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

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).

Before

After

The problem of

observational equivalence

15

15

Before

After

The problem of

observational equivalence

15

9

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.

WIOD

Factor cost shares for each GVC with Leontief transformation using World Input -Output Tables.

Based on publicly available World Input - Output Database(www.wiod.org ), release 2013:

Tables representing flows of goods and services across industries and countries, for 1995 -2007

Includes 40 countries and rest -of - the -world region; and 35 industries

294 final product GVCs (= 21 x 14)

from 14 manufacturing sectors ending in 21 advanced 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

Our units of observation

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!)

G = v(I - A) - 1F

compute value added in all

industries associated to final demand for a

specific product

Industry

1

Industry

N

Industry

1

Industry

N

Industry 1

Industry N

Industry 1

Industry N

World

GDPTotal final output value

Final products of a global value chain,

Value

added

identified by country-industry of completion

Country 1 Country M

Value added from

country-industries

participating in

global value chains

Country 1

Country M

Country

1

Country

M

Industry

1

Industry

N

Industry

1

Industry

N

Industry 1

Industry N

Industry 1

Industry N

Use by country-industries Final use by countries

Total

use

Gross output

Country 1

Country

M

Supply from

country-

industries

Country 1 Country M

Value added by labour and capital

Input cost shares of industries (A)

Factor cost shares of final products (G)

v

F

FROM: World input - output table

TO: GVC cost - share table

The Global Value Chain approach

(Los, Timmer and de Vries, JRS, 2015)

(synthetic estimate)

Econometric set up

Measures of BTC

by the time trends. Note that the (weighted) bilateral biases in technical change.

FBTC is modelled as linear trend. Alternative used is a set of time -dummies (Baltagi and Griffin JPE 1988).

System estimated with fixed effects ISUR (incl. product dummies and country -of- completion dummies).

Declining (average) price of LS jobs in GVCs

Declining cost share of LS jobs in GVCs