WP10 Linkages with Micro Data

Overview, remaining work, and some new resultsBrussels, March 16-17, 2007

Eric BartelsmanThe EUKLEMS project is funded by the European Commission, Research DG, 6th Framework Programme, Priority 8, "Policy Support and Anticipating Scientific and Technological Needs".

•SC LMD

EUKLEMS

Longitudinal Micro Data

National Accounts Industry Data

Single countryMacro and Sectoral

TimeseriesSurveys,

Business Registers

Multiple countries

N.A.

Micro-data linkages

•DMD

EUKLEMS+

•SC LMD+

•Replicated LMD+

WP10 Objectives

• Integrate (existing) micro measures of within industry firm-level distributions with the EU-KLEMS data.

• Conduct empirical work using the augmented EU KLEMS data to explore recent theoretical models linking growth to firm dynamics and heterogeneity.

• Facilitate the analysis of productivity in work package 9 on technology and innovation by providing firm-level data

• Investigate potential to use firm level data in future updates of EU KLEMS Productivity Database.

EUKLEMS+: Integrating micro indicators

• Concordance are done• Firm-level indicators are being

documented/organized. • Looking for pilot-projects: EUKLEMS+

• Transition economies and Turkey (Bartelsman&Scarpetta)

• WP9 ???• Labor market flexibility

Links with WP 9

• WP10 access to micro data may provide indicators for WP9

• We may consider using expertise built up in:• OECD - micro innovation project• Eurostat/ONS ICT-firm performance project.

This project uses linked micro datasets (PS, Ecommerce, R&D) and infrastructure at NSOs. Also EUKLEMS industry hierarchy is used.

Micro-data for updating EUKLEMS• Many indicators, especially those outside of

SNA framework, may be collected directly from linked micro datasets using distributed micro data analysis.• Labor quality• Investment by industry/asset

• Harmonized approach can coordinate technical details, eg sample reweighting.

• An inventory of available sources and metadata needs to be made• Volunteer: someone with overview of WP2/3

 

Distributed micro data analysis

Provision of metadata.Approval of access.Disclosure analysis

Disclosure analysis of Publication

NSO

sR

esea

rche

r

Policy QuestionResearch Design Program Code

Publication

Net

wor

k Metadata

Networkmembers

Cross-countryTables

Creative Destruction and Productivity in the Turkish Manufacturing Industry

Eric BartelsmanVrije Universiteit Amsterdam, Tinbergen Institute, IZA

(joint with Stefano Scarpetta, OECD)

Small manufacturing firms, 1990s (firms with 10 to 20 employees as a percentage of total)

  Firms Employment

Turkey 25.38 3.59Argentina 45.99 10.70

Brazil 46.31 9.23

Chile 27.50 4.96

Colombia 28.70 4.74

Mexico 39.97 5.90

Estonia 36.07 7.21

Hungary 34.88 4.71

Latvia 39.73 7.66

Romania 34.57 1.80

Slovenia 22.47 1.84

Cross-country shift-share analysis of firm size

  Sectoral compositionAverage size of

firms

Interaction between sectoral comp. and

size Total

Turkey 0.01 0.08 0.03 0.12

Argentina 0.07 -0.35 -0.05 -0.34

Brazil -0.02 -0.19 0.00 -0.20

Chile 0.00 -0.03 -0.05 -0.08

Colombia 0.03 -0.07 0.02 -0.03

Mexico 0.06 -0.03 0.02 0.05

Estonia -0.10 -0.18 0.08 -0.20

Hungary 0.00 0.31 -0.16 0.16

Latvia -0.10 -0.07 -0.02 -0.20

Romania 0.56 1.71 0.00 2.27

Slovenia 0.03 1.08 -0.22 0.89

Sectors

Share of

Firms - Turkey

Average Size - Turkey

Share of Firms -

Comparator

Average Size -

Comparator Food Products, Beverages and Tobacco 16.0 98.2 16.1 93.9 Textiles, Textile Products, Leather and Footwear 31.3 108.4 19.2 72.0 Wood and Products of Wood and Cork 2.1 50.3 5.4 43.6 Pulp, Paper, Paper Products, Printing and Publishing 3.6 88.6 11.1 117.5 Coke, Refined Petroleum Products and Nuclear Fuel 0.3 222.8 0.3 333.3 Pharmaceuticals 0.7 217.7 1.3 129.1 Chemicals Excluding Pharmaceuticals 3.1 118.7 3.6 113.9 Rubber and Plastic Products 5.0 68.7 6.1 64.1 Other Non-Metallic Mineral Products 7.8 84.0 6.2 69.3 Basic Metals 3.8 150.1 2.0 169.1 Fabricated Metal Products, except Machinery and Equip. 7.3 62.1 10.4 52.8 Machinery and Equipment, N.E.C. 8.6 73.5 4.7 111.7 Office, Accounting and Computing Machinery 0.1 60.0 0.2 76.9 Electrical Machinery and Apparatus N.E.C. 3.0 103.2 1.7 121.4 Radio, Television and Communication Equipment 0.6 220.1 0.7 138.5 Medical, Precision and Optical Instruments 0.7 65.6 0.9 80.5 Motor Vehicles, Trailers and Semi-Trailers 2.0 195.2 2.4 215.8 Building and Repairing of Ships and Boats 0.3 99.7 0.2 118.7 Railroad Equipment and Transport Equipment N.E.C. 0.2 366.2 0.3 212.3 Aircraft and Spacecraft 0.0 901.5 0.1 189.7 Manufacturing N.E.C., Recycling 3.5 64.4 7.0 60.8

Entry and exit rates, manufacturing, percentage values,

firms with more than 10 employees

0

2

4

6

8

10

12

14

Argenti

na

Estonia

Colombia

Latvi

aBraz

ilChile

Mexico

Slovenia

Romania

Hungar

y

Turke

y

Firm Entry Firm Exit

Relative size of entrants (average size of entrant firms

divided by the average size of incumbents)

Relative size entrants

00.10.20.30.40.50.60.70.80.9

Turke

y

Mexico

Colombia

Brazil

Latvi

a

Romania

Argenti

naChile

Hungar

y

Slovenia

Estonia

Relative size entrants

Survivor size, relative to entry size, by age

Average firm size relative to entry at different lifetimes, 1990s

00.5

11.5

22.5

3

Slovenia

Romania

Colombia

Chile

Turke

y

Estonia

Argenti

na

Hungar

y

Mexico

Latvi

a

age=2 age=4 age=7

Slovenia Hungary

Latvia Romania

0.005.00

10.0015.0020.0025.0030.0035.0040.0045.0050.00

92 93 94 95 96 97 98 99

Gross firm flows Net firm flows

0.005.00

10.0015.0020.0025.0030.0035.0040.0045.0050.00

94 95 96 97 98 99

Gross firm flows Net firm flows

0.005.00

10.0015.0020.0025.0030.0035.0040.0045.0050.00

94 95 96 97

Gross firm flows Net firm flows

0.005.00

10.0015.0020.0025.0030.0035.0040.0045.0050.00

94 95 96 97 98 99

Gross firm flows Net firm flows

Gross and net firm turnover: Transition Economies

Eric Bartelsman, John Haltiwanger and Stefano Scarpetta*

*Vrije Universiteit Amsterdam, Tinbergen Institute and IZA; University of Maryland, NBER, and IZA; OECD and IZA.

January 2007

Cross Country Differences in Productivity: The Role of Allocative Efficiency

Overview• Healthy, market economies exhibit the following features at the

firm level:• Large dispersion of productivity across firms within narrowly

defined sectors.• Must be due to some form of “friction”

• Market power• Economies of scope/decreasing returns• Adjustment frictions and idiosyncratic shocks:

• Search and matching frictions• Labor and capital adjustment costs• Entry and exit costs

• Strong positive covariance between market share and productivity• Static allocative efficiency

• Market selection is productivity enhancing• Market selection yields exit of less productive firms and

establishments• High pace of input (e.g. jobs) and output reallocation

• Uncertainty, experimentation, learning and selection as well as time varying idiosyncratic shocks

Distortions and Allocative Efficiency?• Working conjecture:

• Emerging and transition economies have market structure and institutions that distort allocative efficiency

• Distortions:• Imperfect competition• Subsidies (explicit) or quotas/rationing of allocation to

insiders/incumbents/favored businesses• Credit constraints to young and small businesses• Bribes and corruption (unevenly applied)• Doing Business inefficiencies:

• Costly to start up a business• Costly to adjust employment• Poor or inefficient infrastructure (telephones, roads, electricity)

• Key point: Distortions have idiosyncratic component: cross-sectional allocation is distorted

• Entry barriers act in this way naturally• Credit constraints for young and small businesses• Arbitrary and capricious application of regulations

Aggregate productivity and allocation

• Olley and Pakes (1996) static decomposition:

where: N: # of firms in a sector; The first term is the unweighted average of firm-level productivityThe second term (OP cross term) reflects allocation of resources: do firms with

higher productivity have greater market share.Requires representative cross sectional samples but does not require accurate

longitudinal linkagesCannot quantify directly importance of entry and exit

By construction, cross term takes out country effects in productivity levels, so abstracts from some aspects of measurement error

i i

tittitittt PppNP ))(()/1(__

Allocative efficiency (OP cross term): The gap between

weighted and un-weighted labor productivity, 1990s

0

0.2

0.4

0.6

0.8

Allocative efficiency (Olley Pakes decomposition -- cross term)(weighted averages of industry level cross terms from OP decomposition)

1. Based on the three-year differences

Allocative efficiency OP cross term

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Diff

eren

ce b

etw

een

wei

ghte

d an

d un

wei

ghte

d la

bor p

rodu

ctiv

ity

Hungary: allocative efficiency over the transition(cross-term of the Olley Pakes decomposition, manufacturing)

6.5

7

7.5

8

8.5

9

1995 1996 1997 1998 1999 2000 2001-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

total L productivity un-weighted L productivity Cross term (right scale)

Slovenia: allocative efficiency over the transition(cross-term of the Olley Pakes decomposition, manufacturing)

6.5

7

7.5

8

8.5

9

9.5

1993 1994 1995 1996 1997 1998 1999 2000 2001-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

total L productivity un-weighted L productivity Cross term (right scale)

    

A Model of “Mis”-Allocation (Based on Rogerson and Restuccia (2003) (andsimilar to Hsieh and Klenow (2006))

0

)(t

tt CU

Consumers supply labor inelastically and maximize utility:

Firms maximize profits:•Ex ante firms do not know productivity or distortion but know distribution. •Pay entry fee, learn their draws, decide whether to produce.•Productivity and distortion draws are firm-specific and time invariant•Very low productivity/high distortion firms don’t produce•Decreasing returns yields dispersion in productivity/distortions•Overhead labor yields correlation between TFP and LP

( ) , 1it i it itY A n f k (1 )( ) (1 )it i i it it t it i t itA n f k w n r k

* * ( )(1 )it itrn f k

w

1/(1 )

*

( )(1 ) (1 )

(1 )

i

it

rAwk

r

Entry/Selection

( , , ) ( , , ) /(1 )i i i i i iW A A

, ,

max(0, ( , , )) ( , , ) 0ee

A

W W A dG A c

(1 ) /(1 )R

),,( AG Ex Ante Joint Distribution

Exogenous probability of exiting in each period given by λ

Aggregate Relationships and Steady State Equilibrium

sde NNW ,0

*

,

( , , ) ( , , )t tA

Y y A d A

μ(A,τ,) is ex post distributionE is aggregate entry

*

, ,

( , , ) ( , , )dt t

A

N n A d A

ttett YKcEC

  Note: All reported statistics are at steady state equilibrium reflecting selection.  

•γ = 0.9, •λ = .10, this is consistent with evidence of exit rates in the United States and other OECD countries (Bartelsman et al. 2004) •R=.03 and δ=.12, roughly consistent with long run real interest rates and depreciation rates in OECD countries. •f=.01,log(ce)=12.43•Ex ante A distribution: mean(log(A))=10.57, std(log(A))=0.35

Preliminary Calibration and Numerical Analysis of Model

Case Meanlog(LP)

Stdlog(LP)

OP cross term

log(LP)

Meanlog(A)

Stdlog(A)

OP cross term

log(A)

Avg(K/L) Diff log(cons)

Fraction survive

No distortion 12.05 0.06 0.05 10.72 0.27 0.43 114184 0.00 0.77

Random output distortion

12.15 0.25 -0.15 10.74 0.30 0.38 180224 -0.35 0.44

Random capital distortion

12.14 0.06 0.05 10.74 0.26 0.42 453316 -0.63 0.72

Correlated output distortion

11.72 0.35 -0.10 10.49 0.32 0.16 108946 -0.58 0.64

Relationship Between Productivity and Employment: No Distortions

Relationship Between Labor Productivity and Employment (Random distortion case)

Observations

• Distortions affect various margins• Entry: amount and characteristics• Allocation among survivors• OP cross term reflects allocation for given survivors

• Uncorrelated scale distortion• Slight increase in mean TFP/LP

• Lower survival

• Lower OP cross term for both TFP and LP• Affects entry and selection• Lower consumption (C) through lower productivity

and excess churn

Observations (2)

• Uncorrelated factor-mix distortion• Slight increase in Mean TFP and LP again

• Lower survival• Not much impact on OP cross term for either TFP and LP• K/L too high• Lower C primarily through distorted high capital investment

• Correlated scale distortion• Mean TFP/LP much lower• OP cross term very low• Adverse impact on survival• So: wrong firms survive, allocation poor amongst survivors

and too much churn (everything wrong!)• Much lower C from all of these factors

Open questions

• Can we account for differences across countries in productivity via the distribution of distortions?• If so, what are these distortions?

• Static vs. Dynamic distortions?• Is wedge between marginal product and factor prices because

of adjustment costs (dynamic) or a more permanent distortion (e.g., favored businesses)

• Can we quantify the reduction in distortions in transition economies that account for rising allocative efficiency?

• Implications for growth: transition dynamics vs. steady state growth?

• Missing pieces (many):• E.g., Differentiated products version of model• More frictions? (Experimentation/learning/adjustment costs)