metadata: authors: 0: author: Gonzalez,Alvaro S.
1: author: Iacovone,Leonardo
2: author: Subhash,Hari
count: Russian Federation
docna: 0: docna: Russian volatility : obstacle to firm
            survival and diversification
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Industrial Economics,Construction Industry,Oil Refining & Gas Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Economic Growth,Employment and Unemployment,Business Cycles and Stabilization Policies,Law and Justice Institutions
teratopic: Poverty Reduction,Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: The need for economic diversification
            receives a great deal of attention in Russia. This paper
            looks at a way to improve it that is essential but largely
            ignored: how to help diversifying firms better survive
            economic cycles. By definition, economic diversification
            means doing new things in new sectors and/or in new markets.
            The fate of emerging firms, therefore, should be of great
            concern to policy makers. This paper indicates that the ups
            and downs -- the volatility -- of Russian economic growth
            are key to that fate. Volatility of growth is higher in
            Russia than in comparable economies because its slumps are
            both longer and deeper. They go beyond the cleansing effects
            of eliminating the least efficient firms; relatively
            efficient ones get swept away as well. In fact, an
            incumbency advantage improves a firm's chances of
            weathering the ups and downs of the economy, regardless of a
            firm's relative efficiency. Finally, firms in sectors
            where competition is less intense are less likely to exit
            the market, regardless of their relative efficiency. Two
            policy conclusions emerge from these findings -- one
            macroeconomic and one microeconomic. First, the importance
            of countercyclical policies is heightened to include
            efficiency elements. Second, strengthening competition and
            other factors that support the survival of new, emerging and
            efficient firms will promote economic diversification.
            Efforts to help small and medium enterprises may be better
            spent on removing the obstacles that young, infant firms
            face as they attempt to enter, survive and grow.
geo_reg_key: 517208,517198,82689,517190,517191
repnme: repnme: Russian volatility : obstacle to firm
            survival and diversification
display_title: Russian volatility : obstacle to
            firm survival and diversification
listing_relative_url: /research/2013/09/18263884/russian-volatility-obstacle-firm-survival-diversification
topicv3: Economic Growth m1326241 471,Manufacturing Sectors m1326521 1281,Enterprise Surveys m1504072 2762,Mortality m1504365 3661,Petroleum Industry m1583790 4710
publishtoextweb_dt: 2013-06-01T00:00:00Z
historic_topic: Poverty Reduction,Macroeconomics and Economic Growth,Finance and Financial Sector Development
no_of_pages: 36
author_keys: 0: author_key: 793227
1: author_key: 900307
2: author_key: 1232213
model_extractions: WPS6605 Policy Research Working Paper 6605 Russian Volatility Obstacle to Firm Survival and Diversification _Alvaro S. González Leonardo Iacovone Hari Subhash_ The World Bank Europe and Central Asia Region Financial and Private Sector Development Unit September 2013
Policy Research Working Paper 6605

# **Abstract**

The need for economic diversification receives a great deal of attention in Russia. This paper looks at a way to improve it that is essential but largely ignored: how to help diversifying firms better survive economic cycles. By definition, economic diversification means doing new things in new sectors and/or in new markets. The fate of emerging firms, therefore, should be of great concern to policy makers. This paper indicates that the ups and downs—the volatility—of Russian economic growth are key to that fate. Volatility of growth is higher in Russia than in comparable economies because its slumps are both longer and deeper. They go beyond the cleansing effects of eliminating the least efficient firms; relatively efficient ones get swept away as well. In fact, an incumbency advantage improves a firm’s chances of weathering the ups and downs of the economy, regardless of a firm’s relative efficiency. Finally, firms in sectors where competition is less intense are less likely to exit the market, regardless of their relative efficiency. Two policy conclusions emerge from these findings—one macroeconomic and one microeconomic. First, the importance of countercyclical policies is heightened to include efficiency elements. Second, strengthening competition and other factors that support the survival of new, emerging and efficient firms will promote economic diversification. Efforts to help small and medium enterprises may be better spent on removing the obstacles that young, infant firms face as they attempt to enter, survive and grow.

This paper is a product of the Financial and Private Sector Development Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at agonzalez4@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
# RUSSIAN VOLATILITY: OBSTACLE TO FIRM SURVIVAL AND DIVERSIFICATION

ALVARO S. GONZÁLEZ LEONARDO IACOVONE HARI SUBHASH

**Keywords:** growth, volatility, firm exit, diversification **JEL Classification Codes:** D22, E02, O12, O25, O43

_____________________ agonzalez4@worldbank.org, liacovone@worldbank.org, hsubhash@worldbank.org. The authors would like to thank, Aart Kraay, Alain D’Hoore, Birgit Hansl Lada Strelkova, Michal Rutkowski, Paloma Anos Casero, and Willem Willem van Eeghen, all World Bank colleagues, for helpful comments. Andrew Berg, International Monetary Fund, and Rodrigo Wagner, Tufts University, were especially kind with their time and advice. The views expressed here are the authors' and do not reflect those of the World Bank, its Executive Directors, or the countries they represent. All errors are the authors’ responsibility.
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Finding evidence that businesses are created in times of economic expansion is important because much of the policy debate about diversification is based on the assertion that few emerge. As the argument goes, Russia does not seem to produce much beyond what it has produced in the recent past. This claim is used to support direct intervention to help new economic activities emerge. But one of this study’s hypotheses is that emergence may not be the problem, rather that sustainability is what is lacking in the Russian economy. Therefore, addressing sustainability may be the central economic issue for diversification: it means making sure that efficient firms that emerge in booms survive downturns. Thus, reducing volatility in economic output is a good way to improve their chances of survival.

# LITERATURE

Interest in growth and volatility largely began with macroeconomic studies on booms and busts and the divergence of long-term economic growth between low- and high-income countries. These studies showed that the “peaks-and-valleys” unsustained growth and volatility, characterize lowperforming, poorer countries. Poorer economies tend to have high variances in growth rates across time. In comparison, better economic performers are less volatile and are characterized by “peaks and plateaus”—no valleys (Pritchett, 2000).<sup>3</sup> The current study extends this look at booms and busts, or surges and slumps as they are referred to here, to understand the effects of these on industry and firm-level dynamics.

This study is also closely related to the emerging literature on the links between volatility and economic structure. This new literature points to a reverse causality between a relative lack of diversification and economic volatility. Koren and Tenreyro (2007)<sup>4</sup> decompose volatility into three components: sector-specific shocks, country shocks and covariance between the two to show that less developed countries experience greater growth volatility due to increased concentration in volatile sectors. Moore and Walkes (2010),<sup>5</sup> show that less diversified economies have higher rates of output, investment and consumption growth volatility.

This study explores volatility to question the sustainability of Russian economic growth and whether this type of growth can generate economic diversification. While volatility may hinder economic diversification, at the same time, a lack of diversification characterized by increasing concentration of economic output into a few sectors and/or a few firms may increase the chances of more volatility of this economic output. Breaking this cycle may require concerted effort, maybe from policymakers, but it first needs to be identified, confirmed and then better understood. This study makes progress on identification and understanding.

> 3 Pritchett, Lant (2000) “Understanding patterns of economic growth: Searching for hills among plateaus, mountains, and plains”, _World Bank Economic Review,_ 221–50.

> 4 Koren, Miklós, and Silvana Tenreyro. "Volatility and development*." _Quarterly Journal of Economics_ 122.1 (2007): 24387. Print.

> 5 Moore, Winston, and Carlon Walkes. "Does industrial concentration impact on the relationship between policies and volatility?" _International Review of Applied Economics_ 24.2 (2010): 179-202. Print.

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# COMPARATIVE ANALYSIS OF CONCENTRATION OF RUSSIAN INDUSTRIAL PRODUCTION AND POTENTIAL CONSEQUENCES

There are high levels of concentration of output in a few manufacturing sector in Russia.<sup>6</sup> The bottom quartile of sectors, ranked in order of their size in terms of operating revenue, contribute 0.6 percent of the total manufacturing output in Russia. In comparison, the top quartile contributes 80 percent (Refer to Table A11 a in Annex for a yearly breakdown). The levels of concentration of output within sector (between firms) in Russia is even more noteworthy. The average share of output for the bottom quartile of firms (in terms of operating revenue) in a manufacturing sector<sup>7</sup> is 0.06 percent. The share of the top quartile is 94.7 percent.<sup>8</sup> These relatively high levels of output concentrated in either a few sectors or in a handful of firms may lead to more volatile economic growth. High economic concentration makes an economy vulnerable and sensitive to the fate of fewer economic events such as changes in the price of the most prevalent commodity sold or goods produced. For example, some highly concentrated economies expand and contract in response to rises and dips in the price of the output that dominates total national economic output. In addition, these types of economies are more likely to produce spillover volatility from dominant fluctuating sectors to other sectors that are not directly affected by external events. Evidence shown here supports this characterization of growth volatility in Russia.

In turn, volatility may exacerbate the concentration of economic output. This study also suggests that volatility in growth may increase the likelihood of (premature) exit of new, emerging firms. This means that the structural change that new, emerging firms bring is stunted by high levels of economic volatility. As a result, the economy can experiences a vicious cycle of comparatively higher “premature death” of new firms due to economic volatility and increased volatility driven by an economic structure that remains undiversified or even more concentrated as a result of the high exit rate of new firms.

The reinforcing dynamics between volatility and concentration of output is also a possible explaniation of Russia’s relatively larger manufacturing firms. As the four graphs above indicate, the average size of Russian manufacturing firms, whether measured by annual operating revenue or by the size of their labor force, is larger than the average size of manufacturing firms in the rest of world or in Russia’s closest neighboring economies (Europe and Central Asia<sup>9</sup> ).<sup>10</sup> A relatively

> 6 The characteristics of the dataset used for the descriptive statistics presented here are further explained in the Annex.

> 7 When referring to sectors, these are defined by 4-digit NACE 1.1.  The higher the digit, the more disaggregated the sector data will be.

> 8 See Table A12 of the Annex.

> 9 The 28 economies included in the Europe and Central Asia (ECA) region are (in alphabetical order): Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyz Republic, Latvia, Lithuania, Macedonia, Moldova, Montenegro, Poland, Romania, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine, Uzbekistan. Turkmenistan is not included.

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high mortaility rate of young Russian firms likely explains the size distribution since this eliminates smaller firms from the average size estimation (the left-hand side tail of the distribution). Young firms tend to be small. that younger and smaller manufacturing firms tend to have a high mortality rate (not unusual in any economy) irrespective of their level of efficiency (a relatively less common finding) which is a cause of concern. In addition, as discussed later in more detail, this relatively high mortality rate is associated with the deep and long downturns that characterize some cycles in the short history of the modern Russian economy.

<!-- Start of picture text --> Figure 2: The Russian economy is dominated by larger firms<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2: The Russian economy is dominated by larger firms<br>Size distribution of firms based on labor force (log) Size distribution of firms based on labor force (log)<br>Russia vs. Rest of ECA Russia vs. Rest of the World<br>Rest of the World<br>Rest of ECA Russia<br>Russia<br>0 2 4 6 8 10 0 5 10<br>Distribution of observations Distribution of observations<br>Russia Rest of ECA Russia Rest of the World<br>Source: Enterprise Surveys comprehensive dataset (May 2012) Source: Enterprise Surveys comprehensive dataset (May 2012)<br>Size distribution of firms based on sales revenue (log) Size distribution of firms based on sales revenue (log)<br>Russia vs. Rest of ECA Russia vs. Rest of the World<br>Russia Russia<br>Rest of ECA<br>Rest of the World<br>5 10 15 20 25 30 0 10 20 30 40<br>Distribution of observations Distribution of observations<br>Russia Rest of ECA Russia Rest of the World<br>Source: Enterprise Surveys comprehensive dataset (May 2012) Source: Enteprise Surveys comprehensive dateset (May 2012)<br>.25 .3<br>.2<br>.2<br>.15<br>.1<br>.1<br>kdensity labor force (log) .05 kdensity labor force (log)<br>0 0<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br>kdensity sales revenue (log) kdensity of sales revenue (log)<br>0 0<br><!-- End of picture text --> Of equal concern is the indication that the right-hand side of the size distribution of manufacturing firms in Russia may also be shorter than that of other economies. In other words, the biggest firms do not grow to be as big in Russia as in other parts of the world. Examining Figure 2 (above) once again, the reader can see that the right-hand side tail of the distribution is also shorter for Russia than in other economies. This finding calls into question whether even efficient firms get the resources they require to grow in the Russian economy. In well-functioning economies, markets efficiently allocate resources to the most productive firms irrespective of their size and age (Hsieh

> 10 The data are taken from the World Bank’s Enterprise Surveys on May 2012. For each country, only the latest survey is used. This size comparison controls for differences in the composition of manufacturing sectors across these economies.

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and Klenow 2009).<sup>11</sup> This implies that holding for all other explanatory factors (location, sector and economic activity, for example), firms of the same age, across different economies should employ a similar number of people and make about the same sales revenue if economies are all equally efficient in allocating resources to the most productive firms. If some economies are not allocating the resources that firms need to grow, in economic terminology, they exhibit allocative inefficiencies.

<!-- Start of picture text --> Figure 3: Older firms in Russia employ fewer workers and earn less sales revenue than similar firms in other economies<br><!-- End of picture text -->

**Figure 3: Older firms in Russia employ fewer workers and earn less sales revenue than similar firms in other economies**

<!-- Start of picture text --> Age predicts size of labor force (log) Age predicts sales revenue (log)<br>Russia vs. Rest of the World Russia vs. Rest of the World<br>Rest of the World<br>Rest of the World<br>Russia<br>Russia<br>0 2 4 6 8 10 15 20 25 30<br>Linear prediction Linear prediction<br>95% CI Russia 95% CI Russia<br>Rest of the World Rest of the World<br>Source: Authors' calculations based on comprehensive dataset of Enterprise Surveys (May 2012) Source: Authors' calculations based on comprehensive dataset of Enterprise Surveys (May 2012)<br>Age predicts size of labor force (log) Age predicts sales revenue (log)<br>Russia vs. Rest of ECA Russia vs. Rest of ECA<br>Rest of ECA<br>Rest of ECA<br>Russia Russia<br>0 2 4 6 8 10 15 20 25<br>Linear prediction Linear prediction<br>95% CI Russia 95% CI Russia<br>Rest of ECA Rest of ECA<br>Source: Authors' calculatons based on Enterprise Survey comprehensive dataset (May 2012) Source: Authors' calculations based on Enterprise Surveys comprehensive dataset (May 2012)<br>8 30<br>6 25<br>4 20<br>2 15<br>Actual size of labor (log) Actual sales revenue (log)<br>0 10<br>8 25<br>6<br>20<br>4<br>15<br>2<br>Acutal sales revenue (log)<br>Actual size of labor force (log)<br>0 10<br><!-- End of picture text --> One way to determine the relative allocative efficiency of economies is to compare firm-size and age data across economies. As firms get older and grow, they employ more workers and increase their sales revenue. For that reason, there should be a positive relation between firm size and age and this relation should demonstrate a statistical regularity across economies. Figure 3 depicts this relationship between firm size and age for Russia and other comparator economies. The size of the

> 11 Hsieh, Chaing-Tai, and Peter J. Klenow "Misallocation and manufacturing TFP in China and India." The Quarterly Journal of Economics:124.4 (2009): 1404-447. Print.

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manufacturing firm is measured either by annual sales revenue or number of employees. Indeed, the space between the two, near forty-five degree lines in Figure 3 indicate that firm growth is relatively stunted in Russia compared to other economies. If all firms grew in size at about the same rate in Russia as in other economies, the lines in this figure would be on top of each other and indistinguishable one from the other. They are not; the size-age line trajectories cross and separate at a certain point. The Russian trajectory falls below that of comparator economies. Moreover, the figure indicates that the differences in trajectory are statistically significant to a 95-percent confidence interval. The grey shading around these lines depicts that band of confidence. Where these grey bands do not cross, the reader can conclude that the estimates are statistically significantly different from each other. After a certain age, the size of firms in Russia slows. Based on these data, Russia is seems relatively less allocatively efficient than many of the economies to which it was compared.

At this point, findings on the relatively lower levels of allocative efficiency in the Russian economy are indicative, not conclusive, but nonetheless important. They point to an additional factor that may hamper growth and diversification of the economy. Specifically, the staying power of inefficient firms, stunted in growth, but that do not exit the market may be a problem. In relation to how they affect the entrance of new firms, these stunted firms that stay put hold on to productive resources (labor and finance) that newer, possibly more productive firms in emerging sectors could make use of to survive and grow. The staying power of these stunted firms also calls into question how fierce competition may be since the forces of economic rivalry do not seem to be enough to escort them to the exits. Research is just starting to provide support for the relationship between allocative efficiency, firm entry and competition in other economies.

# COMPARATIVE ANALYSIS OF RUSSIAN ECONOMIC VOLATILITY AND FIRM SURVIVAL **VOLATILITY OF RUSSIA’S SECTOR-LEVEL OUTPUT RELATIVE TO OTHER ECONOMIES**

The first question to answer is whether Russia’s economy is more volatile than others. The study does this by comparing year to year changes in sector-level<sup>12</sup> economic output of the Russian economy, between 1993 and 2009, to that of other economies.<sup>13</sup> To determine if the Russian

> 12 For the sector analysis, a shortened panel that included the period between 1993 and 2009 was used. UNIDO data for Russia start in 1994. In addition, outlier observations – identified as output growth outside 3 standard deviations above or below the mean growth rate for each sector in each country – were removed. Doing this resulted in dropping about 45 percent of the observations in the dataset (Refer to Table 23 Annex for a detailed breakdown of the dataset pre and post sample selection).

> 13 For the sector-level comparative analysis across economies, the following groups of economies and countries are considered: Brazil, India and China, which along with Russia comprise country grouping called BRICs; Australia, Canada, Chile, and New Zealand are high growth countries that like Russia have an abundance of natural resources but, unlike Russia, have largely diversified economies and these are grouped together under Resource Rich Countries; and finally Korea and the set of economies grouped under the Organization of Economic Cooperation and Development (OECD) are compared to Russia because of their relatively long periods of steady and positive growth that serves as reference of longterm economic performance.  Of course, there are overlaps between these groups and some of these economies.  For example, Australia, Canada, Chile, Korea and New Zealand are all members of the OECD.

7
economy is relatively more volatile than other economies, the variance of the average sector-level growth rate across several years is the statistic of import—a high variance means high volatility.

A box and whisker plot (Figure 4) is a graphical depiction that allows the reader to visually determine whether the average annual industrial growth at the sector level in Russia indicates higher variances across time than that of other economies. The vertical line inside the grey box represents the median growth for each country between 1993 and 2009. The right and left boundaries of the grey rectangles represent the middle half of the data; they define the 25<sup>th</sup> percentile to the 75<sup>th</sup> percentile of annual rate of sector-level industrial output growth per <!-- Start of picture text --> Figure 4: The annual growth in output of Russian sectors exhibit<br>relatively higher variances—more volatility.<br><!-- End of picture text -->

**Figure 4: The annual growth in output of Russian sectors exhibit relatively higher variances—more volatility.**

<!-- Start of picture text --> Yearly growth of sector output (1993-2009)<br>OECD<br>Resource rich<br>Brazil<br>Russia<br>India<br>China<br>Korea<br>-1 -.5 0 .5 1<br>Data source: Authors' calculation from UNIDO 2011 Industrial Output Data (4-digit ISIC)<br><!-- End of picture text --> economy or group of economies. The lines or whiskers, outside of these boxes, delineate the most extreme values.<sup>14</sup> As can be verified, both the grey rectangles and the whiskers in the figure are markedly more extended for Russia than any other comparator. This means that the variance of average annual industrial growth in Russia is statistically larger than that of other economies, meaning that Russian sectorlevel growth has higher variances and is more volatile.

Having established that the variance of average annual industrial growth, for the period of time examined here, is higher than that of comparator economies, the next question is whether this volatility is the result of fluctuations in annual growth between sectors or between years. In other words, is the variance of annual growth explained by fluctuations in the growth of some sectors that in certain years grow fast then slow or is it that all sectors, year by year, generally grow fast or slow?

This is an important question because it may point to spillover or to macro-economic drivers of volatility. In other words, if fluctuations are explained by year or temporal fluctuations, where generally all sectors are in slumps or surges at the same time, that may indicate that these

> 14 Inter-Quartile Range (IQR) = x[75] – x[25] Highest Value <= x[75] + 1.5*IQR Lowest Value => x[25] – 1.5*IQR

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industrial sectors are interlinked in such a way that they are all pulled down or up together or there are macroeconomic factors that affect all of them. Alternatively, if a few sectors are continually in flux, while others grow at a steady, even pace throughout the years, this suggests that there are comparatively few spillovers and relatively little linkage between sectors.

The analysis of variances presented in the table below indicates that sector-level growth rates in Russia are highly correlated to each other, year to year. This conclusion is based on the relatively higher coefficient for the year variable as compared to other economies and as compared to the sector variable coefficient as well. These results imply that nearly the entire set of Russian industrial sectors experience fluctuations in growth rates in tandem. This lends support to the spillover hypothesis; namely, that the relatively high levels of concentration of economic output, both across firms and sectors, contributes to volatility.

**Table 1: ANOVA Partial Sum of Squares**

||<br>|**ANOVA**<br>|**FOR 199**<br>|**3-1999**<br>|<br>||**ANOVA**<br>|**FOR 200**<br>|**0-2009**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||**Russia**|**Brazil**|**India**|**China**|**Korea**|**Russia**|**Brazil**|**India**|**China**|**Korea**|
|**Model**|28.35|1.27|14.02|NA|29.24|16.32|4.96|6.68|3.86|7.88|
|**Sector**|4.72|0.21|8.15|NA|7.62|2.25|0.54|1.75|1.27|3.33|
|**Year**|23.63|1.05|5.86|NA|21.62|13.70|4.44|4.92|2.58|4.53|
|**Residual**|21.35|0.95|44.85|NA|37.80|23.15|3.99|25.12|2.54|16.31|
|**Total**|49.70|2.22|58.87|NA|67.03|39.47|8.95|31.80|6.40|24.19|

Source: Author’s calculation from UNIDO 2011 Industrial Output Data (4-digit NACE) The reader will note that the empirical results for the analysis of variances are presented for two separate periods: 1993-1999 and 2000-2009. The first represents the period following the economic collapse of the Soviet Union, between 1993 and 1999. The second covers the years of economic recovery where relatively higher growth (2000-2009) took hold. While these are two dramatically different periods for recent Russian economic history, the empirical results on the possible explanation for the patterns of economic output volatility is remarkably similar for both. In both, the year-to-year fluctuations in sector-level annual industrial output explain more of the variation in growth rates than the composition of sectors that contribute to output growth. This similarity in results demonstrates the persistence in the nature and sources of volatility of the Russian economy. While this temporal effect is seemingly less prominent in the latter period, the data indicate that in Russia, changes in sectors output generally move in tandem across the years.

# **THE NATURE OF VOLATILITY COMPARED WITH OTHER ECONOMIES**

Recent sector-level growth rates in Russia exhibit more volatility than in other economies. All volatility is made up of booms, referred to here as surges, and busts, referred to here as slumps. These two can be examined separately since they are quite different—surges foster firm entry 9
while slumps cause firm exits. But before getting to the dynamics of firm entry and exit, the next task is to understand the characteristics of slumps and surges in the Russian economy.

Slumps and surges have two characteristics: depth and endurance. In the case of slumps, the depth is characterized by how much the economy shrinks. Similarly, to determine the endurance of a slump, the task is to determine from beginning to end, how long a slump lasted without interruption of at least one period of positive growth. With respect to the data, to ascertain the depth of slumps, one looks at period when a slump takes place and one asks how often these slumps are characterized by rates of 0, -1, -2, or -3 percent average annual growth, for example. To get a picture of how long slumps last, one records how long (how many years) each slump remained in negative territory once the slump began.

<!-- Start of picture text --> Figure 5: The average slump in Russia is deeper than in other<br>economies (1993-2009)<br><!-- End of picture text -->

**Figure 5: The average slump in Russia is deeper than in other economies (1993-2009)**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Depth<br>OECD Resource Rich Russia China<br>India Brazil Korea<br>5<br>4<br>3<br>2<br>Density Function<br>1<br>0<br><!-- End of picture text --> Source: Author’s calculation from UNIDO 2011 Industrial Output Data (4-digit NACE) To illustrate the depth of Russian slumps and compare these to that of other economies, a kernel density estimator 15 is used. Figure 5 is a kernel density plot where the horizontal axis, from left to right, indicates progressively deeper slumps (higher negative growth rates). The vertical axis, from bottom to top, records how often a particular negative growth rate is recorded. The data lines record how often a negative growth rate is recorded for all of the slumps that took place in these economies between 1993 and 2009. The respective top of each hill marks the most common negative rate of growth registered in slumps for each economy.

This graph confirms that for Russia—because the top of the hill is to the right of all other comparator economies—the common slump is characterized by higher negative growth than that found in any of the economies to which it is compared.

> 15 Smoothing the duration of slumps data with a kernel density estimator can be more effective than using a histogram to identify features that might be obscured by the choice of histogram bins or sampling variation.

10
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# **DETERMINANTS FOR FIRM SURVIVAL IN RUSSIA**

The comparative analysis of slumps and surges using the UNIDO dataset indicate that the Russian economy exhibits significantly deeper and longer slumps than other economies. But should these features of the Russian economy be of concern? One answer is that these macroeconomic features of the economy may have specific microeconomic consequences. Slumps may slow or halt firm growth, may force the exit of relatively efficient, newer firms and hinder the allocation of resources from less efficient firms to more efficient ones. To see if these concerns are warranted, this section focuses on identifying and describing the link between firm exits and surges and slumps, sectorlevel competition the role firm-level productivity plays into firm mortality.

Given the pattern of deep and long slumps discovered in the previous analysis there is particular emphasis on these results to identify and explain the implications of these slumps on firm mortality. For that reason, only the following findings, out of many, are highlighted and discussed here:<sup>16</sup>

1. More productive firms are relatively less likely to exit than less productive ones. Productivity is more of a factor in improved firm mortality during surges than slumps;

2. Older firms are relatively less likely to exit than younger ones. The age of the firm is also more of a factor in improved firm mortality during surges than slumps; and 3. In sectors where competition is less intense, unproductive firms are less likely to exit than in sectors where competition is more intense.

On average, the likelihood of surviving the ups and downs of the Russian economy improves if a firm is more productive than others, holding for all other factors.<sup>17</sup> The data however, also provide a slight nuance to this result. Being more productive improves the odds of survival during more so surges than during slumps. This nuanced finding supports the conjecture that during a surge (a boom) started by an expansion of demand for goods, the intra-sectoral reallocation of resources between firms will favor those that are more productive. To respond to increased demand, firms expand the purchase of their inputs to increase production. Expanded demand for inputs raises prices for inputs. In this situation, the least productive firms, which by definition are already burdened with higher costs of production, are unable to stay in the market as higher input prices further raises their costs and these cannot be recuperated with higher prices. This forces uncompetitive firms to exit even during economic booms.<sup>18</sup> This finding is good news for the Russian economy. If during surges emerging, more efficient firms enter to present new products to new markets, this dynamic could serve as the basis for economic diversification. However, issues arise during the long and deep Russian economic slumps that were described in previous sections.

> 16 The econometric results are displayed in Tables A17, A18 and A19 of the Annex.

> 17 See Tables A17, A18 and A19 of the Annex where the variable **ln(value added per worker)** serves as a productivity measure.  In all cases, the coefficient for this variable is negative and statistically significant at the 99 percent level.

> 18 This is consistent with a heterogeneous firm-model of Melitz (2003).

12
Slumps, however, temper this positive news. Productivity is expected to be equally important in the survival of firms during both slumps and surges. However, the Russian data indicate that this is not the case.<sup>19</sup> Part of the explanation may be that the dynamics for slumps are dissimilar to those described for surges. The empirical results may just be a reflection of that fact.<sup>20</sup> Nevertheless, while the dynamics may be different, in healthy, competitive economies, productivity is equally important to the survival of firm in the ups and downs. In Russia, during the long and deep slumps, other factors are important in determining the survival of firm.

The age of the firm plays a more significant role during slumps than in surges. Older firms are less likely to exit the market.<sup>21</sup> Regardless of their relative productivity, older, incumbent, firms will remain in the market.<sup>22</sup> This finding, when coupled with the discovery that Russian slumps are more frequent, longer and deeper, there is cause to whether this premium on incumbency and age is an adaptation, a not very healthy one, to the nature of Russian slumps. Incumbents are often not the champions of change and innovation that must be the basis for economic diversification.

The last finding also suggests that firms in less competitive sectors are more likely to survive than would otherwise be the case. This result reinforces the incumbency premium and has implications for the allocative efficiency of the economy. The staying power of relatively inefficient firms in uncompetitive sectors is a problem. Indirectly, these incumbents affect the entrance of new firms, by holding on to the resources that young, emerging, possibly more productive, firms could employ to grow.

Based on the benchmark of health of Russian economic dynamics, namely, whether relatively productive firms stay in the market and grow while inefficient ones exit, there is some room for both optimism and for pessimism. Economic surges reward productivity. On the other hand, the staying power of inefficient, incumbent firms hints at a problem, however.

19 The reader can see in Tables A17, A18 and A19 of the Annex that the coefficient for the interaction term between productivity and slump or surge ( **surge/slump × ln(value added per worker** ) is always negative and statistically significant at the 99 percent confidence level. Since a surge is coded as value=1, the coefficient of this interaction term indicates that during surges, being more productive is more important than during slumps (coded as value=0). If productivity had been as equally important to firm survival during slumps as in surges, the coefficient for this interaction would have been zero.

20 Unlike surges, in slumps demand falls and prices fall; the most efficient firms can meet these prices cuts because they are lower cost producers and survive the slump. During slumps, within sector resource allocation may not be as important in survival as it is in surges. Thru slumps, firms are releasing resources as demand shrinks and this would likely force input prices to drop as well.

> 21 See Tables A17, A18 and A19 of the Annex where the coefficient for the variable **age** , in all cases, is negative and statistically significant at the 99 percent level.

22 In the regression displayed on Tables A17, A18 and A19 of the Annex, the reader will note that the coefficients for the size categories ( **small, medium and large** ) are statistically significant and negative. However, to determine the complete effect of size on the likelihood of survival, the coefficients to all of the interaction terms with age must be considered. Once all coefficients are summed for each size category, they add up to zero, indicating that while there are benefits to being small, medium or large in comparison to a microenterprise (the omitted category absorbed by the constant), there is no statistical difference between being small, medium or large.

13
# CONCLUSION

The results of this study point to three main findings. First, Russian manufacturing output growth is characterized by a higher volatility than other comparator countries. Second, higher volatility is mostly driven by the presence of more numerous, deeper and longer slumps and is mostly associated with aggregate slumps with yearly effects. When the Russian economy slumps or surges, few sectors can escape the gravity of the downward or upward pull. Third, while the economic surges increase the probability that productive firms remain in the market, the same is not true of economic slumps—older firms, not necessarily more productive ones, are more likely to survive the downturn. Furthermore, in sectors in which competition is less fierce, firms in these sectors have a higher likelihood of weathering a slump.

The economic ramifications of these findings to the Russian economy are what matter. In that sense, the evidence presented indicates that slumps affect the nature of firm mortality and allocative efficiency. If Russia is going to rely on new firms in new sectors doing new things in new markets as a source of economic diversification, there will be a need to address volatility, competition and a too heave public policy and programmatic focus on small and medium enterprises to one on young, infant and productive firms.

The econometric results on the relationship between firm exit and competition have important policy implications. First, at the micro-level promoting competition would seem to go a good way forward in addressing them. More specifically, policymakers may want to provide new emphasis to the role of emerging firms, not their size, to address the fact that some of the efficient firms that exit the market are young. Possibly, in a less volatile more competitive economy, these young firms would remain in the market, grow and form the basis for the economic diversification so many Russian policy makers want. However, Russia, like most governments around the world, is focused on SMEs (small and medium enterprises) as a target for policy aid. The findings here indicate that it may be time to change focus to seeing what ails YIFs (young and infant firms) emerging in the Russian market.

Russia’s policy makers may want to worry more about the economic costs of these sharp ups and downs of the economy. At the macro level, Russia, like other resource-rich countries such as Norway and Chile, may want to consider adopting counter-cyclical policies. Historically, many countries have suffered a pattern of pro-cyclical fiscal policy: spending too much in booms and then forced to cut back in recessions. This problem has especially plagued Latin American commodity exporters. Since 2000, fiscal policy in Chile has been governed by a structural budget rule that has succeeded in implementing a countercyclical fiscal policy. Official estimates of trend output and the 10-year price of its main export, copper, are made by expert panels insulated from the political process. Their estimates are essential in highlighting which parts of the budget are structural and which are cyclical. Chile’s fiscal institutions hold useful lessons everywhere, but especially in other commodity-exporting countries like Russia.

14
# ANNEX

## DATA

For the cross-country, sector-level comparative analysis of manufacturing output, the INDSTAT 4 2009 Revision 2<sup>23</sup> and INDSTAT 4 2012 Revision 3<sup>24</sup> datasets from the United Nations Industrial Development Organization (UNIDO) are used. The two UNIDO datasets were combined to create a database representing 84 sectors (4-digit NACE)<sup>25</sup> from 134 countries for the time-period 1977 to 2009. The analysis is supplemented with data from the World Development Indicators,<sup>26</sup> and the World Bank’s _Enterprise Surveys._ The list of countries included in the UNIDO dataset, the average length of the panel per country, the number of observations per country and the number of sectors included in the data is listed in Table A1, below.

### **Table A1: Panel Statistics**

|**COUNTRYNAME**|**AVERAGELENGTH**<br>**OF THEPANEL**|**NO. OFOBS. **|**NUMBER OF**<br>**SECTORS**|
|---|---|---|---|
|**Afghanistan**|6.8|31|4|
|**Albania**|4.7|332|63|
|**Algeria**|0.0|40|40|
|**Argentina**|10.0|339|81|
|**Armenia**|10.3|685|62|
|**Aruba**||0||
|**Australia**|21.6|1539|77|
|**Austria**|22.2|1584|76|
|**Azerbaijan**|15.5|1192|76|
|**Bahamas**|7.7|194|38|
|**Bahrain**|0.0|39|39|
|**Bangladesh**|14.0|911|78|
|**Belarus**|1.0|14|7|
|**Belgium**|14.3|932|68|

> 23 <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=8&Lg=1</u>

> 24 <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=2&Lg=1</u>

25 NACE is the acronym used to designate the various statistical classifications of economic activities developed since 1970 in the European Union (EU). NACE provides the framework for collecting and presenting a large range of statistical data according to economic activity in the fields of economic statistics (e.g. production, employment, national accounts) and in other statistical domains. This classification was designed to delineate broad economic categories, into large economic classes of commodities, distinguishing food, industrial supplies, capital equipment, consumer durables and consumer non-durables. It is broadly used to stand for sectors. The higher the number of digits for the NACE, the more detailed the sector; from the most aggregate to the least, the classifications are organized by Section, Division, Group and finally Class. The analysis here is at the 4-digit NACE level; namely at the Group level. For more information, see <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=27&Lg=1.</u>

> 26 <u>http://data.worldbank.org/data-catalog/world-development-indicators</u>

15
|**COUNTRYNAME**|**AVERAGELENGTH**<br>**OF THEPANEL**|**NO. OFOBS. **|**NUMBER OF**<br>**SECTORS**|
|---|---|---|---|
|**Benin**|3.0|20|5|
|**Bermuda**|2.8|50|13|
|**Bolivia**|11.2|881|73|
|**Bosnia**|.|0||
|**Botswana**|2.6|40|12|
|**Brazil**|10.3|302|27|
|**Bulgaria**|7.1|537|67|
|**Burkina-Faso**||0||
|**Cambodia**|5.3|20|8|
|**Cameroon**|9.6|295|38|
|**Canada**|23.4|1926|79|
|**Cape Verde**|1.6|64|36|
|**Central African Republic**||0||
|**Chad**||0||
|**Chile**|13.6|1012|80|
|**China**|6.0|476|68|
|**Colombia**|22.2|1798|81|
|**Congo **||0||
|**Cook Islands**||0||
|**Costa Rica**|21.3|1705|77|
|**Croatia**||0||
|**Curaçao**||0||
|**Cyprus**|22.6|1490|64|
|**Czech Republic**|8.3|577|64|
|**Côte d'Ivore**|4.1|217|43|
|**Denmark**|15.9|1208|79|
|**Dominican Republic**||0||
|**Ecuador**|21.1|1656|77|
|**Egypt **|20.9|1364|78|
|**El Salvador**|4.3|382|75|
|**Eritrea**|13.6|645|45|
|**Estonia**|15.0|933|63|
|**Ethiopia**|17.1|703|39|
|**Ethiopia and Eritrea**|6.0|278|40|
|**Fiji **|14.7|563|46|
|**Finland**|24.6|2018|81|
|**France**|20.6|1510|73|
|**Gabon**|3.4|128|30|
|**Gambia**|3.6|7|5|
|**Georgia**|9.6|545|56|

16
|**COUNTRYNAME**|**AVERAGELENGTH**<br>**OF THEPANEL**|**NO. OFOBS. **|**NUMBER OF**<br>**SECTORS**|
|---|---|---|---|
|**Germany**|15.0|1266|80|
|**Germany East**|9.8|723|79|
|**Ghana**|15.8|475|70|
|**Greece**|22.4|1170|79|
|**Grenada**||0||
|**Guatemala**|14.2|887|72|
|**Haiti**|9.0|70|7|
|**Honduras**|14.8|993|63|
|**Hong Kong**|19.7|1540|78|
|**Hungary**|15.7|1093|69|
|**Iceland**|15.7|676|41|
|**India**|24.9|2096|81|
|**Indonesia**|24.5|1991|80|
|**Iran**|20.3|1363|78|
|**Iraq**|0.0|4|4|
|**Israel**|12.1|248|19|
|**Italy**|17.1|1337|76|
|**Jamaica**||0||
|**Japan**|19.7|1679|81|
|**Jordan**|18.2|1265|66|
|**Kazakhstan**|12.9|916|67|
|**Kenya **|7.5|119|14|
|**Korea**|24.9|2092|81|
|**Kuwait**|16.4|908|59|
|**Kyrgyzstan**|16.4|1161|70|
|**Lao**|0.0|7|7|
|**Latvia**|16.2|1072|66|
|**Lebanon**|7.0|105|59|
|**Lesotho**||0||
|**Liechtenstein**||0||
|**Lithuania**|13.8|1127|79|
|**Luxembourg**|11.4|329|28|
|**Macao**|12.2|694|62|
|**Macedonia**|7.0|506|68|
|**Madagascar**|9.1|263|37|
|**Malawi**|15.4|455|51|
|**Malaysia**|24.6|1903|78|
|**Maldives**||0||
|**Malta**|19.3|1344|74|
|**Mauritius**|11.8|831|66|

17
|**COUNTRYNAME**|**AVERAGELENGTH**<br>**OF THEPANEL**|**NO. OFOBS. **|**NUMBER OF**<br>**SECTORS**|
|---|---|---|---|
|**Mexico**|18.1|1302|73|
|**Moldova**|12.0|806|63|
|**Mongolia**|8.1|487|62|
|**Morocco**|11.0|948|80|
|**Mozambique**|.|0||
|**Myanmar**|9.6|487|54|
|**Namibia**|0.0|8|8|
|**Nepal**|15.4|524|63|
|**Netherlands**|18.0|1237|72|
|**Netherlands(Antilles)**||0||
|**New Zealand**|2.7|206|78|
|**Nicaragua**|1.8|179|63|
|**Niger**|3.5|73|17|
|**Nigeria**|8.2|484|68|
|**Norway**|23.2|1793|79|
|**Oman**|12.6|694|55|
|**Pakistan**|17.8|480|78|
|**Palestinian**|5.6|380|59|
|**Panama**|18.1|862|60|
|**Papua**||0||
|**Paraguay**|0.9|19|10|
|**Peru**|22.4|1544|81|
|**Philippines**|21.3|1191|79|
|**Poland**|12.9|932|69|
|**Portugal**|24.2|1822|81|
|**Puerto Rico**|15.7|234|14|
|**Qatar**|5.6|191|29|
|**Republic of Ireland**|15.1|851|60|
|**Romania**|12.0|900|73|
|**Russia**|10.1|833|79|
|**Rwanda**|0.0|10|10|
|**Saint Lucia**|3.8|139|29|
|**Saint Vincent**||0||
|**Saudi Arabia**|9.0|19|10|
|**Senegal**|13.7|702|65|
|**Serbia**|4.0|50|10|
|**Sierra Leone**||0||
|**Singapore**|21.4|1498|70|
|**Slovakia**|14.7|1059|74|
|**Slovenia**|13.3|920|69|

18
|**COUNTRYNAME**|**AVERAGELENGTH**|**NO OFOBS**|**NUMBER OF**|
|---|---|---|---|
||**OF THEPANEL**|**. . **|**SECTORS**|
|**Somalia**|0.0|36|36|
|**South Africa**|11.6|783|77|
|**Spain**|25.2|2083|80|
|**Sri Lanka**|15.1|1015|78|
|**Sudan**|0.0|49|49|
|**Suriname**||0||
|**Swaziland**|3.4|150|34|
|**Sweden**|24.9|1898|80|
|**Switzerland**|8.9|64|8|
|**Syrian**|9.0|32|4|
|**Taiwan**|0.0|45|45|
|**Tajikistan**|14.3|621|43|
|**Tanzania**|18.5|888|73|
|**Thailand**|21.8|885|78|
|**Tonga **|5.3|46|8|
|**Trinidad**|12.8|729|60|
|**Tunisia**|12.6|417|33|
|**Turkey**|24.1|1933|81|
|**Turkmenistan**|4.0|30|6|
|**United States**|24.9|2006|80|
|**Uganda**|0.0|55|55|
|**Ukraine**|14.9|1253|79|
|**United Kingdom**|19.5|1562|81|
|**Uruguay**|18.7|884|73|
|**Venezuela**|12.4|911|80|
|**Vietnam**|3.1|211|67|
|**Yemen**|7.3|66|8|
|**Zambia**|7.9|174|62|
|**Zimbabwe**|17.1|759|42|

Source: INDSTAT 4 2009 Revision 2 and INDSTAT 4 2012 Revision 3<sup>27</sup> datasets from the United Nations Industrial Development Organization (UNIDO) <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=2&Lg=1</u> 19
**Table A2: Summary of Output (pre and post sample selection)**

|**COUNTRY AND COMPARATOR GROUPS**|<br>**NUMBER OF**|**MEAN**|**NUMBER OF**|**MEAN**|
|---|---|---|---|---|
||**OBSERVATIONS**|**OUTPUT**|**OBSERVATIONS**|**OUTPUT**|
||**PRE-SAM**|**(IN MILLIONSUS**<br>**PLE SELECTION**|**$)**<br>**POST-SAM**|**(IN MILLIONSUS$)**<br>**PLE SELECTION**|
|**Russia**|833|3,660|738|3,970|
|**OECD**|41,560|6,690|24,661|8,100|
|**Resource rich countries**|4,683|2,770|2,232|3,780|
|**China**|476|60,700|408|66,800|
|**India**|2,096|2,850|1,072|4,290|
|**Korea**|2,092|5,330|1,041|8,600|

Source: Author ’ s calculation from UNIDO 2011 Industrial Output Data (4-digit NACE)

**Table A3: Number of observations removed from UNIDO dataset**

|**E**|**NO. OF**|**NO. OF**<br>**O **|**P**|
|---|---|---|---|
|**CONOMIES**|**OBS. **|**BS. IN**<br>**SAMPLE**|**ERCENT**|
|**Russia**|833|738|88.6%|
|**OECD**|41,560|25,157|60.5%|
|**Resource rich countries**|4,683|2,349|50.2%|
|**China**|476|408|85.7%|
|**India**|2,096|1,137|54.2%|
|**Korea**|2,092|1,097|52.4%|

Source: Author ’ s calculation from UNIDO 2011 Industrial Output Data (4-digit NACE) For the sector analysis, a shortened panel for the period between 1993 and 2009 is used. Since the UNIDO data for Russia start in 1993, this was the earliest period that the pane could begin.

Outlier observations – identified as growth greater than 3 standard deviations above or below the mean – for each sector in each country were removed. This results in dropping about 45 percent of the observations in the dataset. Table A2, above, provides details on the breakdown of the dataset preand post-sample selection.

20
<!-- Start of picture text --> 45000<br>40000<br>35000<br>30000<br>25000<br>m 1977-2009<br>20000<br>@ 1993-2009<br>15000 m 3 Std. Dev. Outside<br>10000<br>5000<br>fe)<br>Russia OECD Resource China India Korea<br>Rich<br>Countries<br><!-- End of picture text -->
**Table A5: Summary statistics with outliers**

|**2 DIGITNACE 1.1 **<br>**(MANUFACTURINGSUBSECTIONS) **|**NUMB**<br>**WOR**|**ER OF**<br>**KERS**|**COST OF**<br>**SOL**|**GOODS**<br>**D**|**OPERA**<br>**REVE**|**TING**<br>**NUE**|
|---|---|---|---|---|---|---|
||**MEAN**|**COUNT**|**MEAN(IN**<br>**MILLIONS)**|**COUNT**|**MEAN(IN**<br>**MILLIONS)**|**COUNT**|
|**Manufacture of food roducts**|||||||
|**p,**<br>**beverages**|127.31|81,216|4.0|117,624|4.9|117,624|
|**Manufacture of textiles and textile**<br>**products**|58.86|34,279|0.9|50,948|1.1|50,948|
|**Manufacture of leather and leather**<br>**products**|61.36|32,291|1.2|47,792|1.4|47,792|
|**Manufacture of pulp, paper and paper**<br>**products**|59.20|32,253|1.8|44,147|2.2|44,147|
|**Manufacture of coke, refined petroleum**|384.39|2,670|46.2|4,049|60.0|4,049|
|**Manufacture of chemicals, chemical**<br>**products**|134.16|25,346|5.1|35,305|6.6|35,305|
|**Manufacture of rubber and plastic**<br>**products**|67.05|30,755|1.9|39,747|2.1|39,747|
|**Manufacture of other non-metallic**<br>**minerals**|107.14|37,138|2.6|50,464|3.3|50,464|
|**Manufacture of basic metals and**<br>**fabrications**|115.85|59,236|5.2|77,808|6.3|77,808|
|**Manufacture of machinery and**<br>**equipment**|90.51|67,617|2.4|94,175|2.8|94,175|
|**Manufacture of electrical and optical**<br>**equipment**|86.50|53,503|2.5|74,309|3.0|74,309|
|**Manufacture of transport equipment**|297.08|15,231|12.2|21,645|13.9|21,645|
|**Manufacturing n.e.c.**|50.63|25,907|1.4|36,497|1.6|36,497|

Source: RUSLANA and Author’s calculations.

22
**Table A6: Summary statistics after removing outliers**

|**2 DIGITNACE 1.1 **<br>**(MANUFACTURINGSUBSECTIONS) **|**NUMB**<br>**WOR**<br>|**ER OF**<br>**KERS**<br>|**COST OF**<br>**SOL**<br>|**GOODS**<br>**D**<br>|**OPERA**<br>**REVE**<br>|**TING**<br>**NUE**<br>|
|---|---|---|---|---|---|---|
||MEAN<br>|COUNT<br>|MEAN(IN<br>MILLIONS)<br>|COUNT<br>|MEAN(IN<br>MILLIONS)<br>|COUNT<br>|
|**Manufacture of food products, beverages**|95.55|72,694|2.46|99,988|2.75|99,988|
|**Manufacture of textiles and textile**<br>**products**|36.31|30,544|0.50|43,418|0.55|43,418|
|**Manufacture of leather and leather**<br>**products**|35.43|27,338|0.59|37,551|0.65|37,551|
|**Manufacture of pulp, paper and paper**<br>**products**|36.91|29,574|0.77|38,915|0.86|38,915|
|**Manufacture of coke, refined petroleum**|215.99|2,070|22.30|2,783|26.80|2,783|
|**Manufacture of chemicals, chemical**<br>**products**|80.12|22,553|2.35|29,078|2.75|29,078|
|**Manufacture of rubber and plastic**<br>**products**|44.33|27,992|1.02|34,272|1.14|34,272|
|**Manufacture of other non-metallic**<br>**minerals**|77.72|32,320|1.67|40,982|1.88|40,982|
|**Manufacture of basic metals and**<br>**fabrications**|62.19|54,406|1.75|67,876|1.94|67,876|
|**Manufacture of machinery and equipment**|59.12|61,269|1.20|81,237|1.35|81,237|
|**Manufacture of electrical and optical**<br>**equipment**|58.72|48,287|1.32|63,982|1.51|63,982|
|**Manufacture of transport equipment**|164.77|13,505|4.76|18,244|5.23|18,244|
|**Manufacturing n.e.c.**|35.72|23,028|0.75|30,611|0.85|30,611|

Source: RUSLANA and Author’s calculations.

Firm size is defined by the number of employees that the firm has. See table below for summary statistics.

**Table A7: Number of observations by size**

|**FIRM SIZE**<br>|**NO. OF**<br>**WORKERS**<br>|**FREQ. **<br>|**PERCENT**<br>|
|---|---|---|---|
|**Micro**|<15|169,762|39.25|
|**Small**|15-99|194,704|45.01|
|**Medium**|100-249|37,141|8.59|
|**Large**|>=250|30,925|7.15|

Source: RUSLANA and Author’s calculations.

23
The names, definition of the key variables and how they are derived are provided in Table A8, below.

**TableA8: Variable definitions**

|**VARIABLENAME**|**DEFINITION**|
|---|---|
|**Cost of goods sold**|Cost of sold goods, production, services. Costs directly related to the production<br>of thegoods sold + depreciation of those costs|
|**Operating revenue**|Total operating revenues (Net sales + Other operating revenues+ Stock<br>variations). The figures do not include VAT.|
|**Current assets**|Total amount of current assets (Stocks + Debtors + Other current assets).|
|**Size**|Whether a firm is micro, small, medium or large. See table above.|
|**Age**|Year of analysis – date of incorporation|
|**Value added**|operating revenue – cost of goods sold|
|**Number of employees**|Total number of employees included in the company's payroll|
|**Value added per worker**|Value added/no. of employees|
||PCM=(operating revenue – cost of goods sold)/cost of goods sold|
|**Price-cost margin(PCM)**|In the case of sector, PCM = median(PCMfirms) of firms in that sector|
|**Entry of firm**|Date of incorporation|
|**Exit of firm**|Two consecutive years of missing or zero operating revenues combined with a<br>status that is not active.|

Table A9 below provides summary statistics of key variables before and after removing outliers.

**Table A9: Mean and median of key variables used in firm-level analysis**

|**VARIABLE**|**MEAN**|**MEDIAN**|
|---|---|---|
|**Operating Revenue**|1,844,960.00|278,885.90|
|**Cost of goods sold**|1,638,562.00|241,428.00|
|**Age**|7.48|6.00|
|**Number of employees**|65.11|21.00|
|**Value added per worker**|2,838.22|1,296.86|
|**Price cost margin**|0.09|0.09|

Source: RUSLANA and Author’s calculations.

24
# _MEASURING FIRM ENTRY AND EXIT_

The firm-level database, RUSLANA, provides information on whether a Russian firm is active or inactive. If a firm is taken out of the dataset, the data also indicates why it has been removed. This is used as one of indications that the firm has exited the market. However, in addition to checking whether firms are active a condition was added that verifies whether a firm has two consecutive years in which either data for that firm are missing or it reports zero turnover. This extra condition provides additional reliability in defining whether a firm has exited rather than just relying solely on the information provided by the dataset.<sup>29</sup> In sum, a firm is considered to have exited the market if it is listed as inactive and is missing two consecutive years of data. RUSLANA also provides the date of incorporation of a company; this is used as the date of establishment for a firm. Table A10 provides a summary of number of exits and entries per year.

**Table A10: Number of exits and entrants per year**

|**Year**|**Number**<br>**of exits**|**Number**<br>**of new**<br>**entrants**|**Number**<br>**of firms**|
|---|---|---|---|
|**1999**|609|2,652|21,585|
|**2000**|659|3,176|28,311|
|**2001**|873|3,584|35,680|
|**2002**|2,390|4,480|53,018|
|**2003**|2,148|6,379|57,395|
|**2004**|5,647|6,055|65,955|
|**2005**|5,366|6,365|65,151|
|**2006**|4,295|6,269|64,549|
|**2007**|4,067|6,105|65,508|
|**2008**|3,588|4,840|64,036|
|**2009**|2,727|3,618|61,045|
|**2010**|2,360|3,839|59,186|

Source: Author’s calculations using RUSLANA An obvious concern with this method is the ability to separate entry and exit in the dataset with that from the economy. In case of entries, this is straightforward since RUSLANA provides information on the date of incorporation (separate from entry into the dataset), thus clearly delineating entry into the economy. The information on exit more complicated since it relies on administrative data, which could experience varying lags in reporting. There is confidence, however, that the additional condition of two consecutive years of missing or zero turnover would be enough to correct for these lags and accurately capture the time of exit since even if the firm is falsely reported as still present in the economy when it has not been operational it will be captured by this condition checking for missing or zero values.

The inability to reliably distinguish between an exit and when a firm merges is acquired or makes other drastic changes of this kind is an issue that could not be resolved using these data. The criteria used here would classify instances where companies were only technically closed due to a merger or an acquisition, a change of name, etc. as an exit. Estimating growth rates, volatility, surges, slumps, depth and duration The methodology used to compare growth rates and, identify growth surges and slumps, and measure the depth (height) and duration of slumps and surges is described below. In addition, the metric used to measure firm exits and entry is explained.

> 29 The information on the reason and time of exit are based on administrative datasets such as the tax directorate or social security, hence the exit of certain firms in the dataset at times occur after few years it has been created.

25
# _CALCULATING GROWTH RATES_

For the purpose of this paper, growth rates are based on the measure proposed in Haltiwanger et al. (2010).<sup>30</sup> The growth rate is calculated as follows: Where _g_ is growth and _yt_ is output at time _t_ and _yt-1_ is output in the previous period. A result in the value of 1, using this measure, implies a growth rate of 200 (Refer to Table A4for correspondences between the Haltiwanger growth measure and the regular measure). This measure has similarities to log differences while also accommodating entry and exit in the growth rate. In addition to the benefit of accommodating entry and exit of firms, this measure is used instead of the more standard measures of growth so there is no need to be concerned about the differences in the size of the base from which growth is calculated; a relatively smaller base yields higher growth rates, all things being equal, meaning that even small changes in output, when starting out from a small base, look like big changes in growth.

This measure of growth rate however, requires particular attention to the start and end of a panel of output figures. For a firm/sector entering the panel at time t output does not exist in t-1, similarly a firm/sector leaving the panel output does not exist at the time of exit. Adjustment for this was made by calculating growth rates from year 2 in the panel and not calculating growth rates for the year of exit. In addition, any change from or to an output level of zero implies a growth rate of either +2/-2 (signifying infinite growth rates). This is particularly tricky when the zero value appears in the middle of the panel. Adjustment for these is made by removing them from the analysis of growth rates.

**Table A4: Comparison with the Haltiwanger growth rate**

|**STANDARD**|**HALTIWANGERGROWTHRATE**|
|---|---|
|**GROWTHRATE**||
|5%|0.05|
|10%|0.10|
|20%|0.18|
|50%|0.40|
|100%|0.67|
|200%|1.00|
|500%|1.43|
|1000%|1.67|
|2000%|1.82|
|5000%|1.92|

In addition, this way of calculating growth yields symmetrical results whether growth is positive or negative. For example, growth in output from 100 to 200 units represents 100 percent increase. However, when growth is negative, from 200 to 100 units, this represents a 50 percent decrease. Using the growth calculation proposed by Haltiwanger et al., using the same example, the growth rates would be 2/3 and -2/3, respectively. This symmetry in positive and negative growth is important for the analysis.

> 30 Haltiwanger, John C., Jarmin, Ron S. and Miranda, Javier, “Who creates jobs?: Small vs. large vs. young (August 1, 2010). United States Census Bureau, Center for Economic Studies, Paper No. CES-WP-10-17. Available at SSRN: <u>http://ssrn.com/abstract=1666157</u> or http://dx.doi.org/10.2139/ssrn.1666157

26
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STATISTICS AND ESTIMATES ON OUTPUT CONCENTRATION IN RUSSIA

**Table A11: Concentration of output**

|**YEAR**|**SHARE OFBOTTOM**|**SHARE OFTOP**|**TOTALOPERATING**|
|---|---|---|---|
||**QUARTILE OF SECTORS**|**QUARTILE OF SECTORS**|**REVENUE(IN**|
||**IN TERMS OF**<br>**OPERATING REVENUE**<br>|**IN TERMS OF**<br>**OPERATING REVENUE**<br>|**MILLIONUSD)**<br>|
|**1999**|0.4%|81.8%|45,700|
|**2000**|0.2%|90.7%|106,000|
|**2001**|0.6%|81.7%|76,300|
|**2002**|0.6%|79.2%|168,000|
|**2003**|0.6%|77.4%|190,000|
|**2004**|0.6%|78.3%|221,000|
|**2005**|0.6%|79.5%|234,000|
|**2006**|0.7%|81.0%|306,000|
|**2007**|0.7%|81.2%|394,000|
|**2008**|0.8%|79.9%|299,000|
|**2009**|0.7%|78.4%|259,000|
|**2010**|0.7%|78.7%|282,000|
|**2011**|0.7%|80.2%|278,000|

Source: Author’s calculations using RUSLANA

**Table A12: Concentration of output within sectors**

|**2 DIGITNACE 1.1**<br>**(MANUFACTURING**<br>**SUBSECTION) **|**AVERAGESHARE OF**<br>**BOTTOMQUARTILE**<br>**(WITHIN4-DIGIT**<br>**NACE1.1) OF FIRMS IN**|**SHARE OFTOP**<br>**QUARTILE(WITHIN4-**<br>**DIGITNACE1.1) OF**<br>**FIRMS IN TERMS OF**|**AVERAGE OFTOTAL**<br>**OPERATINGREVENUE**<br>**IN4-DIGITNACE1.1**<br>**(IN MILLIONUSD)**|
|---|---|---|---|
||**TERMS OF OPERATING**<br>**REVENUE**|**OPERATING REVENUE**||
|**Manufacture of food**<br>**products, beverages**|0.08%|93.6%|29,600|
|**Manufacture of textiles**<br>**and textile products**|0.14%|94.2%|4,160|
|**Manufacture of leather and**<br>**leatherproducts**|0.01%|96.1%|15,400|
|**Manufacture of pulp, paper**<br>**and paper products**|0.14%|94.6%|9,790|
|**Manufacture of coke,**<br>**refinedpetroleum**|0.0002%|99.5%|215,000|
|**Manufacture of chemicals,**<br>**chemical products**|0.02%|97.1%|13,800|
|**Manufacture of rubber and**<br>**plasticproducts**|0.13%|91.9%|11,100|
|**Manufacture of other non-**<br>**metallic minerals**|0.04%|91.9%|16,100|
|**Manufacture of basic**<br>**metals and fabrications**|0.07%|95.8%|19,200|
|**Manufacture of machinery**<br>**and equipment**|0.09%|94.3%|15,800|

29
|**Manufacture of electrical**<br>**and optical equipment**|0.06%|94.8%|19,100|
|---|---|---|---|
|**Manufacture of transport**<br>**equipment**|0.01%|97.7%|48,400|
|**Manufacturing n.e.c.**|0.05%|95.7%|7,630|

Source: Author’s calculations using RUSLANA

# _ANALYSIS OF EXITS, SLUMPS, SURGES AND COMPETITION_

For firm-level data for Russian firms, the study exploits a rich, firm-level dataset called RUSLANA.<sup>32</sup> However, the same method is used to calculate sector-level<sup>33</sup> surges and slumps in RUSLANA. To measure productivity, value added per worker is used.<sup>34</sup> The current assets of the firm are used along with value added per worker to approximate firm-level estimates of total factor productivity. The price cost margin (PCM) measures of competition.<sup>35</sup> Controls are used for regressions: an interaction between 2-digit- industry codes and years to control to time-varying sector specific shocks, and controls for location fixed effects using 85 regions dummies. The controls for years account for overall macroeconomic shocks as well as timevarying industry-level factors such as political economy, demand shocks or technological shocks, while the location controls adjust for region-specific characteristics that are time-invariant. The regressions are also clustered sectors and years as the main explanatory variables do vary at the level of sector and year.<sup>36</sup> Finally also included is the age of the firm and dummies for size<sup>37</sup> as additional controls. Clearly, there are other firm-level time varying shocks which could influence the results that not captured here. However, to the extent that these are uncorrelated with other key variables (surge/slump, competition) conditional on the covariates the results are unbiased.

> 32 As with the growth rate calculation, operating revenue is used to calculate surges and slumps. Operating revenue was summed at the level of 4-digit NACE 1.1 for every year to calculate surges and slumps.

> 33 4-digit NACE 1.1 is used to define sectors.

> 34 The ideal measure of firm productivity would be total factor productivity (TFP) however data limitations do not allow to estimate it. The main drawback of labor productivity is that it is influenced by the capital intensity of the company. In other words, two companies that have exactly the same productivity measured using TFP but differ in their capital intensity will appear to have different labor productivity as the one with more capital will be able to produce more value added per worker. In order to address this problem, all regressions include the stock of capital (assets) of the company.

> 35 PCM=(Operating revenue - Cost of goods sold)/Cost of goods sold. A higher PCM means a lower the level of competition.

36 In this case the variables of interest are at the firm level while some of the main regressors vary at the sector-year level, hence clustering of standard error at the sector-year level is required as suggested by Moulton(1990)

> 37Refer to this link for definitions for micro, small and medium in Russia. - <u>http://www.gks.ru/free_doc/new_site/business/inst preob/obsled/mal_bisnes.htm</u>

30
**Table A13: Firm-level statistics - current assets (in 1000s)**

|**Size**|**N**|**Mean**|**Std. dev.**|**p10**|**p25**|**p50**|**p75**|**p90**|
|---|---|---|---|---|---|---|---|---|
|**Micro**|199,242|201780|4164167|696.0193|4258.19|18245.55|60785.59|172630.1|
|**Small**|202,450|860909.3|1.38E+07|17474.07|65299.63|199388.3|539761.1|1293593|
|**Medium**|40,217|2890061|1.01E+07|209437.4|563468.2|1276528|2713552|5648698|
|**Large **|42,068|1.80E+07|1.81E+08|742631.3|1719325|4249895|1.07E+07|2.86E+07|

Source: Author’s calculations using RUSLANA

**TableA14: Firm-level statistics - value added per worker (in 1000s)**

|**Size**|**N**|**Mean**|**Std. dev**|**p10**|**p25**|**p50**|**p75**|**p90**|
|---|---|---|---|---|---|---|---|---|
|**Micro**|199,242|3370.888|35761.82|-469.968|89.83334|848.6792|3300.341|8142.333|
|**Small**|202,450|3859.066|2.22E+04|6.107754|322.6116|1569.344|4351.495|9116.152|
|**Medium**|40,217|3821.288|6.61E+03|-11.8224|563.0723|2154.832|5117.769|9690.881|
|**Large **|42,068|3.20E+03|4.92E+03|4.85822|569.4197|1932.481|4.36E+03|7.86E+03|

Source: Author’s calculations using RUSLANA

**Table A15: Firm-level statistics - age**

|**Size**|**N**|**Mean**|**Std. dev**|**p10**|**p25**|**p50**|**p75**|**p90**|
|---|---|---|---|---|---|---|---|---|
|**Micro**|199,242|5.4|5.2|0|1|4|9|12|
|**Small**|202,450|7.9|7.0|2|3|6|11|15|
|**Medium**|40,217|10.8|12.5|2|5|9|13|17|
|**Large **|42,068|21.5|30.0|4|8|13|17|57|

Source: Author’s calculations using RUSLANA

**Table A16: Sector-level statistics - price cost margin**

|**Industry Subsection**|**N**|**Mean**|**Std. dev**|**p10**|**p25**|**p50**|**p75**|**p90**|
|---|---|---|---|---|---|---|---|---|
|**Food products, beverages**|117,624|0.09|0.05|0.03|0.05|0.08|0.11|0.16|
|**Textiles and textile products**|50,947|0.07|0.04|0.04|0.05|0.07|0.09|0.12|
|**Leather and leather products**|47,792|0.07|0.03|0.04|0.05|0.07|0.09|0.10|
|**Pulp, paper and paper products**|44,142|0.08|0.05|0.06|0.07|0.08|0.10|0.11|
|**Coke, refined petroleum**|4,049|0.10|0.04|0.06|0.08|0.10|0.12|0.12|
|**Chemicals, chemical products**|35,305|0.14|0.06|0.08|0.10|0.14|0.17|0.24|
|**Rubber and plastic products**|39,747|0.09|0.02|0.06|0.08|0.09|0.10|0.12|
|**Other non-metallic minerals**|50,464|0.09|0.04|0.05|0.06|0.08|0.11|0.14|
|**Basic metals and fabrications**|77,807|0.09|0.03|0.06|0.07|0.09|0.11|0.12|
|**Machinery and equipment**|94,175|0.10|0.03|0.05|0.07|0.09|0.12|0.14|
|**Electrical and optical equipment**|74,309|0.11|0.05|0.07|0.09|0.11|0.14|0.16|
|**Food products, beverages**|21,632|0.09|0.03|0.06|0.07|0.08|0.10|0.11|
|**Textiles and textile products**|36,497|0.11|0.04|0.07|0.08|0.10|0.14|0.15|

Source: Author’s calculations using RUSLANA 31
**Table A17: Firm-level regression – likelihood of exit**

|Year, industry and location controls<br>Clustered at sector (4-digit NACE) and year|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|**Independent variables**|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|
|**surge/slump (surge=1, slump=0)**|0.0106***<br>(0.00334)|-0.00313<br>(0.00212)|0.00437<br>(0.00292)|-0.00345<br>(0.00433)|
|**ln(value added per worker)**|-0.00566***<br>(0.000345)|-0.00538***<br>(0.000331)|-0.00531***<br>(0.000428)|-0.00560***<br>(0.000449)|
|**ln(current assets)**|0.000845**<br>(0.000365)|0.000304<br>(0.000318)|0.000356<br>(0.000420)|0.000981**<br>(0.000482)|
|**small (15-99 employees)**|-0.00436***<br>(0.00125)|-0.00376***<br>(0.00126)|-0.0196***<br>(0.00174)|-0.0210***<br>(0.00173)|
|**medium (100-249 employees)**|-0.00945***|-0.00854***|-0.0217***|-0.0249***|
||(0.00184)|(0.00185)|(0.00260)|(0.00260)|
|**large (250+ employees)**|-0.0171***<br>(0.00205)|-0.0165***<br>(0.00206)|-0.0308***<br>(0.00288)|-0.0358***<br>(0.00287)|
|**age**|-0.000492***<br>(3.32e-05)|-0.000385***<br>(3.21e-05)|-0.000836***<br>(4.46e-05)|-0.000645***<br>(4.09e-05)|
|**surge/slump × ln(value added per worker)**|-0.00202***<br>(0.000597)|-0.00298***<br>(0.000538)|-0.00434***<br>(0.000712)|-0.00381***<br>(0.000792)|
|**surge/slump × ln(current assets)**|-0.00165***|||-0.00250***|
||(0.000502)|||(0.000857)|
|**surge/slump × age**||0.0154***<br>(0.00149)||0.0365***<br>(0.00228)|
|**surge/slump × small**|||0.00182|0.0128***|
||||(0.00240)|(0.00269)|
|**surge/slump × medium**|||-0.00352<br>(0.00356)|0.0154***<br>(0.00433)|
|**surge/slump × large**|||0.00124<br>(0.00346)|0.0259***<br>(0.00471)|
|**Constant**|0.0535***|0.0549***|0.0803***|0.0773***|
||(0.00423)|(0.00407)|(0.00466)|(0.00477)|
|**Observations**|357,252|357,252|256,544|256,544|
|**R-squared**|0.016|0.016|0.019|0.021|

Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: Microenterprises is the omitted category and absorbed by the constant. Source: Author’s calculations using RUSLANA 32
**Table A18: Firm-level regression including PCM variable – likelihood of exit**

|Year, industry and location controls<br>Clustered at sector (4-digit NACE) and year|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|
|**surge/slump (surge=1, slump=0)**|0.0101***|-0.00354*|0.00406|-0.00405|
||(0.00330)|(0.00213)|(0.00277)|(0.00421)|
|**ln(value added per worker)**|-0.00551***|-0.00522***|-0.00506***|-0.00534***|
||(0.000344)|(0.000330)|(0.000428)|(0.000450)|
|**ln(current assets)**|0.000713*<br>(0.000364)|0.000176<br>(0.000313)|0.000136<br>(0.000414)|0.000734<br>(0.000476)|
|**PCMsector**|-0.106***|-0.105***|-0.157***|-0.152***|
||(0.0214)|(0.0216)|(0.0227)|(0.0229)|
|**small (15-99 employees)**|-0.00401***<br>(0.00121)|-0.00341***<br>(0.00122)|-0.0190***<br>(0.00166)|-0.0204***<br>(0.00165)|
|**medium (100-249 employees)**|-0.00930***<br>(0.00182)|-0.00841***<br>(0.00182)|-0.0214***<br>(0.00254)|-0.0245***<br>(0.00253)|
|**large (250+ employees)**|-0.0169***<br>(0.00201)|-0.0163***<br>(0.00201)|-0.0303***<br>(0.00279)|-0.0352***<br>(0.00276)|
|**age**|-0.000479***<br>(3.32e-05)|-0.000373***<br>(3.20e-05)|-0.000819***<br>(4.49e-05)|-0.000629***<br>(4.11e-05)|
|**surge/slump × ln(value added per worker)**|-0.00203***<br>(0.000598)|-0.00299***<br>(0.000537)|-0.00434***<br>(0.000713)|-0.00386***<br>(0.000790)|
|**surge/slump × ln(current assets)**|-0.00164***<br>(0.000504)|||-0.00239***<br>(0.000854)|
|**surge/slump × age**||0.0153***||0.0363***|
|||(0.00148)||(0.00228)|
|**surge/slump × small**|||0.00178<br>(0.00238)|0.0126***<br>(0.00267)|
|**surge/slump × medium**|||-0.00362|0.0149***|
||||(0.00357)|(0.00432)|
|**surge/slump × large**|||0.00111<br>(0.00342)|0.0252***<br>(0.00465)|
|**Constant**|0.0640***|0.0653***|0.0963***|0.0930***|
||(0.00503)|(0.00485)|(0.00522)|(0.00534)|
|**Observations**|357,252|357,252|256,544|256,544|
|**R-squared**|0.016|0.016|0.020|0.022|

Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: Microenterprises is the omitted category and absorbed by the constant. Source: Author’s calculations using RUSLANA 33
**Table A19: Firm-level regression with PCM variable with interactions – likelihood of exit**

|Year, industry and location controls<br>Clustered at sector (4-digit NACE) and year|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|
|**surge/slump (surge=1, slump=0)**|0.0101***|-0.00354*|0.00407|-0.00413|
||(0.00330)|(0.00213)|(0.00277)|(0.00421)|
|**ln(value added per worker)**|-0.00595***|-0.00568***|-0.00553***|-0.00580***|
||(0.000388)|(0.000373)|(0.000472)|(0.000491)|
|**ln(current assets)**|0.000649*|0.000112|6.25e-05|0.000654|
||(0.000364)|(0.000314)|(0.000416)|(0.000477)|
|**PCMsector**|-0.116***|-0.115***|-0.167***|-0.162***|
||(0.0217)|(0.0219)|(0.0232)|(0.0233)|
|**PCMsector× ln(value added per worker)**|0.00203***|0.00207***|0.00226***|0.00222***|
||(0.000647)|(0.000649)|(0.000819)|(0.000814)|
|**small (15-99 employees)**|-0.00376***<br>(0.00122)|-0.00316***<br>(0.00122)|-0.0189***<br>(0.00166)|-0.0203***<br>(0.00165)|
|**medium (100-249 employees)**|-0.00898***|-0.00808***|-0.0211***|-0.0242***|
||(0.00182)|(0.00183)|(0.00254)|(0.00253)|
|**large (250+ employees)**|-0.0164***<br>(0.00202)|-0.0158***<br>(0.00202)|-0.0299***<br>(0.00279)|-0.0348***<br>(0.00276)|
|**age**|-0.000479***|-0.000373***|-0.000819***|-0.000628***|
||(3.32e-05)|(3.20e-05)|(4.50e-05)|(4.11e-05)|
|**surge/slump × ln(value added per**<br>|-0.00200***|-0.00295***|-0.00435***|-0.00387***|
|**worker)**|(0.000601)|(0.000538)|(0.000715)|(0.000792)|
|**surge/slump × ln(current assets)**|-0.00164***<br>(0.000504)|||-0.00236***<br>(0.000854)|
|**surge/slump × age**||0.0153***<br>(0.00148)||0.0363***<br>(0.00228)|
|**surge/slump × small**|||0.00178|0.0125***|
||||(0.00238)|(0.00267)|
|**surge/slump × medium**|||-0.00360<br>(0.00356)|0.0148***<br>(0.00431)|
|**surge/slump × large**|||0.00115<br>(0.00342)|0.0251***<br>(0.00465)|
|**Constant**|0.0645***|0.0657***|0.0969***|0.0935***|
||(0.00504)|(0.00486)|(0.00524)|(0.00536)|
|**Observations**|357,252|357,252|256,544|256,544|
|**R-squared**|0.016|0.017|0.020|0.022|

Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: Microenterprises is the omitted category and absorbed by the constant. Source: Author’s calculations using RUSLANA 34