Location factors for multinationals: Which role for the skill distribution of employment? OECD – Directorate for Science, Technology and Industry Elif KÖKSAL-OUDOT Université Paris 1 Panthéon-Sorbonne CES-MATISSE

1. Introduction 1.1. Topics and purposes Importance of human capital for innovation and economic growth since Becker (1964). Many endogenous growth contributions (Lucas 1988, Romer 1990, Aghion-Howitt (1992) => education as a key determinant 2

1.1. Topics and purposes However assessing skills goes beyond education => challenge Lack of internationally comparable data on labour quality => 1 st objective: Assessing the labour force composition by skills and by industry in OECD countries 3

1.1. Topics and purposes Globalisation : FDI flows into and from OECD countries Especially, FDI in the high technology sectors and internationalisation of R&D Determinants of location decisions of multinationals: what role for the skill composition of employment of the host country? => 2 st objective 4

1.2. Implications Creating a new database: Skills by industry ▫Adding a “skill” dimension to the currently available data at industry level (extending the STructural ANalysis (STAN) Database) Comparing the two skill proxies ▫ Occupation and education Analysing impact of skills on FDI location decisions 5

1.3. Outline 2. Data and descriptive results 3. Empirical estimations 4. Conclusion and further research agenda 6

2. D ata & Descriptive Statistics OECD Skills by Industry (ANSKILL) database OECD Activities of Foreign Affiliates (AFA) Database OECD Foreign Affiliates’ Trade in Services (FATS) Database OECD STAN Database for Structural Analysis 7

2.1. ANSKILL Two proxies for skills: 8 Occupation (ISCO -88) Skill levelsEducation (ISCED-97) Legislators, senior officials, managers HIGH SKILLED Second stage of tertiary education Professionals First stage of tertiary education Technicians, associate professionals Clerks MEDIUM SKILLED Upper secondary education Service/sale workers Skilled agricultural and fishery workers Post-secondary non tertiary education Craft and related trade workers Plant and machine operatorsLOW SKILLED Primary education Elementary occupationsLower secondary education

2.2. AFA and FATS AFA : Manufacturing sectors - Highly aggregated level of detail for industries -16 variables (on production, employment, employee compensation, investment, R&D…) FATS : Service sectors -More recent (since 1995), OECD – Eurostat joint questionnaire -Very detailed breakdown -Less variables (mainly on turnover, value added, employment, exports and imports) 9

2.3. STAN Main OECD database for industry-level time-series data Important country (32 member countries) and time coverage (since 1970’s). Many variables on production, value added employment, labour costs, exports, imports…) 10

2.2. Descriptive statistics High skilled workers, occupation definition,1998 and 2008 As a percentage of all employees Source: OECD, ANSKILL Database, September 2011.

High skilled workers, education definition, 1998 and 2008 As a percentage of all employees Source: OECD, ANSKILL Database, September 2011.

Comparative importance of occupation and educational attainment in skill assessment across countries 13

Employees in high-skill occupations as a percentage of those with at least a university degree, Source: OECD (2010), Measuring Innovation: A New Perspective.

Growth of high skilled workers, EU, Source: OECD, ANSKILL Database, April 2010.

Growth of high skilled workers, United States, Source: OECD, ANSKILL Database, April 2010.

Growth of high skilled workers, Canada, Source: OECD, ANSKILL Database, April 2010.

Growth of high skilled workers, Japan, Source: OECD, ANSKILL Database, April 2010.

Growth of high skilled workers, EU, Source: OECD, ANSKILL Database, April 2010.

Growth of high skilled workers, United States, Source: OECD, ANSKILL Database, April 2010.

Growth of high skilled workers, Canada, Source: OECD, ANSKILL Database, April 2010.

Growth of high skilled workers, Japan, Source: OECD, ANSKILL Database, April 2010.

FDI inflows to OECD countries, as a % of GDP ( average) Source: OECD Economic Globalisation Indicators 2010 based on OECD, International Direct Investment and Annual National Accounts databases, June 2009.

Share of foreign-controlled affiliates in manuf. turnover and employment, 2007 Source: OECD Economic Globalisation Indicators 2010 based on OECD, International Direct Investment and Annual National Accounts databases, June 2009.

Growth of foreign-controlled affiliates and national firms’ share of manufacturing VA (1999 and 2006) Source: OECD, AFA database, December 2009.

Share of foreign-controlled affiliates in high-technology manufacturing turnover, Source: OECD, AFA database, September 2011.

Share of foreign affiliates in the employment of the services and manufacturing sectors, Source: OECD, AFA and FATS databases, December 2009.

Labour productivity of foreign affiliates in manufacturing and services, Source: OECD, AFA and FATS databases, December 2009.

3. Empirical estimation 29 HSoShare of the high skilled workers (occupation definition) MSoShare of the medium skilled workers (occupation definition) LSoShare of the low skilled workers (occupation definition) HSeShare of the high skilled workers (education definition) MSeShare of the medium skilled workers (education definition) LSeShare of the low skilled workers (education definition) NOENumber of foreign affiliate firms XTotal exports (at current $) ITotal imports (at current $) GDPGDP per capita (at current $) POPPopulation FDIForeign direct investment inflows (at current $) VAfValue added of the foreign affiliates (at current $) VATotal value added (at current $) EMPNumber of employees of the foreign affiliates WAGEfAnnual average wage per employee of the foreign affiliates WAGEAnnual average wage per employee of all firms CoopForeign ownership of domestic inventions RDfR&D expenditures of the foreign affiliates (at $ ppp)

3.1. Specification 30 (1)log NOE i,c,t = α 0 + α 1.logHS i,c,t + α 2.logMS i,c,t + α 3 log(X+I) / GDP) c,t + α 4.log(FDI /GDP) c,t + α 5.log(VAf / VA) i,c,t + α 6.log(WAGEf / WAGE) i,c,t + α 7.log(Coop / POP) c,t +  i,c,t

3.1. Specification 31 (2) logVAf / EMP i,c,t = α 0 + α 1.logHS i,c,t + α 2.logMS i,c,t + α 3 log(NOE) i,c,t + α 4.log(FDI / GDP) c,t + α 5.log(WAGEf / WAGE) i,c,t + α 6.log(RD) i,c,t + α 7.log(Coop / POP) c,t +  i,c,t

3.2. Results Log (NOE) estimation results with the occupation proxy model1model2model3model4model5model6model7model8 log(HSo)0.306***0.317***0.255***0.241*** (4.054)(4.004)(3.411)(3.429)(1.310)(1.198)(1.874)(0.884) log(LSo)0.029 (0.578) log(MSo)-0.121* (-1.999)(-0.646)(1.338)(0.608)(-0.937)(-0.740) log((X+I)/GDP)1.627***0.317* **0.442* (14.084)(2.515)(1.278)(2.734)(2.541) log (FDI/GDP)0.609***0.422***0.207***0.033 (11.653)(10.454)(5.090)(0.715) log (VAf / VA)0.436***0.265***0.257*** (9.144)(5.776)(5.647) log (WAGEf/WAGE) (0.236)(0.214) log (Coop/POP)0.407*** (7.071) Constant5.022***5.082***4.833***-2.222***1.300**3.344*** *** (54.888)(35.571)(40.772)(-4.322)(2.721)(5.918)(1.476)(3.738) r N

3.2. Results Log (NOE) estimation results with the education proxy model9model10model11model12model13model14model15model16 log(HSe)0.260***0.192***0.298***0.268***0.148*** 0.149*** (4.815)(3.472)(5.889)(5.634)(3.637)(4.118)(3.548)(1.693) log(LSe)-0.325*** (-4.494) log(MSe)0.259*0.217* (2.452)(2.135)(0.752)(-0.970)(0.495)(-0.758) log((X+I)/GDP)1.694***0.407*** **0.490** (14.276)(3.335)(1.809)(2.811)(2.823) log (FDI/GDP)0.581***0.404***0.213***0.015 (11.233)(10.811)(4.970)(0.271) log (VAf / VA)0.439***0.282***0.273*** (9.155)(5.820)(5.829) log (WAGEf/WAGE) (-0.477)(-0.354) log (Coop/POP)0.464*** (5.804) Constant5.119***4.515***5.398***-2.098***1.179*3.230*** *** (52.888)(26.675)(43.990)(-3.874)(2.435)(5.662)(0.956)(3.753) r N

3.2. Results Log (Vaf/EMP) estimation results with the occupation proxy model1model2model3model4model5model6model7model8 log(HSo)0.354***0.385***0.292***0.222***0.251***0.269***0.461***0.427*** (6.087)(6.073)(4.754)(3.845)(4.383)(4.117)(3.928)(3.590) log(LSo) (1.743) log(MSo)-0.141* (-2.085)(-0.701)(0.120)(0.189)(0.355)(0.498) log (FDI/GDP)0.259***0.157***0.097* (7.956)(4.811)(2.146)(1.868)(0.057) log (NOE)0.144***0.113** (3.664)(2.854)(1.001)(0.728) log (WAGEf/WAGE)0.720***0.581***0.584*** (5.726)(4.110)(4.397) log (RDf)0.050*0.041* (2.514)(2.141) log (Coop/POP)0.210* (2.407) Constant11.586***11.749***11.360***10.459***10.257***20.405***18.673***21.140*** ( )(95.022)(94.581)(69.345)(48.193)(11.713)(9.004)(9.281) r N

3.2. Results Log (Vaf/EMP) estimation results with the education proxy model9model10model11model12model13model14model15model16 log(HSe)0.285***0.230***0.332***0.275***0.243***0.283***0.275***0.254** (6.274)(5.082)(6.635)(5.705)(5.892)(5.090)(3.391)(3.226) log(LSe)-0.263*** (-5.605) log(MSe)0.388***0.288***0.419***0.491***0.293*0.264* (6.435)(4.645)(6.391)(4.182)(2.233)(2.352) log (FDI/GDP)0.230***0.137*** *0.019 (7.137)(4.329)(1.747)(2.099)(0.282) log (NOE)0.127***0.095** (3.409)(2.832)(0.669)(0.360) log (WAGEf/WAGE)0.640***0.497***0.512*** (4.302)(3.602)(3.873) log (RDf)0.059**0.050* (2.632)(2.328) log (Coop/POP)0.209* (2.379) Constant11.688***11.216***12.112***11.111***10.908***20.030***17.724***20.315*** ( )(96.515)( )(62.349)(45.149)(9.357)(8.279)(9.242) r N

4. Conclusion and further research agenda 4.1. Conclusion Strong relationship between the high skilled share of employment in the host country and the presence of multinational firms Both of the proxies => positive and significant while explaining labour productivity of foreign affiliates Occupations should be taken into account, together with the educational attainment for skill assessment 36

4.2. For further research agenda Completing data collection on industry x education matrices and extending the analysis to other OECD countries Robustness checks through logistic panel estimations Focus on the high technology industries and knowledge intensive business services in the econometrical analysis 37

Thank you for your attention! For comments and suggestions: 38