trade, technology and the rise of non-routine jobs

Trade, Technology and the Rise of Non-Routine Jobs

Trade, Technology and the Rise of Non-RoutineJobs

Gaaitzen de Vries(based on joint work with Laurie Reijnders)

Groningen Growth and Development Center, University of GroningenResearch Center for Global Value Chains, University of International Business and Economics

Global Value-Chain Training and Research Workshop, August 2017


Introduction

Fostered by revolutionary advances in ICT, production processes havebeen unbundled across national borders (Baldwin, 2016)

1. Offshoring of tasks that can be summarized in set of well-specifiedrules and no need for face-to-face contact (Levy and Murnane 2004;Blinder 2009)

2. Computers and robots displacing labor in performing routine andnon-cognitive tasks (Autor et al. 2003)

I What are the employment structure changes in routine andnon-routine jobs?

I How to disentangle the role of trade and technology in drivingemployment changes?


The Rise of Non-Routine Jobs

Occupations database

I Employment data from Annual Labour Force Surveys andPopulation Censuses

I 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

I National occupation classifications mapped to a commonharmonized occupation classification

I Country-industry-occupation-year specific employment shares thatmatch with the countries and industries distinguished in the WorldInput-Output Database (Timmer et al. 2015)



Classification of occupations




Note: Change in the employment share of non-routine jobs between 1999 and 2007



What accounts for these changes in the job structure?

I Two key explanations, both based on an examination of the type oftasks 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 forthe diversity in country experiences. Many other factors are also relevant,such as minimum wages, occupational licensing, labor unions, andbusiness cycles



Routine-biased technological change (Autor et al. 2003)

I Many occupations, such as bookkeeping, administrative support andfactory jobs, are relatively routine-task intensive

I A task can be computerized when we know the rules: well-specifiedprocedures, such as copying, calculating, and measuring

I So: spreadsheets replace bookkeepers; robots replace factory workers



Routine-biased technological change (Autor et al. 2003)

I ‘Knowing the rules’ is not a trivial requirement. Procedures foraccomplishing many commonplace tasks not explicitly understood(Polanyi’s paradox)

I Two broad categories of tasks for which we do not know the rules:

(1) Abstract tasks: Requires mental flexibility, problem-solving, andcreativity, such as teachers, doctors, managers, scientists, lawyers,engineers, and artists

(2) Manual tasks: Requires physical adaptability or interpersonalinteractions, such as janitors, security guards, construction workers,home health aides

I A more nuanced view on technological change: from skill-biased toroutine-biased technological change



Offshoring

I When we know the rules of a task it can also be off-shored to acheaper location without a substantial deterioration in quality(Baldwin 2016)

I Relocation of routine-task intensive occupations, such asbookkeeping, administrative support and factory jobs

I Design and innovation is kept at home, while personal servicesoccupations are difficult to offshore since they require physicalpresence



Open questions

I Determining the role of task relocation and technological change inaccounting for job polarization is ultimately an empirical question



What accounts for the rise of non-routine jobs?

We provide new evidence on the role of technological change andproduction relocation.

I Advanced and emerging countries are linked through Global SupplyChains.

I We can determine for each GSC and each occupation:I changes in demand (GSC technology)I changes in the distribution across countries (relocation)I other factors


Intuition of methodology

Intuition: Harmonized occupations data



Intuition: Technological change



Intuition: Task relocation


Task-based model of production in Global Supply Chains

Task-based model of production

I Production function of GSC v :

Yv = Fv (T1v , . . .Tjv , . . . ,TJv )

If tasks are perfect complements then Tjv = αjvYv .

I Task division across countries:

Tjv =∑c

T cjv

I Production function of task j in country c :

T cjv = AcGjv (K c

jv ,Ncjv )



GSC technology

Three types of ‘technology’:

(i) Total Factor Productivity (TFP) in a country Ac

(ii) Overall production function for a supply chain Fv

(iii) Task production functions for a supply chain Gjv

We refer to (ii) and (iii) together as ‘GSC technology’.



Occupational labour demand

If tasks coincide with occupations then Ncjv is the demand for occupation

j in country c by GSC v .

This corresponds to AcNcjv efficiency units of labour.

If capital and labour are perfect complements in task production theneffective labour demand per unit of task output is the same acrosscountries:

AcNcjv

T cjv

= ejv ⇒ Ncjv =

1

AcejvT

cjv


Decomposition of changes in occupational employment

Decomposition

Ncjv =

Ncjv

pvYv

pvYv

WW

(1) within: occupational labour per dollar of output Ncjv/[pvYv ]

(2) between: GSC share pvYv/W

(3) income: world income W , where W =∑

v pvYv



Further decomposition of within component

Ncjv

pvYv=

1

Ac

ejvTjv

pvYv

T cjv

Tjv

(1a) TFP: Total Factor Productivity Ac

(1b) GSC technology : occupational efficiency units per dollar of output

ejvTjv

pvYv=

ejvαjv

pv=

∑c

AcNcjv

pvYv

(1c) Location: task share

T cjv

Tjv=

AcNcjv∑

c′ Ac′Nc′

jv



Decomposition


Data sources

Global Supply Chain data


The World Input-Output Database covers 35 industries and 41 countries(including ‘the rest of the world’)

I World Input-Output Tables: interindustry flows, final demand andgross output by country-industry

I Socio-Economic Accounts: number of persons employed bycountry-industry

We restrict attention to the time period 1999-2007 and use tables inprevious year prices.


Data sources



Empirical definition of a Global Supply Chain: country-industry where thefinal stage of production takes place.

We determine the number of workers employed in every country-industryworldwide on behalf of each GSC.


A Global Supply Chain Perspective

World Input-Output Database (www.wiod.org)



What is in World Input-Output Tables?



Including satellite accounts



A Global Supply Chain in a WIOT



I Over-simplified example!

I Essentially, however, US consumption of cars imported fromGermany generates jobs and income for workers in Germany, Chinaand the USA

I We use the input-output technique from Timmer et al. (2014) tomeasure the direct and indirect jobs related to the production of afinal product


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.


Empirical results

Decomposition


Empirical results

Decomposition results: German cars

Note: Employment in thousands of jobs.


Empirical results

Country-level results

Requires summing decomposition results over all 1435 (41*35) GSCs inour data


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


Concluding remarks

Concluding remarks

I Technological change drives demand for non-routine jobs inadvanced and emerging countries.

I Needs to be recognized and prioritized by policy makers:I Education and job training system to prepare humans with skills that

are complemented by rather than substituted for technologicalchange

I Life long learning and retraining currently much more commonamong high-educated compared to mid-educated. That shouldchange


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 EconomicPerspectives, 28(2), 99-118.

Analysis based on exports:- Los, B., M. P. Timmer, and G. J. de Vries (2015). How important areexports for job growth in China? A demand side analysis. Journal ofComparative Economics, 43(1), 19-32.

Comparison of different methods:- Los, B., and M. P. Timmer (2015). Appendix - Analysis of GlobalProduction Networks: Approaches, Concepts and Data. In J. Amador,and F. di Mauro (Eds.), The Age of Global Value Chains: Maps andPolicy Issues (pp. 201-208). London: CEPR Press.

trade, technology and the rise of non-routine jobs

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