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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.

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Presentation on theme: "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."— Presentation transcript:

1 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".

2 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

3 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

4 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

5 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

6 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; ; ; 500+ (for OECD sample the groups between 1 and 20 and the groups between 100 and 500 are combined) Time (late 1980s – early 2000s)


8 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

9 CaseVariableAggregatorDisaggregationPotential Problems 1aEmploymentMean/SumAggregate or IndustryIndustry misclassification, Sample selection 1b Mean/SumSize ClassSample selection 1c Mean/SumFirm Status (Continuer, Entrant, Exit) Sample selection, Measurement error in longitudinal IDs 2a VarianceAggregate or IndustrySample selection, Classical measurement error 1aProductivityMeanAggregate or IndustryIndustry misclassification, Sample selection, 1bProductivityMeanProductivity quartilesSample selection, Classical measurement error 1cProd changeMeanFirm Status (Continuer, Entrant, Exit) Sample selection, Measurement error in longitudinal IDs, Classical measurement error 2bProductivity and Employment CovarianceAggregate, Industry, Firm Status All of the above

10 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

11 The different dimensions of producer dynamics 1.Firm size 2.Firm demographics: 1. Employment and # of firms for entry, exit, continuers: by industry and size class 3.Firm survival : 1. Employment and # of survivors, by cohort, industry, year 4.Static and dynamic analysis of allocative efficiency: 1. Decompositions of productivity (entry/exit/continuer) 2. Higher moments, covariances, means by quartile In presentation, focus on 2 and 4

12 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

13 Evidence of firm turnover 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 1 employee Total business sector, firms with at least 20 employees

14 Gross and net firm turnover: how the time dimension sheds light on the evolution of market forces in transition economies

15 Allocative efficiency : static analysis – Olley-Pakes decompositon

16 Allocative efficiency : how the allocative efficiency evolved over time in transition economies

17 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

18 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 Argentina Chile Colombia Estonia Finland France Korea Latvia Netherlands Portugal Slovenia Taiwan UK USA Low tech industriesMedium-high-tech industries Contribution of entry to labor productivity growth, five year differencing, gross output

19 Labor Productivity Dispersion Units: Thousand US$ per worker


21 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

22 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).

23 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

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