CROSS-COUNTRY DIFFERENCES IN BUSINESS DYNAMICS AND IN A ALLOCATION OF RESOURCES TO OCATION OF RESOURCES TO PATENTING FIRMS: NEW EVIDENCE FROM MICRO DATA AND THE ROLE OF PO ICIES OF POLICIES Chiara Criscuolo OECD Science Technology and Innovation Directorate OECD, Science, Technology and Innovation Directorate Treasury Guest Lecture The Treasury, Wellington 13 th March 2015
Based on joint work with: Based on joint work with: • The “ DYNEMP team” ( Carlo Menon; Flavio h “ ” ( l l i Calvino and Peter Gal ) • The “Multiprod team” ( Giuseppe Berlingieri and Patrick Blanchenay ) y ) • Dan Andre s Polic St dies Branch • Dan Andrews , Policy Studies Branch Economics Department OECD and Carlo M Menon , Structural Policy Division, Science, St t l P li Di i i S i Technology and Industry Directorate, OECD
Thanks to the network: Responsible for the Working Party of Industry Analysis, OECD Mariagrazia Squicciarini Economic Analysis and Statistics (EAS) Division, STI Chair of the Working Party of Industry Analysis, Census United Javier Miranda Bureau States WIFO Institute (Austrian Institute of Economic Research) Austria Werner Hölzl Federal Planning Bureau Belgium Hilde Spinnewyn, Chantal Kegels, Michel Dumont Instituto de Pesquisa Econômica Aplicada Brazil Carlos Henrique Leite Corseuil, Gabriel Lopes de Ulyssea, Fernanda de Negri Industry Canada, Statistics Canada y , Canada Pierre Therrien, Jay Dixon, Anne-Marie Pierre Therrien, Jay Dixon, Anne Marie Rollin, John Baldwin Mika Maliranta ETLA Finland Lionel Nesta, Flora Bellone National Center for Scientific Research (CNSR) and OFCE France Gabor Katay, Peter Harasztosi Central Bank of Hungary, Central Statistical Office of Hungary Hungary Italian National Institute of Statistics Italy Stefano Costa Kyoji Fukao, Kenta Ikeuchi Institute of Japan Economic Research Hitotsubashi University Economic Research, Hitotsubashi University Leila Ben-Aoun, Anne Dubrocard, Michel STATEC Luxembourg Prombo Michael Polder Statistics Netherlands (Centraal Bureau voor de Statistiek) Netherlands New Zealand Treasury, Motu New Lynda Sanderson, Richard Economic and Public Policy Research Zealand Fabling Arvid Raknerud, Diana Cristina Iancu Arvid Raknerud, Diana Cristina Iancu Ministry of Trade and Industry, Statistics Norway y y, y Norway y Jorge Portugal Presidencia da Republica Portugal Valentin Llorente Garcia Ministry of Industry, Energy and Tourism, Spanish Statistical Spain Office Eva Hagsten Jan Selen Statistics Sweden Sweden
Motivation Motivation • Sluggish productivity growth and stalled job gg p y g j creation. Increasing policy interest in: – Job creation/destruction; creative destruction Job creation/destruction; creative destruction and productivity growth; allocative efficiency; new sources of growth new sources of growth – Firm dynamics and heterogeneous impact of policies li i – Contribution of Innovation to aggregate (long ‐ run) productivity growth – Role of patents and IPR systems p y
Motivation Motivation • Central role of young firms as key drivers of C t l l f fi k d i f job creation: – “Up ‐ or ‐ out” dynamics: high rates of job creation and destruction – Secular decline in start ‐ up rates • Heterogeneous impact of Great Recession: • Heterogeneous impact of Great Recession: – Even though young firms have been hit most they remained net job creators during the crisis • Heterogeneous impact of Policies g p
Motivation Motivation • The allocation and the flow of resources across firms • The allocation and the flow of resources across firms contributes to aggregate differences in productivity l level and growth. l d th • At the firm level productivity is driven by innovation and in turn, the incentives to invest in patents and innovation are affected by the efficiency of resource y y reallocation mechanisms • Public policies can significantly affect the degree of P bli li i i ifi tl ff t th d f static and dynamic allocative efficiency • Very limited cross ‐ country evidence.
Motivation: data needs and challenges g • Data needs: based on firm level data; cross ‐ country; Data needs: based on firm level data; cross country; longitudinal; representative; detailed information on sector of activity; age and size dimensions; sector of activity; age and size dimensions; • Commercial data repositories have well known shortcomings shortcomings • Lack of “timely” cross ‐ country harmonized and “representative” (micro ‐ aggregated) firm ‐ level longitudinal data on job flows and productivity across OECD countries – National Statistical Offices surveys and Business Registers – Access / Confidentiality – Comparability
Our contribution Our contribution • Unveil enterprise dynamics and contribution to Unveil enterprise dynamics and contribution to employment growth using new cross ‐ country evidence from firm level micro data (Dynemp) . • Unveil differences in returns to patenting across Unveil differences in returns to patenting across countries and role of policies in explaining these differences (Matched Orbis ‐ Patstat database) • Ongoing work … g g
Ongoing Work Ongoing Work • Understand the role of policies in explaining Understand the role of policies in explaining differences in post ‐ entry growth performance. • Analyse Long run productivity growth • Look at frontier growth Look at frontier growth • MULTIPROD project to analyse micro driver of differences in aggregate productivity performance using new cross ‐ country evidence from firm level g y micro data. • Depict differences in IPR systems D i t diff i IPR t
The Data The Data • DynEmp provides new cross ‐ country evidence on DynEmp provides new cross country evidence on employment dynamics using microaggregated data data – Coordinated by the DynEmp ‐ team at the OECD and condcuted by delegates from the Working Party of condcuted by delegates from the Working Party of Industry Analysis (WPIA) – Phase I ‐ Dynemp Express : Data for 18 countries (17 Phase I Dynemp Express : Data for 18 countries (17 OECD + Brazil) over 2001 ‐ 2011 – Phase II – Dynemp v.2 : in the field, data for 17 Phase II Dynemp v.2 : in the field, data for 17 countries received, up to 28 countries (OECD and non ‐ OECD) )
The DynEmp Express Database The DynEmp Express Database Dynemp express : y p p • Covers 18 countries over 2001 ‐ 2011 and 3 macro sectors, i.e. manufacturing, non financial services sectors, i.e. manufacturing, non financial services and construction • Contribution to aggregate job flows (creation and Contribution to aggregate job flows (creation and destruction) of firms of different size and age • transition dynamics : follows firms over 3 years transition dynamics : follows firms over 3 years period in 2001; 2004 and 2007 • Caveats: Caveats: – De alio vs de novo entrants – Failures vs acquisitions vs restructuring a u es s acqu s o s s es uc u g – Definition of “inactivity”
The DynEmp v 2 Database The DynEmp v.2 Database In addition to Dynemp express Dynemp v.2 includes: y p p y p • transition dynamics of start ‐ ups : follows cohorts of entrants after 3, 5 and 7 years after entry • more granular analysis at industry level, as data are now aggregated up to 2 ‐ digit sectors , • additional variables and moments of the distribution , e.g. employment growth volatility; average growth rate; gross job creation by the top 10% of the employment gross job creation by the top 10% of the employment growth distribution • “ distributed regressions ”, i.e. regressions conducted at distributed regressions , i.e. regressions conducted at the unit level within each separate country following the same estimation method and model and over the same ti time period i d
The OECD HAN Database The OECD HAN Database Matching commercial (Orbis ‐ unconsolidated accounts) and administrative data d i i i d (P (Patstat) provides Cross ‐ country longitudinal ) id C l i di l microdata on firms patenting activity and performance over 2003 ‐ 2010 • Main measure of firms patent stock: cumulative sum of EPO PCT USTPO granted Main measure of firms’patent stock: cumulative sum of EPO ‐ PCT ‐ USTPO granted patents since 1980, depreciated at 15% p.a. • Size threshold: 20 employees in 2003 or at the time of first appearance if the dataset if >2003 (~582k firms) dataset if >2003 ( 582k firms) Main Caveats: • • ORBIS is a commercial databases which is unlikely to be representative at aggregate level ORBIS is a commercial databases which is unlikely to be representative at aggregate level – Assumption: sample selection is uncorrelated with conditional (on size, country, sector, etc.) patenting probability • Patents are allocated to the firms based on fuzzy matching, we expect substantial measurement error – We restrict the analysis to countries where the matching ratio is good y g g • There is not a 1:1 correspondence between patents and innovation • Measurement of policies is based on synthetic indicators (OECD and World Bank)
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