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Factors determining enterprise location choice in Russia

Factors determining enterprise location choice in Russia. Natalia Davidson Based on Ph.D. thesis supervised by Hubert Jayet, University of Science and Technology of Lille  / Sergey Kadochnikov, Ural Federal University (Ekaterinburg)

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Factors determining enterprise location choice in Russia

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  1. Factors determining enterprise location choice in Russia Natalia Davidson Based on Ph.D. thesis supervised by Hubert Jayet, University of Science and Technology of Lille / Sergey Kadochnikov, Ural Federal University (Ekaterinburg) 3rd ICSID conference Regional Heterogeneity and Incentives for Government and EACES-HSE Workshop Political Economy of Development: a Comparative Perspective May 29, 2014, Pushkin, Saint Petersburg

  2. Motivation Competition between regions and cities for enterprise allocation Location choice of national and foreign enterprises • factors of cities’ attractiveness ? Large territory of Russia; differences among cities => city level analysis City attractiveness for firms and human capital are interrelated (Forslid, Ottaviano, 2003) Enterprise location is linked to availability of jobs and high standards of living Foreign firms: examples of industries. Automobile industry; agricultural machine building (final and intermediate goods producers); food industry.

  3. 1. Introduction and literature Objective: To understand the determinants of enterprise location choice, based on firm level data for Russia Find out agglomeration externalities that firms experience: positiveandnegativeeffectsofscaleandscoperesultingfromeconomicactivityconcentrationin a city (Neffke, 2009) • localization effects (Marshall, Arrow, Romer) – from firms in the same industry • urbanization / diversity effects (Jacobs) – from firms in the other industries Find out the impact of the following factors: • home market potential/ market access (Krugman,1980, 1991; Ottaviano, Thisse, 2004) • transport infrastructure • business environment taking into account specific features of firms, industries and cities Evidence on agglomeration externalities is conflicting (Beaudry and Schiffauerova, 2009) - review of67 studies; De Groot et al. 2009). Depends on the specification, the level of data aggregation, the extent of treating the potential sources of bias (Henderson, 2003; Neffke, 2009).

  4. 1. Introduction and literature Agglomeration externalities: theoretical and empirical background • Microfoundations of agglomeration economies Sharing – firms share common resources (such as infrastructure) Matching – firms can find what they need (for example, specialized workers) Learning – technologicalandknowledgeexternalities, i.e. firms can use knowledge and innovations created by other firms => possibilityofmoreintensiveinnovations Alfred Marshall. Principles of Economics. London: MacMillan, 1920 - in Duranton and Puga (2004) • Life cycle theory (Vernon, 1960) Localizationexternalities: matureenterprises;diversity: newenterprises; (DurantonandPuga, 2001) Localizationexternalities:matureindustries; loc. and div.:newerindustries(Hendersonetal., 1995) • Spatial decay of externalities (Jaffe et al., 1993; Rosenthal and Strange, 2000) County vs. also nearby counties in the same metropolitan areas (Henderson, 2003) • Inverted Ushape of agglomeration externalities (Mills, 1967; Mirrlees, 1972) congestion costs, competition => diminishing positive externalities after a certain point • Locationof firms inacloseproximityremainsimportantintheeraoftelecommunicationtechnologies, partlyduetotacitknowledge(Audretsch, 1998; Robert-Nicaud, 2013) • Industrialdiversityisimportantforsocialsecurityandurbaneconomicgrowth (Jacobs, 1961)

  5. 1. Introduction and literature Empirical evidence Localizationexternalitiesare foundinhigh-tech, butnotinmachineryindustries, anddiversityeconomiesare foundinmachineryindustry – corporatesector for the US firms(Henderson (2003)) Benefitsfromlocalizationeconomies, butnobenefitsfromdiversityeconomiesorcompetitioneffects are found for French firms, based on enterprise level data for theyears 1996-2004. Firmsinternalizebenefitsfromclustering (Martinetal. (2008, 2011)). Transition countries, Russia Localizationanddiversity (urbanization) externalitiesare found bothinmachinemanufacturingandhightechindustries for the Ukrainianfirms.Agglomerationeconomiesfortheforeign-owned firms arethelargestones (Vakhitov (2008), Vakhitov, Bollinger (2010)). InvertedU-shapedlocalizationeconomiesandpositivediversityeconomiesare found for theRussianmanufacturing firms based on data for 2001-2004 (Vorobyev et al., 2010). Doubling of city size – increase in productivity by 3-8% (US data, Rosenthal & Strange, 2004) ‘Centre’ vs. ‘periphery’ – increase in productivity by 20-50% (Okubo, Tomiura, 2010) Evidence for Russia (based on data for 2005-2006): Doubling city size – increase in productivity by 5% (‘Predpriyatiyairynki’, 2010). Firm located within vs. outside an agglomeration – increase in productivity by 46% (KsenyaGonchar, 2010)

  6. 1. Introduction and literature Locationchoiceforacitymadebynationalandforeign firms isanalyzed here. Locationchoicemade byforeignfirms: specific features • theyhavemoreopportunitiestochooseacitythannational firms do; • foreigndirectinvestment (FDI) notonlyproducesspilloversintermsofproductivitygrowthfortheother firms, butalsohasanimpactoninstitutionsandmanagerialskillsthusformingdevelopmentperspectivesinatransitioncountry (Castiglioneetal., 2012). Foreign firms’ location choice depends on MNCsstrategies • traditionalstrategies: marketseeking, assetseeking, efficiencyseeking, naturalresourcesseeking; • morecomplexglobalstrategies (Andreff, 2003).

  7. 2. Methodology and data Territory classification List of agglomeration centers – 17 cities (Gonchar (2008), Vorobyev et al. (2010)) Lists of monotowns: MonotownsinRussia: howtosurvivecrisis? Instituteforregionalpolicy(2008) , Zubarevich (2010)

  8. 2. Methodology and data Diversityindexforthemajorcities

  9. 2. Methodology and data Indices • Localization: • Urbanization: (Martin et al., 2011) • Diversity (variety and inequality): (Vorobyev et al., 2010) • Home market potential: contains home market potential of a city and ofthe othercities: , where (HMP is also in logarithm) Allindicesaremeasuredat 3-digit level of OKVED (classification in Russia - same as NACE in Europe)

  10. 2. Methodology and data Data:individual firms data for manufacturing industries, 8569 firms oftradableindustries; year 2007, augmented with city and regional data. Firm level data (SPARK-Interfax):theorganizationalform, property, theyearoffoundation, location, revenue, labour, costprice, profit. Thesamplecontains 23632 firms, ofwhich 15609 firms belongto the industriesproducingtradablegoods. Thefirmleveldatawasfilteredbasedonlabordata, sothatthemajorityofindicatorsarepresentforthe firms inthesample.Thesampleisrepresentativebycomparisonwithcomplete SPARK databasesandwithRosstatdata. Thesampleincludes 6% of firms withforeignownership. Analysisisbasedonthebalancedpanel. City and regional data (Rosstat) ThecityleveldataistakenfromRosstatdatabaseoncitieswithpopulationexceeding 100 thousandpeople. Fortheyear 2007 thereare 12200 firms fromSPARK-Interfaxdatabaselocatedinthesecities, including 8569 firms oftradableindustries. Drawback of data: it is not known, which country FDI are coming from to an enterprise.

  11. 2. Methodology and data Regionalbusinessclimateindicators (Analytical agency ‘Expert’) Regional investment potential (a quantitative characteristics), contains nine potentials (until 2005 there were eight): natural resources; labour; production; innovation; institutional; infrastructure; financial; consumption; tourist. Regional investment risk (a qualitative characteristics reflecting probability to lose investment and income from investment), contains seven risks: economic, financial, social, ecological, criminal, legislative, governance. The main data sources for the ranking are the following: data compiled by Rosstat, Ministry of Finance of the Russian Federation, Ministry of economic development and trade of RF, Central Bank of RF, Ministry of taxation and levies of RF, Ministry of natural resources of RF, Center for economic environment (Government of RF), legislation database ‘Consultant Plus-Region’, database of the ranking agency ‘Expert RA’. Besides, information provided by certain subjects of federation (from their official web sites or sent upon request) is used. Estimation of weighs of each component in the total potential and integral risk is based on the annual surveys among the experts from the Russian and foreign investment, consulting companies and enterprises. http://www.raexpert.ru/ratings/regions/ratingclass/ The results of investment attractiveness ranking for the Russian regions are published in the journal ‘Expert’ annually since 1996.

  12. 3. Enterprise location choice: empirics Foreign firms’ location choice FDI inflows, in the Commonwealth of Independent States, including Russian Federation, 1990-2012 Source: WorldInvestmentReport 2013:AnnexTables, UNCTAD

  13. 3. Enterprise location choice: empirics Foreign firms’ location choice Uneven location of FDI among regions (Ledyaeva, 2007, Ledyaeva, Karhunen, Kosonen, 2010; Castiglione, Gorbunova, Infante, Smirnova (2012); Gonchar, Marek (2013)) Source: Gonchar, Marek (2013) Natural resource or market seeking FDI in Russia?... (data: from the RUSLANA Database provided by Bureau van Dijk; 2000-2009)

  14. 4. Enterprise location choice: theoretical background Expected profits in each of the possible locations are analyzed to predict the probability that a firm would invest in a location (Head and Mayer, 2004; Combes et al., 2008). It is assumed that firms choose locations where they can earn the highest profits. Conditional logit model is used to estimate the parameters of the profit equation, i.e. the location choice of enterprises for a city out of 172 possibilities (MacFadden, 1974, 1978; Head and Mayer, 2004; Combes et al., 2008). Profits are analyzed based on the equation: where Ar – total factor productivity in the region r; RMPr - real market potential (Harris, 1954); CPr - cost price for each firm, which accounts for wage and composite input price. Sources: Ledyaeva, Karhunen, Kosonen (2010); Gonchar, Marek (2013)) Estimation technique in Stata: Cameron, Trivedi (2009); Baum (2006)

  15. Dependent variable: enterprise location choice for city 5. Enterprise location choice: results and conclusions

  16. 5. Enterprise location choice: results and conclusions Diversity and the home market potential: significant and positive Localization effects: an inverted U shape Regional business environment risksare negative for foreign enterprises National and foreign firms Localization effect is stronger for domestic firms Home market potential is more important for foreign firms Diversity is equally important for both domestic and foreign firms Choice among different types of cities Diversity and localization economies play a more important role in choosing agglomeration center as location than in case of other cities HMPis the most important for enterprises that choose to locate in cities within agglomerations; for the foreign firms HMP is important while choosing also cities outside agglomerations Wage positively affects firms’ decision for location in agglomeration centers; for monotownsthe effect is lower; the effect is negative for cities outside agglomerations (for all tradables, for national firms and for foreign firms); for national firms – positive effect also for cities within agglomerations

  17. 5. Enterprise location choice: results and conclusions Foreign firms’ strategies Estimation shows that foreign enterprises are interested in large demand (‘HMP’), i.e. pursue market seeking strategy; Foreign enterprises do not seem to pursue efficiency seeking strategy, as far as lower wages are concerned; Under assumption that there are more innovations in diversified cities and cities with favourable business climate, strategic asset seeking might be present. Further research More detailed study of business environment using the components of the investment risks indicator by the Analytical Agency Expert; Consideration of the other aspects of institutional environment; more detail on theoretical background; Studying the changes in business environment; completing database with more recent years.

  18. Thank you!

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