knowledge accounting - world klems...francisco pérez and eva benages ivie and universitat de...

[ 1 ]

Knowledge accounting: an economic approach

Francisco Pérez and Eva BenagesIvie and Universitat de València

2012 WORLD KLEMS ConferenceAugust 8-10, 2012Harvard University

[ 2 ]

MotivationHow can we measure the knowledge intensity of economies?Despite this term being frequently used, there is no metric that measures it accurately…

Industry classification according to technology intensity, on the basis of R&D expenditure and the use of high-skilled labor.

Limitations: - It focuses on the current creation of knowledge rather than how the productive system uses it.- Normally based on a single factor: R&D expenditure in the case of manufacturing and human capital in services.- It doesn’t take into account intra-industry heterogeneity among countries.

Synthetic indices that group multiple variables or indicators.Limitations: -They are usually partial and have ambiguous meaning, because

aggregation criteria are not economically clearly founded.

Knowledge Satellite AccountsLimitations: - Complexity and data requirements.

[ 3 ]

Motivation

How can we measure the knowledge intensity of economies?

Our proposal:

• Knowledge accounting seems viable on account of the theoretical and empirical advances achieved in the measurement of physical and human capital.

• Focus: to measure the weight of knowledge in GVA, by calculating the market value of a set of inputs which are knowledge-based and incorporate knowledge into the production processes.

• Application: the methodology can be applied to economies whose national accounts systems offer industry data on various types of labor and capital services and their corresponding compensation (EU KLEMS database).

[ 4 ]

Methodology• Lij is the amount of labor of type i used in sector j

• Khj is the amount of capital of type h used in sector j

• PijL is the unitary wage paid for labor of type i in sector j

• PhjK is the user cost of type h capital in sector j

• Vj is the quantity of sector j value added

• PjV is the price of sector j value added

The value added (GVA) of sector j is distributed among the different inputs included in the production process so that,

We decompose the value of labor and capital services into two parts, one that incorporates knowledge and another one that does not:

assuming that there are f types of low-skilled labor and g assets that don’t incorporate knowledge significantly.

1 1

m nV L K

j j ij ij hj hji h

V P L P K P

1 1 1

fm mL L L

ij ij ij ij ij iji i i f

L P L P L P

1 1 1

gn nK K K

hj hj hj hj hj hjh h h g

K P K P K P

[ 5 ]

MethodologyAssumption of non-separability: the knowledge of a qualified worker is a contribution to the production process non-separable from the contribution of unqualified labor by the same worker (similarly, in knowledge intensive capital the contribution of non knowledge components of this capital, like the iron of the machine, is not separable from the contribution of knowledge-intensive capital).

Value of knowledge intensive labor (KIL) Value of knowledge intensive capital (KIK)

Knowledge-intensive value added (KIV):

Knowledge intensity (ç) of an activity j:

Knowledge intensity (Ç) of the economy depends on the weight of the various industries in the aggregate, on the basis of the value-added generated:

1

nns Kj hj hj

h g

KIK K P

1

mns Lj ij ij

i f

KIL L P

ns ns nsj j jK IV K IL K IK

/ ( )ns ns Vj j j jç KIV V P

1

1

( )Vq

j jnsqj

Vjj j

j

V PÇ ç

V P

[ 6 ]

Methodology

Knowledge-intensive value-

added (KIV)

Human capital (KIL)(high and medium-

skilled labour)

KNOW

LEDG

E-IN

TENS

IVE

INPU

TS

Highlyknowledge-

intensivevalue-added

High-skilledlabour

ICT capital services

Machinery and equipment capital

services (KIK)

HIGH

LY K

NOW

LEDG

E-IN

TENS

IVE

INPU

TS2 x 2 categories of knowledge inputs used in productive activities

[ 7 ]

Data

• EU KLEMS database

• 18 countries

• 28 industries

• 1980-2007

Table 3. Countries and period coverage

Countries Period covered

Germany 1980‐2007

Australia 1980‐2007

Austria 1980‐2007

Korea 1977‐2005

Denmark 1980‐2007

United States 1970‐2007

Slovenia 1995‐2006

Spain 1980‐2007

Finland 1970‐2007

France 1980‐2007

Ireland 1995‐2005

Italy 1970‐2007

Japan 1970‐2006

The Netherlands 1979‐2007

Portugal 1995‐2005

United Kingdom 1970‐2007

Czech Republic 1995‐2007

Sweden 1993‐2007

[ 8 ]

Industry results: knowledge intensity by industries and countriesFIGURE 1. GVA knowledge intensity by industries, 2007. (percentage)a) Germany b) US

0 20 40 60 80 100

EducationMining and quarryingTransport equipment

Renting of m&eq and business…Electrical and optical equipment

Manufacturing, nec; recyclingPulp, paper, printing and…

Public Admin.Health and social work

Financial intermediationRubber and plastic products

Coke, refined petroleum, nuclear…Basic metals and fabricated…

Hotels and restaurantsOther non-metallic mineral…

ConstructionChemicals

Mahinery, necWood and products of wood and…

Wholesale and retail tradeAgriculture and fishing

Food, beverages and tobaccoTransport and storage

Textiles, leather and footwearTOTAL INDUSTRIES

Post and telecommunicationsOther comm., social and…

Electricity, gas and water supplyReal estate activities

0 20 40 60 80 100

Renting of m&eq and business…Electrical and optical equipment

EducationHealth and social work

Transport equipmentMahinery, nec

Pulp, paper, printing and…Transport and storage

ConstructionPublic Admin.

Rubber and plastic productsManufacturing, nec; recycling

Wholesale and retail tradeWood and products of wood and…

Financial intermediationOther comm., social and…

Basic metals and fabricated…Textiles, leather and footwear

Other non-metallic mineral…TOTAL INDUSTRIES

Post and telecommunicationsFood, beverages and tobacco

Hotels and restaurantsCoke, refined petroleum, nuclear…

ChemicalsAgriculture and fishing

Electricity, gas and water supplyMining and quarryingReal estate activities

c) South Korea

0 20 40 60 80 100

Renting of m&eq and business…Education

Wood and products of wood and…Pulp, paper, printing and…

Other comm., social and personal…Electrical and optical equipment

Other non-metallic mineral…Health and social work

Mahinery, necTransport equipment

ConstructionTextiles, leather and footwear

Agriculture and fishingWholesale and retail trade

Hotels and restaurantsRubber and plastic products

Transport and storageManufacturing, nec; recycling

TOTAL INDUSTRIESChemicals

Public Admin.Post and telecommunicationsFood, beverages and tobacco

Basic metals and fabricated metal…Financial intermediation

Coke, refined petroleum, nuclear…Mining and quarrying

Electricity, gas and water supplyReal estate activities

Highly-qualified human capital Medium-qualified human capital ICT Machinery and equipment (non-ICT)

d) Spain

0 20 40 60 80 100

EducationRenting of m&eq and business…Post and telecommunications

Health and social workPublic Admin.

Financial intermediationElectrical and optical equipment

Transport equipmentMahinery, nec

Other comm., social and…Wood and products of wood and…Coke, refined petroleum, nuclear…

Transport and storagePulp, paper, printing and…

Rubber and plastic productsOther non-metallic mineral…

TOTAL INDUSTRIESChemicals

Electricity, gas and water supplyBasic metals and fabricated…Wholesale and retail trade

Food, beverages and tobaccoTextiles, leather and footwear

Mining and quarryingManufacturing, nec; recycling

ConstructionHotels and restaurantsAgriculture and fishing

Real estate activities

Note: 2005 for South Korea. Source: EU KLEMS and own calculations.

[ 9 ]

Industry results: dispersion of knowledge intensityFIGURE 2. Boxplot of GVA knowledge intensity by industries, 2005

Note: The Czech Republic, Ireland, Portugal, Slovenia and Sweden have not been considered .Source: EU KLEMS and own calculations.

[ 10 ]

Industry results: dispersion of knowledge intensityFIGURE 2. Boxplot of GVA knowledge intensity by industries, 2005


[ 11 ]

Industry results: temporal evolution of dispersionFIGURE 3. -convergence in GVA knowledge intensity by industries,1980-2005. (coefficient of variation)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Agriculture, hunting, forestry and fishingMining and quarryingCoke, refined petroleum products and nuclear fuelElectricity, gas and water supply

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Food, beverages and tobaccoTextiles, leather and footwearWood and products of wood and corkPulp, paper, printing and publishing

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

ChemicalsRubber and plastic productsOther non-metallic mineral productsBasic metals and fabricated metal prod.Machinery, nec.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Electrical and optical equipmentTransport equipmentManufacturing, nec; recyclingConstructionWholesale and retail trade

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Hotels and restaurantsTransport and storagePost and telecommunicationsFinancial intermediationBusiness activities

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Public AdministrationEducationHealth and social workOther social and personal servicesReal estate activities


[ 12 ]

Industry results: knowledge contribution to productivity

d) Spain

FIGURE 4. Labour productivity by components and indutries, 2007. (percentage)

a) Germany

0% 20% 40% 60% 80% 100%

TOTAL INDUSTRIESAgriculture and fishingMining and quarrying


Wood and wood and cork productsPulp, paper, printing and publishing

Coke, refined petrol. and nuclear fuelChemicals

Rubber and plastic productsOther non-metallic mineral prod.

Basic metals and fabric. metal prod.Machinery, nec.

Electrical and optical equipmentTransport equipment

Manufacturing, nec; recyclingElectricity, gas and water supply

ConstructionWholesale and retail trade

Hotels and restaurantsTransport and storage

Post and telecommunicationsFinancial intermediation

Real estate activitiesBusiness activities

Public AdministrationEducation

Health and social workOther social and personal services

b) US

c) South Korea

Note: 2005 for South Korea. Source: EU KLEMS and own calculations.

0% 20% 40% 60% 80% 100%















0% 20% 40% 60% 80% 100%















Human capital Machinery and equipment capital Non-qualified labour Real estate capital

0% 20% 40% 60% 80% 100%















[ 13 ]

Industry results: summary• Knowledge intensity in the same industry varies notably among

countries and warns us of the risk of classifying industries in categories by technological intensity.

• Human capital is more determinant in establishing differences in knowledge intensity than machinery, among sectors and within sectors.

• Data of activities specialized in the production of human capital and machinery and equipment confirm that inputs which incorporate knowledge are also produced with knowledge.

• The time series show that the use of knowledge is an increasingly common feature of the countries in the sample.

• Differences among countries in labor productivity for each sector are primarily associated with the contribution of highly qualified labor.

[ 14 ]

FIGURE 5. GVA knowledge intensity by countries, 1980, 1995 y 2007. (percentage)

0 20 40 60 80 100

South Korea

United Kingdom

The Netherlands

US

Japan

Sweden

Slovenia

Italy

Czech Republic

Denmark

Finland

Austria

Germany

France

Ireland

Australia

Spain

Portugal

Note: 2005 for Ireland, Portugal and South Korea and 2006 for Japan and Slovenia.Source: EU KLEMS and own calculations.

a) Knowledge-intensive inputs b) Highly knowledge-intensive inputs

0 20 40 60 80 100

South Korea

US

Finland

Slovenia

Japan

United Kingdom

Spain

Australia

Portugal

Sweden

Czech Republic

Ireland

France

The Netherlands

Austria

Germany

Denmark

Italy

2007 1995 1980

Aggregate results: knowledge intensity by countries

[ 15 ]

Aggregate results: dispersion and convergence

FIGURE 6. Boxplot of GVA knowledge intensity bycountries, 1985-2005

FIGURE 7. -convergence in GVA knowledge intensity. Total industries, 1980-2005. (coefficient of variation)

0.00

0.05

0.10

0.15

0.20

0.25



[ 16 ]

Aggregate results: determinants of differences

-40 -30 -20 -10 0 10 20 30 40

PortugalSpain

AustraliaIrelandFrance

GermanyAustriaFinland

DenmarkCzech Rep.

ItalySloveniaSweden

JapanThe NetherlandsUnited Kingdom

South Korea

Note: 2006 for Japan and Slovenia and 2005 for Ireland, Portugal and South Korea. 2005 for Figure 3c.Source: EU KLEMS and own calculations.

FIGURE 8. Knowledge-intensity shif-share analysis. 2007(absolute differences in percentage points on GVA)

a) Reference country: US b) Reference country: Germany

-40 -30 -20 -10 0 10 20 30 40

PortugalSpain

AustraliaIrelandFranceAustriaFinland

DenmarkCzech Rep.

ItalySloveniaSweden

USJapan

The NetherlandsUnited Kingdom

South Korea

c) Reference country: South Korea d) Reference country: Spain

-40 -30 -20 -10 0 10 20 30 40

PortugalSpain

AustraliaIreland

GermanyFranceAustria

DenmarkFinland

Czech Rep.Italy

SwedenSlovenia

JapanThe Netherlands

USUnited Kingdom

-40 -30 -20 -10 0 10 20 30 40

PortugalAustralia

IrelandFrance

GermanyAustriaFinland

DenmarkCzech Rep.

ItalySloveniaSweden

JapanUS

The NetherlandsUnited Kingdom

South Korea

Country Effect Specialization Effect Allocation Effect Total Effect

[ 17 ]

Aggregate results: knowledge and productivityFIGURE 10. Labour productivity by components, 2007. (2000 PPS euros per hour worked)

0 10 20 30 40 50

US

The Netherlands

Sweden

Germany

Finland

France

Ireland

Denmark

Italy

Australia

Austria

United Kingdom

Spain

Japan

Slovenia

South Korea

Portugal

Czech Rep.

Human capital Machinery and equipment capitalNon-qualified labour Real estate capital

Note: 2006 for Japan and Slovenia and 2005 for Ireland, Portugal and South Korea. Source: EU KLEMS and own calculations.

-30 -25 -20 -15 -10 -5 0 5

Czech Rep.

Portugal

South Korea

Slovenia

Japan

Spain

United Kingdom

Austria

Australia

Italy

Denmark

France

Ireland

Finland

Germany

Sweden

The Netherlands

Knowledge inputs Non-knowledge inputs Total

FIGURE 11. Decomposition of the differences in labourproductivity. Reference country: US, 2007(absolute differences in percentage points on GVA)

Note: 2006 for Japan and Slovenia and 2005 for Ireland, Portugal and South Korea. Source: EU KLEMS and own calculations.

[ 18 ]

Aggregate results: knowledge and GVA growthFIGURE 13. Knowledge contribution to the annual growth of GVA. 1980-1995 and 1995-2007 (percentage points)

-1%

0%

1%

2%

3%

4%

5%

6%

7%

8%

Sout

h Ko

rea

Japa

n

Aust

ralia US

Spai

n

Aust

ria

The

Net

herla

nds

Ger

man

y

Uni

ted

King

dom

Ital

y

Fran

ce

Den

mar

k

Finl

and

Czec

h Re

p.

Irel

and

Port

ugal

Slov

enia

Swed

enNote: 1990-2006 for Japan and Slovenia and 1990-2006 for Ireland, Portugal and South Korea in figure 10b.Source: EU KLEMS and own calculations.

a) 1980-1995

-1%

0%

1%

2%

3%

4%

5%

6%

7%

8%

Irel

and

Slov

enia

Sout

h Ko

rea

Finl

and

Spai

n

Aust

ralia US

Swed

en

Uni

ted

King

dom

The

Net

herla

nds

Port

ugal

Aust

ria

Czec

h Re

p.

Fran

ce

Den

mar

k

Japa

n

Ger

man

y

Ital

yKnowledge-based GVA Non knowledge-based GVA Total GVA

b) 1995-2007

[ 19 ]

Aggregate results: knowledge and productivity growthFIGURE 14. Knowledge contribution to the annual growth of labour productivity. 1980-1995 and 1995-2007 (percentage points)

-1%

0%

1%

2%

3%

4%

5%

6%

7%

8%

Sout

h Ko

rea

Japa

n

Finl

and

Spai

n

Aust

ria

Uni

ted

King

dom

Fran

ce

Ger

man

y

Den

mar

k

Ital

y

Aust

ralia

The

Net

herla

nds

US

Czec

h Re

p.

Irel

and

Port

ugal

Slov

enia

Swed

en

Note: 1990-2006 for Japan and Slovenia and 1990-2006 for Ireland, Portugal and South Korea in figure 14b.Source: EU KLEMS and own calculations.

a) 1980-1995

-1%

0%

1%

2%

3%

4%

5%

6%

7%

8%

Slov

enia

Irel

and

Sout

h Ko

rea

Swed

en

Finl

and

Japa

n

Czec

h Re

p. US

Uni

ted

King

dom

Ger

man

y

Aust

ralia

Fran

ce

Port

ugal

Aust

ria

The

Net

herla

nds

Den

mar

k

Spai

n

Ital

yKnowledge activities Non-knowledge activities Total

b) 1995-2007

[ 20 ]

Aggregate results• Recently, in several advanced economies the intensity of the use of

knowledge has been higher than 75%. The increasing intensity is associated primarily with higher education and ICT.

• Country differences in knowledge intensity decrease, due to the positive evolution of less developed countries.

• The main differences among countries are based on intra-industry specialization, rather than inter-industry specialization.

• The disadvantages in country productivities in comparison with the US stem from the high use of knowledge-intensive factors in the American economy.

• From 1980 to 2007 knowledge has played a crucial role in the growth of GVA and productivity in almost all developed countries.

[ 21 ]

Knowledge accounting: conclusionsThe results of applying the proposed metric of the use of knowledge-based factors in productive activities show that:

• Today advanced economies are extensively based on knowledge: more than half of the GVA remunerates human capital and machinery, factors which incorporate knowledge to the production processes.

• Knowledge is an increasingly common feature of all industries: the factors which incorporate it are the basis for competitiveness and growth.

• The differences among countries in knowledge intensity stem primarily from intra-industry differences: due to the very different activities being carried out within them and to their different weight in each economy.

[ 22 ]

Knowledge accounting: policy implications

• The conventional country classification by categories of technological intensity prevents us from showing that the same industry can differ in the intensity of knowledge among countries what is critical for promoting the use of knowledge is the renewal of the productive structure in each industry.

• The differences among industries in the intensity of use of human capital and machinery do not respond to the dichotomy manufacturing vs. services manufacturing is not superior in the use of knowledge.

• The highest contributions of knowledge to productivity lie on a better use of human capital, particularly, of the workers with higher education if education does not meet the needs of industry or if employment offered by firms does not exploit the potential of education, more value added will not be generated nor wages will be able to increase.

•

[ 23 ]

Extensions

• Case study: Spain• Pérez y Benages (2012): “El PIB basado en el

conocimiento: importancia y contribución al crecimiento”, Ivie, forthcoming.

• GVA vs Total Production knowledge intensity• Knowledge intensity of intermediate inputs• Knowledge intensity of imports

• Knowledge intensity as determinant of productivity and growth: the role of different components

[ 24 ]

Knowledge accounting: an economic approach

Francisco Pérez and Eva BenagesIvie and Universitat de València

2012 WORLD KLEMS ConferenceAugust 8-10, 2012Harvard University

knowledge accounting - world klems...francisco pérez and eva benages ivie and universitat de...

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