Biased Technical Change Through the Lens of Global Value Chains

Transcription

1 Biased Technical Change Through the Lens of Global Value Chains Marcel Timmer (joint work with Laurie S.M. Reijnders and Xianjia Ye) Groningen Growth and Development Centre, University of Groningen Prepared for Fifth World KLEMS meeting, Cambridge, June 4-5, 2018

2 Motivation General consensus that technical change is routine biased leading to polarisation of labour markets (ALM, 2003). Unresolved problem of observational equivalence of biased technical change and offshoring (Feenstra and Hanson, 2003). Studies until now based on cross-country/industry regressions of domestic cost shares (Hijzen et al. 2005; Autor et al. 2008; Michaels et al 2014; Goos et al 2014).Relying on indicators of potential offshorability and of automation. These are highly correlated however (Blinder and Krueger, 2013). This paper measure factor biases in technical change by analysing cost shares in global value chains that include actual offshoring (Antras and Chor, 2013).

3 The problem of observational equivalence Before 15 After 15

4 The problem of observational equivalence Before 15 After 9

5 Contributions 1. Empirical framework to derive factor cost shares in global value chains. Global Value Chain (GVC) approach: trace all activities that are needed in the production process, using a global Leontief input-output model. 2. Econometrically estimate biases in technical change (BTC) within system of factor cost share equations: Strong bias in TC against low-skilled (high-school) workers and in favour of high-skilled (college) workers. 3. Decline in demand for LS jobs: the BTC effect more than outweighs the positive price effect.

6 WIOD Factor cost shares for each GVC with Leontief transformation using World Input-Output Tables. Based on publicly available World Input-Output Database ( release 2013: Tables representing flows of goods and services across industries and countries, for Includes 40 countries and rest-of-the-world region; and 35 industries

7 Our units of observation 294 final product GVCs (= 21 x 14) from 14 manufacturing sectors ending in 21 advanced countries: 15 European (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and United Kingdom) and 6 non-eu (Australia, Canada, Japan, South Korea, Taiwan and the United States). (NB factor inputs can come from 35 sectors and 41 countries!) 15t16 Food, Beverages and Tobacco 17t18 Textiles and Textile Products 19 Leather, Leather Products and Footwear 20 Wood and Products of Wood and Cork 21t22 Pulp, Paper, Printing and Publishing 23 Coke, Refined Petroleum 24 Chemicals and Chemical Products 25 Rubber and Plastics 26 Other Non-Metallic Mineral 27t28 Basic Metals and Fabricated Metal 29 Machinery, Not elsewhere classified 30t33 Electrical and Optical Equipment 34t35 Transport Equipment 36t37 Manufacturing, Not

8 The Global Value Chain approach (Los, Timmer and de Vries, JRS, 2015) FROM: World input-output table Supply from countryindustries Gross output Country 1 Country M Value added from country-industries participating in global value chains Industry 1 Industry N Industry 1 Industry N Value added by labour and capital Country 1 Country M Total final output value Industry 1 Industry 1 Industry N Industry 1 Industry N Country 1 TO: GVC cost-share table Use by country-industries Industry N Industry 1 Country M Industry 1 Input cost shares of industries (A) v Industry N Final use by countries Country 1 Country M Final products of a global value chain, identified by country-industry of completion Country 1 Country M Industry Industry N 1 Factor cost shares of final products (G) F Industry N Total use Value added World GDP G = v(i-a) -1 F Leontief s trick: compute value added in all industries associated to final demand for a specific product

9 GVC observation (synthetic estimate)

10 Econometric set up

11 Measures of BTC The effects of biases in technical change on cost shares are captured by the time trends. Note that the γ coefficients capture the (weighted) bilateral biases in technical change. FBTC is modelled as linear trend. Alternative used is a set of timedummies (Baltagi and Griffin JPE 1988). System estimated with fixed effects ISUR (incl. product dummies and country-of- completion dummies).

12 Declining (average) price of LS jobs in GVCs

13 Declining cost share of LS jobs in GVCs

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15 Magnitudes of BTC effect versus price effect on demand for LS jobs

16 Magnitudes of BTC effect versus price effect on demand for LS jobs Job growth (in log point) Low Medium High Wage effect Bias in TC

17 Magnitudes of BTC effect versus price effect on demand for LS jobs

18 Conclusions 1. Empirical framework to derive factor cost shares in global value chains. Global Value Chain (GVC) approach: trace all activities that are needed in the production process, using a global Leontief input-output model. 2. Econometrically estimate biases in technical change (BTC) within system of factor cost share equations: Strong bias in TC against low-skilled (high-school) workers and in favour of high-skilled (college) workers. 3. Decline in demand for LS jobs: the BTC effect more than outweighs the positive price effect. NB These results are for GVCs of manufacturing goods

19 More on GVC approach Los, B., M. P. Timmer and G. J. de Vries (2016), Tracing Value-Added and Double Counting in Gross Exports: Comment, American Economic Review, 106(7), Timmer, M.P., B.Los, R.Stehrer and G.J. de Vries (2013). Fragmentation, Incomes and Jobs. An Analysis of European Competitiveness. Economic Policy, 28(76), 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, 55(1), 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, 28(2), Timmer, M.P., E. Dietzenbacher, B. Los, R. Stehrer and G.J. de Vries (2015), An Illustrated User Guide to the World Input-Output Database: the Case of Global Automotive Production. Review of International Economics, 23(3), Timmer, M.P., B. Los, R. Stehrer and G.J. de Vries (2016), An Anatomy of the Global Trade Slowdown based on the WIOD 2016 Release, GGDC research memorandum number 162, University of Groningen.