1. “Offshoring, Biased Technical Change and the Increasing Capital Share: an Analysis of Global Manufacturing Production” Marcel Timmer Groningen Growth and Development Centre, University of Groningen, The Netherlands Second SEM conference, Paris, 22-24 July 2015. The World input-output database (WIOD) project was funded by the European Commission, Research Directorate General as part of the 7th Framework Programme, Theme 8: Socio-Economic Sciences and Humanities. Grant Agreement no: 225 281

2.  Two stylised macro-economic facts:  Increasing income share for capital and high-skilled workers.  Polarisation of labour markets  Consensus view that technical change is biased. Mostly based on cross (country/industry) regressions of labour cost shares based on data on domestic production (Author et al. REStat 2008; Michaels et al REStat 2014; Goos et al AER 2014)  Bias in technical change might be observational equivalent to offshoring as long as you only consider domestic factors of production (Feenstra and Hanson, Handbook of International Economics 2003). Motivation

3. Example of car production (cost shares) with offshoring and no change in factor prices Solving the problem of observational equivalence of offshoring and biased technical change BIASED technical change NO BIAS High skill Low skill

4. This paper  Takes vertical integrated production function of a product as unit of observation, which nets out intermediate inputs (Global value chain approach, Timmer et al. JEP, 2014). Production is based on combination of both domestic and foreign factors: Final output = H [K(dom), L(dom), K(for), L(for)]  Estimates biases in technical change within system of factor cost share equations derived from translog cost function (following Baltagi and Rich, 2005 and Hijzen et al. 2005)  Findings:  Strong bias in TC against low-skilled labor and in favour of high- skilled labour and capital.  Which is robust to alternative models and across different data samples

5. Factor content of a global value chain: (introduced by Timmer et al. JEP, 2014) VA by L1 VA by K1 VA by L2 VA by K2 VA by L3 VA by K3

6. Method to derive factor content of GVC: Input-output analysis (Leontief, 1936; Miller and Blair, 2009) F= R(I-Z) -1 Y Y a vector with 1 for product (i,j) and zeros otherwise With F a matrix with elements F(k,h)(i,j) = quantity of production factor k located in country h, used in production of final product (i,j) Z the matrix of intermediate input use of all products per unit of output for each product; I is identity matrix and (I-Z) -1 is the Leontief inverse (= 1+Z+Z 2 +Z 3 +….); R a matrix with direct factor requirements per unit of gross output at country-industry level (NB All matrices of the appropriate dimensions with elements in values)

7. Related literature using the Input-Output methodology  Variations of this approach are also used in literature on  vertical specialisation in trade: Johnson and Noguera (2012, JIE) who extended Hummels, Iishi and Yi (2001, JIE) multi-regional.  Value added content of exports: Koopman, Wei and Zhang (2014, AER) and Bems, Johnson and Yi (2011, AER)  Factor content of trade: Reimer (2006, JIE) and Trefler and Zhu (2010, JIE)  Length of production chains: Dietzenbacher and Romero (2007, IRSR) and Antràs et al. (2012, AER)

8. DATA: World Input-Output Database  Cost shares in production of 280 ‘products’:  14 manufacturing product groups  in 20 advanced countries where last stage of production took place  13 years period: 1995-2007  4 factor inputs: 3 types of labour (by educational attainment) and capital.  World Input-Output Tables representing flows of goods and services across industries and countries, for 1995-2011 (www.wiod.org), based on:www.wiod.org  Times-series of input-output tables benchmarked to national accounts  Bilateral trade classified by end-use

9. A stylized world input-output table Intermediate use (S columns per country) Final use (C columns per country) Total 1…N1…N S Industries, country 1 … S Industries, country N Value added Output

10. Global prices of production factors (change relative to medium-skilled labour)..

11. Kernel distributions of changes in factor income shares between 1995 and 2007 in 280 GVCs. Capital High Skill Medium Skill Low Skill

12. Econometric methodology Standard translog cost framework (following Baltagi and Rich, 2005 and Hijzen et al. 2005). For each GVC :

13. How many factors to consider?  Cost-minimization is modelled as decision making by industry-country where last stage of production takes place. Decision is about how much quantities to use of each factor (irrespective of location), given exogenous global factor prices of labour and capital.  Section 2 contains a task-based model along the lines of Grossman and Rossi- Hansberg (2008, AER) that motivates the exogeneity of factor prices. Non-formal intuition: offshoring of tasks is costless, but limited by the current state of communication technology, as well as openness of potential host countries. These are exogenous to the firm.  An improvement in offshoring opportunities will lead to a decline in global average wage of low skilled workers as a larger share of the tasks will be offshored and carried out at a lower wage.  Factor-specific technical change has no impact on the global factor price as it is assumed to symmetrically affect all workers of the same type irrespective of their location.

14. E’trics (continued)

15. Check on assumptions underlying econmetric model

16. Results: base line

17. Elasticities in base-line model Price elasticities of demand of factor i w.r.t price of j is given by

18. Figure 2 Cumulative factor bias in technological change, 1995-2007 Factor-biased technical change, base-line

19. Results by product group

20. Results: quasi-fixed capital

21. Alternatives

22.  To identify factor bias in technical change one needs to model all inputs in production. To do so, we model production as Global value chains (GVCs) that have factor inputs across all countries.  We find decisively bias in technical change in 280 GVCs of manufacturing goods:  Bias against low-skilled labour and  Bias in favour of high-skilled labour and capital.  Bias against medium-skilled labour is strongly related to ICT-use  Future work: search for determinants of factor bias Concluding remarks

23.  Timmer, Marcel P., Bart Los, Robert Stehrer and Gaaitzen J. de Vries (2013). “Fragmentation, Incomes and Jobs. An Analysis of European Competitiveness.” Economic Policy 28(76):613–661.  Los, B., M.P. Timmer and G.J. de Vries (2015), “How global are Global Value Chains? A New Approach to Measure International Fragmentation”, Journal of Regional Science,  Timmer, M.P., A.A. Erumban, B. Los, R. Stehrer and G.J. de Vries (2014),"Slicing Up Global Value Chains", Journal of Economic Perspectives.  Timmer, Marcel P., Erik Dietzenbacher, Bart Los, Robert Stehrer and Gaaitzen J. de Vries (2015),“An Illustrated User Guide to the World Input- Output Database: the Case of Global Automotive Production”. Review of International Economics More on GVC approach