Are There Urbanization Economies in a Post-Socialist City?

Evidence from Ukrainian Firm-Level Data

Volodymyr Vakhitov

Saint PetersburgOctober 11, 2012

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Outline

• Motivation• Data description• Model and Estimation Issues• Preliminary results

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Agglomeration in the Nutshell

?

Common labor pool?

Relationships between managers and/or owners?

Common market?

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Motivation: Objective

• Agglomeration economies:External (to the firm) economies of scale

• Localization:Internal to the industry Internal to the location

• Urbanization:External to the industry Internal to the location

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Motivation: Urbanization Economies

• Urbanization economies: external to the firm and the industry as a

whole, internal to the location• Jacobs (1969): spillovers matter!

InnovationsKnowledge sharing Borrowing and developing new product[Schumpeterian] churning

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Motivation: Important Issues

• How to measure (Research Policy, 2009)?Size (employment, # of plants)Diversity (similarity and concentration indices)Are measures comparable (robustness)?

• Cluster boundaries (aggregation):What is “the same location”?What is “the same industry”?

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Motivation: Post-Socialist City

• Land “price” and previous allocation• “Lock-in effect” within city boundaries• Ownership issues• Outdated capital stock

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Outline

• Motivation• Data description• Model and Estimation Issues• Preliminary results

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Data Description

Registry:• “Official” data (National Statistics Office) • Panel (2001-2007), submitted by firms• Firm level (plant level on the way)• Excludes budgetary sector and banks

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Data Description

Ownership:• “Official” data (State Property Fund and

FDI statements) • Panel (2001-2005), collected by SPF• Firm level only

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Data: Sample CompositionYear Urban firms, Nonzero Output, K, L Manufacturing Sample*

  Small Big Total  2001 111,587 14,736 126,323 21,346 19,3852002 123,085 14,334 137,419 22,762 20,6822003 130,668 14,008 144,676 23,194 21,0162004 135,810 14,644 150,454 23,759 21,5942005 139,857 15,333 155,190 24,148 21,8232006 143,287 15,543 158,830 24,668 21,8612007 148,511 15,985 164,496 24,138 21,496Total 932,805 104,583 1,037,388 164,015 147,857

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Data Description: Raw Variables

• Territory and industry codes• Output, employment, capital, materials• Ownership (private: domestic / foreign)• Output assortment• Innovation activity• FDI• Purchases from other sectors• Exports and imports

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Data Description: Constructed Variables

• Size:Other employment in the city ln(empl) = ln(# firm) + ln(empl/# firms) (Henderson,

2003)• Diversity:

Market: employment or firm count based HHI Internal: output composition HHIShare of imports in the inputsUse of products from other sectors produced in the

same city (!!!)

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Outline

• Motivation• Data description• Model and Estimation Issues• Preliminary results

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mjtjttmjjtjtjtjty IEX lnlnlnαln 1

Model

• TFP Model (Rosenthal and Strange, 2004):

• Econometric Specification (Henderson, 2003):

KkTij

Iij

Gijjkj

jjj

dddxxsA

XfAgY

),,(),(

)()(

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Estimation issues

• One-stage and two-stage estimation• Restricted to manufacturing in cities • Area and industry-year fixed effects• Robust estimation• 2-stage: TFP by Olley-Pakes, then regress

on agglomeration variables + controls

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Outline

• Motivation• Data description• Model and Estimation Issues• Preliminary results

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Production Function Results  Size-based

ln (Employment) 0.479***

ln(Capital) 0.058***

ln(Materials) 0.461***

 … …Observations 151,775

Number of clusters 41898

R-squared overall 0.85

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Basic specification (XT-FE)  Size Diversity

ln(Local employment) 0.040    

  (0.022)    

ln(# firms)   0.059*  

    (0.028)  

ln(average employment)   0.014  

    (0.027)  

“Anti-diversity index”     -0.026

      (0.020)

Multi-plant 0.115*** 0.115*** 0.112***

  (0.029) (0.029) (0.030)

       

Firms 41,898 41,898 41,240

obs 151,775 151,775 147,857

Overall R2 0.85 0.85 0.85

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Olley-Pakes Two Stage (TFP)  Size Diversity

ln(Local empl) 0.043*    

  (0.019)    

ln(# firms)   0.044*  

    (0.022)  

ln(Average empl)   0.041  

    (0.025)  

“Anti-diversity index”     -0.051**

      (0.018)

Multi-plant firm 0.005 0.005 0.001

  (0.028) (0.028) (0.028)

       

Firms 25,906 25,906 25,541

obs 110,613 110,613 107,737

Overall R2 0.30 0.30 0.30

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“Urban depreciation”

• Ratio:Current value of all fixed assets in the city

toHistorical value of all fixed assets in the city

• Predicted effect: the higher, the better

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“Urban depreciation”  Size OP (1 stage) Diversity

Local employment 0.104***   0.418***  

  (0.038)   (0.550)  

# firms   0.003***    

    (0.001)    

“Anti-diversity index”       -0.004

        (0.023)

Urban depreciation 0.080*** 0.085*** 0.620 0.111***

  (0.038) (0.037) (0.060) (0.038)

Industry* Year f.e. yes yes yes yes

City f.e. yes yes yes yes

firms 42,026 41,898 26,909 41,240

obs 150,311 151,775 73,870 147,857

Overal 0.63 0.85 - 0.85

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Ownership variables

• PO: privately owned• DO: private, majority domestic• FO: private, majority state• PO = FO + DO

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Ownership variables

  State Domestic Foreign Total

2001 1,072 19,342 636 21,050

2002 994 20,753 695 22,442

2003 930 21,209 767 22,906

2004 883 21,734 837 23,454

2005 872 22,073 910 23,855

Total 4,751 105,111 3,845 113,707

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Ownership results  Size Diversity

ln(Local employment) 0.032  

  (0.023)  

Primarily domestic 0.169*** 0.169*** 0.174***

  (0.021) (0.021) (0.021)

Primarily foreign 0.285*** 0.285*** 0.292***

  (0.028) (0.028) (0.028)

ln(# firms) 0.026

  (0.030)

ln(average employment) 0.038

  (0.028)

"Anti-diversity index"   -0.068**

    (0.021)

Multi-plant firm 0.068** 0.069** 0.067**

  (0.025) (0.025) (0.025)

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Conclusions and implications

• Urbanization economies seem to be present

• More pronounced for firm counts based measures, than labor based

• Urban capital depreciation matters• Ownership effect: foreign – private

domestic – state.

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Volodymyr VakhitovKyiv School of Economics: vakhitov@kse.org.ua