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)

• 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

• 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

• Sluggish productivity growth and stalled job creation. Increasing policy interest in: – Job creation/destruction; creative destruction and productivity growth;

allocative efficiency; new sources of growth – Firm dynamics and heterogeneous impact of (horizontal) policies and of

policies that depend on size

• Central role of young firms – Key drivers of job creation – “Up-or-out” dynamics: high rates of job creation and destruction – Secular decline in start-up rates

• Heterogeneous impact of Great Recession

Motivation

• 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

• Lack of “timely” cross-country harmonized and “representative” (micro-aggregated) firm-level longitudinal data on job flows across OECD countries – National Statistical Offices surveys and Business Registers – Access / Confidentiality – Comparability

• Additional benefits: Value-for-Money; Replicability

Motivation: data needs and challenges

• 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

DynEmp: provides new cross-country evidence on employment dynamics using microaggregated data

– Led by the Working Party of Industry Analysis (WPIA) – Coordinated by the DynEmp-team at the OECD – Phase I: Data for 18 countries (17 OECD + Brazil) – Phase II: in the field, up to 28 countries

MultiProd: provides evidence on productivity

The Projects

• Methodology: – Metadata collection – Confidential national business registers – Flexible micro-aggregation along different dimensions

using a distributed microdata (DMD) approach. – Single, thoroughly tested Stata routine:

• Flexible to adapt to differences in data setup • Extensive confidentiality checks and blanking • Internal bridging of different sectoral classifications • Easily extendable over time and countries • Programmed in a modular way: flexible to updates in

methodology and policy issues • Country notes

The methodology

–Annual panel data on • job flows (creation, destruction) • employment and number of firms

–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

• 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

Not all small firms are young…

Note: Small firms defined as 1-249 employees Source: Criscuolo, Gal and Menon, 2014

…but most of young firms are small

Note: Young firms are defined as 5 years old or younger Source: Criscuolo, Gal and Menon, 2014

SMEs are important for job creation and job destruction ...

Source: Criscuolo, Gal and Menon, 2014

…but young SMEs are those which create jobs…

Source: Criscuolo, Gal and Menon, 2014

…and not all SMEs

Source: Criscuolo, Gal and Menon, 2014

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

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

…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

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

• 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

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))

The growth funnel: countries

Source: OECD DynEmp v2 database. Preliminary data.

Average growth index at different percentiles of the growth distribution

Growth index

The growth funnel: sectors Average growth index at different percentiles of the growth distribution

Source: OECD DynEmp v2 database. Preliminary data.

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.

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0 0.5 1 1.5 2 2.5

Firm

chu

rnin

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Average growth index top 10% firms

AUT

BEL

FIN

HUN

ITA

NLD

NOR

NZL

PRT

SWE

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.

• 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

• Cross-country differences in productivity explain a large share of income per capita differences

• Large heterogeneity in firm-level productivity, even in narrowly defined industries: countries might display the same average but very different underlying distributions

• 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

• Cross country differences in firm-level productivity performance: – Schumpeterian process of creative destruction across countries – Characterization of entire firm-level productivity distribution by

industry, and refined by size, age, and ownership categories – Measures of allocative efficiency – Descriptive statistics of firms’ characteristics at different segments of

the productivity level and growth distributions – Firms at the frontier: differences across countries; contribution to

aggregate productivity

• Estimates of misallocation and market inefficiency • Income inequality: drivers of wage dispersion and relationship with

productivity dispersion • Time: before, during and after the recent Great Recession

MultiProd – Output

• Empirical regularities and their impact on policies – Net job creation does not come from all small firms, but only from

those that are young – Growth dynamics of firms differs across countries; in some countries,

firms hardly scale after entry – Growth of young innovative firms means “up” or “out”;

entrepreneurs need flexibility to experiment with new technologies and new business models

– Decline in start-up rates – Co-existence of success and failure (experimentation)

• New data for policy analysis (e.g. size contingent policies)

• 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

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