A model for accessing
international firm-level data
Eric J. Bartelsman
Vrije Universiteit Amsterdam
and Tinbergen Institute
Prepared for OECD/Eurostat Conference
Luxembourg – October 26, 2006
This work is partially funded by the European Commission, Research Directorate General as part of the 6th
Framework Programme, Priority 8, "Policy Support and Anticipating Scientific and Technological Needs".
Overview of Presentation
What: International comparisons of
micro-based indicators
 Why: Meets urgent policy needs
 How: New statistical regulations; remote
execution; remote access
 Who: Networked researchers, NSOs,
Trusted third parties
 Discussion: pros/cons; costs/benefits

Using Microdata for Analysis

Policy analysis
 SNA / Final Expenditures & GDP: demand management
policy
 Indicators for structural policy and policy evaluation:
microdata

Academic research
 Estimation of behavioral responses

Microdata at NSOs
 Improve quality of output
 Reduce response burden
 Provide facilities for outsiders
International Comparisons
Not possible to ‘stack’ data from all
countries
 Cross-country variation in int’l microdata
research provides:

 valuable lessons for policy makers
 identification of effects for academics
Available Data Sources
Longitudinal
Micro Data
Single country
National Accounts
Industry Data
Surveys,
Business Registers
Macro and
Sectoral
Timeseries
•SC LMD
•DMD
N.A.
Multiple
countries
EUKLEMS
EUKLEMS+
Recent Int’l Microdata
Research

There is demand for international
comparisons of micro-based ‘indicators’:
 Firm-level projects for OECD, WB, IADB, Eurostat
 Int’l Wage flexibility project (FRB/ECB)
 IPUMS
 Luxembourg Income Studies
Firm-Level Projects
‘Distributed micro-data analysis’
 Harmonized collection of indicators from
longitudinal micro-level business
datasets

 Firm Demographics: Entry/Exit, Survival
 Productivity: higher moments, conditional
moments, special ‘tabulations’ (by size, ownership
status, etc)
Firms produce
not countries or industries



Variation in firm-level productivity within industry or
country
A country could have a ‘long tail’ problem:
Or a lack of world class firms:
long tail
country1
•Mean productivity may not be a sufficient policy
indicator
country2
Global frontier
The Gap Between Weighted and Un-Weighted
Labor Productivity, 1990s
Five-Year Differencing, Real Gross Output, Manufacturing
0.8
0.6
0.4
0.2
lo
ve
ni
a
La
R tvia
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on
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an
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In ore
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Ta
iw
U
K
o
Fi ld
N n
et la
he nd
rla
nd
W
es Fr s
t G an
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P ny
or
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S
A
A
rg
en
tin
a
C
C hil
ol e
om
bi
a
0.0
Data for Hungary, Indonesia and Romania use Three-Year Differencing.
Excluding Brazil and Venezuela.
OP cross term
Allocative Efficiency (OP Cross Term)
Transition Economies
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Hungary
Latvia
Romania
Slovenia
Estonia
Productivity and market contestability
Labor Productivity - Pooled Manufacturing
Five-Year Differencing, Real Gross Output
Country and Industry Time Averages
1.5
Correlation Coefficient: 0.5800***
1.0
0.5
0.0
-0.5
-1.0
-0.5
0.0
0.5
Net Entry Productivity Growth
Note: Excluding Brazil and Venezuela. Outliers Excluded.
1.0
1.5
How to generate indicators?

Eurostat regulation
 After international debate on definitions, NSOs
must supply the requested indicators

Distributed micro-data analysis
 Networked collection, through remote execution
(or remote access)
Network
Researcher
Distributed micro data research
Policy Question
Research Design
Program Code
Publication
Metadata
Cross-country
Tables
NSOs
Network
members
Provision of metadata.
Approval of access.
Disclosure analysis
Network for International
Microdata

Remote execution using meta-data at
center, and network of NSO contacts
 Coordination issues

Secure remote access to confidential
data at trusted center
 Technical issues
 Legal issues
Participants in Network

Policy analysts and academics
 Answered research questions
 Spillovers from knowledge; launching customer

NSOs
 Meet user needs; fits within organizational goals; learn from
best practice; improve reputation
 Provide facility; Provide expertise and experience

OECD
 Improved comparability of stats; answered policy questions
 Provide institutional support; contribute to analytical
capabilities
Issues for Discussion

What are dangers to NSOs and how to
minimize
 Confidentiality
 Provider of undisputed data
 Costly sink of resources

What are benefits to community and how to
maximize
 Low marginal costs for research output
 Turnaround time lowered
 Learning from broad-based experience