Slides

1. YOUNG SMES, GROWTH AND JOB CREATION: EVIDENCE FROM MICRO-AGGREGATED DATA FOR 18 COUNTRIES
Chiara Criscuolo Structural Policy Division, Directorate for Science, Technology and Innovation (STI)
New Approaches to Economic Challenges
Seminar, 9 September 2014
Peter Gal Structural Surveillance Division, Economics Department (ECO)
Carlo Menon Structural Policy Division, Directorate for Science, Technology and Innovation (STI)
Giuseppe Berlingieri Structural Policy Division, Directorate for Science, Technology and Innovation (STI)

2. •
Motivation for the project
•
DynEmp and MultiProd: an innovative way of getting access to confidential microdata
•
The methodology and the output
•
Results of the first wave of data collection DynEmp Express
•
A preview of new evidence from DynEmp v.2
•
Ongoing research:
–
MultiProd
–
Evidence based policy analysis
Roadmap

3. •
Motivation for the project
•
DynEmp and MultiProd: an innovative way of getting access to confidential microdata
•
The methodology and the output
•
Results of the first wave of data collection DynEmp Express
•
A preview of new evidence from DynEmp v. 2
•
Ongoing research:
–
Multiprod
–
Evidence based policy analysis
Roadmap

4. •
Sluggish productivity growth and stalled job creation. Increasing policy interest in:
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Job creation/destruction; creative destruction and productivity growth; allocative efficiency; new sources of growth
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Firm dynamics and heterogeneous impact of (horizontal) policies and of policies that depend on size
•
Central role of young firms
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Key drivers of job creation
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“Up-or-out” dynamics: high rates of job creation and destruction
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Secular decline in start-up rates
•
Heterogeneous impact of Great Recession
Motivation

5. •
Data needs: based on firm level data; cross-country; longitudinal; representative; detailed information on sector of activity; age and size dimensions;
•
Commercial data repositories have well known shortcomings
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Lack of “timely” cross-country harmonized and “representative” (micro-aggregated) firm-level longitudinal data on job flows across OECD countries
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National Statistical Offices surveys and Business Registers
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Access / Confidentiality
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Comparability
•
Additional benefits: Value-for-Money; Replicability
Motivation: data needs and challenges

6. •
Motivation for the project
•
DynEmp and MultiProd: an innovative way of getting access to confidential microdata
•
The methodology and the output
•
Results of the first wave of data collection: DynEmp Express
•
A preview of new evidence from DynEmp v. 2
•
Ongoing research:
–
Multiprod
–
Evidence based policy analysis
Roadmap

7. DynEmp: provides new cross-country evidence on employment dynamics using microaggregated data
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Led by the Working Party of Industry Analysis (WPIA)
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Coordinated by the DynEmp-team at the OECD
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Phase I: Data for 18 countries (17 OECD + Brazil)
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Phase II: in the field, up to 28 countries
MultiProd: provides evidence on productivity
The Projects

8. •
Methodology:
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Metadata collection
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Confidential national business registers
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Flexible micro-aggregation along different dimensions using a distributed microdata (DMD) approach.
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Single, thoroughly tested Stata routine:
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Flexible to adapt to differences in data setup
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Extensive confidentiality checks and blanking
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Internal bridging of different sectoral classifications
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Easily extendable over time and countries
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Programmed in a modular way: flexible to updates in methodology and policy issues
•
Country notes
The methodology

9. –
Annual panel data on
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job flows (creation, destruction)
•
employment and number of firms
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By:
18 countries (17 OECD + Brazil)
× 3 broad sectors (Manufacturing, construction and non- financial services)
× 5 age classes (0; 1-2; 3-5; 6-10; 11+)
× 6 size classes (Thresholds: 1, 10, 50, 100, 250, 500)
× 11 years (2001-2011) × 3 status (incumbent, entrant, exiting firm)
Phase I: DynEmp Express The database

10. •
Motivation for the project
•
DynEmp and MultiProd: an innovative way of getting access to confidential microdata
•
The methodology and the output
•
Results of the first wave of data collection: DynEmp Express
•
A preview of new evidence from DynEmp v. 2
•
Ongoing research:
–
MultiProd
–
Evidence based policy analysis
Roadmap

11. Not all small firms are young…
Note: Small firms defined as 1-249 employees
Source: Criscuolo, Gal and Menon, 2014

12. …but most of young firms are small
Note: Young firms are defined as 5 years old or younger
Source: Criscuolo, Gal and Menon, 2014

13. SMEs are important for job creation and job destruction ...
Source: Criscuolo, Gal and Menon, 2014

14. …but young SMEs are those which create jobs…
Source: Criscuolo, Gal and Menon, 2014

15. …and not all SMEs
Source: Criscuolo, Gal and Menon, 2014

16. The share of start-ups is declining in most countries
Share of start-ups (less than 3 year old) in all firms - average over the period
Source: Criscuolo, Gal and Menon, 2014

17. Young firms suffered relatively more from the crisis…
Yearly growth rate of young and old firms expressed as difference from the 10-year trend
Source: Criscuolo, Gal and Menon, 2014

18. …but most jobs were destroyed by the downsizing of old incumbents
Contributions to aggregate net job creation by entrants, young/old exitors, and young/old incumbents.
Source: Criscuolo, Gal and Menon, 2014

19. Growth of young firms is a challenge in many countries
Manufacturing
Services
Source: Criscuolo, Gal and Menon, 2014
Average firm size of young and old firms

20. •
Motivation for the project
•
DynEmp and MultiProd: an innovative way of getting access to confidential microdata
•
The methodology and the output
•
Results of the first wave of data collection: DynEmp Express
•
A preview of new evidence from DynEmp v. 2
•
Ongoing research:
–
MultiProd
–
Evidence based policy analysis
Roadmap

21. Who are the top performers…
Source: OECD DynEmp v2 database. Preliminary data.
Mean growth index, average size, median age and share of total employment
of top 10% firms
Note: Growth index = (empt+1-empt)/(0.5*(empt+empt+1))

22. The growth funnel: countries
Source: OECD DynEmp v2 database. Preliminary data.
Average growth index at different percentiles of the growth distribution
Growth index

23. The growth funnel: sectors
Average growth index at different percentiles of the growth distribution
Source: OECD DynEmp v2 database. Preliminary data.

24. The link between churning and growth of top performers
Average growth index at different percentiles of the growth distribution vs. churning rate
Source: OECD DynEmp v2 database. Preliminary data.
0.050.100.150.200.250.300.3500.511.522.5 Firm churning rate Average growth index top 10% firmsAUTBELFINHUNITANLDNORNZLPRTSWE

25. Evidence on growth dynamics of start-ups
Share and growth of surviving micro (<10 emp.) start-ups over 3, 5, and 7 years
Source: OECD DynEmp v2 database. Preliminary data.

26. •
Motivation for the project
•
DynEmp and Multiprod: an innovative way of getting access to confidential microdata
•
The methodology and the output
•
Results of the first wave of data collection: DynEmp Express
•
A preview of new evidence from DynEmp
•
Ongoing research:
–
Multiprod
–
Evidence based policy analysis
Roadmap

27. •
Cross-country differences in productivity explain a large share of income per capita differences
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Large heterogeneity in firm-level productivity, even in narrowly defined industries: countries might display the same average but very different underlying distributions
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Misallocation lowers aggregate productivity
•Distribution matters: low average productivity can be explained by too few firms at the top (lack of innovation) or too many firms at the bottom (weak market selection)
MultiProd – Motivation

28. •
Cross country differences in firm-level productivity performance:
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Schumpeterian process of creative destruction across countries
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Characterization of entire firm-level productivity distribution by industry, and refined by size, age, and ownership categories
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Measures of allocative efficiency
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Descriptive statistics of firms’ characteristics at different segments of the productivity level and growth distributions
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Firms at the frontier: differences across countries; contribution to aggregate productivity
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Estimates of misallocation and market inefficiency
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Income inequality: drivers of wage dispersion and relationship with productivity dispersion
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Time: before, during and after the recent Great Recession
MultiProd – Output

29. •
Empirical regularities and their impact on policies
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Net job creation does not come from all small firms, but only from those that are young
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Growth dynamics of firms differs across countries; in some countries, firms hardly scale after entry
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Growth of young innovative firms means “up” or “out”; entrepreneurs need flexibility to experiment with new technologies and new business models
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Decline in start-up rates
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Co-existence of success and failure (experimentation)
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New data for policy analysis (e.g. size contingent policies)
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Methodology can be replicated to assess different policy domains (e.g. R&D support) and agents (e.g. workers; regions; etc.)
Evidence based policy analysis

30. THANK YOU!
Giuseppe.Berlingieri@oecd.org
Chiara.Criscuolo@oecd.org Carlo.Menon@oecd.org
Peter.Gal@oecd.org
For any additional information on DynEmp
See: www.oecd.org/sti/ind/dynemp.htm
please email: dynemp@oecd.org