Влияние типа собственности на аггломерационные эффекты промышленных предприятий Украины

1. Влияние типа собственностина аггломерационные эффектыпромышленных предприятий Украины Владимир Вахитов Киевская школа экономики 15-16 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 ofscale • 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. Outline • Motivation • Background information & classifications • Data description • Model and Estimation • Results for Machinery and High Tech

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

11. Ukraine: 25 oblasts and borders

12. 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

13. Ukraine: Population Density

14. 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

20. 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

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

22. 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

23. Lattice Data: Raions & QMSA

24. 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 similarto Henderson (2003) • Machinery is more homogenous

25. Machinery: Location in 2001

26. Machinery: Location in 2005

27. High Tech: Location in 2001

28. High Tech: Location in 2005

29. Industry Data: • Firm level and establishment level • Annual (2001-2005), 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

30. Data: Sample composition

31. Data: Employment Dynamics

32. Data: Firms’ Characteristics

33. Data: ownership and size

34. 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

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

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

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

38. 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

39. Model: Dynamics • Year-to-year changes • Lagged agglomeration variables (Et-1)

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

41. Machinery: Localization Results

42. Machinery: Localization + Ownership

43. High Tech: Localization Results

44. High Tech: Localization + Ownership

45. Lagged Variables

46. 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?

47. 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

48. 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

49. vakhitov@kse.org.ua