WP 10 Linkages with firmlevel data

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WP 10 Linkages with firm-level data

• 2nd EUKLEMS Consortium Meeting,
• 9-11 June 2005, Helsinki
• This project is 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".

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Overview of presentation

• WP10 linkages with firm-level data
• Use EUKLEMS data in micro-econometric analysis
• Industry price deflators and PPPs
• Instruments from IO and/or trade matrices Shea
  (94) , BCL(94)
• Add micro-aggregated indicators to EUKLEMS
  database
• Higher moments, covariances, gross flows, etc.
• Integrate micro data sources into EUKLEMS
  statistical process
• First confrontation of different sources of
  productivity measures
• Next consistent, integrated, international data
  creation
• Paper Bartelsman, Scarpetta, Haltiwanger (2005)
• Creating higher moments as addition to EUKLEMS
  database

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Measuring and Analyzing Cross-country
Differences in Firm Dynamics

• Eric Bartelsman, Stefano Scarpetta,
• and John Haltiwanger
• Free University Amsterdam and Tinbergen
  Institute World Bank University of Maryland and
  NBER

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The firm-level project a network of experts

• The firm-level project would have been impossible
  without extensive effort and support of many
  colleagues
• Mika Maliranta, Satu Nurmi, Jonathan Haskel,
  Richard Duhaitois, Pedro Portugal, Thorsten
  Schank, Fabiano Schivardi, Ralf Marten, Ylva
  Heden, Ellen Hogenboom, Mihail Hazans, Jaan
  Masso, John Earle, Milan Vodopivec, Kaplan,
  Maurice Kugler, Mark Roberts...
• The firm-level projects were funded by OECD,
  World Bank, various national government and NSOs

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Distributed micro-data collection

• OECD sample
• Demographics (entry/exit) for 10 countries
• Productivity decompositions for 7 countries
• Survival analysis 7 countries
• World Bank sample
• Same variables, 14 Central and Eastern Europe,
  Latin America and South East Asia
• EU Sample (10 countries), updates and a few new
  countries
• Productivity decompositions
• Sample Stats and correlations by quartile

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Data sources

• Business registers for firm demographics
• Firm level, at least one employee, 2/3-digit
  industry
• Production Stats, enterprise surveys for
  productivity analysis
• Countries
• 10 OECD
• 5 Central and Eastern Europe
• 6 Latin America
• 3 East Asia
• Data are disaggregated by
• industry (2-3 digit)
• size classes 1-9 10-19 20-49 50-99 100-249
  250-499 500 (for OECD sample the groups between
  1 and 20 and the groups between 100 and 500 are
  combined)
• Time (late 1980s early 2000s)

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Measurement Error

• Three sources of error potentially affect
  comparability of indicators built from firm level
  data
• Classical Error of firm-level measure
• Errors in observed firms (sample)
• Method of Aggregation of Indicator
• Aggregation is harmonized in our approach, but
  other errors may or may not cancel out in
  aggregation

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Cross-country Comparisons

• Harmonization
• Sample frames Variable definitions
  Classifications Aggregation Methods
• Make comparisons that control for errors
• Exploit the different dimensions of the data
  (size, industry, time)
• Use difference in differences techniques
• Even in absence of measurement error,
  interpretation of cross-country indicators
  requires careful analysis

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The different dimensions of producer dynamics

• Firm size
• Firm demographics
• Employment and of firms for entry, exit,
  continuers by industry and size class
• Firm survival
• Employment and of survivors, by cohort,
  industry, year
• Static and dynamic analysis of allocative
  efficiency
• Decompositions of productivity (entry/exit/continu
  er)
• Higher moments, covariances, means by quartile
• In presentation, focus on 2 and 4

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Interpretation of Gross Turnover

• Theoretical explanations
• Entry explained by push and pull factors
• Exit barriers may effect characteristics of
  exiting firm more than number of exits
• Measurement errors
• Conceptual differences in measure (e.g. labor)
• Differences in underlying data sources

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Evidence of firm turnover
Total business sector, firms with at least 1
employee

• No major differences across OECD countries,
  especially after controlling for sector and size
  effects
• But large differences in size at entry
• Large net entry in transition economies filling
  the gaps (?)

Total business sector, firms with at least 20
employees
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Gross and net firm turnover how the time
dimension sheds light on the evolution of market
forces in transition economies
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Allocative efficiency static analysis
Olley-Pakes decompositon
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Allocative efficiency how the allocative
efficiency evolved over time in transition
economies
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Dynamic allocative efficiency the role of entry
and exit in reallocating resources towards more
productive uses
We used the FHK approach, but also compared with
Griliches-Regev and Baldwin-Gu
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Dynamic allocative efficiency the importance of
technology factors
We decompose our data for manufacturing into a
low technology group and a medium high tech
group ? Stronger contribution of entry to
productivity growth in medium-to-high tech
industries
Contribution of entry to labor productivity
growth, five year differencing, gross output
1.5
1
0.5
0
-0.5
-1
-1.5
UK
USA
Chile
Korea
Latvia
France
Estonia
Finland
Taiwan
Portugal
Slovenia
Colombia
Argentina
Netherlands
Low tech industries
Medium-high-tech industries
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Labor Productivity Dispersion
Units Thousand US per worker
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Micro-aggregated indicators

• Distributed micro-data research is a practical
  way to exploit information in (confidential)
  firm-level datasets located at separate sites.
• While simple level comparisons may be
  problematic, difference-in-difference approach
  looks more promising
• There is significant cross-country variation in
  firm-level indicators that may be linked to
  differences in policy or market environment

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Integrating micro-level statistical sources

• Using micro-level sources and integration
  framework is flexible way to generate customized
  statistics
• Micro-level sources may provide check on
  aggregate analytical indicators, such as output
  per worker
• e.g. Nominal gross output per worker, aggregated
  from micro data compared with same measure from
  National Accounts (STAN database).
• Different owing to gross output a residual in
  N.A. labor sources in N.A. with different
  industry distribution sampling selectivity at
  micro level unit of observation (firm/estab).

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Further work in WP 10

• Survey paper with 2 components
• Policy research using firm-level data
• Testing hypothesis
• Policy Evaluation
• Linkages with sectoral and micro data
• Merging sectoral data into micro for econometric
  research
• Much literature from US, and increasingly other
  OECD and global
• Using indicators built from micro data for
  sectoral research
• Theoretical in trade/IO/labor, some single
  country and BHS
• Statistics production from integrated micro-level
  sources