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Firm entry and exit during a crisis period: Evidence from Russian regions
expand article infoIchiro Iwasakiℓℓ, Mathilde Maurel₰₰₱₱₳₳, Bogdan Meunier₰₰
ℓℓ Institute of Economic Research, Hitotsubashi University, Tokyo, Japan
₰₰ Centre d’Économie de la Sorbonne, Université Paris 1 Panthéon Sorbonne, Paris, France
₱₱ Centre National de la Recherche Scientifique, Paris, France
₳₳ Fondation pour les Études et Recherches sur le Développement International, Paris, France
Open Access

Abstract

In this paper, we aim to empirically analyze the determinants of firm entry and exit in Russia using a regional-level panel data for the years of 2008–2014, with special emphasis on institutional failures and the politico-economic impact of external crises. We found that these two elements exhibit statistically significant and economically meaningful effects both on the creation and destruction of Russian firms, controlling for potentially explanatory factors. Our empirical results also suggest that the process of firm entry and exit is manifold across Russian regions due to their heterogeneity. Nevertheless, a surprisingly robust estimate of the world oil price (irrespective of the difference in target regions) suggests a possible high exposure of each Russian region to a global crisis. This comes from the importance of oil trade with the world and, accordingly, the ongoing crisis may bring a harmful influence to regeneration of Russian businesses.

Keywords

firm entry, firm exit, institutions, economic integration, crisis, Russia

JEL classification: D22, F15, G01, P31, P33

1. Introduction

One generation after the launch of the perestroika, two features characterize Russian business relative to many other transition countries: first, barriers to entry are considerably more pronounced and second, as a result, the extremely low level of firm entry in Russia by international standards. The Amadeus dataset, which is restricted to firms with more than 50 employees, provides a record of formal entry and shows gross entry rates from 1999 at below one percent. Entry rates are significantly lower in Russia than in other former socialist economies, and even these are (negative) outliers by developed economy standards (Aidis and Estrin, 2006; Aidis and Adachi, 2007; Aidis et al., 2008). For comparison, in Brazil the gross entry rates are up to 14% and net entry rates in China and India of over 6% pa and 3–4% pa, respectively. The picture that emerges is a blessing curse, as growth is fueled by oil exports, while other economically important areas, such as small and medium enterprises (SMEs), are underdeveloped.

The reinforcement of SMEs is key for the emergence of values and norms attached to the market economy. In terms of purely formal constraints, Russia performs relatively well; but enforcement is poor. The rule of law is also weak, creating uncertainty and non-consistency, which is damaging to the firms’ prosperity. The absence of confidence in investing at home is reflected by the net private capital outflows, which stood in 2014 at $150 billion, equivalent to 8% of GDP. One of the main problems faced by Russian business owners is illegal practices such as bribing and corruption, as repetitively reflected in the Russian firms’ survey by the World Bank. The fatality of being involved in corruption activities constitutes the main pervasive and self-reinforcing entry barrier.

The World Bank doing business indicators provide useful information for assessing the quality of the business environment across a large set of countries: ease of doing business, starting a business (licenses), getting electricity, registering property, getting credit, protecting minority investors, enforcing contracts, resolving insolvency. As reported by Doing Business, Russia ranks 13th out of 25, its worst position being registered under the protection of minority investors, trading across borders, and dealing with construction permits items. Two out of those three indicators correspond to our research questions: Trading across borders records the time and cost associated with the logistical process of exporting and importing goods. More precisely, it measures the time and cost (excluding tariffs) associated with three sets of procedures —documentary compliance, border compliance and domestic transport —within the overall process of exporting or importing a shipment of goods. This indicator describes the logistical obstacles towards a deeper integration of Russian enterprises into the world economy. Protection of minority rights measures the strength of minority shareholder's protection against misuse of corporate assets by directors for their personal gain, as well as shareholder rights, governance safeguards and corporate transparency requirements that reduce the risk of abuse. One interesting component in the building of the protection of minority rights is the ease of shareholder suit index, which is one of our variables of interest.

Another pernicious feature of the Russian economic performance over the recent decades is the continuous fall of non-energy goods and services in total exports, echoing the weaknesses of SMEs in the manufacturing and service sectors, while oil represents the lion's share of total trade. In fact, in recent years, the concentration of Russia's economy in the oil and gas sector has steadily increased over time. In addition, Russia's non-energy sector has become less competitive in world markets. While the volume of the nation's non-energy merchandise exports grew at an annual rate of 11% in 2010, they grew by only 7.6% in 2014.1 According to the UNCTAD, cumulative foreign direct investments (FDI) per capita over the period 1989–2014 were low up to 2008 as compared with similar countries (CEECs EU and non-EU) and from 2008 onwards displayed a relatively higher trend. In the first period they stand at $3558, respectively $6562 for CEECs EU countries and $3658 for CEECs non-EU countries. Same data over the second more recent period (2008–2014) delivers a different picture, Russia having attracted $2443 of FDI per capita while CEECs EU countries and CEECs non-EU countries received $2534 and $2174 of FDI per capita, respectively. Needless to say, the top donor of inward FDI is the fuel and energy sector.2 Overall, Russia's comparative advantage in the oil sector is reinforcing in the last decade. It corresponds to a double hypothesis, which our empirical analysis is focusing on: first the country's sensitivity to the crisis passes essentially to the evolution of the world market for oil. This has obvious repercussions for Russian SMEs because the shocks in the oil market are spreading to the rest of the economy. In addition, Russian firms face the curse of the oil rent, which is amplified by the institutional weaknesses such as the low enforcement of property rights and corruption, as mentioned above.

In this paper, we aim to analyze the determinants of firm entry and exit in Russia using a hand-crafted regional-level panel data for the years of 2008–2014, with a special emphasis on the institutional failures and the politico-economic impact of external crises. We found that these two elements exhibit statistically significant and economically meaningful effects both on the creation and destruction of Russian firms, while controlling for potentially explanatory factors. The robust estimate of the world oil price, irrespective of the difference in target regions, indicates that Russian regions are possibly sensitive to a global crisis whatever the circumstances. Accordingly, the ongoing crisis may bring a harmful influence to a regeneration of the Russian business.

The study of firm's birth and death in Russia is relatively scarce. Noticeable exceptions are Rinaldi (2008), Iwasaki (2014), and Sprenger (2014), who investigated the firm-level determinants of survival. This paper complements their findings by identifying the national and regional-level factors that significantly affect both the market entry and exit of Russian firms.

The remainder of the paper is structured as follows: The next section reviews the literature about firms’ net entry and creative destruction, by focusing on transition countries. Section 3 presents statistical evidence on the entry and exit of Russian firms over the period 2008–2014. Section 4 conducts the empirical analysis while Section 5 interprets the empirical results and concludes the paper.

2. Literature review

The process of entry of new firms is an important element in the dynamics of market economies, as underlined by Caves (1998) and Bartelsman et al. (2004). New firm entry helps to transfer resources from low to high productivity activities and underlies competitive pressures dissipating monopoly rents. When analyzing ways in which institutional change affects the performance of developing economies, North (1991) emphasized the role that the institutional environment plays in promoting entrepreneurial development and the impact of informal as well as formal institutions, suggesting that entry barriers may be higher in developing countries where the general business environment is weaker. As a developing economy, Russia is indeed characterized by a complex system of entry barriers or entry fostering mechanisms. The weakness of institutions enhances the market power of incumbents (Djankov et al., 2002), with harmful implications for welfare (Banerjee and Ghatak, 2005). Following these ideas, the work of De Soto (1990) has suggested that regulation of entry represents an important entry barrier in emerging markets. The literature has argued that regulation is introduced by policy makers in their own interests, namely the pursuit of their own remuneration (Shleifer and Vishny, 1993). When analyzing how legal and institutional environments raise the cost of doing business, Friedman et al. (2000) found that the costs of enforcing contracts are associated with higher level of corruption. These additional costs bear more heavily on entrants than incumbents and poor institutional environments enhance the advantages to incumbents yielded by higher levels of regulation.

The empirical evidence on emerging markets strongly supports the predictions with respect to regulations, but the argument with regard to institutions has rarely been tested. Using data on regulations of entry in 85 countries, Djankov et al. (2002) find a positive relationship between the size of informal economy (in turn highly correlated with corruption), and the burden of the entry regulation measured by the number of procedures, time and cost of starting a firm. Similarly, Klapper et al. (2006) find that regulations hinder entry, notably those which are in naturally “high entry” industries. However, regulations are not always welfare reducing; labor regulations reduce entry into labor intensive sectors but property rights protection increases entry in R&D intensive sectors (Bertrand and Kramarz, 2002). Ciccone and Papaionnaou (2007) show that entry rates are higher when the time for registering new businesses is lower, although this is also influenced by demand and technology factors.

In the specific case of Russia, Aidis and Adachi (2007) attempt to find out the reason for a relatively low number of new firms. Since the internationally comparative data do not provide an explanation for this situation, the authors conclude that the low degree of firm creation and the low survival rate of newly created businesses are due to informal impediments associated with the lack of rule of law, inconsistent enforcement of regulations, regional autonomy and pervasive corruption. These findings are consistent with the conclusions of the survey carried out by Estrin and Prevezer (2010) in a selected group of emerging economies. The survey displays widely varying entry and exit rates among the countries, which is due to different institutional settings. In the case of Russia, relatively good formal rules and structures are undermined by informal mechanisms deterring or blocking business entry.

The exiting literature has also highlighted the weak institutional environment in Russia with respect to entrepreneurship (Aidis et al., 2008) with negative informal values towards private business and lack of property rights enforcement (Puffer and McCarthy, 2001; Aidis and Adachi, 2007). Berkowitz and DeJong (2005) show that Russian regional entrepreneurial activity exhibits a statistically and quantitatively significant relationship with subsequent growth. However, the regulatory stance that the Russian national and local governments take toward business and the levels of corruption work to the detriment of private sector development (Frye and Shleifer, 1997). Given these specific institutional weaknesses, recent studies have highlighted different aspects concerning the diffusion of new entrepreneurs in connection with institutions. For instance, Aidis et al. (2008) explore the impact of the entrepreneurial environment by comparing Russia with other economies. Their results suggest that the negative environment for business in Russia has led to low levels of entrepreneurship. The authors conclude that the weakness of Russian institutions is detrimental to entrepreneurial activity and although networks are important, they are not entirely able to offset these deficiencies. In parallel, Bruno et al. (2008), who analyze a three-year panel of Russian firms, conclude that the entry rates in Russia are correlated with institutions and firm size. A recent paper by Iwasaki (2014) confirms the key importance of institutions, by providing strong evidence that the independence of governance bodies from top management is positively correlated with the survival probability of the firm. This result is in line with one key aspect in corporate governance research: outside shareholders and outsider board members feel freer to criticize the company management than employee shareholders and insider directors, who are more likely to support their company's top executives.

In this paper we also try to quantify the extent to which the 2008 financial shock and subsequent economic crisis affected the firm creation and destruction in Russia. The global turmoil impacted the emerging countries in different ways, depending on their previous growth patterns and forms of international integration (Drahokoupil and Myant, 2012). The initial shock in the United States spread over the rest of the world through a contagion/transmission mechanism which transformed the initial sub-prime crisis into a global economic turmoil. Although being major beneficiaries of the economic boom before 2007, the emerging countries became the first victims of the global crisis after 2008 (Dolphin and Chappell, 2010). This is because the world economy is much more integrated and interdependent today than ten or twenty years ago, which implies a limited national economic sovereignty and consequently higher exposure of all countries to international macroeconomic fluctuations (Dabrowski, 2010). Gurtner (2010) considers that the crisis was transmitted primarily by trade and financial flows and the impact on emerging countries was higher in those that were highly integrated in the world economy.

Although Russia entered the crisis with a strong fiscal position, low public debt and large reserves (Bogetic et al., 2010,) the country experienced one of the sharpest declines of GDP. According to Blanchard et al. (2010), the first shock announcing the crisis was related to the war in Georgia, which practically opened the transmission channel to the economic turbulence. A severe decline in the stock market took place in August 2008, compounded by plummeting oil prices, which initiated a severe economic recession (Barannik, 2010). As a result, numerous weaknesses of Russia's export-dependent economy in the money markets and in the financial sector started to evolve. The high impact of the crisis, in spite of very good fiscal situation, was due to three main factors (Ickes and Gaddy, 2010): high dependence of the economy on oil and gas, addiction to resources rents, and the specificity of “Protection Racket” system of political economy. These systemic problems have obstructed the complete recovery of the economy after the 2008 crisis.

Further, in December 2014, Russia entered a new financial crisis which has exposed the real scale of the economic problems that have been growing in the country for several years. The main macroeconomic indicators deteriorated considerably, the confidence of its citizens in the state and in institutions in charge of economic stability declined, the government and business elites became increasingly dissatisfied with the policy direction adopted by the Kremlin, and fighting started over the shrinking resources (Łabuszewska, 2015). It should be noted that in addition to the crisis aspect, the Russian economy has been equally affected by the EU-USA sanctions following the annexation of Crimea and further activities in Eastern Ukraine. Oxenstierna and Olsson (2015) conclude that the targeted economic sanctions have contributed to imposing a cost on the Russian economy. In the presence of a politicized economic system, this gave advantage in resource allocation to rent-addicted loss-making producers loyal to the regime over competitive companies, which damaged the country's economic performance. On top of those factors, the falling oil price on the world markets, coupled with the depreciation of the ruble, have weakened even more the terms of trade.

Based on the above discussions, we conjecture that the firm entry rate into the market is impacted mainly through the evolution of the oil sector, a fact that is entirely compatible with the low integration of Russian SMEs in the world economy. We also give great attention to the institutional failures that reinforce the curse of the oil sector, namely the poor efficiency of the judiciary system. The latter contributes to bail out inefficient firms and to reinforce barriers to entry and exit.

3. Firm entry and exit in Russia: A statistical overview

In this section, we overlook the dynamics and trends of firm entry and exit in Russia overall and its regions during the period 2008–2014. The Federal State Statistics Service (Rosstat) discloses monthly rates of firm entry and exit all over the Russian Federation, and yearly rates for federal districts and constituent entities, which are displayed in Figs. 1 and 2, and in Table 1.

Fig. 1

Dynamics of firm entry and exit in Russia, 2008–2014.

Note: Firm entry and exit rate denote number of newly established and liquidated firms per 1,000 organizations, respectively.

Source: Russian Federal State Statistics Service (http://www.gks.ru).

Fig. 2

Dynamics of firm entry and exit in Russian federal districts, 2008–2014.

Note: Firm entry and exit rate denote number of newly established and liquidated firms per 1,000 organizations, respectively.

Source: Russian Federal State Statistics Service (http://www.gks.ru).

Ranking of Russian regions in terms of firm entry and exit in 2008 and 2014.

Firm entry(a)
2008 2014 Change from 2008
Ranking Region name Entry rate Ranking Region name Entry rate Ranking Entry rate
1 Chechen Republic 155.9 1 Republic of Ingushetia 249.5 79 174.2
2 Perm Territory 148.3 2 Republic of Tatarstan 120.9 14 –8.1
3 Sverdlovsk Region 147.7 3 Lipetsk Region 115.4 8 –18.1
4 Kemerovo Region 142.1 4 St. Petersburg 112.7 33 –0.4
5 Ivanovo Region 142.0 5 Voronezh Region 111.3 66 17.4
6 Chelyabinsk Region 137.4 6 Nizhny Novgorod Region 110.4 2 –24.9
7 Amur Region 136.5 7 Ivanovo Region 108.8 ▴ 2 –33.2
8 Nizhny Novgorod Region 135.3 8 Samara Region 106.2 39 –3.0
9 Novosibirsk Region 134.7 9 Perm Territory 105.9 ▴ 7 –42.4
10 Khabarovsk Territory 134.6 10 Sverdlovsk Region 105.0 ▴ 7 –42.7
11 Lipetsk Region 133.5 11 Vologda Region 103.9 1 –28.5
12 Vologda Region 132.4 12 Republic of Udmurtia 103.2 52 –0.3
13 Belgorod Region 131.0 13 Belgorod Region 102.4 0 –28.6
14 Republic of Buryatia 130.2 14 Novosibirsk Region 102.0 ▴ 5 –32.7
15 Volgograd Region 129.1 15 Krasnoyarsk Territory 101.6 4 –25.1
16 Republic of Tatarstan 129.0 16 Primorsky Territory 101.4 9 –19.9
17 Republic of Sakha (Yakutia) 128.0 17 Altai Territory 101.2 11 –18.6
18 Smolensk Region 126.8 18 Khabarovsk Territory 100.7 ▴8 –33.9
19 Krasnoyarsk Territory 126.7 19 Kirov Region 99.3 30 –8.8
20 Kostroma Region 124.8 20 Yaroslavl Region 99.1 9 –20.4
21 Sakhalin Region 123.6 21 Republic of Buryatia 98.3 ▴7 –31.9
22 Murmansk Region 123.0 22 Ulyanovsk Region 97.7 28 –10.4
23 Bryansk Region 122.3 23 Amur Region 97.2 ▴16 –39.3
24 Vladimir Region 121.7 24 Kemerovo Region 94.6 ▴20 –47.5
25 Primorsky Territory 121.3 25 Chelyabinsk Region 94.6 ▴19 –42.8
26 Republic of Karelia 120.9 26 Omsk Region 93.1 5 –24.5
27 Tula Region 120.4 27 Penza Region 93.1 7 –22.6
28 Altai Territory 119.8 28 Tyumen Region 91.6 8 –22.2
29 Yaroslavl Region 119.5 29 Chuvash Republic 90.5 6 –24.2
30 Tambov Region 117.9 30 Moscow 90.4 12 –21.3
31 Omsk Region 117.6 31 Tambov Region 89.7 ▴1 –28.2
32 Tomsk Region 117.5 32 Republic of Bashkortostan 88.4 11 –23.3
33 Republic of Tuva 117.0 33 Murmansk Region 87.9 ▴11 –35.1
34 Penza Region 115.7 34 Republic of Mari El 87.9 7 –24.1
35 Chuvash Republic 114.7 35 Volgograd Region 87.4 ▴20 –41.7
36 Tyumen Region 113.8 36 Irkutsk Region 87.3 8 –22.6
37 St. Petersburg 113.1 37 Smolensk Region 86.8 ▴ 19 –40.0
38 Novgorod Region 113.0 38 Orenburg Region 86.2 7 –23.3
39 Orel Region 112.7 39 Bryansk Region 85.9 ▴16 –36.4
40 Republic of Daghestan 112.3 40 Vladimir Region 85.9 ▴16 –35.8
41 Republic of Mari El 112.0 41 Tula Region 85.7 ▴14 –34.7
42 Moscow 111.7 42 Jewish Autonomous Region 85.1 12 –22.5
43 Republic of Bashkortostan 111.7 43 Republic of Sakha (Yakutia) 84.9 ▴26 –43.1
44 Irkutsk Region 109.9 44 Ryazan Region 84.0 13 –22.6
45 Orenburg Region 109.5 45 Tver Region 83.9 30 –0.6
46 Kaliningrad Region 109.4 46 Chechen Republic 82.4 ▴45 –73.5
47 Samara Region 109.2 47 Republic of North Ossetia —Alania 82.3 18 –20.8
48 Kurgan Region 109.1 48 Yamal-Nenets Autonomous Area 80.1 4 –27.7
49 Kirov Region 108.1 49 Saratov Region 80.0 29 1.1
50 Ulyanovsk Region 108.1 50 Krasnodar Territory 79.7 8 –26.9
51 Kaluga Region 108.0 51 Republic of Karelia 79.5 ▴25 –41.4
52 Yamal-Nenets Autonomous Area 107.8 52 Rostov Region 78.9 17 –18.0
53 Arkhangelsk Region 107.6 53 Kostroma Region 77.9 ▴33 –46.9
54 Jewish Autonomous Region 107.6 54 Sakhalin Region 77.7 ▴33 –45.9
55 Astrakhan Region 107.2 55 Kaliningrad Region 77.3 ▴9 –32.1
56 Republic of Khakasia 107.0 56 Astrakhan Region 77.1 ▴1 –30.1
57 Ryazan Region 106.6 57 Kursk Region 77.1 2 –28.8
58 Krasnodar Territory 106.6 58 Tomsk Region 76.7 ▴26 –40.8
59 Kursk Region 105.9 59 Arkhangelsk Region 76.4 ▴6 –31.2
60 Republic of Mordovia 105.4 60 Republic of Adygeya 76.3 7 –24.4
61 Moscow Region 104.2 61 Khanty-Mansi Autonomous Area —Yugra 76.1 11 –16.3
62 Republic of Komi 104.2 62 Zabaikalsk Territory 75.6 1 –28.2
63 Zabaikalsk Territory 103.8 63 Republic of Khakasia 74.5 ▴7 –32.5
64 Republic of Udmurtia 103.5 64 Kamchatka Territory 73.7 9 –17.6
65 Republic of North Ossetia —Alania 103.1 65 Nenets Autonomous Area 73.3 11 –11.1
66 Karachayevo-Circassian Republic 102.5 66 Republic of Daghestan 72.4 ▴26 –39.9
67 Republic of Adygeya 100.7 67 Republic of Komi 72.0 ▴5 –32.2
68 Kabardino-Balkarian Republic 98.9 68 Novgorod Region 71.3 ▴30 –41.7
69 Rostov Region 96.9 69 Republic of Altai 70.3 1 –24.9
70 Republic of Altai 95.2 70 Moscow Region 69.7 ▴ 9 –34.5
71 Voronezh Region 93.9 71 Orel Region 68.8 ▴32 –43.9
72 Khanty-Mansi Autonomous Area —Yugra 92.4 72 Kaluga Region 68.8 ▴21 –39.2
73 Kamchatka Territory 91.3 73 Pskov Region 67.7 1 –20.4
74 Pskov Region 88.1 74 Republic of Mordovia 66.6 ▴14 –38.8
75 Tver Region 84.5 75 Republic of Kalmykia 66.6 8 15.9
76 Nenets Autonomous Area 84.4 76 Kurgan Region 66.3 ▴28 –42.8
77 Stavropol Territory 83.9 77 Kabardino-Balkarian Republic 59.0 ▴9 –39.9
78 Saratov Region 78.9 78 Karachayevo-Circassian Republic 58.6 ▴12 –43.9
79 Leningrad Region 77.4 79 Republic of Tuva 58.3 ▴46 –58.7
80 Republic of Ingushetia 75.3 80 Magadan Region 57.9 1 –13.8
81 Magadan Region 71.7 81 Stavropol Territory 53.8 ▴4 –30.1
82 Chukotka Autonomous Area 71.2 82 Leningrad Region 51.6 ▴3 –25.8
83 Republic of Kalmykia 50.7 83 Chukotka Autonomous Area 49.8 ▴1 –21.4
(b) Firm exit
2008 2014 Change from 2008
Ranking Region name Exit rate Ranking Region name Exit rate Ranking Exit rate
1 Republic of North Ossetia —Alania 123.3 1 Tula Region 130.3 72 101.5
2 Republic of Altai 101.8 2 Moscow 120.0 78 98.3
3 Altai Territory 77.3 3 Jewish Autonomous Region 117.5 19 61.3
4 Chuvash Republic 75.4 4 Republic of Kalmykia 113.1 46 74.4
5 Omsk Region 72.7 5 Arkhangelsk Region 111.3 67 82.1
6 Sakhalin Region 70.5 6 Perm Territory 109.4 59 77.2
7 Kabardino-Balkarian Republic 69.4 7 Novosibirsk Region 109.0 5 43.7
8 Tver Region 68.7 8 Republic of Bashkortostan 103.9 16 48.6
9 Chechen Republic 67.0 9 St. Petersburg 103.0 73 83.9
10 Republic of Udmurtia 66.7 10 Yaroslavl Region 101.5 52 67.9
11 Tambov Region 65.8 11 Republic of Mari El 101.4 4 38.6
12 Novosibirsk Region 65.3 12 Penza Region 100.1 15 47.5
13 Zabaikalsk Territory 63.8 13 Kostroma Region 93.4 41 55.9
14 Kemerovo Region 63.3 14 Republic of Altai 93.0 ▴12 –8.8
15 Republic of Mari El 62.8 15 Chechen Republic 90.9 ▴6 23.9
16 Orenburg Region 62.3 16 Tambov Region 90.3 ▴5 24.5
17 Republic of Ingushetia 61.3 17 Yamal-Nenets Autonomous Area 90.1 60 64.3
18 Lipetsk Region 59.1 18 Sverdlovsk Region 89.6 49 57.8
19 Republic of Tuva 58.3 19 Omsk Region 86.8 ▴14 14.1
20 Bryansk Region 57.5 20 Chukotka Autonomous Area 86.5 21 43.5
21 Ulyanovsk Region 57.5 21 Kamchatka Territory 86.5 55 59.5
22 Jewish Autonomous Region 56.2 22 Ulyanovsk Region 85.5 ▴1 28.0
23 Kurgan Region 55.7 23 Altai Territory 83.4 ▴20 6.1
24 Republic of Bashkortostan 55.3 24 Zabaikalsk Territory 81.3 ▴11 17.5
25 Pskov Region 55.1 25 Nenets Autonomous Area 80.9 31 45.4
26 Smolensk Region 54.0 26 Lipetsk Region 80.1 ▴8 21.0
27 Penza Region 52.6 27 Kabardino-Balkarian Republic 78.9 ▴20 9.5
28 Kursk Region 51.9 28 Murmansk Region 75.0 7 29.1
29 Volgograd Region 51.6 29 Vladimir Region 74.9 39 43.3
30 Novgorod Region 51.4 30 Samara Region 74.8 10 30.8
31 Stavropol Territory 48.9 31 Republic of Ingushetia 74.7 ▴14 13.4
32 Voronezh Region 47.3 32 Chelyabinsk Region 74.2 19 35.7
33 Republic of Karelia 47.2 33 Kemerovo Region 73.7 ▴ 19 10.4
34 Amur Region 46.7 34 Tver Region 73.5 ▴ 26 4.8
35 Murmansk Region 45.9 35 Primorsky Territory 73.3 39 45.3
36 Kaliningrad Region 45.4 36 Republic of Sakha (Yakutia) 72.7 8 31.4
37 Khabarovsk Territory 45.3 37 Vologda Region 71.9 12 32.1
38 Republic of Mordovia 45.0 38 Kirov Region 71.7 8 31.0
39 Tomsk Region 44.6 39 Republic of North Ossetia —Alania 71.0 ▴38 –52.3
40 Samara Region 44.0 40 Orenburg Region 70.7 ▴ 24 8.4
41 Chukotka Autonomous Area 43.0 41 Tyumen Region 70.5 29 38.9
42 Saratov Region 42.8 42 Voronezh Region 70.3 ▴ 10 23.0
43 Rostov Region 41.6 43 Amur Region 69.7 ▴9 23.0
44 Republic of Sakha (Yakutia) 41.3 44 Republic of Tatarstan 69.4 15 34.6
45 Republic of Buryatia 41.0 45 Nizhny Novgorod Region 66.2 2 25.5
46 Kirov Region 40.7 46 Republic of Tuva 65.9 ▴ 27 7.6
47 Nizhny Novgorod Region 40.7 47 Saratov Region 65.6 ▴ 5 22.8
48 Orel Region 40.0 48 Krasnoyarsk Territory 64.2 16 31.7
49 Vologda Region 39.8 49 Republic of Karelia 63.2 ▴16 16.0
50 Republic of Kalmykia 38.7 50 Khabarovsk Territory 62.3 ▴13 17.0
51 Chelyabinsk Region 38.5 51 Astrakhan Region 59.9 4 23.8
52 Ivanovo Region 38.2 52 Smolensk Region 59.7 ▴26 5.7
53 Karachayevo-Circassian Republic 37.6 53 Kurgan Region 59.6 ▴ 30 3.9
54 Kostroma Region 37.5 54 Volgograd Region 59.6 ▴ 25 8.0
55 Astrakhan Region 36.1 55 Sakhalin Region 59.3 ▴ 49 –11.2
56 Nenets Autonomous Area 35.5 56 Tomsk Region 59.2 ▴ 17 14.6
57 Krasnodar Territory 35.5 57 Ivanovo Region 59.0 ▴ 5 20.8
58 Ryazan Region 35.4 58 Rostov Region 57.7 ▴ 15 16.1
59 Republic of Tatarstan 34.8 59 Orel Region 57.5 ▴ 11 17.5
60 Irkutsk Region 34.4 60 Irkutsk Region 57.5 0 23.1
61 Magadan Region 34.2 61 Magadan Region 57.5 0 23.3
62 Yaroslavl Region 33.6 62 Belgorod Region 57.5 4 25.7
63 Republic of Daghestan 32.7 63 Leningrad Region 57.4 16 32.6
64 Krasnoyarsk Territory 32.5 64 Krasnodar Territory 56.3 ▴ 7 20.8
65 Perm Territory 32.2 65 Ryazan Region 56.0 ▴ 7 20.6
66 Belgorod Region 31.8 66 Republic of Mordovia 55.8 ▴ 28 10.8
67 Sverdlovsk Region 31.8 67 Pskov Region 55.7 ▴ 42 0.6
68 Vladimir Region 31.6 68 Chuvash Republic 55.5 ▴ 64 –19.9
69 Republic of Komi 31.6 69 Republic of Khakasia 53.1 2 22.4
70 Tyumen Region 31.6 70 Bryansk Region 52.7 ▴ 50 –4.8
71 Republic of Khakasia 30.7 71 Republic of Adygeya 52.5 7 27.5
72 Arkhangelsk Region 29.2 72 Kaliningrad Region 51.8 ▴ 36 6.4
73 Tula Region 28.8 73 Republic of Udmurtia 51.4 ▴ 63 –15.3
74 Primorsky Territory 28.0 74 Karachayevo-Circassian Republic 51.1 ▴ 21 13.5
75 Kaluga Region 27.5 75 Republic of Komi 46.8 ▴ 6 15.2
76 Kamchatka Territory 27.0 76 Republic of Buryatia 45.1 ▴ 31 4.1
77 Yamal-Nenets Autonomous Area 25.8 77 Kaluga Region 42.4 ▴ 2 14.9
78 Republic of Adygeya 25.0 78 Khanty-Mansi Autonomous Area —Yugra 42.2 3 22.6
79 Leningrad Region 24.8 79 Republic of Daghestan 39.3 ▴ 16 6.6
80 Moscow 21.7 80 Moscow Region 36.8 3 19.2
81 Khanty-Mansi Autonomous Area —Yugra 19.6 81 Kursk Region 36.7 ▴ 53 –15.2
82 St. Petersburg 19.1 82 Novgorod Region 35.6 ▴ 52 –15.8
83 Moscow Region 17.6 83 Stavropol Territory 35.3 ▴ 52 –13.6

Panel (a) of Fig. 1 shows that, as argued in the previous section, firm entry rates in Russia are relatively low and depict a slight decreasing trend (from more than 9 newly established companies per 1,000 existing firms to 8 per 1,000 between 2008 and 2014). On the other hand, as shown in Panel (b) of the same figure, the number of liquidated companies per 1,000 firms is even lower, but exhibits a much stronger and significant increase (by about two points during the same period). In fact, a simple OLS regression of the firm entry and exit rates on a time trend variable shows that the monthly firm entry rate decreases by 0.0145 and the firm exit rate increases by 0.0673 at 5% and 1% level of statistical significance, respectively.3

Regarding the entry of new firms into the market, Panel (a) of Fig. 1 shows that the trend started to revert towards mid–2012 with a slight increasing tendency in the beginning of the second semester of 2014. Over the whole period of observation the evolution of firm entry is rather chaotic, with successive peaks and bottoms; for example, in October 2008 the firm entry rate was 10.1% but in January 2009 it fell to 5.2%, and then again increased to 8.6% by March 2009. This erratic evolution can be observed during the whole time horizon 2008–2014, which shows a significant degree of instability in the economy coupled with high uncertainty related to legislation, political and institutional environment, and discretionary bureaucratic practices in granting new business licenses. On annual basis Panel (a) of Fig. 2 shows that the entry of Russian firms experienced a significant decline from 115.2 per 1,000 firms in 2008 to 88.7 in 2009, remaining afterwards at practically the same level between 2009 and 2014.

With respect to the firm exit, Panel (b) of Fig. 1 shows a relatively constant increase of the indicator until the beginning of 2011, followed by a highly volatile evolution afterwards. This can be attributed to the previous factors — uncertainty and instability — related to informal mechanisms deterring the net expansion of businesses. As a result, towards the end of 2014 the new entries are entirely annulled by the number of firms quitting the market. On annual basis, as Panel (b) of Fig. 2 exhibits, the firm exit rate depicts a modest increase until 2010, then a sudden jump in 2011, followed by a declining trend in the last two years of the period. The high difference in the firm exit rates between 2010 and 2011 can be attributed to the world economic crisis, whose impact arrived with some time- lag as compared to the entry rates, where the effect of the global turmoil took place immediately.

Fig. 2 also illustrates time-series changes of firm entry and exit rates at the level of federal districts. From this figure we can observe that there is a weak but negative trend in firm entry, while a notable increasing trend exists in the case of firm exit. In other words, as in the whole federation, Russian districts have experienced an accelerated decrease of turnover (number of firms created plus the number of firms that left the market) since January 2008, with a much more significant contribution of the firm exit. The declining trend of entries, coupled with an increasing tendency of exit rates, suggest an overall low survival rate of Russian companies on the market, as discussed in Aidis and Adachi (2007).

Table 1 reports the rankings of Russian regions in terms of firm entry and exit rates in 2008 and 2014. This table suggests that the situation of each particular region changed substantially during the period. In fact, according to Panel (a) of Table 1, out of the first 10 regions ranked with respect to the firm entry rates in 2008, only four are present in the top 10 six years later, in 2014 (Perm Territory, Sverdlovsk, Ivanovo and Nizhny Novgorod). The best performer of 2008 (Chechen Republic) is ranked only 46th in 2014. With few exceptions, we therefore cannot speak about constantly good performers. The business development in a particular region and in a specific period is probably subject to the political orientation of local authorities; when local governments are politically consonant with the central power the region performs well.

Similarly, as indicated in Panel (b) of Table 1, out of the top 10 regions recording the lowest firm exit rates in 2008 only three of them (Kaluga, Moscow region and Khanty-Mansi Area) are ranked in 2014 among the first ten best performers; the Moscow capital, for instance, lost 78 positions over the period, passing from an exit rate of 21.7 in 2008 to 120 six years later. At the same time, the Russian Federation contains regions where both firm entry and exit rates are high in 2014 (Saint Petersburg: entry rate 112.7; exit rate 103), as well as regions where both indicators are low (Stavropol: entry rate 53.8; exit rate 35.3). The two categories of regions are relatively balanced and record overall a net rate of business creation. Nevertheless, there are certain regions where the entry rate is low and the exit rate high (Chukotka Area: entry rate 49.8; exit rate 86.5), which implies a net rate of firm destruction and consequently economic depression. There are only very few regions where high firm entry rates coexist with low exit rates, implying a good development potential: only one such region is found within the top 20 with respect to high entry, respectively low exit rates — Republic of Udmurtia: entry rate 103.2; exit rate 51.4.

In sum, the aforementioned findings suggest firstly that the market entry of Russian firms was discouraged by the global financial crisis, which started in September 2008 and lasted at the end of 2014, a period characterized by subsequent economic downturns. Firms’ exit exhibited a strong upward trend and it is conceivable that this phenomenon was also triggered by the 2008 financial shock from the US and amplified by the following crises in Europe and Ukraine. Second, the above tendency was observed in all federal districts, suggesting that the recent crises have negatively influenced Russian firms and entrepreneurs whatever their geographical location and specialization. Third, the level and volatility of firms’ entry and exit rates vary greatly across Russian regions and over time. This suggests that the impact of the crisis was different, depending on the vulnerability to the world crisis and the reliance on the oil sector, but, in a more discriminative way, on the differences in institutional and business environments.

4. Empirical analysis

In this section, we will empirically examine the determinants of firm entry and exit in Russia using a regional-level panel data for the period between 2008 and 2014. Here, inspired by the arguments in the existing literature and the observations reported in the previous section, we intend to perform regression analysis paying a special attention to two aspects: institutional failures and the recent politico-economic crises. More specifically, we will estimate a multivariate regression equation, which takes a natural logarithm of firm entry or exit rate as a dependent variable and proxies for the regional-level institutional quality and the economic integration as independent variables, while controlling for other potentially explanatory factors. The next subsection gives a detailed explanation of independent variables used in our regression analysis. Subsection 4.2 reports estimation results, and Subsection 4.3 checks statistical robustness of the empirical evidence.

4.1. Selection of independent variables

To assess the impact of institutional failures on the creation and destruction of Russian firms, we employed four proxies for the efficiency of the judiciary system which are available at the regional level. They include: the mean and median duration of lawsuit at a regional arbitration court, the acceptance rate of petition by regional arbitration courts, and lawsuits application number to regional arbitration courts per 1,000 organizations. As emphasized in Dmitrieva et al. (2012), the duration of a lawsuit at a regional arbitration is a key aspect, as it affects the functioning of a business activity. Slowness and poor performance of the judiciary system to resolve disputes relating to the non-compliance with contracts and the non-payment of arrears can jeopardize the business of SMEs. For the latter, delayed payments combined with a limited access to bank credit can result in the inability to continue their business. The Russian Federation is classified as a “high compliance country”, which refers to the level of compliance with international standards for corporate governance. The country is considered therefore as having a sound legal framework at a par with OECD ones (EBRD, 2005). Official data on entry costs (like number of days for starting a business and contract enforcement) puts Russia as the best performer of the BRIC countries. The time needed to resolve overdue payments fell from 7 to 6 weeks between 2002 and 2005. Formal barriers are relatively low. But the de facto reality is far from this de jure situation. Law enforcement is indeed arbitrary, with over 80% of Russian entrepreneurs suffering from broken contracts (Radaev, 2002). Johnson et al. (1999) indicate that relational contracting plays a significant role in Russia where the court system is inefficient, which is a strong obstacle to the normal development of SMEs. Overall, it seems that the legal and regulatory framework is blurred by numerous inconsistencies, with many Soviet regulations still in force; “No one really knows which laws and regulations are implemented and observed, although it is clear that many are not implemented at all, or only partially” (OECD, 2005, p. 5). These arguments suggest that the above four variables are likely to be negatively related to the net entry rate of Russian firms. We believe that, although these variables are cross-sectional due to information constrains, they reflect well the effectiveness of regional courts; not being derived formally from official definitions, they depict what really happens on the ground.

In addition to the above court-related variables, we will also utilize a criminal risk ranking evaluated by the Expert rating agency—a famous Russian think-tank, which is a regional-level time-variant variable, as an additional proxy for the institutional quality. According to the Transparency International, Russia has much worse levels of corruption than Brazil, China and India for 1998 — 2004, improving somewhat to converge with Indian levels in 2002. The Business Environment and Enterprise Performance Survey (BEEPS) reports a significant worsening in the perception of corruption between 2002 and 2005 as compared with transition countries, where the situation improved (EBRD, 2005). Corruption refers to the percentage of contract paid to secure a government contract, the percentage of respondents who agreed to pay irregular payments or gifts and losses due to crime as a share of sales, all of which appear to be significant in Russia. This corruption cannot be considered as being wheel-greasing, but it is rather linked to deficiencies in law enforcement, whereby legislation can be interpreted in discretionary ways by the authorities. Radaev (2002) finds that over 80% of Russian entrepreneurs had suffered from broken contracts. Guriev and Rachinsky (2004) argue that while industrial concentration has increased, the influence of incumbents increased; in certain regions, the governor's influence has protected incumbents who are members of the family: the region of Oryol, for instance (Aidis and Adachi, 2007), Kursk (Kryshtanovskaya and White, 2005). The Expert criminal ranking is regarded as a good proxy for the level of corruption and the institutional failure in general. Accordingly, we will estimate the coefficient of this variable together with the court-related variables. These five variables are called “institutional variables” hereinafter.

To assess possible impact of the politico-economic crises on firm entry and exit, we adopted four variables according to the discussion in Section 2. They comprise: a natural logarithm of world oil price, total trade volume as percentage of gross regional products (GRP), inflow of FDI in % of GRP, as well as a natural logarithm of direct distance between Brussels and the capital cities of Russian regions. As suggested in Kuboniwa (2014), the world oil price is extremely volatile against a global crisis and greatly influences the economic and business circumstances in Russia. Hence, we expect that the world oil price effectively captures macro-economic shocks of the recent crises on the whole Russian economy. A crisis may affect Russian firms and entrepreneurs through shrinking of international trade and FDI. The total trade and FDI in percent of GRP are used to grasp these indirect effects of a crisis. The distance of Brussels and a regional capital is employed as a proxy for the geographical proximity of Russian regions to the EU. We assume that if other conditions are equal, Russian regions located near to the EU market tend to be negatively affected by a crisis much stronger than other regions. We call these four variables as “economic integration variables” hereinafter.

In the regression estimation, we will also control for a series of factors that represent medium-term economic development, financial constraints, quality of social infrastructure, degree of inter-firm competition, market density as well as natural obstacles for business. The medium-term economic development is expressed by the GRP growth rate. The financial constraint is measured by a natural logarithm of number of credit organizations and their branches per 100,000 residents, which is considered as one of major barriers to both starting up and expansion of businesses in Russia (Puffer and McCarthy, 2001; Pissarides et al., 2003). The quality of social infrastructure is proxied by a natural logarithm of number of PCs with internet connection per 100 workers, taking into account the fact that the information technology plays an important role in the contemporary Russian business. A natural logarithm of firms and organizations per 10,000 residents and population density are utilized to capture the effect of interfirm competition and market density, respectively. We expect that the former factor is negatively associated with firm entry and positively related to firm exit. On the other hand, we predict that the latter factor has a promoting effect of firm entry and exit. Average temperature in January serves as a proxy for natural obstacles for business, assuming that more severe climate conditions force Russian firms and entrepreneurs to take higher initial sunk costs that may restrict firm entry and exit (Iwasaki and Suganuma, 2005). In addition to the above six factors, we will also control for the time trend and district-level fixed effects, which, according to Figs. 1 and 2, may have a statistically significant impact both on firm entry and exit.

With regard to the time-variant variables, to avoid possible endogeneity with the dependent variables, a lagged three year moving average is utilized except for the world oil price, which is obviously exogenous for the overwhelming majority of Russian firms and entrepreneurs. The definition, descriptive statistics, and source of the aforesaid variables are described in Table 2.

Definition, descriptive statistics, and source of variables used in the empirical analysis.

Variable group and name Additional definition Descriptive statistics Source
mean s.d median max. min.
Firm entry and exit variables
Log of firm entry Log of number of newly established firms per 1,000 organizations 4.50 0.21 4.50 5.52 3.57 Rosstat (http://www.gks.ru/)
Log of firm exit Log of number of liqudated firms per 1,000 organizations 4.07 0.50 4.07 5.66 1.36 Rosstat
Institutional variables
Log of duration of a lawsuit at a regional arbitration court (mean) Data during 2007–2011 4.26 0.22 4.23 5.53 3.80 Dmitrieva et al. (2012)
Log of duration of a lawsuit at a regional arbitration court (median) Data during 2007–2011 4.04 0.19 4.07 4.68 3.37 Dmitrieva et al. (2012)
Acceptance rate of petition by regional arbitration courts (%) Data in 2014 94.75 2.21 95.10 98.54 87.70 Federal Arbitration court (http://www.arbitr.ru/)
Log of lawsuit application number to regional arbitration courts per 1,000 organization Data in 2014 5.96 0.41 5.92 7.04 5.02 Federal Arbitration court (http://www.arbitr.ru/)
Expert criminal risk ranking Lagged 3 year moving average 43.19 21.48 43.33 87.00 1.33 Expert rating agency (http://www.raexpert.ru/)
Economic integration variables
Log of world oil price Log of simple average of spot prices of Dated Brent, West Texas 4.51 0.18 4.58 4.65 4.12 IMF Commodity Price Database
(http://www.imf.org/external/np/res/commod/index.aspx/)
Intermediate, and the Dubai Fateh computed using monthly data
Total trade in % of GRP Lagged 3 year moving average 23.85 21.33 17.27 133.93 0.03 Rosstat
FDI inflow in % of GRP Lagged 3 year moving average 3.82 5.85 2.02 68.59 0.00 Rosstat
Log of direct distance from Brussels Distance from region capital in kilometers 8.11 0.44 7.99 9.04 7.04 Authors’ calculation
Control variables
GRP growth rate (%) Percent to previous year; lagged 3 year moving average 3.98 3.49 3.80 16.27 –7.27 Rosstat
Log of number of credit organizations and their branches per 100,000 residents Lagged 3 year moving average 0.99 0.49 0.97 2.61 –2.48 Rosstat
Log of number of PCs with internet connection per 100 workers Lagged 3 year moving average 2.48 0.50 2.54 3.90 0.29 Rosstat
Log of number of firms and organizations per 10,000 residents Lagged 3 year moving average 5.49 0.40 5.49 6.95 4.20 Rosstat
Log of population density Per km2; lagged 3 year moving 2.71 1.79 3.10 8.43 –2.65 Rosstat
average
Average temperature in January In centigrade; lagged 3 year moving average –12.29 7.67 –10.87 1.40 –34.10 Rosstat
Time trend dummy 2008 = 0 3.00 2.00 3.00 6.00 0.00 Authors’ calculation

4.2. Estimation results

Tables 3 and 4 show the estimation results of firm entry and exit models, respectively. To obtain coefficients of the time-invariant variables, we carry out a regression analysis by using a pooling OLS and a random-effects estimators and report one of these two estimation results according to the Breusch–Pagan test of the null-hypothesis that the variance of regional individual effects is zero. In both tables, Models [1] to [4] represent this kind of estimation results. In addition, to check statistical robustness of the time-variant variables, we also estimated a fixed-effect model and reported its result as Model [5].

Panel data estimation of firm entry model in Russian regions.

Dependent variable Log of firm entry
Estimation period 2008–2014
Target regions All regions
Estimator Random-effects Random-effects Random-effects Random-effects Fixed-effects
Model [1] [2] [3] [4] [5]
Institutional variables
Log of duration of a lawsuit at a regional arbitration court (mean) –0.1148 (–0.960)
Log of duration of a lawsuit at a regional arbitration court (median) –0.0227 (–0.120)
Acceptance rate of petition by regional arbitration courts (%) –0.0184* (–1.810)
Log of lawsuit application number to regional arbitration courts per 1,000 organization –0.1705*** (–2.820)
Expert criminal risk ranking –0.0004 (–0.800) –0.0004 (–0.800) –0.0003 (–0.610) –0.0002 (–0.460) –0.0009* (–1.790)
Economic integration variables
Log of world oil price 0.4424*** (14.850) 0.4425*** (14.970) 0.4161*** (13.360) 0.4164*** (13.610) 0.4217*** (14.530)
Total trade in % of GRP –0.0013 (–1.460) –0.0013 (–1.520) –0.0011 (–1.040) –0.0012 (–1.120) –0.0007 (–0.550)
FDI inflow in % of GRP –0.0015 (–0.740) –0.0016 (–0.780) –0.0014 (–0.620) –0.0016 (–0.680) –0.0012 (–0.450)
Log of direct distance from Brussels 0.1453 (0.870) 0.1187 (0.690) 0.1285 (0.760) 0.1299 (0.840)
Control variables
GRP growth rate (%) 0.0137*** (6.900) 0.0136*** (6.850) 0.0122*** (4.450) 0.0118*** (4.330) 0.0104*** (4.280)
Log of number of credit organizations and their branches per 100,000 residents 0.0478 (0.750) 0.0406 (0.620) –0.1032 (–1.530) –0.0976 (–1.530) –0.1923* (–1.690)
Log of number of PCs with internet connection per 100 workers 0.2361* (1.800) 0.2307* (1.730) 0.1647 (1.120) 0.1492 (1.040) 0.1369 (1.020)
Log of number of firms and organizations per 10,000 residents –0.3374*** (–3.270) –0.3517*** (–3.470) –0.2732** (–2.230) –0.3032** (–2.460) –0.7550 (–5.310)
Log of population density 0.0817*** (4.320) 0.0764*** (4.070) 0.0738*** (5.070) 0.0556*** (4.150) 0.1892 (0.280)
Average temperature in January 0.0024 (0.820) 0.0025 (0.870) –0.0021 (–0.620) –0.0016 (–0.500) –0.0030 (–0.850)
Time trend dummy –0.0766*** (–3.280) –0.0759*** (–3.210) –0.0718*** (–2.710) –0.0686*** (–2.640) –0.0702*** (–2.700)
Constant term 3.0643** (2.290) 2.9831** (2.230) 4.5010** (2.360) 4.0051*** (2.750) 6.2841*** (3.370)
Control for district-level fixed-effects Yes Yes Yes Yes No
Control for individual effects of regions Yes Yes Yes Yes Yes
N 563 563 576 576 576
R 2 0.28 0.26 0.29 0.31 0.51
Breusch–Pagan testa 481.18*** 491.66*** 422.07*** 407.13*** 430.93***
Hausman-testb 154.50***
Wald test/F-test (χ2)c 872.52*** 881.37*** 677.00*** 598.94*** 57.26***

Panel data estimation of firm exit model in Russian regions.

Dependent variable Log of firm exit
Estimation period 2008–2014
Target regions All regions
Estimator Random-effects Random-effects Random-effects Random-effects Fixed-effects
Model [1] [2] [3] [4] [5]
Institutional variables
Log of duration of a lawsuit at a regional arbitration court (mean) –0.4439** (–2.370)
Log of duration of a lawsuit at a regional arbitration court (median) –0.6972*** (–3.190)
Acceptance rate of petition by regional arbitration courts (%) –0.0341** (–2.180)
Log of lawsuit application number to regional arbitration courts per 1,000 organization 0.1286 (1.250)
Expert criminal risk ranking –0.0025** (–2.090) –0.0023** (–1.980) –0.0023** (–1.990) –0.0019* (–1.640) –0.0018* (–1.770)
Economic integration variables
Log of world oil price 0.6838*** (7.760) 0.6796*** (7.790) 0.7019*** (8.090) 0.7029*** (8.190) 0.6603*** (7.560)
Total trade in % of GRP –0.0037*** (–3.370) –0.0028*** (–2.630) –0.0035*** (–3.190) –0.0035*** (–3.060) –0.0035* (–1.640)
FDI inflow in % of GRP 0.0117*** (2.740) 0.0119*** (2.950) 0.0082* (1.640) 0.0095* (1.880) 0.0104** (2.090)
Log of direct distance from Brussels –0.3021 (–1.490) –0.1465 (–0.730) –0.3574* (–1.740) –0.4942** (–2.280)
Control variables
GRP growth rate (%) –0.0288*** (–5.030) –0.0294*** (–5.040) –0.0307*** (–5.560) –0.0302*** (–5.660) –0.0274*** (–4.390)
Log of number of credit organizations and their branches per 100,000 residents 0.0719 (0.590) 0.0821 (0.710) –0.0662 (–0.550) –0.0452 (–0.360) 0.1927 (1.270)
Log of number of PCs with internet connection per 100 workers –0.2784* (–1.770) –0.2533 (–1.540) –0.2187 (–1.350) –0.2312 (–1.450) –0.1517 (–0.970)
Log of number of firms and organizations per 10,000 residents 0.3455** (2.240) 0.3395** (2.270) 0.2623* (1.840) 0.3067* (1.840) 0.3301 (0.570)
Log of population density 0.0695*** (3.190) 0.0675*** (3.300) 0.0526** (2.390) 0.0499** (2.140) 2.0269 (1.300)
Average temperature in January –0.0265*** (–4.080) –0.0269*** (–4.340) –0.0259*** (–3.720) –0.0253*** (–3.690) –0.0456*** (–4.840)
Time trend dummy 0.0937*** (2.720) 0.0897*** (2.580) 0.0771** (2.130) 0.0811** (2.250) 0.0742** (1.970)
Constant term 3.3873** (1.990) 3.0776* (1.790) 5.6176** (2.410) 2.4230 (1.370) –6.6001 (–1.050)
Control for district-level fixed-effects Yes Yes Yes Yes No
Control for individual effects of regions Yes Yes Yes Yes Yes
N 562 562 575 575 575
R2 0.43 0.44 0.42 0.41 0.46
Breusch–Pagan testa 49.88*** 39.46*** 42.14*** 48.34*** 53.04***
Hausman-testb 32.37***
Wald test/F-test (χ2)c 557.34*** 538.54*** 531.72*** 511.81 *** 42.58***

With respect to the institutional variables, Table 3 shows that the acceptance rate of petition by regional arbitration courts and the lawsuits application number to regional arbitration courts are negatively related to firm entry with a statistical significance at the 10% and 1% level, respectively, in line with our prediction. In the same table, the Expert criminal ranking is insignificant as well as the mean and median duration of a lawsuit at a regional arbitration court in the random-effects models [1] to [4]. But it is given a significant and negative estimate in the fixed-effects model [5]. As shown in Table 4, four of the five institutional variables are significantly associated with firm exit and all their coefficients are negative, suggesting that the destruction of Russian firms is closely linked with institutional failure.

With regard to the economic integration variables, the world oil price displays a remarkably robust estimate in the firm entry model. In fact, as reported in Table 3, this variable is estimated at 1% significance level with a positive sign in all the five models, irrespective of the difference in model specification and estimator. In contrast, estimates of the other economic integration variables suggest that regional dependence on international trade and direct investment from abroad and geographical proximity to the EU do not significantly affect the decision-making of Russian entrepreneurs who consider new entry to the market. On the other hand, the estimation results in Table 4 demonstrate that the destiny of Russian firms is nowadays deeply connected with the world economic dynamism. Indeed, not only the world oil price but also the other four economic integration variables are estimated with a significant coefficient, implying that the 2008 financial shock and subsequent crises greatly damaged Russian businesses as witnessed by Iwasaki (2014) in the case of large and medium-sized industrial companies and by Sprenger (2014) in the case of former socialist enterprises.

Many estimates of the control variables are significant, which corresponds to our predictions: medium-term economic growth encourages firm creation and, at the same time, keeps existing organizations alive. Intensive inter-firm competition blocks new comers and evicts poorly performing companies from the market. High market density is a key promoting factor for both firm entry and exit. In regions with hard climate conditions, firms tend to stay in the market probably due to high initial sunk costs.

As a next step, we addressed the issue of heterogeneity of Russian regions. It is argued that Russia is a huge country and hence there exists a great variation among regions in terms of the socio-economic systems as well as the market/industrial structure. To examine possible influence of regional heterogeneity, we re-estimated Model [4] in Table 3 and Model [2] in Table 4 by dividing observations into four subsample groups taking historical and geographical similarities of the federal districts into consideration.

The results are reported in Table 5. The statistical significance of the key variables is remarkably different between the subsamples, implying that the system of firm creation and destruction is indeed manifold across Russian regions. The world oil price, however, takes a significant and positive coefficient in all the eight models corresponding to the above estimation results. The surprisingly robust estimate of the world oil price suggests the possibility that every Russian region is extremely sensitive to a global crisis and, accordingly, the regeneration of Russian firms was greatly damaged during the recent period.

Examination of heterogeneity among Russian regions.

(a) Firm entry
Dependent variable Log of firm entry
Estimation period 2008–2014
Target regions North Caucasus and Southern Districts Central and Northwestern Districts Volga and Ural Districts Siberian and Far East Districts
Estimator Pooling OLS Random-effects Random-effects Random-effects
Model [1] [2] [3] [4]
Institutional variables
Log of lawsuit application number to regional arbitration courts per 1,000 organization 0.0360 (0.330) –0.2088** (–1.990) –0.0954 (–0.530) –0.2357*** (–3.730)
Expert criminal risk ranking 0.0028 (1.160) –0.0002 (–0.250) –0.0001 (–0.150) –0.00037 (–0.610)
Economic integration variables
Log of world oil price 0.5613*** (4.400) 0.4811*** (9.000) 0.4131*** (9.670) 0.3730*** (6.490)
Total trade in % of GRP –0.0051** (–2.470) 0.0001 (0.060) 0.0032 (1.210) 0.0000 (–0.040)
FDI inflow in % of GRP –0.0307 (–1.050) –0.0048 (–0.960) –0.0070 (–1.280) 0.0001 (0.070)
Log of direct distance from Brussels –0.4690 (–0.500) 0.6934*** (3.230) –0.1648 (–0.270) 0.1763 (1.210)
Control variables
GRP growth rate (%) 0.0227* (1.740) 0.0070** (2.040) 0.0146*** (3.760) 0.0095*** (3.060)
Log of number of credit organizations and their branchs per 100,000 residents –0.0409 (–0.610) 0.0298 (0.240) 0.1291 (0.930) –0.1484*** (–2.820)
Log of number of PCs with internet connection per 100 workers –0.0054 (–0.040) –0.1838* (–1.750) –0.0993 (–0.650) –0.0021 (–0.040)
Log of number of firms and organizations per 10,000 residents –0.1159 (–0.890) –0.1930 (–1.430) –0.1216 (–0.730) –0.1013 (–1.200)
Log of population density 0.2299*** (3.730) 0.0703*** (2.670) 0.0334 (0.720) 0.0660** (2.540)
Average temperature in January –0.0408*** (–2.770) 0.0179*** (3.430) 0.0025 (0.750) –0.0101*** (–2.820)
Time trend dummy –0.0233 (–0.690) 0.0062 (0.290) 0.0027 (0.080) –0.0563*** (–4.990)
Constant term 5.1432 (0.680) –0.2498 (–0.120) 5.2981 (0.880) 3.3762** (2.130)
Control for district-level fixed-effects Yes Yes Yes Yes
Control for individual effects of regions No Yes Yes Yes
N 90 203 140 143
R 2 0.49 0.33 0.22 0.67
Breusch–Pagan testa 0.00 206.42*** 55.14*** 26.41***
Wald test/F-test (χ2)b 6.48*** 377.07*** 2207.91*** 1766.72***
Institutional variables
Log of duration of a lawsuit at a regional arbitration court (median) –0.5783* (–1.670) –0.7549** (–2.250) –1.1080* (–1.770) –0.1469 (–0.470)
Expert criminal risk ranking –0.0049 (–1.150) –0.0039* (–1.800) –0.0059*** (–2.750) –0.0005 (–0.260)
Economic integration variables
Log of world oil price 0.8414** (2.530) 0.4158*** (3.230) 0.5878*** (3.060) 0.7513*** (4.310)
Total trade in % of GRP 0.0004 (0.060) 0.0008 (0.350) –0.0097* (–1.830) –0.0052*** (–2.660)
FDI inflow in % of GRP 0.0061 (0.110) –0.0055 (–0.470) 0.0274* (1.660) 0.0102*** (3.010)
Log of direct distance from Brussels 4.4697** (2.160) –0.5696 (–1.110) 1.7801* (1.680) 0.0310 (0.080)
Control variables
GRP growth rate (%) –0.0413** (–2.310) –0.0397*** (–3.860) –0.0209 (–1.590) –0.0090 (–0.850)
Log of number of credit organizations and their branches per 100,000 residents –0.9893* (–1.870) 0.1923 (1.130) 0.7060*** (2.930) 0.1871 (1.240)
Log of number of PCs with internet connection per 100 workers 0.8113** (2.160) –0.4048* (–1.850) –0.8634*** (–2.930) –0.6831*** (–5.490)
Log of number of firms and organizations per 10,000 residents 0.9106** (2.190) 0.0100 (0.050) 0.7951*** (2.910) 0.4955*** (3.770)
Log of population density 0.4113** (2.510) 0.1487*** (3.880) 0.2502*** (2.920) 0.1045* (1.920)
Average temperature in January –0.0566** (–2.010) –0.0796*** (–5.550) –0.0276** (–2.040) –0.0118 (–1.140)
Time trend dummy –0.1791** (–2.190) 0.1133** (2.270) 0.2206*** (3.450) 0.1768*** (6.520)
Constant term –39.5232** (–2.240) 9.1650*** (2.620) –13.0383* (–1.760) –2.0756 (–0.520)
Control for district-level fixed-effects Yes Yes Yes Yes
Control for individual effects of regions No Yes Yes No
N 84 196 140 142
R 2 0.58 0.52 0.50 0.48
Breusch–Pagan testa 0.00 3.45** 1.84* 0.19
Wald test/F-test (χ2)b 4.77*** 369.94*** 444.54*** 10.09***

4.3. Robustness check

In the previous subsection we mainly utilized a random-effects model to obtain estimates of the time-invariant variables and checked the statistical robustness of the time-variant variables by estimating a fixed-effects model. As additional robustness check, we also conducted the estimation using a population-average estimator, a between-effects estimator, a Hausman–Tylor estimator and a system GMM estimator, and found no remarkable differences from the estimation results reported in Tables 3, 4, and 5.4

Furthermore, we also carried out a supplementary estimation, in which various sample restrictions were placed on the regression models and confirmed that these sample restrictions do not substantially change our major empirical findings. More specifically, supplementary regressions were conducted with the following five settings: (a) excluding Moscow and St. Petersburg, which are under direct control of the Federal government (i.e., federal cities), from observations; (b) excluding so-called “resource-rich” regions from observations; (c) dividing observations into those for the years of 2008–2011 and those for the years of 2012–2014; (d) limiting regions to those with FDI in % of GRP within the mean ±1 standard deviation; (e) limiting regions to those with total trade in % of GRP within the mean ±1 standard deviation.

The above findings led us to the judgment that the estimation results reported in this paper are robust across the various specifications, apart from the regional heterogeneity discussed in the previous subsection.

5. Conclusions

This paper is based upon a rich and unpublished panel dataset of regional-level data for the period 2008–2014. Our regression analysis gives a special attention to two aspects: institutional failures and the recent economic crises and downturns that characterize the period of investigation. More specifically, we take the natural logarithm of firm entry or exit rate as the dependent variable and focus on two explanatory sets of variables: four proxies for the efficiency of the judiciary system which are provided by Dmitrieva et al. (2012) and the federal arbitration court, and variables that measure the extent to which Russian firms are integrated in the world economy: the natural logarithm of world oil price, total trade volume in percent of GRP, inflow of FDI in % of GRP and finally the natural logarithm of the distance between Brussels and the capital cities of Russian regions.

The results lead us to the overall conclusion that the Russian economy functions within a legal, institutional and political environment that hinders the competition among firms, which is detrimental for economic development. On top of that, corruption and vested interests complicate furthermore the overall framework where private companies are forced to operate. As a result, entry rates of new businesses are declining over the period of the analysis, while the number of firms leaving the market is increasing. This is the case, in spite of already low entry rates as compared to other transitional economies.

More specifically, the entry and exit rates of businesses depend upon the efficiency of the judiciary system in resolving disputes related to the non-compliance with contracts and the non-payment of arrears. The resulting uncertainty and instability amplified by discretionary bureaucratic practices of public administrations distort dramatically the economic governance principles at all levels. In terms of entry/exit of businesses, this translates into a chaotic evolution of the two rates, with successive peaks and bottoms over the period.

Two major external factors bring additional menaces to the already fragile economic conditions. Firstly, Russian firms, mainly SMEs, are highly sensitive to the variation of the world oil price, regardless of their geographical location and specialization. The oil price is the only variable pertaining to the integration of Russia in the world economy which is robustly related to firms’ entry and exit. Our findings have two policy implications, namely that the oil curse can be a blessing if the rent is not a source of widespread corruption and does not impede the development of other sectors employing entrepreneurs and workers; and a rule of the law favorable to the emergence of a sound business environment is key to allow SMEs to enter/exit the market in a creative way. Russian economy, based essentially on oil and gas related activities, is therefore insufficiently diversified to cope with the negative impact of falling prices of these natural resources.

No after-oil strategic perspectives of development exist, which will definitely have serious implications in the future. The second important external factor is related to the vulnerability of the economy to world crises. Although the degree of integration of Russian economy into the global economy is relatively low, the 2008 crisis did have impact on the entry and exit of Russian businesses.

The internal and external factors damaging the economic performance influence the firm creation and destruction at national, regional and district levels. Specifically, the entries are declining as a consequence of those factors while exits are increasing, which leads to a low survival rate of enterprises. This tendency is somehow attenuated in certain regions where the local governments are in political harmony with the central power, which grants them some privileges to cope with the adverse effects of exogenous influences. Overall, the impact of external threats on various regions vary according to the degree of vulnerability to outside crises, which in turn depends on the extent to which a particular region is more or less isolated from the source of the crisis.

To conclude, the firm creation and firm destruction in Russia is a multifarious process, in particular at regional level. As a general rule, the medium-term perspectives of economic growth encourages the entries of new businesses, as it is common to all countries. At the same time, those perspectives keep alive the majority of existing organizations. Strong inter-enterprise competition limits the entry of newcomers and evicts poorly performing companies from the market. High market density represents a major promoting factor both for entries and exits.

Acknowledgements

This research work is financially supported by the Japan Securities Scholarship Foundation, the Nomura Foundation, the Suntory Foundation, and the Joint Usage and Research Center, Institute of Economic Research, Hitotsubashi University. We thank Tatiana Mikhailova and other participants in the XVII International Academic Conference on Economic and Social Development at the National Research University Higher School of Economics, Moscow, 21 April 2016, for their valuable comments and suggestions.

1

Authors’ calculation based on the official statistics of the Federal State Statistical Service of Russia (http://www.gks.ru).

2

UNCTAD database (http://unctadstat.unctad.org). See Iwasaki and Suganuma (2015a, 2015b) for details of the recent trend of international trade and FDI inflow in Russia.

3

The estimation results are the following:12.44) (9.15) Figures in parentheses beneath regression coefficients are t-statistics computed based on robust standard errors. *** and ** denote statistical significance at the 1% and 5% levels, respectively. Null hypothesis of the F-test is that all coefficients are zero.

4

The Hausman–Tylor and system GMM estimations were attempted to estimate and endogenize non-lagged time-variant independent variables.

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