Abstract
This chapter discusses the methodology and lays the foundation for the empirical analyses in this book. After presenting a detailed view of the data used in this work (in Sect. 5.2), two methodological perspectives on the research question are provided. Section 5.3 takes a Tobit regression model approach in order to determine and compare the impact of different factors on spatial entrepreneurial activity in Russia. In contrast, Sect. 5.4 describes the development of a prediction model for entrepreneurial entry in Russia’s regions based on Brieden and Gritzmann’s (SIAM Journal of Discrete Mathematics, 26(2), 415–434, 2012) innovative geometric clustering approach.
All we can know is that we know nothing. And that’s the height of human wisdom.
Leo Tolstoy, War and Peace
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Notes
- 1.
NACE refers to the Statistical Classification of Economic Activities in the European Community.
- 2.
The data set is based on an August 2016 excerpt from Orbis. The database contains extensive information covering private and listed companies in a broad set of countries.
- 3.
Net entry would be measured by the gross number of firm entry minus all suspended or bankrupt businesses per year.
- 4.
Notably, according to Rosstat, in 2016 employment in the informal sector of Russia’s economy peaked at a record level for the last 10 years with 21.2% (Rosstat 2018). Regarding the observation period of this thesis, those figures are more moderate.
- 5.
The sample refers to the EU-27, excluding Greece, Malta, and Cyprus due to technical reasons.
- 6.
The average correlation of industry entry between consecutive years is 95.25% in the EU sample, with an average deviation of 0.64%. Industry entry in the post-socialist sample correlates with 86.84% and an average standard deviation of 1.75%. The highest fluctuation can be observed in Russia, with an average correlation of 75.28% and an average standard deviation of 1.68% (cf. Annex A.1).
- 7.
The sample covers the Eastern European countries of Bosnia and Herzegovina, Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Macedonia, Poland, Romania, Serbia, Slovakia, Slovenia, and Ukraine. Unfortunately, data availability for other post-socialist countries (i.e., Commonwealth of Independent States, CIS) was scarce and highly fragmented.
- 8.
Data based on Orbis used the same filter criteria as outlined in Sect. 5.2.1; data based on Eurostat only used criteria 1 and 4 as the other were already accounted for in the figures provided by Eurostat.
- 9.
Since gubernatorial elections were abandoned in 2004, those evaluations were set up by a presidential decree in order to assess the public opinion about the functioning and transparency of regional administrations. They were intended to be used to allocate fiscal transfers to the best performers among Russian regions (the indicators have been prepared until 2010/11 pursuant to the President’s Decree No. 825 of 28 June 2007, “On Evaluating the Performance of Regional Government Authorities in the Russian Federation”).
- 10.
This procedure is the same for all variables drawn from OPORA reports, relating to administrative barriers, financial capital, human capital, and all other categories.
- 11.
Considering all Opora reports, they cover a set of eight indicators relating to corruption. Specifically, the indicators considered were bribes to public officials (вэятки чиновникам), sum of illegal payments to officials as share of company revenues (Доля незаконных выплат чиновникам в выручке компаний), freedom from corruption and raiding (Свобода от коррупции и рейдерства), total extent of corruption (Общий уровень коррупции), freedom from corruption in typical situations (Свобода от коррупции в типичных ситуациях), and the frequency of illegal actions against entrepreneurs by officials/by representatives of the Ministry of Internal Affairs/by employees of control and supervisory bodies (как часто предприниматели региона сталкиваются с противоправными действиями со стороны чиновников/представителей мвд/сотрудников контрольно-надзорных органов).
- 12.
The excerpt was downloaded on the 5th of July, 2017.
- 13.
Specifically, the indicators considered are availability of machinery and equipment suppliers (Доступность поставщиков машин и оборудования), the availability of suppliers of business services (Доступность поставщиков бизнес-услуг), availability of component suppliers (Доступность поставщиков комплектующих) and the existence of SME subcontracting/supply for large firms in a given region (насколько в регионе развита такая практика когда малые предприятия выполняют какие-либо работы или поставки по заказу крупных?).
- 14.
For example, Fritsch and Schroeter (2011) have analyzed German regions and drawn the conclusion that the levels of agglomeration and population density shape the relation between new firm entry and growth, competition, and a number of indirect effects.
- 15.
Particularly cities like Kazan, Novosibirsk, Rostov-on-Don, and Yekaterinburg experienced a rapid evolution towards major service industry centers. Other cities, for example, Chelyabinsk, Omsk, Perm, Ufa, and Volgograd, have shown similar developments. Even though their economic dependence on former Soviet large-scale industry such as oil refineries or metallurgical plants is still high, the latters’ importance as the previously largest employers has waned, and the cities have gradually moved towards modernization (Zubarevich 2013).
- 16.
With regard to the thresholds, in all clusters, an evaluation tolerance of up to 5% was allowed.
- 17.
It needs to be emphasized that some allocation decisions between clusters two or three were ambiguous from a technical perspective. Hence, expert assessments and manual cluster allocation were applied for individual cases. For example, there is the notable case of Tyumen Oblast, which is the major producer of oil and gas in Russia and which made the region by far the richest federal subject of the country in terms of GRP per capita. Although Zubarevich (2013) has allocated the city of Tyumen, which hosts roughly 500,000 inhabitants, to second Russia, it only makes up roughly 17% of the oblast’s overall population. Moreover, the oblast is characterized by considerable inequalities, as profits from resource exploitations are not equally distributed among different shares of the population, leading to a poverty rate in the region’s rural parts that is significantly higher than in Tyumen city (Buccellato and Mickiewicz 2009). Hence, I adhere to the basic idea of the clustering strategy and allocate Tyumen oblast, according to the characteristics of the majority of its population, to cluster three.
- 18.
This can easily be illustrated by the example of automobile factories in Russia that are concentrated in the Kaluga and Leningrad regions, i.e., those close to large and growing consumer markets such as Moscow and St. Petersburg. By contrast, distant Russian regions, such as the Republics of Altai and Tuva, are subject to unfavorable landlocked positions far from the main traffic flows and major economic centers, which might contribute considerably to their poor economic development.
- 19.
That is, ports in the Arkhangelsk, Kaliningrad, Leningrad, Murmansk, Rostov, St. Petersburg, Krasnodar, and Primorsky regions, plus Moscow’s international airports.
- 20.
For an overview on border countries n and regions e, refer to Annex A.2 (Table A.4).
- 21.
Although the Calinski/Harabasz pseudo-F was maximized for a number of six clusters (39.02), I want to avoid defining clusters with only one or two regions and confronting the danger of lacking observations for the subsequent analyses. As the three-cluster pseudo-F was only slightly lower (34.07), I decided to use three clusters. A plot of the identified clusters, i.e., the basis of the scheme presented in Fig. 5.3, is provided in Annex A.2 (Fig. A.1).
- 22.
Notably, all variables sourced from Opora Rossii, as well as the data on Internet use, were normalized during the process of variable construction. This is primarily due to the changing number of surveyed regions in each Opora report.
- 23.
Coming from the observed distributions, I further investigated the issue of zero entry. Annex A.5 shows the impact of the given set of variables on the likelihood of observing an entry rate equal to zero (i.e., 1 on the Y-axis) vs. a non-zero entry rate (i.e., 0 on the Y-axis). The blue mean curve represents the likelihood of observing entries of 0 given the value on the X-axis. Except from minor effects for the HHI and post-soc. entry variables, there are no obvious relationships. The former may be explained by the fact that some industries are generally not very prone to entry in Russia (or other post-socialist countries), for example, tobacco or banking.
- 24.
There are marginal effects observable for the MA_intensity, op_ad_noprsc, op_art_corr, op_art_spl, op_fin_long, and op_infra variables in some regions. Regarding the industry perspective, only HHI_ind shows some effects because it is one of the few variables that vary across industries instead of regions. Hence, these variables are, to some extent, promising to show significant results in the analyses of the following chapters.
- 25.
The Tobit model is suitable for corner solutions, where non-negativity constraints force (theoretically negative) values to be zero. Under these circumstances, OLS estimates would be distorted downward and inconsistent, whereas Tobit estimates are asymptotically normal and consistent (Stewart 2009; Amemiya 1973). Since the distribution of the entry rate variable implies a strong concentration of censored entry rates with value 0 (as illustrated in Annex A.6), the model is expected to result in more reliable results than OLS models.
- 26.
To account for unobserved regional effects, I created dummy variables for regions. Regional differences apart from the considered institutional factors may result in different levels of entrepreneurial activity across regions (Busenitz et al. 2000; Baumol 1996; North 1990). Similarly, I controlled for time effects across all regions (Caprio and Klingebiel 2002) and for specific industry effects.
- 27.
As HSE professor Alexander Chepurenko strikingly puts it, this would be similar to making conclusions by “measuring the average temperature of all patients in a hospital.”
- 28.
For example, in a study focusing on Germany, Fritsch and Schroeter (2011) have confirmed a stronger overall economic impact of new firms in knowledge-intensive service branches. This perspective is also supported by prior results from Falck (2007) and Fritsch and Noseleit (2009), which confirm that particularly high-innovation start-ups that were able to survive a critical minimum period have the highest economic impact.
- 29.
The reduced sample selection is based on the 2009–2015 long-term view of the Reuters 2016 State of Innovation report (Reuters 2016), which highlights the following as the most constantly innovative industries: automotive, biotechnology, cosmetics and well-being, food, beverage and tobacco, home appliances, information technology, medical devices, pharmaceuticals, semiconductors, and telecommunications. In accordance with Sect. 5.2.1, I exclude the state sector-dominated aerospace and defense and oil and gas industries.
- 30.
The composition of the post-socialist natural entry data is outlined in Sect. 5.2.2. The model specifications utilizing post-socialist natural entry rates lead to different significant results in only 10.4% of all regressions compared to using EU industry natural entry rates (Annex B.2). Given the fact that post-socialist entry rates show higher volatility than EU rates, I deem this discrepancy to be in a tolerable range.
- 31.
In total, the Bingham et al. (1998) approach produces different significant results in only 4.5% of all regressions compared to the linear imputation method (Annex B.3). We may thus conclude that the likelihood of a serious estimation bias due to employing simple linear imputation is comparatively low.
- 32.
For a more detailed explanation of the algorithm procedure, refer to Brieden and Gritzmann (2012). The main ideas on classifying heterogeneous data into homogeneous collectives according to the specific algorithm are based on the work of Brieden and Gritzmann (2003, 2004, 2010). A more detailed description of the methodology applied to the case of entrepreneurial entry is provided in a forthcoming paper of Schlattau et al. (2019).
- 33.
This is another argument for the sectoral NACE view, since, in contrast to the two digit NACE view, this perspective warrants for a sufficient number of observations in every quantile.
- 34.
Since the standard deviation of the test data collective is unknown, I needed to use an estimator. Following Chambers and Clark (2012, p. 86), I used an unbiased estimator for the unknown variance of outcome in a given collective Cli.
- 35.
For example, the Altai region now accounts for 16% of the country’s cheese production, and production volumes grow at an average of 33% per year. In the course of this development, some dairy entrepreneurs in the remote region even became billionaires (Daschkowski 2015).
- 36.
MAPE measures the size of the error in percentage terms by \( \frac{1}{C}\sum \limits_{i=1}^C\left|\frac{A_i-{P}_i}{A_i}\right| \), with C denoting the number of collectives for a given model, Ai the actual entry rate for a collective i, and Pi the prediction for collective i.
- 37.
One exception in this regard might be the financial services sector K, where at least in terms of banking services, considerable amounts of initial investment capital would be required.
- 38.
For example, residents from Ingushetia stated that, for many years 3G and 4G, networks were disconnected every time a rally or public action was scheduled (Roskomsvoboda 2018).
- 39.
For example, according to a recent survey from global investment bank Morgan Stanley, e-commerce ventures based in Moscow and St. Petersburg have received substantial funding over the past decade in a market that can be expected to reach US$52 billion by 2023 (Henni 2018). This is notable, as particularly in relatively remote regions, Internet-based entrepreneurial activity might help counterbalance other geographic disadvantages.
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Schlattau, M. (2021). Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis. In: Tilting at the Windmills of Transition. Societies and Political Orders in Transition. Springer, Cham. https://doi.org/10.1007/978-3-030-54909-1_5
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