wiod why wiod? erik dietzenbacher this project is funded by the european commission, research...

WIODWhy WIOD?

Erik Dietzenbacher

This project is 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

1. What is (in) WIOD

2. Factor intensity of trade

3. Vertical specialization

4. CO2 emissions: Consumer responsibilities and their sensitivity

5. Does the Rest of the World matter?

6. Is there a life after WIOD?

WIODWORLD INPUT-OUTPUT DATABASE:

Construction and Applications

Background

Policies are designed at a detailed level of industries and products

Production is characterized by interdependent structures

Globalization increases the importance of cross-border interdependencies, which makes inclusion of trade in analyses more essential than ever

Analyzing policy issues requires an all-encompassing database with three dimensions:

time, industries/products and countries

Objectives of WIOD

To build a time series of global inter-country input-output tables;

To build socio-economic and environmental satellite accounts;

To measure and analyze trends in trade, economic growth, technological change and environmental pressures;

To provide policy support to the European Commission on socio-economic and environmental issues.

Full database will become publiclyavailable in May 2012

WIOD: Data and Coverage

Time series in current and constant prices of: Harmonized national supply and use tables Harmonized IO tables Bilateral trade flows of goods and services Inter-country IO tables Socio-economic accounts and environmental accounts

The tables in the WIOD-database will cover: The period from 1995 to 2006

(and for some major countries back to 1980) 27 EU countries and 13 other major countries More than 30 industries and at least 60 products

WIOD: Work Packages

WP1-3: Construction of harmonized supply and use tables, national input-output tables, price deflators, trade flows and intercountry input-output tables

WP4: Construction of environmental satellite accounts (energy use, greenhouse gas emissions, etc.)

WP5: Construction of socio-economic satellite accounts (skill levels, investment, accumulation of intangibles)

WP6: Methodological researchWP7-9: Development of new models and

extension/adaptation of models with track record within EC

Who is in WIOD?

University of Groningen (The Netherlands)

Institute for Prospective Technological Studies (Sevilla, Spain)

Wiener Institut für Internationale Wirtschaftsvergleiche (Vienna, Austria)

Zentrum für Europäische Wirtschaftsforschung (Mannheim, Germany)

Österreichisches Institut für Wirtschaftsforschung (Vienna, Austria)

Konstanz University of Applied Sciences (Germany)

The Conference Board Europe (Brussels, Belgium)

CPB Netherlands Bureau for Economic Policy Analysis (The Hague, The Netherlands)

Institute of Communication and Computer Systems (Athens, Greece)

Central Recherche SA (Paris, France)

* Organization for Economic Co-operation and Development (Paris France)

My personal interest:

• What (types of) questions can be answered?

• What difference does it make?

Examples of preliminary studies

Factor intensity of trade

Leontief paradox:labor content of 1 million $ of exports

versus labor content of 1 million $ of imports

Crucial:same technology assumption

use the matrix of technical input coefficients($ of steel per $ of US cars, no matter

whether US steel or German steel is used)


Problem 1:labor content of US exports includes German workerssolution: use domestic input coefficients

Problem 2:domestic input coefficients of the UScannot be used for labor content of US imports

Countries that are “similar” in terms of technical input coefficients, may have very different domestic input coefficients

because their dependence on imported inputs differs


Exercise:Intercountry IO tables for 6 European countriesGE, FR, IT, NL, BE, DK1985, 1975


1975 1985

GE 12 15

FR 11 13

IT 10 11

NL 28 28

BE 27 36

DK 16 17

Exports as % of total output

Exports as % of total output


Exercise:

LAB(GE→FR) = GE labor embodied in GE exports to FRLAB(FR→GE) = FR labor embodied in FR exports to GE

all exports amount to: 1 million ECU

K/L ratios for GE and FR


LAB(GE→FR) = GE labor embodied in GE exports to FRLAB(FR→GE) = FR labor embodied in FR exports to GE

K/L ratios for GE and FR

K/LGE > K/LFR : according to HO, FR “exports” labor to GE : LAB(FR→GE) > LAB(GE→FR)

Bilateral comparisons:If yes: GREENIf no: RED

1975

GE FR IT NL BE DK

GE ---

FR ---

IT ---

NL ---

BE ---

DK ---

1975

GE FR IT NL BE DK

GE ---

FR ---

IT ---

NL ---

BE ---

DK ---

Large countries behave according to HOSmall countries behave according to HO

1985

GE FR IT NL BE DK

GE ---

FR ---

IT ---

NL ---

BE ---

DK ---

1985

GE FR IT NL BE DK

GE ---

FR ---

IT ---

NL ---

BE ---

DK ---

Large countries behave almost according to HOSmall countries behave according to HO


6 out of 6 in 1975and 5 out of 6 in 1985

is a wonderful score!


6 out of 6 in 1975and 5 out of 6 in 1985

is a wonderful score!

But (admittedly) the “ sample size” is rather small

Use WIOD:to include more countriesto include refinements (types of labor, capital)

Vertical Specialization

Production processes more and more split upin subsequent phases, carried out in different countries

→ Trade in intermediate goods and services becomes more and more important

→ increase in interconnectedness of industries across countries

→ intercountry IO tables reflect exactly that→ measure vertical specialization in intercountry IO tables


Measuring vertical specializationHummels, Ishii & Yi (JIE, 2001): import content of the exports


Z c e x

M

v´

x´

Z = intermediate deliveries matrixc = domestic consumption vectore = gross exports vectorx = gross output vectorM = imports matrixv´ = value added vector


Z c e x

M

v´

x´

Z = intermediate deliveries→ A = input coefficients→ (I – A)-1 = Leontief inverse


Z c e x

M

v´

x´

Z = intermediate deliveries→ A = input coefficients→ (I – A)-1 = Leontief inverse

M(I – A)-1e = imports necessary for exportss´M(I – A)-1e = total imports necessary for exports, s´ = summation row vector = (1,…,1)

es

eAIMs

1)(

VS

Z11 Z12 c1 e1 x1

Z21 Z22 c2 e2 x2

ZR1 ZR2

(v1)´ (v2)´

(x1)´ (x2)´

Z12 = intermediate deliveries from 1 to 2c1 = domestic consumption in country 1e1 = exports to consumers in country 2 plus all exports to the Rest of the World (= R)ZR1 = imports from R to country 1

Z11 Z12 c1 e1 x1

Z21 Z22 c2 e2 x2

ZR1 ZR2

(v1)´ (v2)´

(x1)´ (x2)´

Three types of final demand that drive the model

Exports Imports from 2 Imports from R

e1


e1

(production in 1)


e1

(production in 1) inputs inputs


e1

(production in 1) inputs(production in 2)

inputs


e1


inputs

inputs(production in 1)


e1


inputs

inputs(production in 1) inputs

(production in 2)inputs


e1


inputs





Consumption Exports Imports from 2 Imports from R

c1


inputs





Final demand 2 Exports Imports from 2 Imports from R

c2 + e2

(production in 2)






Collect all exports and all imports (from 2 and from R)

Use some matrix algebra, then:it is exactly the same as for the single country case

Conclusion:to measure vertical specialization of a countryit is not necessary to use an intercountry IO table

VS = all imports from 2 + all imports from R

all exports by 1

Two exercises on CO2

Central question: does it matter whether we use an intercountry IO table (and how much)?

Data from Nori Yamano37 countries, 16 sectors, 80% of world-GDP

Two exercises on CO2

Abuse the data:exports to RoW become part of domestic consumptionimports from RoW become part of value added

Why?we want to work with a perfect world-IO tableif you cannot construct the table, adapt your world

Hence:our world consists of 37 countriesthat is, USA ≠ USA, USA = “USA”

Consumer responsibility CO2

Consumer responsibility (CR) of country 1= all CO2 emissions (all over the world) that are necessary for producing the “consumption” in country 1

Crucial element of the carbon footprint of country 1

Calculate the CR of country 1:using the full world-table yields “true” answervarious cases with limited informationmeasure the percentage error for country 1Do this for country 1, …, country 37

→ (unweighted) average % error

Z → input coefficients Aemission coefficients, row vectors (w1)´, (w2)´, (w3)´

“true” consumer responsibility for country 1:

[(w1)´, (w2)´, (w3)´](I – A)-1f•1

A11 A12 A13 f11 x1

A21 A22 A23 f21 x2

A31 A32 A33 f31 x3

↓ ↓ ↓

A f•1 x

Case 1: only technical coefficients available for country 1

consumer responsibility (w1)´(I – A1)-1f1

Average error: -37.5%

i.e. reported CR is (on average) only 62.5% of “true” CR

A11 f11 x1

+A21 +f21 x2

+A31 +f31 x3

↓ ↓ ↓

A1 f1 x

Case 2: information for imports from RoW

• use true emission coefficients: (w1)´, (w2)´, (w3)´[(w1)´, (w2)´, (w3)´](I – A)-1f•1

average error: -27.6%• use emission coefficients of country 1 only

[(w1)´, (w1)´, (w1)´](I – A)-1f•1

average error: -31.0%

A11 0 0 f11 x1

A21 0 0 f21 x2

A31 0 0 f31 x3

↓ ↓ ↓

A f•1 x

Case 3: technical coefficients for all other countries

• use true emission coefficients: (w1)´, (w2)´, (w3)´[(w1)´, (w2)´, (w3)´](I – A)-1f•1

average error: +0.3%• use emission coefficients of country 1 only

[(w1)´, (w1)´, (w1)´](I – A)-1f•1


A11 0 0 f11 x1

A21 A2 0 f21 x2

A31 0 A3 f31 x3

↓ ↓ ↓

A f•1 x

Case 4: aggregated RoW

• use true emission coefficients: (w1)´, (wRoW)´[(w1)´, (wRoW)´](I – A)-1f•1


[(w1)´, (w1)´](I – A)-1f•1


A11 0 f11 x1

A21+A31 0 f21+f31 x2+x3

↓ ↓ ↓

A f•1 x

Case 5: aggregated RoW, estimate RoW using country 1

• use true emission coefficients: (w1)´, (wRoW)´[(w1)´, (wRoW)´](I – A)-1f•1


[(w1)´, (w1)´](I – A)-1f•1


A11 0 f11 x1

A21+A31 A1 f21+f31 x2+x3

↓ ↓ ↓

A f•1 x

Consumer responsibility CO2

Conclusion:

underestimation is (on average) substantialunless a lot of information is available (Case 3)

Estimating RoW effects

We will never be able to cover all countriesthere will always remain a RoW

How does this affect our findings, and what can we do?

Our world covers only 37 countries“delete” one of them (which plays RoW)and consider the effects on consumer responsibility CO2

large effects: neglecting RoW matterssmall effects: who cares about RoW?

Delete country 3Case 1: CR country 1: [(w1)´, (w2)´, (wav)´](I – A)-1f•1

→ % error country 1 CR country 2: [(w1)´, (w2)´, (wav)´](I – A)-1f•2

→ % error country 2

wav = average emission coefficients (of countries 1 and 2)

A11 A12 0 f11 f12 x1

A21 A22 0 f21 f22 x2

0 0 0 f31 f32 x3

↓ ↓ ↓ ↓

A f•1 f•2 x

Delete country 3

% error country 1, % error country 2finally: weighted average % error

Example: delete China (i.e. treat China as RoW)% error in any other country is on average -5%

A11 A12 0 f11 f12 x1

A21 A22 0 f21 f22 x2

0 0 0 f31 f32 x3

↓ ↓ ↓ ↓

A f•1 f•2 x

Delete country 3Case 2: extra information on imports from RoW CR country 1: [(w1)´, (w2)´, (wav)´](I – A)-1f•1



A11 A12 0 f11 f12 x1

A21 A22 0 f21 f22 x2

A31 A32 0 f31 f32 x3

↓ ↓ ↓ ↓

A f•1 f•2 x

Delete country 3Case 3: extra information on imports from RoW and estimate technical coefficients of RoW CR country 1: [(w1)´, (w2)´, (wav)´](I – A)-1f•1



Aav = average technical coeffientsof countries 1 and 2

A11 A12 0 f11 f12 x1

A21 A22 0 f21 f22 x2

A31 A32 Aav f31 f32 x3

↓ ↓ ↓ ↓

A f•1 f•2 x

Case 1 Case 2 Case 3

Germany -2.3 -1.5 +1.4

Japan -1.3 -0.8 +2.0

USA -4.8 -3.4 +2.2

China -5.3 -4.9 -3.0

Russia -4.8 -4.5 -4.0

Average -0.9 -0.7 +0.2

Estimating RoW effects

Average % errors


Germany -2.3 -1.5 +1.4

Japan -1.3 -0.8 +2.0

USA -4.8 -3.4 +2.2

China -5.3 -4.9 -3.0

Russia -4.8 -4.5 -4.0

Average -0.9 -0.7 +0.2

• Move from left to right: add more information average underestimation decreases• Delete GE/USA/Japan (“clean and efficient” countries) estimate them by the average (which is less “clean and efficient”) NL depends on USA, but US emissions are overestimated hence consumer responsibility of NL is overestimated


Germany -2.3 -1.5 +1.4

Japan -1.3 -0.8 +2.0

USA -4.8 -3.4 +2.2

China -5.3 -4.9 -3.0

Russia -4.8 -4.5 -4.0

Average -0.9 -0.7 +0.2

Tentative conclusion for WIOD:• GDP of RoW in WIOD is approximately 15%• omission of USA from “37 world”

has limited effects (less than 5%)• suggests that WIOD will generate reliable results• but: these are average results

• for specific countries the errors may be substantial• oil-producing countries are not included in WIOD

Is there a life after WIOD?

Consider the effects involved in off-shoringor processing trade in general

Lot of imports and exports, little value addedlittle activity, little CO2 emissions

Recent debate about China’s CO2 emissionsclaim: a substantial part is in China’s exports

Use an IO analysis


In 2002:of all CO2 emitted in production activities20.3% due to exports (and thus 79.7% due to domestic “consumption”)

20.6% for SO2, 20.7% for Nox

Strongly increasing over time:Weber et al (2008), for CO2

21% in 200233% in 2005


Claim: these numbers are seriously overestimated

Why?Production activities related to processing traderequires: essentially imported inputs

almost no domestically produced inputsinvolves little emissions at home

Ordinary production:large dependence on domestically produced inputs little dependence on imported inputs


Input-output tables reflect the “average” production structure processing trade 50% of exports, but a minor share of production

(>1 billion Chinese consumers) production related to processing trade

receives a small weight in the “average” production

Conclusion: ordinary IO tables cannot do the jobwe need special IO tablesbetter reflecting the input structure of processing trade

Tripartite IO table (Chen et al., 2006)

ZDD ZDP ZDN fD 0 xD

0 0 0 0 eP xP

ZND ZNP ZNN fN eN xN

MD MP MN

(vD)’ (vP)’ (vN)’

(xD)’ (xP)’ (xN)’

Distinguish between three types of production D = for domestic use only P = for processing exports N = for non-processing exports

and other production of foreign-invested enterprises

(high dependence on imports and many indirect exports)


In 2002:of all CO2 emitted in production activities20.3% due to exports

Using the tripartite IO table:of all CO2 emitted in production activities12.6% due to exports

CO2 emissions due to exports are overestimated by 61%!!


In 2002:of all CO2 emitted in production activities12.6% due to exports

CO2 emissions due to exports are overestimated by 61%!!

Is this to be viewed as a ‘measurement error’?

The ‘measurement error’ has the size of the total CO2 emissions of the Netherlands, or Poland

or 1.5 times Turkey


Conclusion:• using “ordinary” IO tables grossly overestimates the CO2 emissions involved in exports when processing trade is present• this applies to countries for which:

processing trade is a major part of exports large domestic market characteristic production structure is not reflected in

the average production structure (which is dominated by the structure for domestic production)


Yes, there is a life after WIOD!


Yes, there is a life after WIOD!

Thank you for your attention.

wiod why wiod? erik dietzenbacher this project is funded by the european commission, research...

Documents

fr slide

fr labor

ho slide

fr exports labor

ge labor

ge exports

ec slide

red slide