Trade, Technology and the Rise of Non-Routine Jobs

Transcription

1 Trade, Technology and the Rise of Non-Routine Jobs Gaaitzen de Vries (based on joint work with Laurie Reijnders) Groningen Growth and Development Center, University of Groningen Research Center for Global Value Chains, University of International Business and Economics Global Value-Chain Training and Research Workshop, August 2017

2 Introduction Fostered by revolutionary advances in ICT, production processes have been unbundled across national borders (Baldwin, 2016) 1. Offshoring of tasks that can be summarized in set of well-specified rules and no need for face-to-face contact (Levy and Murnane 2004; Blinder 2009) 2. Computers and robots displacing labor in performing routine and non-cognitive tasks (Autor et al. 2003) What are the employment structure changes in routine and non-routine jobs? How to disentangle the role of trade and technology in driving employment changes?

3 The Rise of Non-Routine Jobs Occupations database Employment data from Annual Labour Force Surveys and Population Censuses Countries covered are the 27 members of the EU (per January 2007) plus Australia, Brazil, Canada, China, India, Indonesia, Japan, Mexico, Russia, South Korea, Taiwan, Turkey and the US National occupation classifications mapped to a common harmonized occupation classification Country-industry-occupation-year specific employment shares that match with the countries and industries distinguished in the World Input-Output Database (Timmer et al. 2015)

4 The Rise of Non-Routine Jobs Classification of occupations

5 The Rise of Non-Routine Jobs The Rise of Non-Routine Jobs Note: Change in the employment share of non-routine jobs between 1999 and 2007

6 The Rise of Non-Routine Jobs What accounts for these changes in the job structure? Two key explanations, both based on an examination of the type of tasks workers perform (1) Routine-biased technological change (2) Routine task relocation to low-cost destinations A word of caution: No single cause or explanation can fully account for the diversity in country experiences. Many other factors are also relevant, such as minimum wages, occupational licensing, labor unions, and business cycles

7 The Rise of Non-Routine Jobs Routine-biased technological change (Autor et al. 2003) Many occupations, such as bookkeeping, administrative support and factory jobs, are relatively routine-task intensive A task can be computerized when we know the rules: well-specified procedures, such as copying, calculating, and measuring So: spreadsheets replace bookkeepers; robots replace factory workers

8 The Rise of Non-Routine Jobs Routine-biased technological change (Autor et al. 2003) Knowing the rules is not a trivial requirement. Procedures for accomplishing many commonplace tasks not explicitly understood (Polanyi s paradox) Two broad categories of tasks for which we do not know the rules: (1) Abstract tasks: Requires mental flexibility, problem-solving, and creativity, such as teachers, doctors, managers, scientists, lawyers, engineers, and artists (2) Manual tasks: Requires physical adaptability or interpersonal interactions, such as janitors, security guards, construction workers, home health aides A more nuanced view on technological change: from skill-biased to routine-biased technological change

9 The Rise of Non-Routine Jobs Offshoring When we know the rules of a task it can also be off-shored to a cheaper location without a substantial deterioration in quality (Baldwin 2016) Relocation of routine-task intensive occupations, such as bookkeeping, administrative support and factory jobs Design and innovation is kept at home, while personal services occupations are difficult to offshore since they require physical presence

10 The Rise of Non-Routine Jobs Open questions Determining the role of task relocation and technological change in accounting for job polarization is ultimately an empirical question

11 The Rise of Non-Routine Jobs What accounts for the rise of non-routine jobs? We provide new evidence on the role of technological change and production relocation. Advanced and emerging countries are linked through Global Supply Chains. We can determine for each GSC and each occupation: changes in demand (GSC technology) changes in the distribution across countries (relocation) other factors

12 Intuition of methodology Intuition: Harmonized occupations data

13 Intuition of methodology Intuition: Technological change

14 Intuition of methodology Intuition: Task relocation

15 Task-based model of production in Global Supply Chains Task-based model of production Production function of GSC v: Y v = F v (T 1v,... T jv,..., T Jv ) If tasks are perfect complements then T jv = α jv Y v. Task division across countries: T jv = c T c jv Production function of task j in country c: T c jv = A c G jv (K c jv, N c jv )

16 Task-based model of production in Global Supply Chains GSC technology Three types of technology : (i) Total Factor Productivity (TFP) in a country A c (ii) Overall production function for a supply chain F v (iii) Task production functions for a supply chain G jv We refer to (ii) and (iii) together as GSC technology.

17 Task-based model of production in Global Supply Chains Occupational labour demand If tasks coincide with occupations then Njv c j in country c by GSC v. is the demand for occupation This corresponds to A c N c jv efficiency units of labour. If capital and labour are perfect complements in task production then effective labour demand per unit of task output is the same across countries: A c N c jv T c jv = e jv N c jv = 1 A c e jv T c jv

18 Decomposition of changes in occupational employment Decomposition N c jv = Nc jv p v Y v p v Y v W W (1) within: occupational labour per dollar of output N c jv /[p v Y v ] (2) between: GSC share p v Y v /W (3) income: world income W, where W = v p v Y v

19 Decomposition of changes in occupational employment Further decomposition of within component Njv c = 1 e jv T jv Tjv c p v Y v A c p v Y v T jv (1a) TFP: Total Factor Productivity A c (1b) GSC technology: occupational efficiency units per dollar of output e jv T jv p v Y v = e jv α jv p v = c A c N c jv p v Y v (1c) Location: task share Tjv c A c Njv c = T jv c N Ac jv c

20 Decomposition of changes in occupational employment Decomposition

21 Data sources Global Supply Chain data Global Supply Chain data The World Input-Output Database covers 35 industries and 41 countries (including the rest of the world ) World Input-Output Tables: interindustry flows, final demand and gross output by country-industry Socio-Economic Accounts: number of persons employed by country-industry We restrict attention to the time period and use tables in previous year prices.

22 Data sources Global Supply Chain data Global Supply Chain data Empirical definition of a Global Supply Chain: country-industry where the final stage of production takes place. We determine the number of workers employed in every country-industry worldwide on behalf of each GSC.

23 A Global Supply Chain Perspective World Input-Output Database (

24 A Global Supply Chain Perspective What is in World Input-Output Tables?

25 A Global Supply Chain Perspective What is in World Input-Output Tables?

26 A Global Supply Chain Perspective What is in World Input-Output Tables?

27 A Global Supply Chain Perspective Including satellite accounts

28 A Global Supply Chain Perspective A Global Supply Chain in a WIOT

29 A Global Supply Chain Perspective A Global Supply Chain in a WIOT

30 A Global Supply Chain Perspective A Global Supply Chain in a WIOT

31 A Global Supply Chain Perspective A Global Supply Chain in a WIOT

32 A Global Supply Chain Perspective Over-simplified example! Essentially, however, US consumption of cars imported from Germany generates jobs and income for workers in Germany, China and the USA We use the input-output technique from Timmer et al. (2014) to measure the direct and indirect jobs related to the production of a final product

33 Empirical results Example: German cars (=cars finalized in Germany) Note: Employment in thousands of jobs. Illustration shows results for 4/11 occupations and for 3/40 countries.

34 Empirical results Decomposition

35 Empirical results Decomposition results: German cars Note: Employment in thousands of jobs.

36 Empirical results Country-level results Requires summing decomposition results over all 1435 (41*35) GSCs in our data

37 Empirical results Country-level results: the role of trade and technology Note: Change in the employment share of non-routine jobs due to trade and technology between 1999 and 2007

38 Concluding remarks Concluding remarks Technological change drives demand for non-routine jobs in advanced and emerging countries. Needs to be recognized and prioritized by policy makers: Education and job training system to prepare humans with skills that are complemented by rather than substituted for technological change Life long learning and retraining currently much more common among high-educated compared to mid-educated. That should change

39 Concluding remarks References Introducing the GVC method: - 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), Analysis based on exports: - Los, B., M. P. Timmer, and G. J. de Vries (2015). How important are exports for job growth in China? A demand side analysis. Journal of Comparative Economics, 43(1), Comparison of different methods: - Los, B., and M. P. Timmer (2015). Appendix - Analysis of Global Production Networks: Approaches, Concepts and Data. In J. Amador, and F. di Mauro (Eds.), The Age of Global Value Chains: Maps and Policy Issues (pp ). London: CEPR Press.