Влияние типа собственности на аггломерационные эффекты промышленных предприятий Украины Владимир Вахитов Киевская школа экономики февраля, 2013

2 Outline Motivation Background information & classifications Data description Model and Estimation Results for Machinery and High Tech

3 Outline Motivation Background information & classifications Data description Model and Estimation Results for Machinery and High Tech

4 Motivation: Objective Measuring localization economies:  external economies of scale  external to the firm  internal to the location

5 Agglomeration in the Nutshell ? Common labor pool? Relationships between managers and/or owners? Common market?

6 Agglomeration in the Nutshell

7 Motivation: Important Questions Localization economies:  external to the firm, internal to the location Cluster boundaries:  What is “the same industry?  What is “the same location”? How to measure? Can we compare our measures to others’?

8 Motivation: This Paper Two channels of interaction and spillovers:  Common employment  Interactions between firms Two cuts of the space:  Greater area, smaller industry size  Smaller area, greater industry size Other external factors:  Soviet inheritance (predetermined)  Ownership structure (dynamics)

9 Motivation Big factory towns (internal scale economies) Massive privatization and restructuring Resource-oriented industries Are there any particular issues of the post- Soviet economy? Does ownership structure matter?

10 Outline Motivation Background information & classifications Data description Model and Estimation Results for Machinery and High Tech

11 Background: Ukraine Comparable to France and Texas by size Population: 46 million people Territory: 25 oblasts

12 Ukraine: 25 oblasts and borders

13 Background: Territory structure Smaller regions: 490 raions, 179 cities Raions are comparable to US counties by size and administrative role Administrative units inherited from USSR Industrialized (part of the Soviet economy) Urbanized: 2/3 of population

14 Ukraine: Population Density

15 Background: Diversity, Depopulation Population and employment fell from 52M in 1991 to 46M in 2006 Employment fell drastically ~ 4 M leaved for private entrepreneurship ~ 2 M retired in rural areas ~ ??? Emigration and work migration

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21 Background: Transition First stage of transition was over in 2001  Accounting standards reform  Industry classification reform  Privatization is mostly over with  By 2001, only 3% of firms are state-owned  Less than 5% are foreign-owned

22 Outline Motivation Background information & classifications Data description Model and Estimation Results for Machinery and High Tech

23 Lattice Data: Raions & QMSA “Quasi-MSA” construction:  Population-based (Census 2001)  Located around big cities in hierarchical order  Conjectured commuting distances (60 km)  56 QMSAs

24 Lattice Data: Raions & QMSA

25 Industry data: Machinery & High Tech: KVED: NACE compatible Machinery : 29.1, 29.2, 29.4, 29.5 High-Tech : 29.6, 30.0, 32.1, 33.1, 35.3 Groups composition is taken similar to Henderson (2003) Machinery is more homogenous

26 Machinery: Location in 2001

27 Machinery: Location in 2005

28 High Tech: Location in 2001

29 High Tech: Location in 2005

30 Industry Data: Firm level and establishment level Annual ( ), submitted by firms National Committee on Statistics, State Property Fund Budgetary sector and banks excluded Territory, industry codes, output, employment, capital Ownership, subsidiary and urban dummies

31 Data: Sample composition Manufacturing Total Firms total44,77048,15149,00849,94650,719242,594 Establishments45,84049,65051,20552,28853,096252,079 Positive L and Y35,98938,04039,07638,78038,634190,519 QMSA33,76735,77136,79036,60436,568179,500 Urban25,81727,51928,31228,47428,739138,861 Machinery total3,0423,2253,3903,3753,31216,344 High-Tech total1,0101,0781,0231,0331,0275,171

32 Data: Employment Dynamics Y2001Y2002Y2003Y2004Y2005 Machinery, Small Firms High Tech, Small Firms Machinery, Large Firms High Tech, Large Firms

33 Data: Firms’ Characteristics MachineryHigh Tech LargeSmallLargeSmall Urban87%89%90%94% Majority Private86%98%62%97% Foreign Owned3%1%2%

34 Data: ownership and size Full sample StateDomestic Private Foreign Private Machinery High Tech

35 Data: Agglomeration Measures Two measures within the same cluster:  Interaction between firms: plants counts  Labor pool: employment Industry aggregation: Group, KVED3 Spatial aggregation: QMSA, Raion

36 Data: Agglomeration Measures Two experiments: 1)3-digit industry in QMSA (Greater physical distance, close in the industrial space) 2)Industry Group in a Raion (Short physical distance, loose industrial bonds)  Both industrial and physical distances matter

37 Outline Motivation Background information & classifications Data description Model and Estimation Results for Machinery and High Tech

38 Model Model (Rosenthal and Strange, 2004): Econometric Specification (Henderson, 2003): Fixed effects panel data estimation

39 Model: Issues Fixed effects: MSA, 3-digit industry-year cross-effects E : Agglomeration variable I : Institutional variables: urban, subsidiary, set of ownership dummies Industry-year dummies to capture sector- specific inflation

40 Model: Dynamics Year-to-year changes Lagged agglomeration variables ( E t-1 )

41 Outline Motivation Background information & classifications Data description Model and estimation Results for Machinery and High Tech

42 Machinery: Localization Results Group-RaionKV3 - QMSA EmplPlantsEmplPlants ln (Capital)0.072 a a a (0.017) ln (Labor)0.938 a a a (0.026) (0.025) Localization Effect0.074 a a b (0.017)(0.024)(0.017)(0.044) Subsidiary a a a a (0.058)(0.056)(0.064) Urban a a (0.073) Observations Number of QMSA's56 R-squared0.63

43 Machinery: Localization + Ownership Group-RaionKV3 - QMSA EmplPlantsEmplPlants Primarily domestic (DO)0.683 a a a a (0.089)(0.111)(0.084)(0.203) Primarily foreign (FO)1.272 a b a c (0.170)(0.331)(0.182)(0.459) Localization Effect0.074 c (0.042)(0.053)(0.051)(0.091) Localization + Domestic Cross-effect (0.035)(0.041)(0.044)(0.064) Localization + Foreign - Cross effect c (0.073)(0.090)(0.084)(0.123)

44 High Tech: Localization Results Group-RaionKV3 - QMSA EmplPlantsEmplPlants ln (Capital)0.117 a a a a (0.036) (0.039) ln (Labor)0.963 a a a a (0.037)(0.036)(0.043) Localization Effect0.117 a a b (0.015)(0.032)(0.021)(0.061) Subsidiary a a a (0.110)(0.109)(0.117)(0.116) Urban a a (0.114)(0.113) Observations Number of QMSA's48 R-squared

45 High Tech: Localization + Ownership Group-RaionKV3 - QMSA EmplPlantsEmplPlants Primarily domestic (DO)0.541 a a (0.188)(0.234)(0.211)(0.239) Primarily foreign (FO)1.020 a a (0.326)(0.696)(0.208)(0.588) Localization Effect c b (0.032)(0.048)(0.040)(0.089) Localization + Domestic Cross-effect b a a (0.035)(0.038) (0.051) Localization + Foreign - Cross effect (0.130)(0.129)(0.139)(0.138)

46 Lagged Variables MachineryGroup-RaionKV3 - QMSA EmplPlantsEmplPlants Localization effect (0.019)(0.020)(0.023)(0.048) Lagged localization effect0.069 a a a (0.014)(0.011)(0.018)(0.016) High TechGroup-RaionKV3 - QMSA EmplPlantsEmplPlants Localization effect0.086 a a a a (0.026)(0.034)(0.031)(0.072) Lagged localization effect0.051 a a a a (0.013)(0.010)(0.033)(0.042)

47 Major Results Effects are present in both groups and consistent with previous studies Effects are stronger in High Tech group Effects are stronger for plants measures: management matters?

48 Major Results II Effects are stronger for Group-Raion than for 3-digit industry-MSA (local) Effects are stronger for private firms FO is more important in Machinery DO is more important in High Tech Lagged effects are stronger Older (past-Soviet) firms are less efficient

49 Policy implications Improve relationships between firms Attract foreign investors Do not expect immediate results Increase density and size of clusters Restructure sooner “Urbanization” effects: study on the way

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