other hand, the unexplained component remains positive, suggesting that unobservable factors still favor the innovative companies, and served to reduce the sales growth gap between the two firms’ categories. Nevertheless, it is also remarkable that this residual was significantly reduced after the crisis, which can be seen as evidence that the “premium” for the ability to innovate (“animal spirits”) has declined after the economic downturn.

Table 6 presents the JMP decomposition technique, before and after the crisis, to examine the gap of sales growth performance between young and older companies. As the aim of the exercise is to explain the sales growth performance of young companies relative to older firms, the three effects that explain the sales performance gap between the two groups are considered from the point of view of young companies.

17
**Table 6 – Decomposing the total difference of sales growth rate between young and older firms: JMP results before and after the crisis**<sup>#*</sup>

||**Total Difference**|**Characteristics Effect**|**Return Effect**|**Unexplained Effect**|
|---|---|---|---|---|
|**Before**|19.261<br>(9.164)|20.985<br>(7.485)|15.559<br>(1.491)|‐17.283<br>(6.569)|
||100%|109.0%|80.8%|‐89.7%|
||‐10.696|‐4.434|‐5.011|‐1.252|
|**After**|(3.828)|(1.775)|(1.005)|(0.559)|
||100%|41.4%|46.8%|11.7%|

# The results refer to the sample means of the sales growth rate distribution * Bootstrap standard error in parentheses

For the pre‐crisis period, Table 6 shows that the total difference between sales growth performance of young and older companies is 19.26 percentage points (in favor of young). The observable characteristics effect and the return effect were also positive implying that the characteristics of young firms were actually more favorable than those of older companies, especially regarding use of skilled workforce and export orientation (see Table V in the Annex). The returns of the observable characteristics were higher for younger companies. However, the residual (or unexplained) effect was negative, which we interpreted as evidence that the ability of young firms to benefit from the learning process was inferior to the ability of older companies. This negative effect acted to reduce the positive gap of sales growth between the two groups of firms before the crisis.

In the post‐crisis period, the results show a negative gap between the sales growth performance of young and older companies, indicating a reversal of the comparative sales performance of the two groups of firms. The observable characteristic effect have also turned to be negative, which means that, on average, the characteristics of young firms turned to be less favorable than those of older companies after the crisis. If we separate this negative characteristic effect, we see that the major contributions for this result came from size (see Table V in Annex). This could be interpreted as evidence that after the crisis young companies might have faced difficulties evolving into larger firms.

The unexplained component was again negative, suggesting that the ability of young firms to capture the learning benefits remained inferior to the ability of older companies. It is worth mentioning, however, that this residual effect turned to be less negative in the 18
post‐crisis period, possibly suggesting that young firms were forced to improve their learning capabilities in order to operate in a more competitive environment. Yet this improvement was not enough to compensate the worse sales growth performance of young enterprises after the crisis.

# **4 ­ Conclusions**

In this paper we examined the effects of the global downturn on innovative firms and on young firms in order to understand the growth prospects of Eastern European countries using a unique panel data for 1,686 firms in six countries (Bulgaria, Hungary, Latvia, Lithuania, Romania, and Turkey) covering manufacturing, retail and other service sectors. Two empirical methods were used. First, we applied a panel‐data analysis in order to assess the difference of sales growth performance between innovative and non‐ innovative firms, and between young and older firms, over the pre and post‐crisis periods, when controlling for certain firm characteristics. Second, we used the Juhn ‐Murphy‐Pierce technique to decompose the difference in sales growth performance between innovative (young) non‐innovative (older) companies, over time, into three effects: characteristics effect; returns effect and unexplained effect.

The results show that innovative firms have suffered more than non‐innovative companies, regardless the estimator applied (population‐averaged estimator or FGLS), and the criteria used to categorize innovation (introduction of a product or a process or development of R&D activities). We also showed that the decrease of sales growth rate of younger firms was more severe than of older companies.

When comparing innovative and non‐innovative companies, the decomposition technique showed three main outcomes. First, the positive difference in sales growth performance in favor of innovative companies has been reversed after the crisis. Second, while before the crisis that positive gap in favor of innovative companies was due to their better characteristics, especially regarding size, and, mainly, to higher returns obtained by innovative firms, after the crisis the gap reversion was due mostly to the deterioration of characteristics of innovative companies. Finally, the results showed that the residual (or unexplained) effect was always positive, before and after the crisis, suggesting that the 19
unobservable factors favor the innovative companies, which could also be interpreted as a premium for the entrepreneurship ability, or “animal spirits”, of innovative companies. However, it is also remarkable that this residual was significantly reduced after the crisis, which can be seen as evidence that the “premium” for innovative companies was diminished over time.

When contrasting young and older companies, the use of Juhn‐Murphy‐Pierce decomposition also revealed three main results. First, we found evidence that the positive gap in sales growth performance in favor of young companies has been reversed after the crisis. Second, the results showed that after the crisis there was a deterioration of the characteristics of the young companies, especially regarding firm size. Finally, the results showed that the residual (or unexplained) effect was always negative, before and after the crisis, suggesting that the ability of young firms to capture the learning benefits was always inferior to the ability of older companies. It is worth mentioning, however, that this residual effect turned to be less negative in the post‐crisis period, which can suggest that young firms were forced to improve their learning capabilities in order to operate in a more competitive environment. Nevertheless, this improvement was not enough to compensate the worse sales growth performance of young enterprises after the crisis.

In sum, our results seem to provide robust evidence that innovative and young companies were more affected by the global downturn. Two obstacles for the achievement of pre‐crisis growth rates in the region related to this result are a premature exit among young firms and a decline on innovation performance. The exit of potentially viable young firms – assuming the same firm entry rates of the pre‐crisis period – and the consequent premature “aging” of the enterprise sector might soften market selection and therefore reduce productivity gains. The impact on innovative firms may hold back investment in R&D among other reasons because lower sales – in a context where diminished access to credit shifted internal funding to finance working capital ‐‐ imply less funding for innovation. For those reasons, we interpret our results as indication that the achievement of pre‐crisis growth rates in those countries may be difficult.

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