The NEMESIS Reference Manual

Transcription

The NEMESIS Reference Manual
Coordination
Erasme Team,France
Prof. P. Zagamé
B. Boitier,A. Fougeyrollas,P. Le Mouël
Core Teams
National Technical University of Athens, Greece
Prof. P. Capros,N. Kouvaritakis
Federal Planning Bureau, Belgium
F. Bossier,F. Thierry, A. Melon
The NEMESIS model had been partially funded by the research programs of the European
Commission
2
Contents
Introduction to NEMESIS
8
I. The core economic Model
15
I.1. Current version of the endogenous technical change module . . . . . . . . 15
I.1.1.
The stock of knowledge . . . . . . . . . . . . . . . . . . . . . . . . 16
I.1.2.
From stock of knowledge to innovation
I.1.3.
innovation to economic performance . . . . . . . . . . . . . . . . . 18
I.1.4.
Calibration in the model . . . . . . . . . . . . . . . . . . . . . . . . 21
. . . . . . . . . . . . . . . 17
I.2. New production functions with embodied endogenous technical change
and skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
I.2.1.
Multi-level, putty-semi-putty CES production functions . . . . . . 22
I.2.2.
The Endogeneization of Technical Change . . . . . . . . . . . . . . 26
I.2.3.
Estimation results . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
I.3. Households’ final consumption
. . . . . . . . . . . . . . . . . . . . . . . . 47
I.3.1.
Aggregate consumption . . . . . . . . . . . . . . . . . . . . . . . . 47
I.3.2.
Allocation of aggregate Consumption . . . . . . . . . . . . . . . . . 49
I.4. External trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
I.4.1.
Intra-European trade . . . . . . . . . . . . . . . . . . . . . . . . . . 54
I.4.2.
Extra European Trade . . . . . . . . . . . . . . . . . . . . . . . . . 57
I.4.3.
Imports and Exports prices . . . . . . . . . . . . . . . . . . . . . . 59
I.5. Wage setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
I.5.1.
Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3
Contents
I.5.2.
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
I.5.3.
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
I.5.4.
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
I.6. Labour supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
I.6.1.
The data on participation rates of working-age population . . . . . 76
I.6.2.
Determinants of participation rates . . . . . . . . . . . . . . . . . . 78
I.6.3.
Calibration of labour supply
. . . . . . . . . . . . . . . . . . . . . 83
I.7. Taxation and subsidies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
I.7.1.
Institutional sectors accounts . . . . . . . . . . . . . . . . . . . . . 87
I.7.2.
Public finances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
I.7.3.
Focus on most important taxations system . . . . . . . . . . . . . 89
I.8. Sectoral Interdependencies . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
I.8.1.
Demand flows to products . . . . . . . . . . . . . . . . . . . . . . . 92
I.8.2.
technological progress interactions. . . . . . . . . . . . . . . . . . . 95
I.9. housing investments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
I.9.1.
Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
I.9.2.
The data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
I.9.3.
Model estimate and results . . . . . . . . . . . . . . . . . . . . . . 103
I.9.4.
Sensibility analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
I.9.5.
Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4
List of Figures
.1.
Basic functioning of the model . . . . . . . . . . . . . . . . . . . . . . . . 10
.2.
NEMESIS modularity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
I.1. From R&D expenditure to the R&D stock
. . . . . . . . . . . . . . . . . 17
I.2. The stock of knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
I.3. Two types of innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
I.4. Process innovation and economic performance . . . . . . . . . . . . . . . . 18
I.5. Product innovation and economic performance . . . . . . . . . . . . . . . 19
I.6. CES nesting structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
I.7. Ex-ante and ex-post isoquants . . . . . . . . . . . . . . . . . . . . . . . . . 25
I.8. EU National share of high skill on total employment, in 2005 (source:
Eurostat) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
I.9. European high skill share in total employment for NEMESIS sectors,
(source EU-KLEMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
I.10. European high skill share in total employment for NEMESIS sectors . . . 39
I.11. Sectoral illustration of the final results . . . . . . . . . . . . . . . . . . . . 40
I.12. Ratio of European employee unit cost between high and low skills at
sectoral level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
I.13. Corrected European share of compensation of employees for high skill at
sectoral level in 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
I.14. Allocation of Durable Goods . . . . . . . . . . . . . . . . . . . . . . . . . 50
I.15. Allocation of Non Durable Goods . . . . . . . . . . . . . . . . . . . . . . . 50
5
List of Figures
I.16. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
I.17. results whole model
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
I.18. Sectoral results P1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
I.19. sectoral results P2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
I.20. Participation rates to labour market of men and women aged 25 to 64,
EU27 + Norway, 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
I.21. Participation rates to labour market of women aged 50 and 64 by skill,
EU27 + Norway, 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
I.22. Social Contribution paid . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
I.23. Social contribution received . . . . . . . . . . . . . . . . . . . . . . . . . . 92
I.24. Sectoral interdependencies in NEMESIS . . . . . . . . . . . . . . . . . . . 94
I.25. Knowledge spillovers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
I.26. Rent Spillovers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
I.27. Sensibility analysis with common adjustment coefficient . . . . . . . . . . 107
I.28. Model response to 1% shock on households real disposable income: Comparison according to adjustment coefficients . . . . . . . . . . . . . . . . . 108
6
List of Tables
I.1. Labour compensation growth, period 1998-2005 . . . . . . . . . . . . . . . 65
I.2. Unemployment rate , period 1998-2005 . . . . . . . . . . . . . . . . . . . . 66
I.3. Price growth and high skill share , period 1998-2005 . . . . . . . . . . . . 67
I.4. Labour productivty growth, period 1998-2005 . . . . . . . . . . . . . . . . 68
I.5. Coefficients summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
I.6. Estimation results of participation rates . . . . . . . . . . . . . . . . . . . 82
I.7. Elasticities of activity rates in NEMESIS in 2008 . . . . . . . . . . . . . . 85
I.8. Estimates results of households gross fixed capital formation error correction model
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
I.9. Estimates results for short term model with individualised adjustment
coefficients
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
7
The NEMESIS model (New Econometric Model of Evaluation by Sectoral Interdependency and Supply), has been partialy funded under the fifth and sixth RTD Framework
Programs of European Commission General Directorate of Research1 . It is a system
of economic models for every European country (EU27 less Bulgaria and Cyprus, plus
Norway), USA and Japan, devoted to study issues that link economic development,
competitiveness, employment and public accounts to economic policies, and notably all
structural policies that involve long term effects: RTD, environment and energy regulation, general fiscal reform, etc. The essential purpose of the model is to provide a
framework for making forecasts, or ‘Business As Usual’ (BAU) scenarios, up to 25 to
30 years, and to assess for the implementation of all extra policies not already involved
in the BAU. NEMESIS uses as main data source EUROSTAT, and specific databases
for external trade (OECD, New CRONOS), technology (OECD and EPO) and land use
(CORINE 2000). NEMESIS is recursive dynamic, with annual steps, and includes more
than 160.000 equations.
The main mechanisms of the model are based on the behaviour of representative
agents: Enterprises, Households, Government and rest of the world. These mechanisms
are based on econometrics works.
1
The core teams of the NEMESIS model are :
• ERASME (France) as coordinator
• CCIP (France)
• Federal Planning Bureau (Belgium)
• National Technical University of Athens (Greece)
8
The main originality of the model, when compared to others used for similar policies,
lies in the belief that the medium and long term of macroeconomics path is the result of
strong interdependencies between sectoral activities that are very heterogeneous from a
dynamic point of view, with leading activities grounded on Research and Development,
and from environment and sustainable development with a huge concentration of pollutants on few activities. These interdependencies are exchanges of goods and services
on markets but also of external effects, as positive technological spillovers and negative
environmental externalities.
Another originality of NEMESIS is that it is a “Framework model” with different
possibilities on the several mechanisms involved in the functioning (see figure .2 for the
different available modules). Although econometrics, the model cannot be classified as
a neo-keynesian model, in the new version that built-in the new theories of growth; it
escapes also to the classification of general equilibrium model, as it incorporates original
mechanisms that do not refer to the strict orthodoxy of the mainstream neo-classical
approach, on which was based the general equilibrium approach.
We now present the main mechanisms, outputs and uses of NEMESIS.
Main NEMESIS’ mechanisms
On the supply side, NEMESIS distinguishes 32 production sectors, including Agriculture, Forestry, Fisheries, Transportations (4), Energy (6), Intermediate Goods (5)
Capital goods (5), Final Consumption Goods (3), Private (5) and Public Services. Each
sector is modeled with a representative firm that takes its production decisions given
its expectations on production capacity expansion and input prices. Firms behaviour
includes very innovative features grounded on new growth theories, principally endogeneous R&D decisions that allow firms improving their process productivity and product
quality. Production in sectors is in this way represented with CES production functions
(with the exception of Agriculture which uses Translog functions, and Forestry and Fisheries where technology is represented with Leontief functions) with 5 production factors
: capital, unskilled labour, skilled labour, energy and intermediate consumption, where
also endogenous innovations of firms come modify the efficiency of the different inputs
(biased technical change) and the quality of output (Hicks neutral technical change). The
production function was estimated by the dual approach and estimation and calibration
of links between R&D expenditures, innovations and economic performance were picked
up from the abundant literature on the subject. The pricing of enterprises results from
9
Figure .1.: Basic functioning of the model
10
Figure .2.: NEMESIS modularity
an arbitrage between firms engaged in competitive behaviour and those with a pricing
by mark-up (due to innovation that creates monopoly situations). Interdependencies
between sectors and countries are finally caught up by a collection of convert matrices
describing the exchanges of intermediary goods, of capital goods and of knowledge in
terms of technological spillovers, and the description of substitutions between consumption goods by a very detailed consumption module enhance these interdependencies.
On the demand side, representative households’ aggregate consumption is dependent
on current income. Total earnings are a function of regional disposable income, a measure of wealth for the households, interest rates and inflation. Variables covering child
and old-age dependency rates are also included in an attempt to capture any change
in consumption patterns caused by an ageing population. The unemployment rate is
used, in the short-term equation (only), as a proxy for the degree of uncertainty in the
economy. Consistent with the other behavioural equations, the disaggregated consumption module is based on the assumption that there exists a long-run equilibrium but
rigidities are present which prevent immediate adjustment to that long-term solution.
Altogether, the total households aggregated consumption is indirectly affected by 27
different consumption sub-functions through their impact on relative prices and total
income, to which demographic changes are added. Government public final consump-
11
tion and its repartition between Education, Health, Defence and Other Expenditures,
are also influenced by demographic changes.
For external trade, it is treated in NEMESIS as if it takes place through two channels:
intra-EU, and trade with the rest of the world. Data availability was an important factor
in this choice – it allowed an emphasis to be put on intra-EU trade flows, which are a
large portion of the total trade in the EU. The intra- and extra-EU export equations can
be separated into two components, income and prices. The income effect is captured by
a variable representing economic activity in the rest of the EU for intra-EU trade, and a
variable representing economic activity in the rest of the world for extra-EU trade. Prices
are split into two sources of impacts in each of the two equations (intra- and extra-EU
trade). For intra-EU trade, they are the price of exports for the exporting country and
the price of exports in other EU countries. For extra-EU trade, prices impacts come
through the price of exports for the exporting country, and a rest-of-the-world price
variable. The stock of innovations in a country (which, in NEMESIS, is taken relative to
the total innovation stock in Europe in a particular sector) is also included in the export
equations in order to capture the role of innovations in trade performance and structural
competitiveness. For imports, equations are identical for both intra- and extra-EU trade.
The income effect is captured through domestic sales by domestic producers, while the
price effects are represented in both the import price, as well as the price of domestic
sales by domestic producers. The stock of innovations is again included to account for
the effects of innovations on trade performance.
The wage equations, which determine in NEMESIS the dynamics of prices and incomes, are based on a theory of wage-setting decisions made by utility maximising
unions. The unions calculate utility from higher levels of employment and from higher
real wages (relative to wages outside the sector) in the sector, subject to the labourdemand constraint imposed by firms’ profit-maximisation. The implication of this form
of wage equation is that conditions in the labour market are important for determining
wage and real wages in a given sector will rise if there are positive productivity shocks,
changes in the unemployment rate, or changes in the real wage outside that sector.
Another important NEMESIS characteristic is finally its land-use module that extended the field of policies the model could explore to the areas of Agriculture, Forestry,
Bio-energies, Tourism, Transportations, Urbanization and Nature Conservation, through
their implications on land-use. These six sectors are actually of significant importance
for land use; they are at the origin of all possible land claims which modelling in NEMESIS is cross sectoral to this extent that all sectors are competing for land. Land claim
by each sector sum up in a common land balance for all sectors which, confrontation to
12
the supply land function allows deriving the equilibrium rental price for land.
Main NEMESIS’ inputs and outputs
On the input side, NEMESIS uses for its functioning assumptions on a set of exogenous
variables concerning word assumptions including interest rates, exchange rates, activity
proxies for the rest of the world, prices of wholesales commodities and specially oil; demographic assumptions by country such total Population, population and participation
rates to labour force by gender per 5 years cohorts; national policies assumptions and notably fiscal policies (indirect and direct taxes, social security benefits and contributions)
and government expenditures (defence, health, education, infrastructures, others expenditures) and investments; and energy and environment assumptions as excises duties and
other energy tax rates, CO2 taxation, etc.
On the output side, NEMESIS can deliver results at EU25, country and regional
NUTS2 levels for key economic indicators. The indicators the model calculates are
macro-economic, as GDP (European, National or Regional) and its counterparts (final
consumption, investment, exports, imports, etc.), sectoral, as production, value added
and employment per NACE economic sector or sector clusters, or agent based (Governement, Non Financial Corporations, Financial Corporations, Households including
NPIH, and outside).
Beyond economic indicators as GDP, prices and competitiveness, employment and
revenues, financial balances for the main agents, etc., NEMESIS Energy Environment
Module (NEEM) gives detailed results on energy supply and demand by fuel type and
technology, and on various pollutants emissions: CO2, SO2, NOX, HFC, PFC and CF6;
it computes also a carbon price (Taxation or tradable permit price associated to a carbon
constraint). The inclusion in the model of detailed data on population and working force,
allows also the model delivering many social indicators as employment, unemployment
and labour force participation rates by gender, GINI coefficient for wages and earnings,
and a set of indicators dedicated to measure inequalities between European countries and
regions for key variables as GDP and final consumption per capita. Additional original
indicators concern land use by 6 sectors: Agriculture, Forestry, Nature Conservation,
Urbanization, Transport and Energy Infrastructures and Tourism, and 8 land categories.
NEMESIS calculates also, at country level, the equilibrium rental price of land, which
impact strongly on housholds’ cost of living and firms’ investment price.
13
Main NEMESIS uses
With its original characteristics and great detail level results, NEMESIS can be used
for many purposes as short and medium-term economic and industrial “forecasts” for
business, government and local authorities; analysing Business As Usual (BAU) scenarios and economy long-term structural change, energy supply and demand, environment,
land-use and more generally sustainable development; revealing the long term challenges of Europe and identifying issues of central importance for all European, national,
regional scale structural policies; assessing for most of Lisbon agenda related policies
and especially knowledge (RTD and human capital) policies; emphasizing the RTD aspect of structural policies that allows new assessments (founded on endogenous technical
change) for policies, and new policy design based on knowledge: Education, Skill and
Human Capital and RTD.
NEMESIS has notably been used to study BAU scenarios for European Union and reveal the implication for European growth, competitiveness and sustainable development
of the Barcelona 3% GDP RTD objective, of the 7th Research Framework Program of
European Commission, of National RTD Action Plans of European countries, of European Kyoto and post-Kyoto policies, of increase in oil price, of European action plan for
Renewable Energies, of European Nuclear Phasing in/out, etc. NEMESIS is currently
used to assess for European Action plan for Environmental and energy technologies, for
European financial perspective (CAP reform) and for Lisbon agenda, with in deep development on the modelling of RTD, Human Capital and labour market and European
regions.
14
CHAPTER I
The core economic Model
We will present in this section two versions of the supply side and of the endogenous
technical change module: the first one, the current version implemented in the model,
had been used in numerous studies related to R&D and innovations, while the second
one is currently tested and enhance the previous formulation.
Section I.1
Current version of the endogenous technical change module
The endogenisation of technical progress in applied models is a very recent phenomenon.
It has mainly been used in overall balance models. Some of these models follow on from
the work carried out by Arrow [17]. Here, the rate of technical progress is linked to
expertise or experience, measured by gross accumulated investment. They therefore
take up a similar viewpoint to the AK model in which the capital K variable contains
information relating to the state of the technology. This approach has been adopted in
15
I.1. CURRENT VERSION OF THE ENDOGENOUS TECHNICAL
CHANGE MODULE
certain models relating to climate change, such as those of Goulder and Mathai [156]
and Grubb [163]. In this field, the characteristics of technologies are often linked to
experience curves. Making technical progress endogenous provides a more effective,
immediate implementation of policies to fight against greenhouse gases as a result of the
experience acquired in the field.
Other overall balance models use R&D expenditure to make technical progress endogenous. This is the case in models dealing with issues relating to international trade
(such as those of Diao and Roe [101], Baldwin and Forslid [24] and Diao et alii [102]) and
in models applied to the environment and climate change (such as Nordhaus’ RDICE
model [254] or Fougeyrollas et alii’s GEM-E3 model [147]). Endogenisation through
R&D expenditure is not easy. The first difficulty comes in calculating the relationship
between R&D and process or product innovations. The second comes from the possibility that there may be a number of balances. The third stems from the diversity of R&D
levels of intensity and results in the sectors. Only a few sectors, such as those linked
to ICT and the pharmaceutical sector, are R&D intensive. It is therefore necessary to
adopt a detailed sector-based approach so that the endogenisation of technical progress
is appropriate. Few models manage to overcome this difficulty.
Econometric models containing technical progress mechanisms endogenised by R&D
are rare. To our knowledge, only the International Monetary Fund Multimod model,
which is highly agregated, includes R&D stocks at a sector level. The Nemesis model
takes it place in this new family of macro-econometric models with endogenous technical
progress. The special feature in Nemesis is the endogenisation of technical progress
across three phases: from R&D to the stock of knowledge, from the stock of knowledge
to innovation and from innovation to economic performance, the second feature is the
level of disagregation.
I.1.1 The stock of knowledge
The variable that plays a vital role in the endogenisation of technical progress in Nemesis is the variable “knowledge” (KN OW ) that arises out of the R&D stock. A sector’s
R&D stock is determined by its R&D expenditure and by a constant displacement rate.
It is constituted as a stock of capital, with displacement being the gradual deletion of
knowledge (figure I.1).
“Knowledge” is not determined only by the sector’s R&D stock but also by all the
knowledge spillovers in all national and foreign sectors (figure I.2). Knowledge spillovers
16
CHAPTER I. THE CORE ECONOMIC MODEL
Figure I.1.: From R&D expenditure to the R&D stock
from other sectors are dependent on their stocks of R&D, via technological flow matrices. These matrices, which are differentiated by sector and by country, are constructed
according to the methodology developed by Johnson for the OECD (Johnson, 2002).
This consists of identifying, for every patent registered at the European Office, the sectors producing and using the innovation described in the patent. This is then used to
determine the proportion in which the knowledge accumulated in a sector will benefit
others, by calculating knowledge transfer coefficients, the knowledge being, by assumption, borne by the patents. This work is done in great detail (over 100 sectors) and
the results are re-agglomerated in Nemesis’ sector-based nomenclature in the form of
technological flow matrices. “Knowledge” also feeds on the R&D stock in foreign sectors
and on the public sector R&D stock.
KNOW
R&D Stock of the Sector
Technology Flow
Matrices
R&D Stocks of Foreign
Sectors
R&D Stock of Other
Sectors
Public R&D Stock
Figure I.2.: The stock of knowledge
I.1.2 From stock of knowledge to innovation
Innovations are determined by the variant in the stock of knowledge (figure I.3). The
two types of innovation are considered here:
• process innovations that increase the global productivity of factors in the specification that we have chosen;
• product innovations, which, in the fixed nomenclature of national accounting that
17
CHANGE MODULE
under-pins Nemesis, are considered as quality improvements1 .
These two types of innovation act very differently on economic performance:
∆KNOW
Process Innovation
Product Innovation
Figure I.3.: Two types of innovation
I.1.3 innovation to economic performance
Process innovation does not lead to the same effects as product innovation. Process
innovation increases the global productivity of factors, thus increasing product supply
and reducing the unit production cost, and therefore the price. This price reduction
leads to increased demand, which is dependent on demand price elasticity (figure I.4).
Productivity Growth
Increase in Supply
Process Innovation
Price Fall
Supply Side
Demand Side
Demand Price Elasticity ε
Increase in Demand
Figure I.4.: Process innovation and economic performance
Growth in demand helps to absorb extra supply (at a constant usage level) if demand
price elasticity is higher than or equal to one. However, econometric estimates in chrono1
National accounts already take, partially, the increasing quality of goods and services into account
in their calculus, however this accounting is relatively rough. In the NEMESIS model we consider
the quality improvements that are additional what are very important whenever we increase R&D
efforts.
18
logical series reveal an elasticity generally lower than one for each sector, and thus for
the whole economy. This result comes from the assumption of a representative firm per
sector: we do not consider the innovative firm in competition with the other companies
in its activity sector. This amounts to assuming that all firms in the sector innovate
and reduce their prices. Increased demand then depends on the capacity for absorption
represented by elasticity lower than one. In this case process innovation reduces the use
of factors as the effects of supply outweigh the effects of demand.
Product innovation acts like an increase in efficiency per volume unit and increases
demand for units of efficiency (figure I.5). Volume production is only maintained if the
increase in demand for the new efficiency is just equal to the increase in efficiency due
to innovation. Generally, product innovation does more than compensate for the fall
in factor usage due to process innovation. R&D therefore leads simultaneously to an
increase in GDP and in the use of factors.
Increase in efficiency per
volume
Fall in price of efficiency unit
Product Innovation
Supply Side
Demand Side
variation of demand volume
Increase in demand of
efficiency unit
Figure I.5.: Product innovation and economic performance
The ex ante effects of innovation on GDP depend on the effects of the increase in
knowledge on the global productivity of factors and on quality and thus on demand:
increased production is in fact linked to increases in demand arising from process innovation and quality innovation respectively (box 1).
19
CHANGE MODULE
Box 1. The effects of innovation on economic performance
Process innovation: the accumulation of knowledge (KN OW ) generates an increase in
the global productivity of factors (T F P ).
∆T F P
∆KN OW
=α
TFP
KN OW
Product innovation: the accumulation of knowledge (KN OW ) leads to an improvement
in quality (QU AL).
∆QU AL
∆KN OW
= α0
QU AL
KN OW
Economic performance: increased production (Y ) depends on increased demand due to
innovation depending of two elasticities and 0 .
∆Y
∆T F P
∆QU AL
=
+ 0
Y
TFP
QU AL
i.e.
∆KN OW
∆KN OW
∆Y
= α + 0 α0
=β
Y
KN OW
KN OW
Finally, economic performance, measured by increased production due to increased
knowledge, is written as follows:
∆Y
∆KN OW
=β
Y
KN OW
Most of the available econometric studies link increased production with an increase
in R&D stock (SRD) using the following formula :
∆Y
∆SRD
=α
Y
SRD
The difference between these two approaches is an explicit integration of all the
spillovers in the first and an implicit or nil integration in the second. Econometric studies (Mohnen [245], Mairesse and Sassenou [237], Grilliches [160], Nadiri [249], Cameron
(1998)[49], . . . ) reveal a fairly broad range for parameter β of 0.05 to 0.2. The results
are independent of the methods chosen. However, where β is estimated using instant
cross-section series (inter-companies), it is higher than when chronological estimates are
used.
I.1.4 Calibration in the model
20
In the NEMESIS model the α and α0 had been calibrated to reproduce the desired
values of the mean β parameter. This had been done by making sensitivity analysis over
the historical data in order to reproduce past trends, in order to be as close as possible
of the historical facts. After that, the βparameters are differentiated using sectoral R&D
intensities.
Section I.2
New production functions with embodied endogenous technical
change and skills
Since their conception in the late Fifties and early Sixties (see for example: Jorgenson
[203], Salter [280], Solow [299]; Solow, Tobin et al. [300]), vintage models have been
often adopted by applied modellers for representing the links existing between technical
change and economic growth. These models gave important insights regarding the complementarities between productivity growth on one hand and investment on the other
one, through the technical progress embodied in the new vintages. The development
of new growth theories from the Eighties stated furthermore that technical change was
itself an endogenous process based on R&D and innovations decisions of private and
public actors. We therefore adopted for NEMESIS an embodiment approach, close from
the one already implemented in GEM-E3 model, and where technical change results
from investment decisions for new equipment goods and machineries on the one hand,
and from investments in R&D based innovations that modify both the rate and the
direction of technical change. The modelling of production technologies was inspired by
the approach that was developed by Adriaan Van Zon [324] and Huub Meijers & Adriaan Van Zon (1999) that they called RUM Putty-Semi-Putty vintage model. ’RUM’
means ’Recursive Update Model’, that allows to obtain the aggregate levels of production factor demands from a set of simple recursive update rules. This model is based
on a putty-semi-putty vintage production structure, and it is precisely the possibility of
limited substitution possibilities ex-post which enable to reduce to a set of few equations,
the book-keeping account of all existing vintage, necessary when economic scrapping is
endogenised, as for the Putty-Clay and the Clay-Clay vintage production models as they
were first introduced by Johansen [196] and Salter [280]. RUM makes positive use of the
fact that it is often not necessary to know all the details of every individual vintage: from
21
I.2. NEW PRODUCTION FUNCTIONS WITH EMBODIED
ENDOGENOUS TECHNICAL CHANGE AND SKILLS
a macro-economic point of view, only the average characteristics of the capital stock are
important.
In a first subsection, we show how this vintage approach was introduced in NEMESIS,
with the use of “Putty-Semi-Putty” multi-level CES production functions. A second subsection, we describes then the modelling of technical change, endogenized on R&D, and
incorporated in production vintages. The resolution of firms’ optimization program, and
of the main equations that where introduced in NEMESIS for R&D, innovation and production factor demands, are available in NEMESIS reference manual, the presentation
here focusing on methodological issues.
I.2.1 Multi-level, putty-semi-putty CES production functions
The nested CES framework
The multi-level nested CES production functions, pioneered by Sato [283], have recently
been widely used in macroeconomics. Its flexibility, and its usefulness to implement
and analyse endogenous growth makes it an attractive choice for many applications in
economic theory, applied modelling and empirics (cf. Acemoglu (2002), Papageorgiou
and Saam (2006) and McAdam et al. (2007)). In NEMESIS, apart for the Power sector,
which has a special modelling, the other 29 production sectors were modelled with fourlevel nested CES production functions that differ only from the values of substitution
elasticities, and of share parameters.
NEMESIS production functions
NEMESIS production functions were extended to include low skilled and high skilled
labour, and 5 productive inputs : Capital K, Low Skilled LabourLL , High Skilled Labour
LH , Energy E and Materials M , .
The choice of factor bundles was based on the results of separability tests, abundant
in the econometric literature. As it is illustrated by I.6 Source du renvoi introuvable.,
at a first stage Materials are combined with a bundle regrouping all other production
factors. At a second stage, Low Skilled Labour was separated from Capital, High Skilled
22
Figure I.6.: CES nesting structure
Y
M
KELHSLLS
LLS
KELHS
LHS
KE
K
E
Labour and Energy, that we supposed to be gross complements. High Skilled labour
is separated from the bundle formed by Capital and Energy at a third level, and then
Capital was combined with Energy at a fourth level.
This grouping means that at each level, the value of the partial substitution elasticities
between the factor that is separated, and each production factors in the bundle formed
by other inputs, are identical. Partial substitution elasticities are noted:
• σ1 for substitutions between M and K, LH LL , E;
• σ2 for substitutions between LH and K, LL E ;
• σ3 for substitutions between LL and K E ;
• and σ4 for substitutions betweenK and E.
The next sub-section will details the expression of the nested production functions.
Production technology: A Putty-Semi Putty Vintage Model
For technologies of production, the underlying idea is that substitutions possibilities
between production factors are greater ex-ante than ex-post, that is to say, they are
greater at the moment of the investment in the new vintage than when the marginal
production capacity was already installed. We have then to distinguish the ex-ante from
the ex-post production functions.
23
The Ex-Ante Production Function
To begin with the production function for the first level of the nesting, that combines
materials and the bundle of all other inputs to produce the output in volume QY , lets
consider the following production function with constant returns to scale:
−ρa
h
−ρa
a
a
1
1
· Mt,t+1
QYt,t+1 = δM
+ δKLE
· KLEt,t+1
t
t
i−
1
ρa
1
(I.1)
Where:
• QYt,t+1 is the output in volume,
• Mt,t+1 is the amount of materials associated to vintage t at date t + 1;
• KLEt,t+1 is the amount of the bundle formed by Capital, Energy and High skilled
Labour associated to Low skilled labour on vintage t at date t + 1;
a and δ a
• δM
KLEt are the ex-ante distribution parameters;
t
• σ1a is the ex-ante partial elasticity of substitution between M and KLE with
σ1a =
1
(1+ρa1 )
.
By assumption, the capital associated to the new vintage, installed at date t, needs one
year to be productive: date t + 1.
The Ex-Post Production Function
The ex-post production function has the same CES, constant returns to scale, specification that the ex-ante function (I.4):
QYt,t+1 =
p
δM
t
·
−ρp1
Mt,t+1
+
p
δKLE
t,t+1
−
· KLEt,t+1
1
p
ρ
1
(I.2)
p
p
where δM
, δKLE
and σ1p are the ex-post parameters of the CES function, and with
t
t
σ1p =
1
. By assumption, ex-post substitution possibilities between KLE and M are
(1+ρp1 )
limited and we have σ1p < σ1a .
Ex-ante and ex-post substitution possibilities
24
Figure I.7.: Ex-ante and ex-post isoquants
To illustrate the difference between ex-ante and ex-post substitution possibilities one
can express the ex-ante and ex-post production functions in term of factor coefficients,
respectively:
−ρa
h
−ρa
i−
1
ρa
1
−ρp1
vt,t+1
−
1
ρa
1
a
a
1
1
1 = δM
· mt,t+1
+ δKLE
· vt,t+1
t
t
(I.3)
and
1=
p
δM
t
·
−ρp1
mt,t+1
+
p
δKLE
t
·
(I.4)
with :
• v = KLE
QY , the coefficient for the factors inside the bundle and
• m=
M
QY
the factor coefficient for Materials.
Figure I.7 shows that ex-post isoquants (e.p), associated with certain ex-ante technologies
on the curve (e.a), have a stronger curvature than ex-ante isoquants, reflecting that the
substitutions possibilities between the two categories of factors are reduced ex-post.
By definition, at the date of installation of the last vintage, the ex-ante and expost production functions are equal and there is only the technique (m, ν), on the exante isoquant, in common with the ex-post isoquants. This technique, defined as the
tangential technique, allows determining the exact position of the ex-post isoquant on
figure I.7.
25
One can also express the ex-post parameters in terms of ex-ante parameters and of
the tangential technique (m, ν), by identifying equations I.3 and I.4 above :
p −ρa
p
a
δM
= δM
[mt ]ρ
t
t
(I.5)
and
p
a
δKLE
= δKLE
[ν t ]ρ
t
t
p −ρa
(I.6)
Equations I.3 and I.4, that characterize the ex-ante and ex-post production technologies can also be re-expressed in terms of the tangential technique (m, ν) and of the
ex-ante parameters :
a
h
a
a
+ δKLE
gt (mt , ν t ) = δM
m−ρ
ν −ρ
t
t t
t
a
i−
1
ρa
=1
(I.7)
with gt (mt , ν t ) the ex-ante production function, in terms of factor coefficients, associated with the vintage t;
and :
h
(ρp −ρa )
a
ft,t+1 (mt,t+1 , νt,t+1 , mt , ν t ) = δM
mt
t
p
i 1
(ρp −ρa ) −ρp − ρa
νt,t+1
a
m−ρ
t,t+1 + δKLEt ν t
= 1 (I.8)
with ft,t+1 (mt,t+1 , νt,t+1 , mt , ν t ) the ex-post production function, in terms of factor
coefficients, associated to the vintage t at instant t + 1.
These characteristics of the ex-ante and ex-post technologies are also valid for the
three other levels of the production function, that exhibit also constant returns to scale.
I.2.2 The Endogeneization of Technical Change
We show in this section how, in each production sector, the firms can increase the
quality of their products, and the productivity of their inputs, by investing in R&D
activities and by buying certain amounts of innovations. The underlying idea is, as
for the optimal choice of production factors, that substitutions possibilities are greater
ex-ante than ex-post. The representative fim invests ex-ante in R&D activities to improve the quality of its products, and the productivity of its inputs, on the marginal
production capacity. Ex-post, once the marginal production capacity installed, there are
26
no possibilities for modifying the quality of products or the productivity of production
factors.
By assumption firms run in-house R&D, with constant return to scale innovation
technologies. They beneficiate of positive knowledge spillovers from R&D activities in
other production sectors, but also from other countries and from public laboratories.
They have also negative knowledge spillovers from their past innovations (fishing-out
effect), as in Jones (1995). These knowledge externalities, by modifying the productivity
of R&D, give the possibility of increasing returns to scale and endogenous growth at the
industry level, even if each representative firm operates with constant returns to scale
technologies. We have therefore six different sources of endogenous technical change in
NEMESIS. One is Hicks-neutral, with the improvement of products quality, and the other
are biased with the endogenous improvement of individual factors productivity (Capital,
, High Skilled Labour, Low Skilled Labour, Energy and Materials). This general setting
allows furthermore taking into account the possible crowding-in or crowding-out effects
between the different innovation activities. We describe first how the quality of products
is combined with the volume produced to form the output Y , and, similarly, how the
input-specific innovations are combined to the volumes of inputs used to form the efficient
inputs. We then present the innovation functions of firms and the formation of knowledge
spillovers.
The incorporation of innovations in output
In every production sectors, product innovations are incorporated in the new production
vintage. The characteristics of products, measured by the ‘marginal quality index’ , are
chosen ex-ante, and once again, we must make the distinction between the ex-ante and
the ex-post production technologies.
The ex-ante trade-off between improving products quality and increasing volume of output
In NEMESIS, the marginal production capacity, Y , is expressed in efficient units, and
it is a CES function of the production volume, QY , and of product innovations, IY :
− 1a
−ρa
ρ
0
−ρa
a
0
Yt,t+1 = δQ
QYt,t+1
+ δIaY IYt
Y
t
t
0
(I.9)
a
where δQ
and δIaY are the distribution parameters of respectively product innovations
Y
t
t
27
1
.
(1+ρa0 )
is set to 1 for the base year of NEMESIS (2000), where the levels
and output in volume, and σ0a =
By convention, IY
of production in efficient and in ordinary units (volume) are equal:Yt,t+1 = QYt,t+1 .
Furthermore, product innovations are considered as fixed inputs that become productive
one year after their date of invention.
Products characteristics are fixed ex-post and embodied in new vintages
By assumption, the technological characteristics of products are fixed ex-post, and
products innovations are embodied in the new production vintages from ex-ante optimal
production and innovation choices, that will be described later.
The marginal production capacity, measured in efficient units, is given ex-post by the
following linear production function:
Yt,t+1 = apYt QYt,t+1
(I.10)
with apt the ex-post marginal quality index of output.
The correspondence between ex-ante and ex-post decisions for producing the efficient
marginal output
It is possible, similarly to marginal output in volume, QY , to re-express equations I.9
and I.10 above, in terms of factor coefficients, respectively:
i− 1a
−ρa
ρ
0
−ρa
h
a
0
1 = δQ
qyt,t+1
+ δIaY iyt
Y
t
0
t
(I.11)
and
1 = apYt .qyt,t+1
with qy =
QY
y
and iy =
IY
y
(I.12)
successively the factor coefficients for marginal output in
volume and for products marginal quality index.
We can then, from I.11 and I.12 above, re-express apYt in terms of the tangentiel
technique (q̄yt , īyt )and of the ex-ante parameters:
"
apYt (q̄yt , īyt )
=
a
δQ
Yt
+
28
q̄y
δIaY . t
t ī
yt
#− 1a
ρ
−ρa
0
0
(I.13)
which is ex-post a fix parameter.
The incorporation of innovations in productive inputs
In NEMESIS, inputs used on the new production vintage are measured in physical
volumes, X, and in efficient units QX , with X = K, LL , LH , E and M respectively the
Capital, Low skilled labour, High Skilled Labour, Energy and Materials.
For a given marginal output in volume, QY , we have the following system of four levels
nested CES functions of productive input used:
i
δM
t
QYt,t+1 =
·
−ρi1
Mt,t+1
·
−ρi2
LLt,t+1
KLEt,t+1 =
δLi L
t
+
i
δKEL
t
+
i
δKL
H Et
·
−ρi1
KLE t,,t+1
·
−
−ρi2
KLH E t,,t+1
−ρi
−ρi
i
3
3
KLH Et,t+1 = δLi HS,t · LHt,t+1
+ δKE
· KEt,t+1
t
i
δE
t
KEt,t+1 =
·
−ρi4
Et,t+1
+
i
δKt
·
1
ρi
1
−ρi
Kt 4
−
1
ρi
4
(I.14)
−
−
1
ρi
2
1
ρi
3
(I.15)
(I.16)
(I.17)
with i = a, p for , respectively , ex-ante and ex-post production technologies. The
capital used on the new vintage, Kt , is a fixed factor that become productive after one
year (intallation delay).
We then have ex-ante, for ,X = K, LL , LH , M, E:
h
−ρx
a
a
x
Xt,t+1 = δQ
Q−ρ
Xt,t+1 + δIX IXt
X
t
t
i−
1
ρx
.
(I.18)
The ex-ante efficient units of inputs are CES combinations of the volume of the factor
used and of factor efficiency indexes, IX , with IX = 1 for the base year of NEMESIS
(2000). The factor efficiency indexes act as fixed factors, with an installation delay of
one year, as for physical capital.
By assumption, ex-post, efficient units of inputs are linear functions of the volume of
factors used:
Xt,t+1 = apxt QXt,t+1 .
29
(I.19)
Finally, the ex-post productivity parameters of inputs, apXt , can, , similarly than for the
ex-post marginal quality index of output, be expressed in terms of the ex-ante parameters
and of the tangential techniques (q̄xt , īxt ):
a
a
−ρx
x
apxt (q̄xt , īxt ) = δQ
q −ρ
xt + δIX īxt
X
t
with qx =
QX
X
and ix =
IX
X
−
1
ρx
t
(I.20)
respectively the factor coefficients for volume of inputs
and factor specific innovations used on the new production vintage.
The marginal productivity of inputs in volume, apxt , that depends only of the choice
of the tangential technique ex-ante (date t), is consequently constant ex-post.
The innovation functions
The innovation indexes for output and inputs on last production vintage, Ij,t , are modelled is NEMESIS as innovations stocks:
Ij,t = Ij,t−1 + innovj,t
(I.21)
with j = Y, K, LL , LH , E and M , and where innovjt are the new innovations produced at date t.
The flow of innovations innovj,t , is produced with the following constant returns innovation function:
innovj,t = αj,t · RDj,t
(I.22)
where RDj,t and αj,t are respectively the R&D expenditure at constant prices of the
representative firm for the innovation type j, and the R&D productivity.
The originality of this formulation is that research productivity in one sector and one
category, j, of innovation, αj,t , is influenced by two externalities, as in Jones (1995):
αj,t = αj
KN OWj,t
N Ej,t
(I.23)
with αj a constant and positive parameter, KN OWj,t the knowledge stock of the sector
for innovation type j, and N Ej,t the ‘Research Difficulty’ index that is a positive function of all past successful innovations realized by the sector (Jones (1995), ’Fishing-out’
effect):
30
N Ej,t = (Ij,t−1 )βj
with βj a positive parameter.
The knowledge externality, KN OWj,t , reduces R&D costs by innovation, and reflects
the fact that if innovations are specific to sectors who produce it, the technological
knowledge is to a large extend common to all sectors and all countries.
This modelling of research productivity, αj,t , is particularly important in NEMESIS for
the reason that if the knowledge externality, KN OWk,j,t grows faster than the Research
Difficulty index, N Ej,t , research productivity will increase in time. We will then have
increasing returns to scale, at the global level, while representative firms are supposed to
operate with constant return to scale production and innovation functions, compatible
with pure and perfect competition on all product markets. In this case, every policies
stimulating R&D expenditures will have long term positive impacts on the growth rate
of the economy, while these impacts will stay limited in time in knowledge externalities
do not grow fast enough to compensate the rising difficulty in time of innovating.
The modelling of knowledge spillovers
In NEMESIS knowledge externalities result from past R&D expenditures with the following generic accumulation for R&D:
SRDt = (1 − δ) · SRDt−1 + RDt−τ
(I.24)
with SRDt the R&D stock at date t, δ the ’radioactive’ rate of decay and τ > 0
measuring the delay for R&D expenditures to transform into formal knowledge that will
influence the productivity of R&D, αt .
R&D expenditures are realized by private firms and by public universities and research
centers with a repartition, in 2008, of respectively 60% and 40% for private and public
R&D in EU-27. These two sources of research externalities have distinct impacts on
economic performance of European firms, private R&D being more oriented toward industrial applications of inventions, and public R&D toward basic research. Econometric
studies reveal to that extent a greater contribution to economic growth of basic research
compared to applied research, but also greater maturation delays. In NEMESIS, τ was
set to one year for private R&D expenditures, which implies that, if one takes also into account the one year delay for innovations to be introduced in production, that knowledge
31
externalities from private origin will influence economic performance with an average lag
of two years. From knowledge externalities coming from the public sector, τ was set to
3 and it needs four years for public research to influence economic performance.
Knowledge spillovers from private R&D expenditures are measured in NEMESIS with
Johnson (2002) OECD Technology Concordance (OTC) that transforms patent applications data, based on the International Patent Classification (IPC), into patent counts by
sector of the economy. OTC is a matrix that is used for dispatching the R&D performed
by the industrial sectors (’Industries of Manufacture’) in the sectors that will the most
likely use the process or product innovations that they realize (’Sectors of Use’). From
this methodology, knowledge externalities flow from industrial sectors to industrial sectors themselves and toward service sectors, but there are no externalities from service
sectors toward industrial sectors. This is a limitation due to IPC definitions that do
not include innovations in softwares and in services. OTC matrices were calculated for
NEMESIS at country level, from European Patent Office (EPO) database.
Knowledge spillovers from public research are sent to sectors with a ’grandfathering’
approach consisting in a split of public knowledge stock between sectors proportionnal to
their share in total private R&D expenditure. This approach was retained in NEMESIS
for the reason that there do not exist precise information in existing databases on how
the public R&D contribute to sectoral innovation performance. There exist data in
EUROSTAT on the repartition of public R&D by socio-economic objectives, but this
repartion doesn’t help much for retrieving the amount of spillovers that flow to economic
sectors. Our assumption of grandfathering follows then the idea that these spillovers at
sectoral as important that the specific sectors are engaged in R&D activities; also, we
did supposed in NEMESIS that research
For knowledge externalities from foreign sources, NEMESIS uses trade flows of goods
and services. The assumption is that knowledge transfers between countries are bare
by traded goods, that is to say by the imports realized by the country that receives the
externality. For illustration, the intra-sectoral knowledge spillover for a country i and a
sector s (source 1) is measured in the following way:
s
SRDi,s,j,t
=
X
P RODi,s,t
IM P i,c,s,t
.SRDi,s,j,t +
P RODi,s,t +IM Pi,s,t .SRDc,s,j,t
P RODi,s,t + IM Pi,s,t
c6=i
,
where P RODi,s,t is the production of good s in country i, IM Pi,s,t is the total imports
of good s by country i, IM P i,c,s,t is the import of good s in country i from country c,
32
SRDi,s,j,t−τj in the R&D stock of sector s in country i and SRDc,s,j,t−τj the R&D stock
in the foreign country c. The R&D stock have the following generic equation:
SRDs,j,t = (1 − δs ) · SRDs,j,t−1 + RDs,j,t−τj .
(I.25)
For one sector s in a country c:
In each country c, the knowledge in a sector s accumulates accordingly to the following
formula:
PN
PF
−I
I
KN OWc,s,j,t = SRDc,s,j,t
+ SRDc,s,j,t
+ SRDc,s,j,t
+ SRDc,s,j,t
where:
I
1. SRDc,s,j,t
represents the past R&D efforts realized in the production sector, by
national and foreign fims. It is the intra-sectoral knowledge spillover;
−I
represents the past R&D efforts realized in the other production sectors.
2. SRDc,s,j,t
It is the inter-sectoral knowledge spillover;
PN
3. SRDc,s,j,t
represents R&D externalities coming from the public laboratories in the
country, that beneficiate to the sector;
PF
4. SRDc,s,j,t
represents finally R&D externalities emanating from public laboratories
in foreign countries, that beneficiate to the sector.
Johnson D. (2002), The OECD Technology Concordance (OTC): Patents by Industry
of Manufacture and Sector of Use, OECD working paper n° 2002/5.
I.2.3 Estimation results
This section presents the estimation of the new production block for the NEMESIS
model. We estimated the aggregated ex-post production function for output and inpurs
measured in efficiency units. We start this section by presenting the econometrical
specifications of the production block and the construction of data we used. In the third
part we present the econometric results. The fourth implements shock price simulations
in order to test the robustness of the estimations and to obtain the direct elasticities of
substitution between factors of different bundle. Finally, we discuss issues concerning
the ex-ante estimates.
33
Estimation specifications for the efficient form
In this part we estimate the production block only for the specification of input and
output in efficient units. The determination of the whole block with biased technical
change will be realized using calibration methods. We estimate the five factor demand
equations with the FIML method (Full information maximum likehood). FIML is the
asymptotically efficient estimator for linear and nonlinear simultaneous models, under
the assumption that the disturbances are multivariate normal.
Bundle prices and production prices used in the regression are the unit costs of production. For instance, for the last level, we consider:
h
1−σ4 σ4
σ4
PKE = PK
δK + PE1−σ4 δE
i
1
1−σ4
We define the distribution parameter with the share value of the input, for example,
if we consider the materials:
δM =
PM · M
PM · M + PKELLS LHS · KELLS LHS
To avoid problems of endogeneity, we use lagged values for input and prices in the
distribution parameter δ.
From a technical standpoint, we add a scale parameter which takes into account
independent technical progress, named A (in a non-biased form). Thus, we use an
exogenous form, which takes a deterministic linear trend, i.e. A = A0i,c et where A0i,c
the scale parameter and et is the technology growth rate.
Because factors do not adjust immediately we need to take into account adjustment
delays. Thus, we transform the factor demand equation into the following:
"
log (Ms,t ) = ρM,s log Y s,t − log
!
X
dc A0s,c − αs · t + σ1,s log
c
PY ,s · δM,s
!#
PM,s
+ (1 − ρM,s ) · log (Ms,t−1 )
Where s is the sector index, c the country index and ρ is the time adjustment paramis the time necessary for an adjustment at a 50% level.
eter. Thus, 1−ρ
ρ
Finally, we consider a sector for all countries, with the underlying assumption that
the elasticities of substitution are common to all countries, but not between sectors. In
order to measure certain country specificities, a dummy variable is introduced next to
34
the scale parameter, which therefore represents a fixed effect for each sector and country.
Low and high skilled labour demand data
In order to estimate the production block we have to carry out the construction of data
on labour demand by skill. Specifically we construct two types of data: one related to
the share of employees or total employment and the other one on the labour remunerations. In addition to being used for the production block estimation, the data will be
incorporated into the NEMESIS model. As already mentioned, we define two categories
of job skills:
• High skilled labour corresponding to the INSEAD5 to INSEAD6 classes
• Low skilled labour that corresponds to the INSEAD1 to INSEAD4 classes
The sectoral dimension of the NEMESIS model implies the availability of information
on employment by skills at the sectoral level, and there are few complete data sources
on this subject. According to our knowledge, only two databases provide such dataset:
• The Eurostat database which provides skilled labour data by age, gender, occupational jobs, etc . . . but only at the national level
• The EU-KLEMS database in which we can find the share of labour employment
and the share of labour compensation by skills at a sectoral level.
Thus, the EU-KLEMS database seems to be well suited, regarding its sectoral dimension,
to be used as a basis for the NEMESIS model. However the skills definitions are different
between each country, leading to a consistency problem that could imply surprising
results in the model. The Eurostat database is homogenous but provides data at the
national level only. As a consequence, we choose to use the sectoral information provided
by EU-KLEMS and to apply a national correction using the Eurostat database.
We present in the following sections how these corrections were made to obtain the
share of low and high skilled labour on total employment and employees and the share
of low and high skilled labour on total labour compensation at the sectoral level, and
we illustrate our corrections with statistical figures.
Employment and employees data
35
Figure I.8.: EU National share of high skill on total employment, in 2005 (source:
Eurostat)
40%
35%
30%
25%
20%
15%
10%
5%
0%
BE EE
FI NO DK ES
IE
UK NL LT LU SE FR DE EU GR LV
SI
PL HU AT SK MT
IT
CZ PT RO
National shares (Eurostat)
eurostat )
Starting from the Eurostat database, we calculate the share of high skilled (SX,HS,C
eurostat ) labour demand at national levels as follows:
and low skilled (SX,LS,C
0
eurostat
SX,HS,C
HSc
=
0
HSc + M Sc0 + LSc0
eurostat
eurostat
SX,LS,C
= 1 − SX,HS,C
(I.26)
(I.27)
Where c is the country index, X = EM P, SAL the total employment or the total
0
0
0
employees and HS , M S and LS are respectively the INSEAD1-2, INSEAD3-4 and
INSEAD5-6 in Eurostat.
The Figure I.8 shows the share of high skilled labour in total employment according
to the Eurostat database for each EU countries. We find the share to be superior to 35%
in Belgium, Estonia and Finland with respectively 36.8%, 35.9% and 30.1% whereas the
lowest shares are inferior to 15% with 14.7% in Italy, 14.6% in Czech Republic, 13.4%
in Portugal and 12.6% in Romania. In average, the European share of high skilled jobs
in the total employment is about 25% in 2005.
Sectoral levels (EU-KLEMS)
36
EU KLEM S ) and low skilled (X EU KLEM S )
We also calculate the total high skilled (XHS,C,S
LS,C,S
labour and the total high skilled and low skilled number employees, at the sectoral level
using EU-KLEMS database.
EU KLEM S
EU KLEM S
N EM ESIS
XHS,C,S
= SHS,C,S
· XC,S
EU KLEM S
EU KLEM S
N EM ESIS
XLS,C,S
= 1 − SHS,C,S
· XC,S
(I.28)
(I.29)
EU KLEM S the share of hours worked by high skilled
Where s is the sectoral index, SHS,C,S
workers in the EU-KLEMS database once converted to the NEMESIS sectoral nomenN EM ESIS , the total employment/employees from the NEMESIS database
clature and XC,S
(Eurostat being the original source).
We made here an important assumption (see equations I.28 and I.29); since we assumed
that high skilled and low skilled workers work the same time by employment unit. This
represents of course a relatively important hypothesis, but the lack of information and
data on this subject does not allow us to overcome this issue.
We present in Figure I.9 the share of high skilled workers in total employment for the
year 2005 at the European sectoral level, as deduced from the previous computation.
First, we can observe very low level of the high skilled labour in almost all sectors.
The sector that employs the most highly skilled workers is the services sector where
the share can reach between 25% and 30% of the total labour demand. Although the
sectoral repartition seems relatively logical, the levels appear to be very low.
Sectoral shares and sectoral levels (Eurostat & EU-KLEMS)
We now calculate the sectoral shares of high skilled and low skilled labour (
EU KLEM S , δ EU KLEM S )
δX,HS,C,S
X,LS,C,S
in the national total employment and in the total number of
employees using the EU-KLEMS database:
EU KLEM S
XHS,C,S
EU KLEM S
δX,HS,C,S
=P
EU KLEM S
C XHS,C,S
(I.30)
EU KLEM S
XLS,C,S
EU KLEM S
δX,LS,C,S
=P
EU KLEM S
C XLS,C,S
(I.31)
N EW , X N EW
We then compute the national levels for high skilled and low skilled labour (XHS,C
LS,C
EU ROST AT ) and taking the national labour and
) using the Eurostat national share (SX,HS,C
37
Figure I.9.: European high skill share in total employment for NEMESIS sectors, (source
EU-KLEMS)
35%
30%
25%
20%
15%
10%
5%
C
oa
l a Ag
O nd ri.
il
& C ok
G
as e
G Ex
as t.
D
R
ef ist
in
ed .
Fe
O
il
rr.
&
W E
no at lec
e
N nF r .
on
S
e
M rr. up.
et
M
.M e
t
in als
.
C Pro
he
m d.
Ag Me ica
r. tal ls
&
P
In rod
d
O .M .
ffi
c ach
El e M .
ec ac
h
T t.
Fo ran G o .
od sp od
.
Te , D E s
q
x.
, C rink uip
.
Pa loth & T
p. . & ob
.
&
F
R Pri oot
ub nt
w
be . P .
r & ro
d
O Pla .
th
er stic
C M
on an
st uf
r
.
D uct
i
Lo stri ion
dg bu
t
io
.
n
In &
Se lan Cat
er
d
a
T
O & A ra .
th
er ir T nsp
Tr ra .
n
a
C ns sp
om p. .
S
Ba mu er
v
n
O nk, ica .
th
t
er Fin ion
.
M
&
N
on ark Ins
M et S .
ar
ke erv
tS .
er
v.
0%
number of employees (XCN EM ESIS ) of the NEMESIS model.
N EW
EU ROST AT
XHS,C
= SX,HS,C
· XCN EM ESIS
N EW
EU ROST AT
XLS,C
= 1 − SX,HS,C
· XCN EM ESIS
(I.32)
(I.33)
N EW and X N EW ) and the share of high skilled and low
Using these national levels (XHS,C
LS,C
EU KLEM S and δ EU KLEM S ), we can calculate the new sectoral levels for
skilled labour (δX,HS,C,S
X,LS,C,S
N EW ) and low skilled (X N EW ) labour:
high skilled (XHS,C,S
LS,C,S
N EW
EU KLEM S
N EW
XHS,C,S
= δX,HS,C,S
· XHS,C
(I.34)
N EW
EU KLEM S
N EW
XLS,C,S
= δX,LS,C,S
· XLS,C
(I.35)
Final correction
Finally, to achieve the consistency between countries, we compute the new shares by
N EW and δ N EW ) at the sectoral level, using the employment and the number
skills (δHS,C,S
LS,C,S
N EW and X N EW ). These shares will be used to
of employees previously calculated (XHS,C,S
LS,C,S
38
Figure I.10.: European high skill share in total employment for NEMESIS sectors
45%
40%
35%
30%
25%
20%
15%
10%
5%
C
oa
la A
O nd gri.
il
& Co
G ke
as
G Ex
a
R s D t.
ef is
in t.
ed
Fe
O
rr.
i
&
W
El l
n
e
N on ate c.
r
on F
S
M err. up
et
. M Me .
in tals
C . Pr
he o
d
Ag Me mic .
r. t a als
& lP
In ro
d
O . M d.
ffi
c a
El e M ch.
ec a
c
Fo Tra t. G h.
od nsp oo
Te ,
d
.
x. Dr Eq s
, C in u
k
Pa lot & ip.
p. h. & To
b.
&
R P r F oo
ub in
be t. P tw .
r & ro
O P l d.
t h as
e
t
C r M ic
on a
st nu
D ruc f.
Lo istr tio
dg ibu n
t
.
I & io
Se nla Ca n
a nd ter
O & T
th A ra .
er ir ns
Tr Tra p.
C an s ns
om p p.
.
Ba mu Se
O nk nic rv.
t h , F at
er in io
n
N Ma . &
on rk In
M et s.
ar Se
ke rv
tS .
er
v.
0%
compute data on high skilled and low skilled labour at the sectoral level.
N EW
δHS,C,S
=
N EW
XHS,C,S
N EW + X N EW
XHS,C,S
LS,C,S
N EW
N EW
δLS,C,S
= 1 − δHS,C,S
(I.36)
(I.37)
Figure I.10 shows the corrected European high skilled labour share in total employment at the sectoral level for the year 2005. We can see that once corrected the services
sector has a high skilled labour share between 35% and 45%, whereas it was only about
25% and 30% before the correction.
Figure I.11 give some illustration of the high skilled labour share in total employment
in “Agriculture”, “Chemicals” and “Bank, Finance and Insurance” sectors across the
EU countries. Looking at “Agriculture”, the highest share of high skilled labour is in
Estonia with 27.3% followed by Finland, Norway and Latvia with 25.4%, 23.2% and
22.6% respectively. We find the lowest share for agriculture in Italy and Austria with
1.7%. If, we look at the “Bank, Finance and Insurance” sector, the highest shares are
in Sweden, Belgium and Poland with more than 50% whereas the lowest is about 16%
in Italy.
39
Figure I.11.: Sectoral illustration of the final results
65%
60%
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
AT
BE DE DK ES
FI
FR GR
Agriculture
IE
IT
LU
NL
PT SE
Chemicals
UK CZ EE HU
LT
LV
MT
PL RO
SI
SK NO
Bank, Finance and insurance
Compensation of employees data
Eurostat and EU-KLEMS data
A similar calculus is realized to correct for the EU-KLEMS shares of high skill and
EU KLEM S ). We start by comlow skill in the total compensation of employees (θHS,C,S
puting EU-KLEMS compensation of employees for high skilled and low skilled labour
N EM ESIS and COM P N EM ESIS ) at the sectoral level, using the EU-KLEMS
(COM PHS,C,S
LS,C,S
N EM ESIS - Eushares and the NEMESIS sectoral compensation of employees (COM PC,S
rostat being the original source).
EU KLEM S
N EM ESIS
EU KLEM S
COM PHS,C,S
= COM PC,S
· θHS,C,S
EU KLEM S
N EM ESIS
EU KLEM S
COM PLS,C,S
= COM PC,S
· 1 − θHS,C,S
(I.38)
(I.39)
Thus, using the EU-KLEMS data on high skilled and low skilled employees previously
computed (equations I.28 and I.29), we can find the EUKLEMS cost per employee.
40
Figure I.12.: Ratio of European employee unit cost between high and low skills at sectoral
level
2.25
2
1.75
1.5
1.25
1
0.75
0.5
0.25
C
oa
la A
O nd gri.
il
& Co
G ke
as
G Ex
a
t
R sD .
ef is
in t.
e
Fe
d
O
rr.
il
&
no Wa Ele
N n ter c.
on F
S
M err . up
et
. M Me .
in tals
C . Pr
he od
Ag Me mic .
r. t a als
& lP
In r o
d
O . M d.
ffi
c a
E l e M ch .
e
T ct ac
Fo ra . G h.
o
o n
Te d, sp. ods
x. Dr Eq
, C in u
k i
Pa lot & p.
p. h. & To
b.
&
R P r Fo o
ub in
be t. P tw.
r & ro
O Pl d.
th as
e
t
C r M ic
on a
st nu
D ruc f .
Lo istr tion
dg ibu
t
.
In & ion
Se la Ca
a nd ter
O & T
t h A ra .
er ir ns
Tr Tra p.
C ans ns
om p p.
.
Ba mu Se
O nk nic rv.
t h , F at
er in io
n
N Ma . &
on rk In
M et s.
ar Se
ke r v
tS .
er
v.
0
EU KLEM S
U CHS,C,S
=
EU KLEM S
U CLS,C,S
=
EU KLEM S
COM PHS,C,S
KLEM S
SALEU
HS,C,S
EU KLEM S
COM PLS,C,S
KLEM S
SALEU
LS,C,S
(I.40)
(I.41)
The unit costs by skill at the sectoral level allow us to compute the ratio between high
and low skills compensations at the sectoral level for each country.
KLEM S
µEU
=
C,S
EU KLEM S
U CHS,C,S
EU KLEM S
U CLS,C,S
(I.42)
Figure I.12 presents the ratio of European employee’s unit cost between high skill and
low skill at the sectoral level. In average, we can see that high skilled workers are paid
between 50% and 100% more than low skilled ones. For instance, we can observe that
in the “Electrical Goods” sector, a high skilled employee cost 81% more in Europe than
a low skill employee.
Final correction
41
N EW )
Now, we compute the corrected compensation of employees for the high (COM PHS,C,S
N EW ) skills with the corrected shares of high and low skilled
and low (COM PLS,C,S
EW
N EW
employees,SALN
HS,C,S and SALLS,C,S (see equations I.34 and I.35), the NEMESIS comN EM ESIS ) and the cost ratios computed just before.
pensation of employees (COM PC,S
N EW
COM PHS,C,S
=
EW
SALN
HS,C,S
EW
SALN
HS,C,S
N EW
COM PLS,C,S
=
+
EW
SALN
LS,C,S
N EM ESIS
KLEM S
· COM PC,S
· µEU
C,S
EW
SALN
LS,C,S
EW
SALN
HS,C,S
+
EW
SALN
LS,C,S
N EM ESIS
· COM PC,S
(I.43)
(I.44)
Finally, using both last results we compute the corrected share of high and low skill
in the total compensation of employees that will be used for rest of the study
N EW
=
θHS,C,S
N EW
COM PHS,C,S
N EW + COM P N EW
COM PHS,C,S
LS,C,S
N EW
N EW
θLS,C,S
= 1 − θHS,C,S
(I.45)
(I.46)
Figure I.13 displays the corrected European share of employees’ compensation for high
skill at the sectoral level. Thus in average, the share of high skills’ cost in the total cost
of employees is between 20% and 25% on average, but it is more than 40% in “Bank,
Finance and Insurance”, “Other Market Services” and “Non Market Services” sectors,
with respectively 40%, 53% and 45%.
Labour input and labour cost in the production function
Labour input
Low and high skilled labour (LLS and LHS ), are the product of their share in total
employees and the number of hours worked:
employees
LLS,C,S,t = HEM P EC,S,t · δLS,C,S,t
employees
LHS,C,S,t = HEM P EC,S,t · δHS,C,S,t
42
Figure I.13.: Corrected European share of compensation of employees for high skill at
sectoral level in 2005
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
C
oa
l a Ag
O nd ri.
il
& Co
G ke
as
G Ex
as t.
R
ef Dis
in t.
ed
Fe
O
rr.
il
&
W E
no a lec
N n ter .
on Fe S
M rr . up
.
et
. M Me
in tals
.P
C
he rod
m .
Ag Me ica
r. tal ls
&
P
In ro
d
d
O .M .
ffi
c ac
E l e M h.
e
a
T ct. ch
Fo ran Go .
od s p od
Te ,
.
s
x. Dri Equ
,C n
k ip
Pa loth & T .
p. . & ob
&
F .
R Pri oo
ub n
t
be t. P w.
r & ro
d.
P
O
th las
e
t
C r M ic
on an
s t uf
r
D uct .
Lo istri ion
dg bu
ti
.
In & on
Se lan Ca
te
d
a
O & Tr r.
t h A an
er ir
s
Tr Tra p.
a
C ns ns p
om p. .
Ba mu Ser
n
O nk, ica v.
th
er Fin tion
.&
M
N
on ark In
M et S s .
ar
k e er v
tS .
er
v.
0%
Where HEM P E is the total number of hours worked by employees (millions) or the
N EM ESIS and δ employees is the corrected share of high-skilled persons engaged
previous XC,S,
HS,C,S,t
(share in total hours) which has been computed previously.
Labour cost
Concerning the cost of labour i.e. PLLS and PLHS , we use the same method as for
the labour input:
PLHS,C,S,t = COM PC,S,t · θHS,C,S,t
PLLS,C,S,t = COM PC,S,t · θLS,C,S,t
Where COM P is the Compensation of employees (in millions of Euros) or the previous
N EM ESIS and θ
COM PC,S
HS,,C,S is the high-skilled labour compensation (share in total
labour compensation) which has been computed previously.
43
Estimation results for the efficient specification
The data sample includes 11 countries over 12 years (from 1989 to 2000) for each of
NEMESIS’ 30 sectors. The sample of countries is restricted to 11 due to the lack of
data over a long period for most European countries. Countries included in the sample
are: Austria, Belgium, Denmark, Germany, Finland, France, Italy, Netherland, Spain,
Sweden and the United Kingdom.
Due to lagged values, the sample data is restricted to 11 years, which gives 121 observations per sector. As mentioned previously, we use a pooled panel estimation method
with the FIML estimator.
Estimations are made sector by sector. The small sample size imposes that the parameters are common to all countries (but can vary by sector) with the exception of the scale
parameters. We estimate four elasticities of substitution, five delays, one technological
trend and 11x5 scale parameters for each sector.
Specifically, we estimate sector parameters in three stages. First, we estimate the scale
parameters and technological trends with values constraints on other parameters. In the
second step, we relax the constraints on elasticities. Finally, we relax all constraints on
the parameters.
If some estimated values give an unrealistic result which does not allow convergence,
we constrain them to an extreme value. For example, the Chemical sector (sector 5),
presents an estimated value for sigma-1 close to zero. We constrain it to 0.05 so that it
corresponds to our limit value, allowing some substitution between factors. The most
constrained parameters appear for the delay parameter on capital. In this case, the delay
seems to be infinite. The constrained value corresponds to a median delay of 20 years.
Table 18??? reports estimates of the four substitution elasticities. Constrained parameters take the value n.a. for their P-Value. All elasticities are positive, highlighting
a positive substitution effect between factors for all sectors. All sector elasticities lie
between 0 and 1, meaning that factors and factor bundle on the same level, are gross
complements (for elasticity values higher than 1 we talk about gross substitutes). Results differ between sectors but some trends are well identified. The first and fourth
levels present lower elasticities values than intermediate levels. Labour (both skills) is
more easily substitutable than other factors.
Table 19 ??? reports estimates of the technological trend and delays. It appears that
only a few technological trends are significant (only 15 .T. are significant at 10%). On
the 15 significant values, 9 exhibit positive estimated parameters. Since technological
44
advances are very different between countries, common technological trends are neither
relevant nor significant. Delays are highly significant and there is a common trend
between sectors. Materials and Labour have a short adjustment delay (a mean of 0.9
years for Materials, 0.7 for low skilled and 1 for high skilled Labour). Energy presents a
medium delay of adjustment due to its high complementarity with Capital (a mean of
1.8 years). Capital exhibits a relatively long delay (a mean of 14 years).
Table 20 reports the R-Squared of the estimations. The R-Squared are as a whole
relatively high, partly because we use panel data with individual fixed effect. In this
case an important part of the variability between observations is explained by these fixed
effects.
Table 27 presents all scale parameters (Ao,s,c for M, LLS, LHS, K and E). The poor
quality of the data for a long period enables us only to present results for 11 countries.
Besides, one shoud recall that these previous data are the results of econometric estimations and are not a necessary condition for the implementation of the 27 countries
into the model. Since the scale parameters are sectoral, they are substituted with the
calibrated variables (which only need data for one year).
Independent Simulations
In order to assess the robustness of our estimations and to determine the direct elasticities
of substitution between all factors, we simulate the demand factor evolutions in response
to different price shocks. Table 21 to Table 26 present these simulations, which are
independent of the rest of the NEMESIS model. We increase each factor price by 10%
and we report the factor demand evolutions. Results are consistent with theory: direct
price elasticity is negative and the sign of indirect price elasticity depend of the synergy
between factors. An increase of a factor price will induce a decrease in the demand of
this factor, an increase in subsidiary factors and a decrease in complementary factors.
All factors are subsidiary (negative indirect elasticities), except for energy and capital
which are complementary for most of the sectors.
Estimations of the Substitution Possibilities Ex-ante and Ex-post
For the vintage and ex-ante/ex-post approach, we should use the following methodology to distinguish and estimate the different parameters of the ex-ante and ex-post
45
production functions.
Ex-post, the technical level being fixed, we then just have to estimate or calibrate the
parameters of the factors’ (or bundle of factor) efficiency demands. So we could use the
previous values of substitution elasticities σ p . Moreover the ex-post parameters δip are
endogenously calculated with the tangential technique as describe previously. We obtain
the following parameters, for example, in the first level of the nested CES:
p
a
δM,t+1
= δM
[At+1 · mt+1 ]ρ
p −ρa
and
p
a
δKEL
= δKEL
[At+1 · ν t+1 ]ρ
HS LLS
HS LLS ,t+1
p −ρa
In the ex-ante side we have to determine also the substitution elasticities σ a that are
superior to the σ p previously estimated. Referring to Meijers and Van Zon (1994), we
could find σ a ≈ 1.5 · σ p . As achieved in the article by Meijers and Van Zon, we could be
more accurate and determine the ratio σ a /σ p by sectors.
Furthermore, the ex-ante parameters δia are defined like in the example corresponding
to the first level of factor efficiency demand of the nested CES:
a
δM,t
=
PM,t−t · Mt−t
PM,t−t · Mt−t + PKELHS LLS ,t−t · KELHS LLS,t−t
Finally, contrarily to the ex-post functions, we need to determine the parameters
relative to the biased technical change. That is to say the parameters of the following
function for each factor and for the global production:
1
−ρI − ρI
I
It = δQI,t · Q−ρ
I,t + δI t · I t
Physical volume I and Technological level QI equation parameters could be calibrated
using patent data for the level of productivity and with the European Community Innovation Survey (CIS). Using the amount of R&D expenditures and the CIS or patent
data, we could split these expenditures between factors and global productivity or product. After that, using the innovation function, we can determine the parameters δQI
that represent the weight in the cost of the quality or productivity improvement. The
parameters σ should be determined the same way as describe above.
46
Section I.3
Households’ final consumption
The consumption behaviour is divided in two stages. The first is the agregate consumption that splits households’ incomes in consumption and global saving. At the second
stage, the agragate consumption is allocated in 27 consumption functions.
I.3.1 Aggregate consumption
At the begining of the aggregate consumption equation, there is the model of Davidson et al. [92] in which the consumption is linked to income and wealth by an Error
Correction Model. The econometrics estimate at first the long term relationship, then
the dynamics.
In the first version of the model, the wealth was represented by a permanent income
function that was computed as a mean of the lagged revenues
2
; later, the cumulated
investment in dwelling was used as a proxy for the housing stock of households, and was
added to the wealth effect.
Other significant variables on the link between wealth on different support and consumption are interest rates and inflationary pressures. The unemployment rate is used
as a proxy for the degree of uncertainty in the economy.
Researches on the aggregate consumption are always going on, and they are now
extended for the NEMESIS model in two directions : at first the building of a genuine
wealth variable in a forward looking module that would be isolated from the rest of
the model ; aggregate consumption function could the be the result of two type of
behaviours ; that of “liquidity constrained” households which is founded on the current
revenue ; that of “neoclassical households” which can borrow or lend liquidities without
restrictions and which is grounded on wealth, discounted sum of future revenues.
This structure could allow to focus attention on the effects of financial liberalization on
consumption. CARRUTH and HENLEY [55] ; this focus could be achieved in increasing
with liberalization the past of “neoclassical households” SEFTON and VELD[288].
Co-Integrating Long term equation
2
The long run elasticity of consumption in relation to incomes has been set to one to ensure that the
lifecycle theory is fulfilled
47
I.3. HOUSEHOLDS’ FINAL CONSUMPTION
ln
CON SN AT N Qc
P OPc
=
lrscnn0c
+
lrscnn1 · ln
IN CGDISPc
P CON SN AT T OTc
P OPc
+
P OP RETc
lrscnn2 · ln
P OPc
P OP CHIc
lrscnn3 · ln
P OPc
lrscnn4 · ln(RRLRc )
+
lrscnn5 · DU M 97
+
+
Dynamic equation
∆ ln
CON SN AT N Qc
P OPc
=
crscnn0c
+
+
+
+
crscnn1 · ∆ ln
IN CGDISPc
P CON SN AT T OTc
P OPc
P OP RETc
P OPc
P OP CHIc
crscnn3 · ∆ ln
P OPc
crscnn4 · ∆ ln(RRLRc )
crscnn2 · ∆ ln
+
crscnn5 · ∆ ln
P CON SN AT T OTc
P CON SN AT T OTc−1
CON SN AT N Q−1
c
+
crscnn6 · ∆ ln
+
crscnn7 · ERR−1
+
crscnn8 · DU M 97
with:
•
P OPc , Population
•
IN CGDISPc , Gross Disposable Income
•
P CON SN AT T OTc , Consumers’ Price
•
P OP RETc Retired Population
•
P OP CHIc Child Population
•
RRLRc Interest Rate
•
ERR, the Error Term
•
DU M 97, a dummy variable
48
P OPc−1
Parameters Restrictions:
lrscnn4 < 0
crscnn1 > 0
crscnn4 < 0
crscnn5 < 0
0 < crscnn6 < 1
0 > crscnn7 > −1
I.3.2 Allocation of aggregate Consumption
We will present in this section the theoretical and empicical grounds of the system
that will allow to disaggregate the macroeonomic consumption determined above. The
basic The presentation thereof are from Bracke I. and Meyermans E. [38]. The only
difference with respect to their work as far as the econometric analyze is concerned,
is that now panel estimation is applied instead of ’individual’ OLS regressions. The
econometric allocation system is derived from the theory of rational consumer and restrictions imposed by it are implemented in a flexible way thanks to a CBS version of the
system. The total aggregate consumption is therefore divided into 27 components as a
function of relative prices and total income (to which are added demographic changes).
Furthermore, that allocation module assumes groupwise separability, meaning that the
consumer faces a decision problem in several stages. In the particular, the representative
consumer decides, in a first stage, how much he will spend on "durable and complementary non-durable goods" on the one hand and on "other non-durable goods" on the other
hand. In a second stage, he decides how to spend the money allocated in the first stage
within the group i.e. how much of the amount dedicated to the durable goods will be
allocated to clothing, household utilities and transportation. Transportation includes
public transportation, equipment (such as cars) and energy, divided into petrol, heavy
fuel and oil. A further decision stage takes place in the non-durable goods group. It
consists of the choice between "necessities" (including food, beverages, tobacco, education, rent, health, electricity and other expenditure items) and "luxuries"(including
communication, tourism and domestic services).
Based on the CBS parametrization, the long-run equilibrium relationship is:
49
Figure I.14.: Allocation of Durable Goods
04 Clothing and footwear
D Durable goods
10 Furnitures etc
11 Households textile
12 Major appliance
13 Hardware
14 Household operation
15 Domestic services
FUR Furniture and equip.
17 Cars etc
T Transport
18 Petrol etc
19 Rail Transports
20 Buses and coaches
21 Air Transports
22 Other Transports
OT Purchased Transport
Figure I.15.: Allocation of Non Durable Goods
FB Food Bev. and tob.
01 Food
02 Beverages
03 Tobacco
05 Gross rent and water
06 Electricity
07 Gas
08 Liquid Fuels
09 Other Fuels
NEC Necessities
FU Fuel and power
16 Medical care
ND Non Durable goods
23 Communication
24 Equipment and accessories incl repair
25 Recreation
26 Hotel and restaurant
27 Misc. Goods and Services
LUX Luxuries
wc,i ln
CON Sc,i
IN CRDISPc
= cc,i + bi ln(IN CRDISPc ) +
27
X
si,j · ln (P CON Sc,j )
j=1
+ g1,i ln(DEM Pc ) + g2,i ln(DEM Wc ) + ϑc,i
50
and the short-run one is:
CON Sc,i
wc,i ∆ ln
IN CRDISPc
= bsi ∆ ln(IN CRDISPc ) +
27
X
ssi,j · ∆ ln (P CON Sc,j ) +
j=1
26
X
j=1
+ hs1,i ∆ ln(DEM Pc ) + hs2,i ∆ ln(DEM Wc ) + uc,i
where :
• i, j = 1 to 27 consumption categories
• c = 1 to 26 countries
• CON S consumption of commodity (1995m euros)
• IN CRDISP regional real personal disposable income (1995m euros)
• P CON S commodity price
• DEM W share of people of working age in total population
• DEM P share of old age people in total population
More specifically, under groupwise separability, the equations that follow were estimated.
They show the interactions within a group of commodities and between groups of commodities.
Within a group I, the long-run equilibrium relationship is:
CON Sc,i
wc,i ln
QIc
=
cIc,i
+
I
g2,i
ln(DEM Wc )
+
bIi
ln(QIc )
+
27
X
I
sIi,j · ln (P CON Sc,j ) + g1,i
ln(DEM Pc )
j=1
+ ϑIc,i
for i ∈ I and where
• the scale effect of group I is defined by ln(QIc ) =
P
i∈I
I ln(CON S )
wc,i
c,i
• bIi : the income coefficient of commodity i in group I
• sIi,j : the compensated price effect of commodity j on I, both elements of I
I : the budget share of commodity i in group I,
• wc,i
51
s −1
fi,j
ϑc,i
I
wc,i
∆ ln
CON Sc,i
QIc
I
= bs,I
i ∆ ln(Qc ) +
+
27
X
s,I
26
X
s,I
j=1
j=1
si,j · ∆ ln (P CON Sc,j ) +
hs,I
1,i ∆ ln(DEM Pc )
+
hs,I
2,i ∆ ln(DEM Wc )
fi,j (ϑIc,i )−1
+ uc,i
for i ∈ I
Between groups of commodities, the long-run equilibrium relationship is:
I
w ln
QIc
IN CRDISPc
!
= cIc + bI ln(IN CRDISPc ) +
k
X
sIJ · ln (P CON Sc,J )
J=1
+
g1I
ln(DEM Pc ) +
g2I
ln(DEM Wc ) + ϑIc
for I = 1, ..., k groups and where
• ln(QIc ) =
P
i∈I
• ln(P CRcI ) =
I ln(CON S )
wc,i
c,i
P
i∈I
I ln(P CON S )
wc,i
c,i
• wI : the budget share of group I
I
w ∆ ln
QIc
IN CRDISPc
!
= bs,I ∆ ln(Qc ) +
+
k
X
s,I
J=1
s,I
h1 ∆ ln(DEM Pc )
sJ · ∆ ln (P CON Sc,J ) +
k−1
X
fJs,I (ϑIc )−1
J=1
+
hs,I
2 ∆ ln(DEM Wc )
+ uc,I
for I = 1, ..., k groups.
From those intra- and inter-group interactions, the overall interactions, which are
defined as the interactions between commodities of different groups, may be computed.
For the long-run overall coefficients:
mi = mI · mIi ∀i, I
si,j
= sIi,j · wI · δi,j + mIi · S IJ · mJj ∀i, I, j, J
with δi,j = 1 only if I, j ∈ I and = 0 elsewhere and where
52
• mIi : the marginal propensity to spend on commodity I in group I
• mI : the marginal propensity to spend on group I
• mi : the overall marginal propensity to spend on commodity i (in the case of the
CBS parametrization, the marginal propensity to consume is defined as mi = bi +wi
)
• sIi,j : the compensated price effect of commodity j on i in group I (non zero only
if i, j ∈ I)
• sI,J : the compensated price effect of group J on group I
• si,j : the overall compensated price effect of j on i
• wI : the budget share of group I.
Mutatis mutandis, those equations may also be applied to compute the short-run overall
coefficients.
Restrictions
• Summability :
0,
Pn
s
i=1 si,j
Pn
i=1 cc,i
= 0,
Pn
i=1 bi
= 0,
Pn
i=1 si,j
= 0,
Pn
s
i=1 bi
=
=0
• Homogeneity :
Pn
j=1 si,j
= 0,
Pn
s
j=1 si,j
=0
• Symmetry: si,j = sj,i , ssi,j = ssj,i , ∀i, j
• Negativity : sii < 0, ssii < 0
The consumption per category is then allocated to consumption by product using consumption transition matrix (mcons) with fixed coefficient.
ADDCON SQc,s =
27
X
(mconsc,co,s · CON Sc,co )
co=1
This transition matrix is also used for calculating consumption price per category using
sectoral production and import prices prices to which VAT taxes and Excises duties are
added:
30
P
P CON Sc,co =
(mconsc,co,s · CON Sc,co · P ADDDEMc,s ) + V AT CPc,co + EXCIP AHc,co
s=01
1995 + EXCIP AH 1995
CON Sc,co + V AT CPc,co
c,co
53
I.4. EXTERNAL TRADE
Section I.4
External trade
External trade is of a crucial importance in applied models such as NEMESIS, indeed,
one of the most important transmission effects between the different countries in the
model goes through trade in goods and services. This matter of fact is reinforced by the
strong European integration that as led to an increasing degree of openness, resulting
in a increasing share of external trade ratio to the final demand. External trade is
modelised in the models through a three sets of equations:
1. Intra-European trade in volume
2. Extra-European trade in volume
3. Exports and imports prices equations
If it were possible to separate intra and extra European trade in volume, this is not yet
possible for prices, that’s the reason why no distinction is made between intra and non
prices modelisation, except the fact that rest of the world trade prices includes trade
barriers such as import duties, that are not present into intra European trade.
I.4.1 Intra-European trade
The basic assumption regarding intra-European trade is that it take place into a “trade
pool”, i.e. into the same distribution network, that is to say that all European countries
exports to this pool and imports from it. One of the major drawback of this kind of
modelisation is that as exports and imports are both econometrically estimated, nothing
insure that at the global European level, total exports and total imports are equals3 .
As underlined by Satchi [282] it is not yet possible to estimate trade equations without
bilateral data, that follows straightforwardly this constraint. However, this “adding up”
problem was solved by modifying the exports equations in order to insure the equilibrium
3
The modelling of bilateral trade flows insure this “adding up” constraint, we are currently studying
the possibility to modelise bilateral trade flows, at least for goods, as bilateral trade flows of services
data are too weak for the moment.
54
between the sums of exports and the sums of imports per sector, this implicitly signify
that imports equations are better modelised than exports ones.
Numerous attempt had be made for integrating in external trade equations (particularly in exports equation) the so called non price competitiveness, one convicing attempt
was made using quality index build up with using made on importors by Crozet et alii
[86]. In our framework however, such quality indices are not available for the 27 modelised countries, and hence we had to estimate this effect through the Knowledge variable.
Of course, taking knowledge as a proxy variable for quality covers as noted by Crozet et
alii [86] not exactly the same content as quality indices, and may focus on a particular
dimension of quality, technological differentiation. Moreover, empirical testing shows
that bilateral trade flows are more suitable for estimating such quality effects, as this
allows for changes in the direction of this trade (see Hallak [171]), that one of the reason why the possibility for implementing bilateral trade flows in NEMESIS is currently
studied.
A great part of international trade theory nowadays concerns the so called Home
Market effect (See for instance Crozet et alii [87] or Corsetti et alii [85]) explaining that
big countries have an advantage for specialising their production to increasing return to
scale sectors, and on the contrary, small countries are more focused on constant return
to scale production. However, this effect refers largely to world trade, and the European
Integration tends to largely reduce this effect.
Finaly, the borders effect was not taking into account in our modeling framework the
“trade pool” hypothesis does not allow for bilateral trade, moreover, one can argue that
the European Market integration tends to reduce this effect (see Chen [63])
Imports equations
The three main effects integrated in the trade equations are income and prices effects
and non prices effects. For imports equations, these effetcs are taken into account with
the following variables
• The income effect for a country is taken into account through a demand variable,
represented by the demands addressed to the sector
• The price effect is represented by the ratio of the import price to the domestic
price.
55
I.4. EXTERNAL TRADE
• the non price effect is taken into account through national knowledge stock to
European knowledge stock ratio
ln(IM P EU Qc,s ) = limpeu0c,s
+ limpeu1s · ln(ADDDEM Qc,s )
!
+ limpeu2s · ln
P IM Pc,s
P P RODc,s
!
+ limpeu3s · ln
KN OWc,s
KN OWeu,s
with :
• ADDDEM Qc,s Total domestic Demand by products
• P IM Pc,s The Price of Imports
• P P RODc,s The production Price
• KN OWc,s the national knowledge Stock
• KN OWeu,s the European knowledge Stock
limpeu1s > 0
limpeu2s < 0
limpeu3s < 0
Exports Equations
For exports equations, the incomes and prices effetcs are taken into account with the
following variables
• The income effect for a country j is taken into account through a demand variable,
resulting from the demands of partners’ countries, weighted past trade intensities
(in a matrix form, for year 2000)
• The price effect is represented by the ratio of the export price to a European price
index, which is a weighted variable of other EU countries export prices.
56
ln(EXP EU Qc, s) = lexpeu0c,s
+ lexpeu1s · ln(IN DACT EUc,s )
+ lexpeu2s · ln
P EXPc,s
P IN DICEXP EUc,s
+ lexpeu3s · ln
KN OWc,s
KN OWeu,s
!
!
• IN DACT EU c,s , indicator of activity
• P EXPc,s , the Export Price
• P IN DICEXP EUc,s , Indicator of competing Prices
• KN OWeu,s the Global European knowledge Stock
lexpeu1s > 0
lexpeu2s < 0
lexpeu3s > 0
I.4.2 Extra European Trade
Extra European trade vis à vis of the rest of world (divided into ten exogenous areas),
follows broadly the same formalisation than intra European Trade and includes therefore
the same effects as described above.
Imports equations
The three main effects integrated in the trade equations are income and prices effects
and non prices effects. For imports equations, these effetcs are taken into account with
the following variables
57
I.4. EXTERNAL TRADE
• The income effect for a country is taken into account through a demand variable,
represented by the demands addressed to the sector
• The price effect is represented by the ratio of the import price to the domestic
price.
• the non price effect is taken into account through national R&D stock to the extra
European zone R&D stock ratio
ln(IM P ROW Qc,s ) = limprow0c,s
+ limprow1s · ln(ADDDEM Qc,s )
+ limprow2s · ln
P IM P ROWc,s
P P RODc,s
+ limprow3s · ln
KN OWc,s
KN OWz,s
!
!
with :
• ADDDEM Qc,s Total domestic Demand by products
• P IM P ROWc,s The Price of Imports for extra European imports
• P P RODc,s The production Price
• KN OWz,s the extra European zone knowledge Stock
limprow1s > 0
limprow2s < 0
limprow3s < 0
Exports Equations
For exports equations, the incomes and prices effetcs are taken into account with the
following variables
58
• The income effect for a country j is taken into account through a demand variable,
resulting from the demands of partners’ countries, weighted past trade intensities
(in a matrix form, for year 2000)
• The price effect is represented by the ratio of the export price to a non European
price index, which is a weighted variable of other extra European zone export
prices.
ln(EXP ROW Qc, s) = lexprow0c,s
+ lexprow1s · ln(IN DACT ROWc,s )
+ lexprow2s · ln
P EXP ROWc,s
P IN DICEXP ROWc,s
+ lexprow3s · ln
KN OWc,s
KN OWz,s
!
!
• IN DACT ROW c,s , indicator of activity
• P EXP ROWc,s , the Export Price
• P IN DICEXP ROWc,s , Indicator of competing Prices
• KN OWz,s the extra European zone knowledge Stock
lexprow1s > 0
lexprow2s < 0
lexprow3s > 0
I.4.3 Imports and Exports prices
Exports and imports prices play a large role in determining trade volumes. the basic
feature of trade prices in NEMESIS assume that European countries operate in oligopolictic markets, following this assumption, importers and exporters sets mark-ups on
their prices taking others partners prices into account. As noted above, the lack of data
59
I.4. EXTERNAL TRADE
regarding import and exports prices differentiated per trade partners, make that the
distinction between Intra-European and Rest of the World distinction was not possible,
however, in order to take into account for possible trade barriers between the EU and the
rest of the world, we sets two different prices, the sole difference between the two prices
lay precisely in the existing trade barriers (import duties...) that multiplies the global
import and exports prices. Other partners prices are weighted in the same manner than
for volume equations, exchange rates are directly taken into acccount in the European
(making a clear distinction between intra and extra Euro zone) and ROW price index .
The majority of trade prices are treted in the same manner (with the notable exception
of crude oil and gas, that are treated exogenously)
Export prices
ln(P EXP c, s) = lpexp0c,s
+ lpexp1s · ln(P IN DICEXP EUc,s )
+ lpexp2s · ln(P IN DICEXP RWc,s )
+ lpexp3s · ln(P P RODc,s )
• P IN DICEXP EUc,s , Price Index for competing Exports in Europe
• P IN DICEXP RWc,s , Price Index for competing Exports in the Rest of the World
• P P RODc,s , Production Price
lpexp1s + lpexp2s + lexp3s = 1
Import prices
60
ln(P IM P c, s) = lpimp0c,s
+ lpimp1s · ln(P IN DICIM P EUc,s )
+ lpimp2s · ln(P IN DICIM P RWc,s )
+ lpimp3s · ln(P P RODc,s )
• P IN DICIM P EUc,s , Price Index for competing Imports in Europe
• P IN DICIM P RWc,s , Price Index for competing Imports in the Rest of the World
• P P ROD, Production Price
lpimp1s + lpimp2s + lpimp3s = 1
Section I.5
Wage setting
In section we will present the specification, the estimation and the implementation of
this modified labour market for the Nemesis model. Indeed, the integration of different
labour skills in the model implies to reformulate and to extend the labour market of
NEMESIS. This will allows us to revisit the latest theoretical developments and to
proceed to an econometric analysis at a disaggregated level. This section is organized
as follows, we first analyse the theoretical issues and the consensus that has emerged in
the last years, and then in a second part we will define the formalization that will be
implemented in the model. The third part is dedicated to the presentation of the data
used in the econometric estimation that will be presented in the fourth part. Finally, we
will present the functional form implemented in the NEMESIS model.
I.5.1 Theory
61
I.5. WAGE SETTING
The formulation of wage process suffers from a lack of consensus arisen from a long
and stormy history. Main empirical and theoretical controversy opposes proponents of
the Philips curve to those of the WS-PS model. Philips curve is an empirical relation
that highlights the negative relation between nominal wage and unemployment. It could
be well represented by :
4w = c + 4pe − bU
(I.47)
All variables are expressed in logarithm except U which is express in level. 4w is
the variation of nominal wage (w − w−1 ), 4pe is the expected inflation (it is equal to
4p if expectations are perfect) and is U the unemployment rate. Whereas Philips curve
estimates well wage formation over the course of a business cycle, it suffers from a lack
of theoretical foundation.
At the opposite, the WS-PS models are theoretical based but present some unrealistics
assumptions about their key concepts. Almost all these models4 are founded on assumption that real wage fluctuates around a reservation wage which representes the income
opportunity of employees outside the firm. Main models explain wage reservation by
unemployment benefit, labor productivity, positive trend or lagged real wage. Recent
litterature (Chagny and ali (2002)[58] and Reynes (2006)[134]) rejects the theoretical
underpinning of the first three explanations and retains the latter, leading to a Philips
curve specification. Almost all theoretical models based of bargain model or efficiency
wage can be represented as:
∼
∼r
w = w − p = w + Z − bU
(I.48)
∼r
∼
w is the real wage, w is the reservation wage and Z embodied all other variables that
can explain wage formation (almost institutional variables). As highlight by Manning
(1993)[4], Blanchard & Katz (1999)[255], if we consider reservation wage as the lagger
∼r
∼
real wage (w = w−1 = w−1 − p−1 ), equation can be transformed into a Philips curve.
4w = 4p + Z − bU
(I.49)
In assuming that the reservation wage is the lagged wage, the Philips curve theoretical
underpinnings are as valid as those of the WS setting. Chagny and ali (2002) and
Reynes (2006) go father in narrowing the empirical difference between the two approachs.
Their models allows “a clear distinction between medium run of equilibrium rate of
4
For instance efficiency wage model, matching model or competitive wage competition...
62
unemployment (ERU) and the long run ERU” which the the key difference between
Philips curve and WS-PS models. Let’s assume that the medium run wage formation is
directed by :
4w = Z + a4pcons − b1 U − b2 (U − U−1 ) + d4π − f 4tcs
(I.50)
In this specification, wage formations may be: indexed on consumer price pcons , hystheris or not b2 , depend of labour productivity π, employer’s social contribution tcs and
influenced by a pool of institutional variables Z.
The long run ERU is:
UELR = (Z − (1 − d)4π − (1 − a)4p0 )/b1
(I.51)
where 4p0 is the inflation target of the monetary authorities. UELR differs from the
medium run (assume that b2 = 0) by:
UEM R = UELR + (w − wd )/b1 T
(I.52)
where T is the number of quarter during which authorities are implicitly assumed to
correct the unemployment gap.
I.5.2 Model
We extend the model developped by Chagny and ali (2002) and Reynes (2006) in
order to estimates the wage formation. We transform equation I.56 to take into account
Nemesis specificities. Since Nemesis models is sectorial and intergates two kinds of labour
(high skill and low skill), equation retains is as following:
4wi,l,c,t = Zi,l,c + ac,l (L)4pcl,c,t − b1,c,l (L)Ul,c,t − b2,c,l (L)(Ul,c,t − U Tl,c,t )
+dc,l (L)4πi,l,c,t + i,l,c,t
Where
• t = 1; ...; 11 is a time index ranging from t = 1992 to 2005.
• c is a country index, c = 1, .., 19
63
(I.53)
I.5. WAGE SETTING
• l corresponds to the labour qualification
• i is a sector index, i = 2, ..., 29
• Zi,l,c represents institutional variables.
U T correspond to the tendential unemployment rate. Due to lack in data we limit
our estimated sample to 19 countries (instead of 27) and 28 sectors (instead of 30).
Institutional variables Zi,l,c are treated as country-sector fixed effect.
I.5.3 Data
Wage
Unfortunately there is no data available for wi,l,c (or 4wi,l,c ), which make the distinction
between different kinds of labour. By definition the variation of wage equal the variation
of labour compensation and the variation of employer’s social security rate, i.e.4wi,l,c =
4Compi,l,c + 4tcs,i,l,c with Compi,l,c the labour compensation. Under hypothesis that
4tcs,i,l,c = 0, wage variation equal labour compensation variation, which is available in
the EUKLEMS[309] database. Compi,l,c is built as follows:
CompHS =
COM P ∗ LABHS
100
HEM P E ∗ HHS
100
Where HEM P E is the Total hours worked by employees (millions), COM P the
Compensation of employees (in millions of Euros), LABHS the High-skilled labour
compensation (share in total labour compensation) and HHS the Hours worked by
high-skilled persons engaged (share in total hours). CompHS is thus the hours labour
compensation for high skill workers. Same method is used for the low skill compensation.
Unemployment
Ul,c is the unemployment rate. Because there is no data available and we assume that
workers are mobile through sectors, unemployment rate is not defined by sector. Data
64
AT
BE
DK
GE
FI
FR
GR
IR
IT
NL
PT
SP
SW
UK
CZ
HU
PL
SN
SK
Low skil
mean standard error
2.07
0.84
2.35
1.23
3.34
0.70
1.45
1.13
3.04
1.60
3.63
1.91
5.27
1.23
5.49
1.64
1.76
1.86
3.75
1.32
3.38
2.62
2.59
0.81
3.65
2.18
5.19
1.67
6.80
2.80
10.40
5.71
7.28
5.66
7.92
2.62
8.72
3.51
High Skill
mean standard error
1.79
0.74
2.49
1.57
3.70
0.75
2.01
1.55
3.85
1.37
1.50
2.10
3.66
2.28
5.70
2.73
3.42
3.07
4.30
2.40
3.68
3.31
2.80
0.94
2.87
2.08
3.05
4.00
7.00
3.60
11.33
5.34
6.89
4.72
6.99
4.89
8.20
3.39
Table I.1.: Labour compensation growth, period 1998-2005
65
I.5. WAGE SETTING
AT
BE
DK
GE
FI
FR
GR
IR
IT
NL
PT
SP
SW
UK
CZ
HU
PL
SN
SK
mean
5.39
9.66
5.28
10.60
13.54
11.03
11.40
6.02
10.34
4.02
5.68
13.76
7.45
6.28
8.52
7.58
18.80
7.50
18.84
Low skil
standard error
0.48
1.39
0.54
1.55
1.60
1.68
0.88
1.72
1.72
1.25
1.50
3.15
1.88
0.83
1.03
1.23
4.16
0.73
2.64
mean
2.33
3.60
3.60
4.86
4.80
6.09
7.73
2.33
6.10
2.13
3.99
9.90
3.59
2.64
2.41
1.76
5.59
2.84
4.59
High Skill
standard error
0.39
0.52
0.67
0.57
0.76
0.79
0.61
0.47
0.78
0.55
1.37
2.92
0.88
0.32
0.43
0.53
1.93
0.53
0.92
Table I.2.: Unemployment rate , period 1998-2005
are taken from Eurostat, we use set called “Unemployment rates by sex, age groups and
highest level of education attained (%)”. We retain as “age groups”, the 15-64 years old.
We convert ISCE classification (International Standard Classification of Education) into
low and high skill classification.
Price
pcl,c is taken from the OECD database: “Consumer price indices (MEI)” for all countries
except for Slovenia. Since database not available for Slovenia we use Eurostat price (with
lower time coverage).
66
AT
BE
DK
GE
FI
FR
GR
IR
IT
NL
PT
SP
SW
UK
CZ
HU
PL
SN
SK
HHS share
mean standard error
12.50
0.99
14.71
0.60
7.50
0.69
9.28
0.39
33.84
1.00
13.86
0.89
19.91
1.63
15.88
1.98
10.78
1.29
10.73
1.35
9.41
1.42
19.38
1.51
16.84
2.52
16.78
1.69
12.71
0.97
17.62
1.82
14.72
2.66
16.63
2.52
14.20
1.49
P CONS
mean standard error
1.75
0.74
1.90
0.68
2.14
0.53
1.33
0.45
1.43
0.96
1.55
0.62
3.45
0.64
3.41
1.46
2.28
0.39
2.38
0.93
2.98
0.72
2.96
0.60
1.05
0.98
1.37
0.36
3.50
3.22
7.91
3.51
5.28
3.97
6.35
2.34
7.32
3.24
Table I.3.: Price growth and high skill share , period 1998-2005
67
I.5. WAGE SETTING
AT
BE
DK
GE
FI
FR
GR
IR
IT
NL
PT
SP
SW
UK
CZ
HU
PL
SN
SK
Low skil
mean
3.21
1.71
2.16
2.31
2.62
2.68
1.68
3.69
1.08
1.97
1.37
2.05
3.44
2.72
6.09
5.69
6.50
4.17
4.67
standard error
1.20
1.68
1.09
1.01
1.21
1.60
2.56
1.99
1.06
1.84
1.40
1.01
1.85
1.33
2.96
1.87
2.21
2.68
3.51
High Skill
mean
-0.57
-0.23
-1.56
0.26
1.47
-0.83
-2.07
-1.84
-4.61
-2.85
-2.47
-1.89
-3.35
-2.35
2.80
1.02
-0.61
-1.61
0.55
standard error
1.94
2.03
2.12
4.85
2.46
2.27
5.11
4.21
1.14
8.89
7.49
1.37
5.65
1.78
3.38
5.53
4.96
9.68
5.13
Table I.4.: Labour productivty growth, period 1998-2005
Labour productivity
πi,l,c is the labour productivity. It is calculated as usual way, i.e. the GDP divided
by the number of hours worked (πHs =
GOQI
HEM P E∗(1− HHS
)
100
GOQI
HEM P E∗ HHS
100
for unskilled labour and πLs =
for skilled labour). GOQI is also taken from the EUKLEMS database
(Gross output, volume indices, 1995 = 100).
I.5.4 Results
In order to highlight the difference between the macro and the sectorial view, we
implement two set of regression. First set is made at the macro level. Second set is
made with sectorial specifications. To deal with error autocorrelation we use an order-1
68
LS coef
se
Price
0.42
-0.32
Macro b2=0
Unemp
-0.46
0.33
Productivity
0.41
0.19
Price
0.76
0.46
Sec b2=0
Unemp
-0.58
0.52
Productivity
0.24
0.17
HS coef
se
0.69
0.43
-1.34
1.09
0.29
0.37
1.05
0.54
-0.52
0.50
0.15
0.08
Table I.5.: Coefficients summary
auto-regressive model (AR(1)).
Macro
The related equation for regressions at macro level is a transformed form of equation
I.53:
4wl,c,t = Zl,c + ac,l 4pcl,c,t − b1,c,l U − b2,c,l (Ul,c,t − U Tl,c,t ) + dc,l 4πl,c,t + l,c,t
(I.54)
In a first time we presume the non existence of hysteresis phenomena. In that case we
constraint b2 = 0. Results of macro regression without hysteresis are given in Figure1.
In a second step we test the presence of hysteresis phenomena, results are given in Figure
2. Results of first estimation present expected coefficient sign for 20 specifications on
38 (sample include 19 countries with two labour market that give 38 specifications). 6
countries: GE,FR,IR,NL,HU and SN present expected sign for both markets. In some
case coefficient value exceed the unity, it’s arise only on the high skill market (except
for Hungary). Table I.5 gives aggregated results from regressions which present value
of the expected sign and lower than 2. Price and unemployment seem to play a more
important role for high skill wage, at the opposite productivity has a stronger effect on
low skill wage. Figure I.17 presents coefficient for the whole model. Most of the results
are in the opposite sign than expected one. For this reason we reject this specification.
69
I.5. WAGE SETTING
Figure I.16.:
70
Figure I.17.: results whole model
71
I.5. WAGE SETTING
Sectorials results
The related equation for sectorial regressions is transformed form of equation I.53:
4wi,l,c,t = Zi,l,c +ac,l 4pcl,c,t −b1,c,l Ul,c,t −b2,c,l (Ul,c,t −U Tl,c,t )+dc,l 4πi,l,c,t +i,l,c,t (I.55)
Main difference with equation I.54 is the sectorial specification of labour compensation
and labour productivity. Results of sectorial regression are given in Figure I.18 and
in Figure I.19. We proceed in the same way than in macro specification, in the first
step we estimate equation I.55 with b2 = 0 then we relax the constraint. First set
of results (Figure I.18) present expected coefficient sign for 24 specifications on 38. 8
equations, which present non expected value are the same than in first regression (AT:HS,
BE:LS, FI, SW, PL:HS and SK:LS). Table I.5 presents aggregated results for regressions
related for Figure I.18. Price seems to play a more important role for high skill wage.
Unemployment has a same effect for both kind of labour and productivity has a stronger
effect on low skill wage. In comparison with macro results coefficient value are higher
for price and unemployment coefficient but lower on productivity coefficient. Figure I.19
present coefficient for the whole model. Most of the results are in the opposite sign than
expected one. For this reason we reject this specification
ANNEX
Long run equilibrium rate of unemployment UELR
Recall the relevant wage setting equation:
4w = Z + a4pcons − b1 U − b2 (U − U−1 ) + d4π − f 4tcs
(I.56)
Consider 4p0 is the constraint inflation target of the monetary authorities. In the
long run 4tcs = 0, U − U−1 = 0 it follows:
72
Figure I.18.: Sectoral results P1
73
I.5. WAGE SETTING
Figure I.19.: sectoral results P2
74
4w = Z + a4p0 − b1 U + d4π
The unemployment rate consistent with the target inflation rate and growth rate of
labour productivitty is:
4w − 4p0 = 4π = Z + (a − 1)4p0 − b1 U + d4π
We note the long run equilibrium rate of unemployment U = UELR .
UELR = (Z − (1 − d)4π − (1 − a)4p0 )/b1
(I.57)
Section I.6
Labour supply
This section presents the modelling of labour supply in NEMESIS. The decision by
individuals to participate or not to labour market relies on many socio-economic and
institutional factors, such as the levels of wages, of reservation wages, of social transfers,
and the dynamism of labour market.
A first sub-section states the situation prevailing in the different EU member States
for participation rates of working age population by categories. Then a second subsection presents the econometric works realized for MODELS for endogenizing the labour
supply for the different population categories. The data sets that were used to model
participation rates in NEMESIS did not allowed distinguishing, for a given age group,
the supplies by high skilled and low skilled workers, but only the supply by gender
categories. The results allowed nevertheless giving parameter values to calibrate the
labour supply for the different skills that were introduced in NEMESIS for MODELS,
as it is explained in the third sub-section. The last sub-section concludes.
I.6.1 The data on participation rates of working-
75
I.6. LABOUR SUPPLY
Figure I.20.: Participation rates to labour market of men and women aged 25 to 64,
EU27 + Norway, 2005
96%
94%
92%
90%
88%
86%
84%
82%
80%
78%
BE DK DE IE GR ES FR IT LU NL AT PT FI SE UK CZ EE LV LT HU MT PL RO SI SK NO
Males
Females
age population
Data on labour supply are numerous. EUROSTAT provides participation rates by sex,
age groups and skills for each EU country either annually or on a quarterly basis. The
Figure I.20displays the participation rate of the high skilled women and men between 25
and 64 years old in 2005. One can observe on this figure an important diversity between
countries especially for women. The participation rate is about 80% in Malta and Czech
Republic whereas it is more than 90% in Sweden and Portugal. The males’ participation
rates differ less between countries and range between 89% in Austria and Romania and
94% in Ireland. Finally, the data confirm that the activity rates of men are, in average,
higher that those of women, but relatively close in few countries as Portugal, Sweden
and Romania.
Figure I.21 shows the activity rates for women aged from 50 to 65 and for low skilled
and high skilled populations in 2005. For this elder population category, participation
rates are always superior for high skilled than for low skilled women, of up to about 8 to
10% to the population composing this age group in countries like Italy, Belgium, Spain,
Greece and Luxembourg. One can state finally on this figure that there exist fewer
discrepancies among EU countries for participation rates of the high skilled women that
for the low skilled women population for this age group.
The previous figures illustrate the contrasts existing into the behaviour of labour sup-
76
Figure I.21.: Participation rates to labour market of women aged 50 and 64 by skill,
EU27 + Norway, 2005
77
I.6. LABOUR SUPPLY
ply between countries, age groups, sex, and skills. The next sub-section will then try
identifying the influence of key socio-economic indicators on the decision by individuals to participate to labour market. In economic theory, individuals realize a trade-off
between work that provides income for consumption and leisure. Another important determinant of the labour supply behaviour, linked to this arbitrage between consumption
and leisure, is the value of the reservation wage that one can proxy by the amount of
social transfers per head received by individuals. Also, a dynamic labour market, with
growing employment, will encourages individuals to provide their labour force, while,
on the contrary, high unemployment rates will discourage them to enter on it. There
exists also strong trends affecting the behaviour of labour supply reflecting structural
changes occurring in European societies, that occur for example in the context of labour
market regulation, in retirement policies and in social and professional aspirations of the
young generations. The use of exogenous time trends, and of proxy variables such as the
share of population aged 55-64 in total working-age population, to catch the effects of
retirement policies, and of school enrolment ratios, to catch changes in youngest population social aspirations, may help us to control the influence of these additional factors
affecting the labour supply.
We had also to deal with data scarcity problems which have constrained us to limit
our analysis to age groups and gender categories, and to leave the skill dimension to
future works.
I.6.2 Determinants of participation rates
The goal of this sub-section was to find functional forms for participation rates to
labour market of the different population categories that were introduced in the model
NEMESIS. As it was underlined just above, the skill dimension was leaved away, in
reason of the lack of data on labour compensations and social transfers at skill level.
After a presentation of the data used, we introduce the functional forms that were used
and we end by a comment of the econometric results.
The data and the choice and construction of indicators
The choice of indicators for the modelling of participation rates to labour market were
based of a set of empirical studies on participation rates (Mincer [244], Hughes [180],
78
Jacobsen [192], Schrier (2000), Cutler and Turnbull [88], Conesa et al. [82], Huber [179]
and Balleer et al. [25]). The data were taken from EUROSTAT. They cover all EU27
countries plus Norway, have an annual periodicity, and range from 1998 to 2004 . The
S,A
participation rates by sex and age groups (T xi,t ) were modelled by using the following
set of explanatory variables:
• Gwi,t =
wi,t /pi,t
wi,t−1 /pi,t−1 that
• GLSi,t =
LS
i,t
Li,t−1 ,
represents the change in real wage between t − 1 and t,
the increase in jobs creation between t − 1 and t,
U n +SB poor +SB f arm /p U
U n +SB poor /p U
SBi,t
(SBi,t
i,t i,t
i,t
i,t i,t ) i,t i,t
M =
,
GSB
U n +SB poor /p
i,t
U n +SB poor +SB f arm /p
SBi,t−1
(SBi,t−1
i,t−1 Ui,t−1
i,t−1 ) i,t−1 Ui,t−1
i,t−1
i,t−1
U n +SB poor +SB old /p U
(SBi,t
i,t ) i,t i,t
i,t
O =
, that are indicators of change in
and GSBi,t
poor
U
n
old
+SBi,t−1
(SBi,t−1 +SBi,t−1
)/pi,t−1 Ui,t−1
social benefits between t − 1 and t,
Y =
• GSBi,t
S =
• SSTi,t
S
STi,t
S,Y
P OPi,t−1
, the share of student in ISCED 5 and ISCED 6 in the 15 to 24
years old population,
S =
• and SP OPi,t
Y +P OP M
P OPi,t
i,t
M +P OP O ,
Y
P OPi,t +P OPi,t
i,t
the share of the young and medium age
groups in the total population (except the cohorts between 0 to 14 years)
We have:
• S = M, W the gender index,
• A = Y, M, O the index of age groups, the class Y represents population between
15 and 24 years; the class M regroups population between 25 and 64 years and the
class O includes the population aged 65 years and more,
• i the index for countries,
• t the time index,
• LSi,t the total employment by gender,
S,A
• P OPi,t
, the total population by sex and age groups,
U n ,SB P oor ,SB f arm ,SB Old , the social transfers for, respectively, unemployment,
• SBi,t
i,t
i,t
i,t
poverty:, family: and oldness,
• wi,t , the nominal wage rate,
• pi,t , the price index of final consumption,
79
I.6. LABOUR SUPPLY
S , the total number of student in ISCED 5 and 6 by sex,
• STi,t
S , the total number of unemployed by sex.
• Ui,t
Among the explanatory variables, the change in real wages will capture the importance
of the trade-off between work and leisure or between consumption and leisure. The wage
rate used is identical for men and women and all age groups. The change in employment
will allow measuring the flexion of labour supply to the dynamism of labour market.
An alternative consists in using the evolution of unemployment rate, but this rate,
at the difference of the change in employment, is partially endogenous, as it is relies
by construction on the participation rate to labour market; the use of unemployment
rate in the econometric specification could consequently lead to biased estimators. For
social benefits, an indicator was constructed for each cohort, even if data on social
benefits do not cover age groups, nor population genders. We constructed nevertheless
indicators oriented toward particular age groups, by normalizing the social benefits by
the respective unemployment rates of the different age groups. This allows taking into
account the impact of changes in unemployment onto the evolution of social benefits per
head in volume (deflated with final consumption price). For population aged 15 to 24,
social benefits include unemployment allowances and poverty assistance, for population
aged 25 to 64, they include also assistance to families, and for population over 64,
the retirement pensions. In addition, the share of population aged between 15 and 24
engaged in tertiary education traduces the trade-off between work and studies. Finally,
the share of young and medium in total working-age population aims capturing the
influence of retirement policies on the labour supply behaviour of the eldest age group.
The modelling of participation rates
The participation rates were estimated econometrically from temporal trends that take
the form of logistic curves, and the set of indicators that was described above. The
logistic curves have the following form:

ln 
T xS,A
i,t − µ
λ − T xS,A
i,t

 = σ · time + ρ
(I.58)
with µ and λ respectively the limit activity rates high and low, σ the diffusion speed
of activity behaviours and − (ρ/σ) the year of inflexion of the activity rate. We obtain
by developing:
80
T xS,A
i,t =
µ + λ · e(σ·time+ρ)
1 + e(σ·time+ρ)
(I.59)
As the participation rates were estimated with pooled panel techniques, µ and λ were
set respectively to 0 and 1, and the parameters σ and ρ were individualised by country.
We have:
S,A
T xS,A
i,t
S,A
e(σi ·time+ρi )
=
S,A
S,A
1 + e(σi ·time+ρi )
(I.60)
By introducing the other determinants of participation rates, we get finally the expressions that were estimated:
S,A
S,A
S,A
S,A
A
T xS,A
= βw
· GWi,t + βLS,A · GLSi,t + βSB
· GSBi,t
+ βST
· SSTi,t
i,t
+
βPS,A
OP
·
S,A
SP OPi,t
S,A
S,A
e(σi ·time+ρi )
+ S,A
+
i,t
σiS,A ·time+ρS,A
)
(
i
1+e
(I.61)
The elasticity parameters βX are common to all countries and i are the i.i.d. error
terms. Parameter restrictions were imposed with respect to the population groups that
are considered:
S,A
= 0 when A = Y, M i.e. we suppose a null effect of the student share (SST )
• βST
on superior age groups.
• βPS,A
OP = 0 when A = Y, M , i.e. we suppose a null effect of the share of 15 to 64
years old population (SP OP ) on young and medium age groups.
The model was estimated by the Full Information Maximum Likelihood procedure
(FIML) with annual data and 92 independent observations. There are, depending of
the population category, 3 to 4 elasticity parameters βX and 21 country specific constant ρS,A
and time trends σiS,A to estimate.
i
Estimation Results
Table I.6 displays the estimation results for the parameters , which measure the short
term impacts of changes in economic conditions, in population structure and in educa-
81
I.6. LABOUR SUPPLY
Table I.6.: Estimation results of participation rates
Gender
age
15-24
Males
25-64
65-max
15-24
Females
25-64
65-max
Gw
GLS
GSBA
SSTS
SPOPS
Adjusted R2
DW
-0.0088
0.3154***
-0.0171
-0.7719**
--
0.98
2.04
(0.0299)
(0.0514)
(0.0133)
(0.3136)
0.0631***
0.3040***
-0.0285***
--
--
0.99
2.51
(0.0177)
(0.3040)
0.0076)
0.0327
0.0078
-0.0226**
--
-0.5028*
0.98
2.1
(0.0216)
(0.0646)
(0.0092)
-0.02969
0.3664***
-0.0266*
-0.9362***
--
0.99
2.23
(0.0477)
(0.05571)
(0.0142)
(0.1283)
-0.0142
0.2531***
-0.0136*
--
--
0.97
1.98
(0.0142)
(0.0478)
(0.0079)
-0.0002
-0.0214
-0.0067
--
-0.4758***
0.97
1.61
(0.01461)
(0.0474)
(0.0046)
(0.2899)
(0.0540)
*,**,***: parameter significantly different to zero at 10%, 5% and 1% respectively
tion, on the evolution of activity rates5 .
The parameters βw can be interpreted as the semi-elasticity of activity rates to variations in the corresponding explanatory variable.
For real wages, one can see in the first column of the table that the parameter βw
is significant only for men aged between 25 and 64. For this population category, an
increase of 1% of the growth of real wages increases labour supply of about 0.06 point,
which represents an augmentation of 0.64%.
For employment effect, that measures the sensitivity of labour supply to an amelioration, or a deterioration of labour market, the second column of the table indicates that
the parameters βL are significant at 1% level for every categories, except persons aged 65
and over, with anyway very low participation rates to labour market. The parameters
values are important and range between 0.25, for women aged between 25 and 64, and
0.37 for those aged between 15 and 24. These values mean that in average, an increase
of 1% of employment will lead to a rise of about 0.3 point of activity rate of population
in working-age, that is to say to an increase of about 0.35% of the labour supply. These
results indicate, in other words, that, in average, if 3 new jobs are created, it will reduce
the number of unemployed of 2.
For reservation wage, or replacement revenue, the third column of Table I.6 shows
that the growth of these revenues represents a discouragement to offer its labour on the
market, for the major part of population in working-age, and for men aged over 64.
The importance of this effect is nevertheless quite small, but generally significant with
parameter values that range between -0.0136 and -0.0285, for women and men aged 25
5
For information, Table I.6 shows also adjusted R-squared, that exhibit values superior to 0.97, and
Durbin-Watson statistics that do not reveal serious autocorrelation of error terms, with values close
from 2 generally
82
to 64 respectively.
Finally, as expected, the share of young aged between 15 and 24 engaged in tertiary
education reduces very significantly, and mechanically, the activity rate of this population category, for both men and women, while the share of working –age population
in population aged 15 and over reduces also significantly, and mechanically, the labour
supply by population at (or close from) retirement age.
I.6.3 Calibration of labour supply
This last sub-section describes the calibration of labour supply behaviour for the
different population categories that the model NEMESIS distinguishes, presented in 0??
The expressions of activity rates that were used are identical to those presented above,
with as main explanatory variables the variation of real wages, the variation of social
benefits and the variation in employment over two periods:
S,A
S,A
S,A
A
S,A
· SSTi,t
· GSBi,t
+ βST
T xS,A
= βw
· GWi,t + βLS,A · GLSi,t + βSB
i,t
S,A
+ βPS,A
OP · SP OPi,t +
e(
σiS,A ·time+ρS,A
i
(I.62)
)
S,A
S,A
1 + e(σi ·time+ρi )
(I.63)
As we did not dispose of data by skill to perform the econometric estimations, we then
used, for the different age-groups, the same value of elasticity parameters for the two
skill categories. The value of elasticity parameters were retrieved from the estimation
results presented in Table I.6. Also, for the age groups of NEMESIS 25-54 and 55-64, the
same elasticity parameters were used, that correspond to the econometric estimations
realized for the global category 25-64.
For wages, the elasticity parameter estimated for women aged between 25 and 64
was not significative, and had the wrong sign, and we consequently used for women the
elasticity parameter estimated for men. For the categories aged between 15 and 24, we
kept the assumption, revealed by econometric estimations, that changes in real wages
are not a determinant of activity rates.
For social benefits, the parameters estimated where globally significative, or close
from significativity level, and we kept the values estimated, that are quite homogenous
between population categories.
For employment also, the estimated values, are always significative and homogenous
83
I.6. LABOUR SUPPLY
between population categories were kept.
For the youngest categories, aged between 15 and 24, we retained also as explanatory
variable the share of students, with the elasticity parameters that were estimated. For
women, an increase of one point of school enrolment ratio, decreases of 0.94 point the
participation rate, and of 0.77 point for men. The impact is less than proportional for
the reason that students may cumulate studies with a professional activity.
The participation rates for the population aged 65 and over, that present very low
values, and for which the econometric estimation dir not provided good results, were not
modelled, and were kept them exogenous at this stage in NEMESIS.
The other determinant of the participation rates are finally the logistic trends, traducing the influence of other factors, exogenous in NEMESIS.
For illustration of our calibration procedure, the Table I.7 sums-up the elasticities of
activity rates with respect to one point change in their explanatory variables. For wages,
social benefits and employment, the values represent the percentage change of activity
rate, that is to say of labour supply, of the category, with respect to a 1% increase in the
growth rate of the explanatory variables. For the share of students and the categories
aged between 15 and 24, the values are semi-elasticities measuring the percentage change
of activity rates with respect to a one point variation of school enrolment ratio of the
category.
One can see on Table I.7 that even if we use identical parameters for low skilled and
high skilled populations, the values of elasticities are different for the two categories,
and that they differ also between countries. For a given age and gender category, the
elasticities of labour supply are inversely proportional to the initial value of the activity
rates. There are consequently in every country stronger for women, who have activity
rates inferior to men, and for low skilled persons, that have participation rates inferior
to high skilled ones. We’ll have also superior values of elasticities for the age categories
15-24 and 54-65, the youngest and eldest populations having inferior activity rates than
population aged between 25 and 54.
For wages, an increase of 1% in labour remuneration will rise between 0.07% and 0.09%
the labour supply of population aged between 25 and 54, which represents the major
part of the labour force, with few discrepancies between skills, genders and countries.
In other words, a permanent increase of 1% in the growth rate of real wage will increase
by less that 0.1% the labour supply of the medium aged population, confirming the very
weak impact of wages onto the labour supply behaviour. There is no impact at all for
population aged between 15 and 24. For the eldest category, the impact of wages range
between 0.09% for high skilled men and 0.25% for low skilled women, and 0.13% for the
84
Table I.7.: Elasticities of activity rates in NEMESIS in 2008
Female
Wages:
15-24
25-54
55-64
Social benefits:
15-24
Male
Low
High
Low
High
0.09
0.07
0.07
0.07
(CZ: 0.07; PT: 0.09)
(RO: 0.07; BE: 0.08)
(IE: 0.07; LU: 0.08)
(GR: 0.06; FI: 0.08)
0.25
0.13
0.13
0.09
(SK: 0.11; AT: 0.35)
(UK: 0.08; AT: 0.15)
(EE: 0.09; LT: 0.18)
(UK: 0.07; LU: 0.11)
-0.08
-0.04
-0.04
-0.03
(CZ: -0.04; LU: -0.17) (SK: -0.03; LT: -0.06) (CZ: -0.02; LV: -0.06) (SK: -0.02; FR: -0.06)
25-54
-0.02
-0.02
-0.03
(CZ: -0.02; PT: -0.02) (RO: -0.02; BE: -0.02)) (IE: -0.03; LU: -0.04)
55-64
-0.05
-0.03
-0.06
-0.03
(GR: -0.03; FI: -0.03)
-0.04
(SK: -0.02; AT: -0.08) (UK: -0.03; AT: -0.08) (EE: -0.04; LT: -0.08) (UK: -0.03; LU: -0.05)
Employment:
15-24
25-54
55-64
Share students
15-24
1.12
0.52
0.74
0.49
(CZ: 0.5; LU: 2.29)
(SK: 0.4; LT: 0.89)
(CZ: 0.42; LV: 1.37
(SK: 0.34; FR: 1.05)
0.31
0.25
0.30
0.28
(CZ: 0.29; PT: 0.44)
(RO: 0.26; BE: 0.31))
(IE: 0.32; LU: 0.38)
(GR: 0.31; FI: 0.33)
1.00
0.50
0.60
0.43
(SK: 0.45; AT: 1.41)
(UK: 0.36; AT: 0.42)
(EE: 0.43; LT: 0.95)
(UK: 0.35; LU: 0.54)
-2.87
-1.33
-1.80
-1.20
(CZ: -1.27; LU: -5.88) (SK: -1.03; LT: -2.29) (CZ: -1.01; LV: -2.57) (SK: -0.82; FR: -2.57)
Values are non weighted European averages
In brackets: minimum and maximum values
85
I.6. LABOUR SUPPLY
two other categories. The strongest impact is found in Austria for high skilled women:
0.35%.
For social benefits, the impacts are again weaker than for wages. A permanent increase
of 1% of the growth rate of social benefits will reduce labour supply between 0.02% for
high skilled women aged between 25 and 54 and 0.08% for low skilled women aged
between 15 and 24.
The main endogenous determinant of participation rates to labour market are finally
jobs creation. A 1% increase of jobs creations raises labour supply of 0.25% for high
skilled women aged between 25 and 54 and of 1.12% for low skilled women aged between
15 and 24. This last figure underlines the difficulty of reducing unemployment for young
women with low school attainment level and for which an increase of 1% of job opportunities could provoke an augmentation more than proportional of their labour supply in
the short term. It it nevertheless not the case for every country, the impact depending on
the initial participation rate to labour market of this category of women. The smallest
value of the flexion coefficient of labour supply to job opportutnities is found for Czech
Republic, with 0.5 and the highest value for Luxembourg where it reaches 2.29. We find
also high values of flexion coefficients for low skilled women aged between 55 and 64,
with 1 in average, a minimum value of 0.45 in Slovakia and a maximum value of 1.41 in
Austria. For the greater population category, aged between 25 and 54, the value is 0.28
in average, with few differences between skill and gender groups and between countries.
For the youngest population category, aged between 15 and 24, one can see also
than school enrolment ratios, than can be endogenized on expenditures in eduction
in NEMESIS, could influence very importantly the activity rates, and therefore the
unemployment rates of both women and men, and of both low skilled and high skilled
persons. In Europe, in average, an increase of 1 point of the school enrolment ratio of
low skilled women reduces their labour supply of 2.87%, with a minimum of 1.27% in
Czech republic and a maximum of 5.88% in Luxembourg. We have the more limited
impacts of changes in school enrolment ratios on activity rates for high skilled men, with
an elasticity of 1.2% in Europe in average, a minimum value of 0.82 in slovakia and a
maximum value of 2.57 in France.
86
Section I.7
Taxation and subsidies
I.7.1 Institutional sectors accounts
The main data source was the Eurostat database, completed if necessary by national
sources (mainly for Luxemburg, Denmark and Norway). The data availability for some
countries (Ireland, Luxemburg, Hungary, Malta,Slovenia and Romania) were too weak
for building agents account for them, but main taxes and subsidies were integrated. All
data and the sequence of accounts follows the European accounting framework ESA956 .
The different institutional sectors that are represented are the General Government(GG),
Households and Non-Profit Institutions Serving Households (HNPISH), Financial Corporations (FC), Non-Financial Corporations (NFC), all of which are of course linked to
the sectoral nomenclature of the model.
The split of households and NPISH’s was not possible for most countries, so it had
been decided not to separate them for the moment, this will be done as soon as data
will be available. This huge database has been checked (agregations, paid/received...)
completely and corrected if errors were encountered.
Agents accounts are implemented from the production account up to the Acquisition
of non financial assets account (i.e. up to the b9 Net lending (+) /net borrowing (-)).
I.7.2 Public finances
The main taxes and subsidies considered are see [126] for more information:
Taxes on production and imports (D.2)
• Taxes on products (D.21)
6
ESA: European System of Accounts
87
I.7. TAXATION AND SUBSIDIES
– value added type taxes (D.211)
– Taxes and duties on imports excluding VAT (D.212)
– Taxes on products, except VAT and import taxes (D.214)
– Excises duties and consumption taxes (D.214a)
∗ Mineral oil
∗ Alcoholic beverage
∗ Tobacco
∗ Electricity
∗ Non alcoholic beverages
∗ ...
– Other taxes on products (D214-D214a)
• Other taxes on production (D.29)
Subsidies (D.3)
• Subsidies on products (D.31)
• Other subsidies on production (D.39)
Current taxes on income, wealth, etc. (D.5)
• Taxes on income (D.51)
• Other current taxes (D.59)
Social Contributions (D.61)
• Actual social contributions (D.611)
– Employer’s actual social contributions (D.6111)
– Employees’ social contributions (D.6112)
88
– Social contributions by self and non-employed persons (D.6113)
• Imputed social contributions (D.612)
Capital Transfers (D.9)
• Capital Taxes (D.91)
• Investment Grants (D.92)
I.7.3 Focus on most important taxations system
We will focus here on the main important taxation part of the model. The main
difficulties in sectoral applied modelling is to apply the right taxation rate and/or subsidy
to the right sector. For a part of the taxation system, some information are availlable
in the EUROSTAT datasets, while for others some assumptions had been made.
Value added type taxes (D.211)
The VAT is probably the most difficult tax to be implemented in a model such as
NEMESIS. Firstly if we consider the datasets needed the available information on VAT
particularities is often too detailed for modeling as on one side the sectoral disaggregation
of the model allow a strict differentiation of the different VAT rates applicable to the
different products and services, but on the other side, calculating VAT rates applicable to
one sector based on actual rates is fastidious and need numerous assumptions concerning
the sharing of each rate in the same sector, taking into account existing exemptions
and therefore complicate more the linking of the sectoral taxation system up to the
macro-econoomic one. Secondly, considering the formalisation in itself, the traditional
framework in applied modeling for integrating VAT, is to calculate implicit tax rates for
each sector, with the drawback that for analysing the consequences of the modification
of one VAT rate, the implicit rate has to be recalculated ex-ante with all errors that it
may imply.
In the NEMESIS model the implicit VAT rate is fully modelised flowing from the
actual VAT rates up to the product/sector implicit rate. The implicit rate is thus the
89
I.7. TAXATION AND SUBSIDIES
result of linear combination of the different rates (0 rate, super reduced rate, reduced
rate 1, reduced rate 2, normal rate and the parking rate) and of the different shares of
each products in the consumption.
The main information sources for building the data neede are furnished by the commission and the taxation and customs DG.
For calculating final consumption VAT rates as precisely as possible, the most disagregated data of the COICOP nomenclature were used.
Starting from this the VAT bloc is composed of three components:
1. The actual VAT rates series for the period 1980-2007, for all european countries.
2. The share for each COICOP three digit category of the different rates applied
3. Finaly coefficients allowing to flow from the COICOP three digit nomenclature to
the NEMESIS one
Hence our final implicit VAT rate is the linear combination of these three datasets (the
exemple shown below is for the NEMESIS Medical Care category):
T V AIM P medcar = shpmedc ·
X pmed
αT,c
·T
T
+ sheconsc ·
X
econs
αT,c
·T
T
+ shhospc ·
X hosp
αT,c · T
T
Avec:
• shpmed, sheconset shhosp, respectively the share of the COICOP three digit
«medical products and apparel», «external consultation???» et «hospital services»
categories in the Medical care category of NEMESIS
• T = T 0, T SR, T R1, T R2, T N, T P , the differnet existing rates, 0 rate, super reduced rate, reduced rate(s) (sometimes two rates), normal rate and the parking
rate.
pmed
• αT,c
, the share of the pmed category to which we apply the rate T in country c.
Taxes on products, except VAT and import taxes (D.214)
90
These taxes were splited into two broad taxes, Excises duties an consumption taxes
(D214a) on one part, and other taxes on products (D214-D214a) on the other part. The
distinction between the different excises duties and there allocation between the sectors
were made possible using DG taxation and custom Union informations (see [122]) , the
same database was used for allocating the rest of Taxes on products, except VAT and
import taxes. Hence, aside the three main Excises duties (alcoholic beverages, tobacco
and mineral oil), some countries have other excises duties (electricity, non alcoholic
beverage...), all of which had been incorporated in the model.
Social contributions
Employers’ social contribution (D6111) are splited into sectors using the sectoral data
on D11 wage and salaries and D1 Compensation of employees that are available on
Eurostat, employees’ social contribution (D6112) as well as imputed social contribution
(D612) are splited between sectors depending on relative compensation of employees
as no other data were available, while Social contributions by self and non-employed
persons (D6113) are calculated only at the macroeconomic level, the figures I.22 and
I.23 sums up the functiuning of the social contribution bloc. Then each type of social
contribution is allocated to institutional sectors account ( Gov: general governement,
FC: financial corporations, NFC, Non Financial corporations, H&NPISH: Households
and non profit institutions serving households) through fixed shares.
Figure I.22.: Social Contribution paid
91
I.8. SECTORAL INTERDEPENDENCIES
Figure I.23.: Social contribution received
Section I.8
Sectoral Interdependencies
In sectorally detailled models, macroeconomic dynamics is driven by sectoral ones, the
mix of the 32 sectors evolutions will descibe the strength and weaknesses of each european
economy modeled, and hence describe their respective macoeconomic results in terms
of economic growth, employment,etc.... Therefore, interlinkages between sectors are
thus an important part of the model scheme as they will reflect the different sectoral
tendencies either in the short/medium term or in the long term.
I.8.1 Demand flows to products
Each sector, in order to produce a certain quantity of its product (supposed to be
homogenous), needs production factors: the five factors described in NEMESIS are
employment, intermediate energy demands, final energy demands, materials demands
and investments. A sixth factor could be added, even if it is not directly treatened
as a pure production factor, this is the research and development expenditures. In
the NEMESIS model, sectoral interdependencies are handled through energy demands
(intermediate and final), materials demands, investment demands, and through R&D
rent and knowledge spillovers (that will be explained separately). Each of this factor
92
demands is addressed to one or more sectors, other sectors but also the demanding sector
(reflecting the intra-branch cousumption). These interactions are presented in figure I.24
below.
These interactions between the sectors are threatened in two ways in the NEMESIS
model depending on the simulations runs term.
In the short/medium term, one can consider that substitutions between products
are rather weak, as input substitution often requires changes in the production process
(employees’ formation, capital structure,...) thus the coefficients of the different matrices
are considered to be fixed and the demands are formulated as:
j
j
DEMC,i
= βc,i
· F ACT DC,i
(I.64)
With this formulation a sector can not shift from a product to an other. If the sector
that produces product j improve its productivity (that is produce the same product but
with a lower price), every sectors i that uses the product j will face a lower investment
price (ceteris paribus). By using¯fixed coefficient matrices, this will only leed for the i
to a smaller global investment price, but this sector can not choose to buy more of the
j’s good instead of other ones. Consequently, we can easily see that the j has no gain
to make TFP in order to lower its price. Theoretically, if the i’s sector lowers its price,
this must lead to improve its market share. The fīxed coefficient matrices are therefore
not compatible with the developments proposed.
Consequently, in order to keep the global theoretical coherency of the model, we have
to endogeneise these coefficients. We choose to endogeneise the share of each product j
in the total factor demand of sector i as a cost minimisation on a CES function. Firms
determine their global factor demand using its production function, then minimises
the cost of approvisioning this global demand from a C.E.S function of elasticity of
substitution i .
The substitution elasticity of that C.E.S function ought to be sufficiently slack to not
to conduct to too sharp fluctuations for technical coefficients. In order to lowers as more
as possible quick shifting between the different sources of supply, we add in this derived
shares adjustment delays, the formulation we choose for each type of firms matrices
(Intermediate Consumption, final Energy demand and Investment) is the following:
j
COEFC,i
j
= λi · coef matC,i ·
P DC,i
j
P VC
with
93
!i
j
+ (1 − λi ) · coef matC,i
(I.65)
Figure I.24.: Sectoral interdependencies in NEMESIS
94
• λi the adjustment delay,
• P DC,i the global factor demand price of sector i
• P VCj the sale price of the product j and
• i the price elasticity.
Using this formulation, if the sector j decrease its price (other sectors unchanged) the
share of demand of the sector i asked to sector j will increase depending on the price
elasticity and the adjustment delay.
The price elasticities can not be estimated and was selected from other studies between
0.05 and 0.1, and the delays taken between 4 years and 10 years.
Of course, endogeneising all these coefficient increases dramatically the number of
equations of the model (around 60 000 equation added).
Some remarks must be here formulated:
1. The adoption of an optimisation procedure for the choice of these coefficients,
grounded on a re-agregation function of a C.E.S. type, allows to easily explicitate
the products components of investment, intermediate consumption and energy sectoral demands and is moreover fully coherent with the framework we choose for
closing- up the NEMESIS supply side to grounded microeconomic behaviour. Nevertheless, coefficients so calculated are not those determined by national accounts
statisticians.
2. But in the baseline projections, coefficients evolution must be exogeneised, the
endogenous determination of thousands of coefficients complicate the model resolution.
This formalisation of matrices’ coefficients had been tested using several economic and
environmental policies and is operational.
I.8.2 technological progress interactions.
Endogenous technical change in NEMESIS needs to takes into account technologicals
interactions between sector. T.C needs three kind of interactions: knowledge spillover,
rent spillover and technology flows.
95
Knowledge spillovers
Knowledge spillover represent the case that one sector could benefit from R&D activities of another sector without pay monetary compensation. An example of knowledge
spillovers is when one invention might lead to a new ideas for different inventor. In
Nemesis model knowledge spillover relies positively R&D expenditure of one sector to
the knowledge stock of another. We distinguish national and international knowledge
spillover.
Knows,c,t = f (RDs,c,t0 , SKNs,c,t00 , SKIs,c,t00 , RD/Y )
• s:sector, c: country and t: time
• Know is the knowledge stock of sector
• RD is the R&D expenditure of sector
• SKN is the national Knowledge spillovers
• SKI is the international Knowledge spillovers
Measure of knowledge spillover follow the seminal work of Jaffe [194] and Verspagen
[317] which develop methods to take into account non-incorporated or disembodied R&D
spillovers. This concept of technological link is called technological proximity because
it is derived from the relative position of sector in a technological space. Concretely
technological proximity matrix assume that the main IPC code into which a patent is
classified provides a good proxy of the producing sector of the knowledge, and the listed
supplementary IPC codes given an indication for technology spillovers to other industrial
sectors. The more two sector are close to each other, the higher is the effect of R&D
expenditure.
For national knowledge spillover we assume:
SKNi,c,t =
X
θij · R&Dj,c,t0
j6=i
For international knowledge spillover we assume :
SKIi,c,t =
XX
βcd θij · R&Dj,d,t0 ·
d j6=i
96
Figure I.25.: Knowledge spillovers
where θij is the technology proximity between sector i and j and βcd is the economy
distance between c and d
Matrix used is Verspagen matrix transformed to Nemesis sectorial classification.
Rent Spillovers
Second kind of technological interaction is rent spillovers. Rent spillovers refer to the
case where R&D intensive input are purshased from other industries at less than their
fully adjusted price. This failure to embody correctly a higher quality into output price
is the consequence of imperfectly monopolistic pricing arising from competitive pressure
on innovating industry. For Griliches [159] rent spillover is a problem of measuring
capital equipement, materials and their price correctly and not a case of pure knowledge
spillovers. If innovation are sold at prices that entirely reflect quality improvment i.e.
on hedonic price index, problem does not arise.
In Nemesis model, we assume that prices do not reflect totaly quality improvment.
Importance of rent spillovers relatively to the adjustment of price will depend on the
degree of competition. Low degree induce more importance on rent spillovers effect than
on price adjsutment.
In Nemesis model, rent spillover originate exclusively from economic transaction. We
assume that rent spillover diffuse proportionaly to the level of intermediate input flows
between sectors. This level is simply measured by Input-Output matrices. It reslut
that factor productivity is not only affected by its own R&D but also by productivity
improvment in another sector to the extend of its purchase.
97
I.9. HOUSING INVESTMENTS
Figure I.26.: Rent Spillovers
RentSi,t =
X
δij Innovj,c,t0
j6=i
Where δij is the I-O matrix coefficient and Innov is product innovation of sector j.
Technology flows
Nemesis model allows some innovation to be produce in one sector and implemented
in another. Innovation which improve productivity could be made in own sector or
purchase to another throught patent transaction. To link sector innovation in userproducer principle we use the so-called “Yale matrices”. This matrix is constructed on
the basis of data from the Canadian patent Office. This last (exclusively in the world)
assigns principal user and producing sectors to each patent. We use matrices made by
Johnson [197] and extent it to other country.
Section I.9
housing investments
I.9.1 Methodology7
98
Households investment was already modelled in the NEMESIS model but in a very
roughly way, and the implementation of the land use module implies a better modelling
of it. We will present in this section the new formalisation and estimate of households
investment in the NEMESIS model.
Either at theoretical or empirical point of view, interactions between residential market and macroeconomic are not very analysed (Leung, 2004[230]), this explains that the
modelling of households investment in large applied economic model is not very developed, or at least is not highlighted compared to others macroeconomic variables. This
fact is reinforced by the lack of consensus regarding households investments formalisation, mainly due to national regular regimes but also because of real estate bubbles
(Baghli et al., 2004[23]). Furthermore, there are two aspects on the housing investments.
The first one is associated with the services provides by the housing which can be view
as a consumption and the second one concerns the wealth effect related with the ownership of housing8 . We analyse how some large applied economic models and especially
econometrics ones represent the housing investments9 .
In the INTERLINK model developed by the OECD (Richardson 1988[275]), Egebo
and Lienert (1988[117]) estimate housing stocks for six main OECD countries with a
stock adjustment model. In their modelling, the variation of the housing stock is a
function of the households real disposable income per capita, the real interest rates (in
moving average), the housing relative price (either relative price of housing services or
relative price of housing investment), the existing housing stock per capita at the previous
period, the variation of unemployment rate and finally a partial adjustment term10 .
For the MIMOSA model11 , Chauffour and Fourmann (1990[59]) formalise the investment rate (i.e. the ratio between housing investment and housing stock) as a function of households income per capita (smoothed variable), real housing investment price
(smoothed variable), real interest rate, previous housing stock per capita and unemployment rate change. A very similar version of housing investment model is developed for the
french economy (Bonnet et al. 1994[33]) in the AMADEUS model (INSEE 1998[185]),
they also estimate the investment rate but they replace households income per capita by
7
The section depends for a part on a study realised in the ERASME laboratory (Lécina, 2008[224])
and especially for the literature survey.
8
We do not treat the trade-off between buying a housing or renting it (see e.g. Arrondel and Lefebvre
2001[16] or Henderson and Ioannides 1983[173]). Furthermore, from macroeconomic point of view,
the housing investment only concerns new housing purchase, the second hand market is not considered
even if it follows the new housing purchase market, as demonstrated by Demers (2005[98]) for Canada.
9
We do not present the estimate results of these studies but we will use them to compare our estimate
results in the following section.
10
All variables are expressed in logarithm expect for real interest rate and unemployment rate.
11
See Delessy et al. 1996[97] for a description of the MIMOSA model.
99
gross households income and unemployment change by employment variation.
Another interesting model is the European HERMES model (1993[191]) composed by
seven individual macro-sectoral models for Belgium, Netherlands, France, Germany, Ireland, Italy and United-Kingdom. Only four models introduce the housing investment in
their respective presentation but in one of them, the Dutch model (Mot et al. 1993[247]),
housing investment is described as exogenous. According to the French HERMES model
(Assouline and Epaulard 1993[20]), housing investment is described as a model based on
the desired housing stock adjusted with the help of an error correction model (Engle and
Granger 1987[119]). This desired housing stock depends on households income, relative
price, active population and interest rates. In the Irish HERMES model (Bradley et al.
1993[39]), the housing investment is modelled by the housing investment per capita that
is a linear function of real per capita personal disposable income, government transfers
for housing, interest rates and inflation12 . Finally, Bosi et al. (1993[35]) use similar
housing investment model for the Italian HERMES model. They relate, in logarithmic
form, housing investment per capita with income per capita and relative price of housing
investment and they include also a dynamic partial adjustment.
More recently, Chauvin et al. (2002[60]) develop a error correction model for the
Emod.fr model in which they model housing investment rates (the ratio between housing investment and households real disposable income) with real disposable income,
unemployment rate and interest rate as explanatory variables. In this case, the use of
housing investment rates, in a error correction model, imposes a long term elasticity
between households investment and real disposable income equals to one13 .
Finally, a very recent description of the MESANGE model (see Klein and Simon
2010[214]) housing investment is also modelled with an error correction model in which
they link for short term, housing investment variation with previous variation, housing
investment price variation, real short term interest rate variation (3 month) and unemployment rate variation while in the long term equation, they only keep the link between
real disposable income and real long term interest rate (10 years)14 .
As one can see in the previous descriptions, there are few differences in the explanatory
variable used to describe households investment in the models, even if the endogenous
variable are slightly different (investments rates, investment in level and stock of housing,
...). This can be summarised as follows:
• Firstly, the real disposable income allows taking into account the purchase ability
12
All variables are expressed in logarithm.
We will specify these properties in the following section.
14
All variables are expressed in logarithm except interest rates and unemployment rate.
13
100
as well as the borrowing power of households.
• The purchase of housing, in most case, requires a long term loan. This aspect
is included with the help of interest rates, the payback power being reduce when
interest rates increase.
• In addition, socio-economic aspect can also act on the housing investment, and
particularly, the demography can play a non negligible role, it is why some models
use per capita variables as explanatory variables.
• The relative housing investment price, generally the ratio between housing investment price and consumption price, allow the modelling of the traditional substitution effect. But, in the case of housing, which is a asset for the households, the
investment price acts also on the expected wealth - insomuch as it follows housing
stock price - and this wealth effect can be effectively very important as illustrated
by the recent real estate bubble. Thus, housing investment price plays a double
role, it increases purchase cost but it also raises housing expected value.
• The general economic context is generally represented through unemployment rate
or employment which are relatively important for the households expectations on
economic futures and then for their confidence on their payback capacities.
• Finally, other variables such as government subsidies for housing, like in Irish
HERMES model (Bradley et al. 1993[39]), can act on households investment
decision. Some of them are already included in the real disposable income, this is
the case for instance of transfers. It could also be interesting to include specific
variables that could reflect change in national regular regimes but as we use a panel
of 12 countries, the time required to get good and reliable information constrains
us to exclude this option.
According to the modelling, we can see that the most recent studies (Chauvin et al.
2002[60] and Klein and Simon 2010[214]) use the error correction model that we also
choose for NEMESIS because error correction model allow the distinction between two
models: one for short term and a second for long term (equilibrium). Nevertheless, the
error correcting model requires a deep examination of variables with numerous econometric time series tests, and especially, it requires unit roots and cointegration tests. Thus,
we present in the following sections the data used for the modelling, the unit roots
and cointegration tests, following by the error correction model estimate and finally we
display some sensibility analysis on the estimated model.
101
I.9.2 The data
All economic data used for the estimate come from the Annual Macro-ECOnomic
database (AMECO 2008[13]) of the European Commission’s Directorate General for
Economic and Financial Affairs (DG ECFIN) which provides structured and coherent
data on national account and especially times series for prices. Population data come
from Eurostat Population database (Eurostat 2008[130]). Thus we have the following
variables for 12 European countries15 from 1995 to 2008:
• Households and Non-Profit Organisation (NPO) real gross fixed capital formation16
(GF CF ) which is the GF CF in value divided by the total economy gross fixed
capital formation price (PGF CF )17 ,
R
• The real total economy gross fixed capital formation price (PGF
CF ) which is the
ratio between PGF CF and the consumption price (PCON S )
• The households real disposable income (REV Q ) which is the ratio between households disposable income and consumption price,
• The long term and short term real interest rates (T X LT and T X CT ) which are the
ratio between interest rates and consumption price,
• The number of unemployed persons (U N EM P ),
• And the population divided in 5 age groups, the “very young” (P OP Y Y ) between
0 and 4 years, the “young” (P OP Y ) between 0 and 19 years old, the “medium”
(P OP M ) between 20 and 39 years, the “medium-old” (P OP M O ) between 40 and
59 years old and the “old” (P OP O ) more than 60 years.
All these variables are transformed in logarithm except for the real interest rates. We
present in the following section the unit root tests and cointegration tests realised on
these variables.
15
Only EU-15 countries: Belgium, Denmark, Germany, Spain, France, Italy, Netherlands, Austria,
Portugal, Finland, Sweden and United-Kingdom.
16
Households gross fixed capital formation and households and NPO gross fixed capital formation in
residential are unavailable.
17
Price of households and NPO gross fixed capital formation is unavailable.
102
I.9.3 Model estimate and results
We estimate a error correction model (Engle and Granger, 1987[119]) with the cointegrated variables presented above. Thus, we have two models, one for long term relationship and a second for short term relationship. The long term relationship is defined
by the general model I.66 whereas short term equation is defined by general model I.67.
pR
Q
Q
pop
gf cfi,t = αi + δi ti + βirev revi,t
+ βi gf cf pR
gf cf,i,t + βi popi,t
T X LT
+ βiunemp unempi,t + βi
LT
T Xi,t
+ εi,t
pR
Q
Q
pop
4gf cfi,t = µi + θirev 4revi,t
+ θi gf cf 4pR
gf cf,i,t + θi 4popi,t
+
θiunemp 4unempi,t
+
(I.66)
LT
LT
θiT X 4T Xi,t
+
θires ε̂i,t−1
(I.67)
+ ui,t
As, we use panel data, we impose identical parameters for each country for long term
and short term equations (see equation I.68) except for intercept (αi and µi ) and trend
(δi ). ε̂ are estimated residuals from long term relationship.
βiU = β U ∀i
(I.68)
θiZ = θZ ∀i
LT and Z = rev Q , pR
LT , ε̂.
Where U = rev Q , pR
gf cf , pop, unemp, T X
gf cf , pop, unemp, T X
We also estimate the models I.66 and I.67, either keeping free the long term relationship between households gross fixed capital formation and households real disposable
R
Q
income (β rev ) and gross fixed capital formation real price (β pgf cf ) or constraining these
relationships. Table I.8 displays the estimated results of both models, with and without
constrained parameters.
Looking at the unconstrained models, we can see that parameters of long term model
are all significantly different to zero except for population. The long term elasticities
of households gross fixed capital formation (GF CF ) with respect to households real
disposable income (REV Q ) is equal to 0.52. If this elasticity can appear relatively good,
this result supposes a progressive decrease of the ratio between households investment
in level and their income in level i.e. the share of households investment in their budget
tends to decline. Thus, we can not keep this results, and we must impose, as in the studies
103
presented above (for instance in Chauvin et al. 2002[60]), the long term relationship for
households real income.
In addition, the elasticity of households gross fixed capital formation with respect to
R ) is estimated to 1.47. This
households gross fixed capital formation real price (Pgf
cf
positive and superior to unity value of price elasticity can be quite surprising, however,
as we mentioned above, housing is a spending for households but it is also an asset, as
a consequence, an increase of investment price increases purchase cost but raises also
the anticipated value of the asset. Thus, as our data cover the 1995 to 2008 period, it
includes the recent real estates bubbles that occurs in most of the EU-15 countries, and
an elasticity of 1.47 can traduce the bubble effect of the housing price. Nevertheless, the
introduction of such a parameter value in a economic model such as NEMESIS would lead
to misleading results, therefore we constrained this elasticity at -0.5%, value in adequacy
with those estimated in the literature. For instance, Egebo and Lienert (1988[117]) find
elasticities of -0.45% for France, -0.56% for United Kingdom and -0.44 for Italy while
Chauffour and Fourmann (1990[59]) find elasticities about -0.4% for France, -0.4% for
Italy and -0.3% for West Germany. More recent studies, imposed an elasticity or exclude
the real price of housing investment of their models in order to avoid such results.
In the constrained models, all parameters are significantly different to zero, at least at
10% level except for long term interest rate in the short run relationship and population
in the long run one. Regarding the effect of the long term interest rate, the null parameter
seems not so surprising, and even using the short term interest rate does not provide
better parameters estimates, consequently we keep the hypothesis that long term interest
rate has no impact at short term. The parameter estimates for the population appears
to be strong in the short run (+3%)18 , but does not influence households investment
in the long run. The unemployed persons elasticity is negatively related to housing
investment with a short term elasticity of -0.28% and a long term elasticity a little bit
lower with -0.13%. For households investment prices, the positive short term elasticity
(1.2%) represent the bubble effect where households anticipate the increase of the value
of their assets, while in the long run the more traditional behaviour dominates and
explains the negative parameter value (-0.5%). Finally, an increase of 1% of households
real disposable income raises the households gross fixed capital formation about 0.66%
at short term and 1% at long term.
We tried to individualise some coefficients either in long term or short term model,
but due to our limited sample (168 obs.) and the increasing number of parameters, the
18
We will limit the short term effect at 1.5% in the implemented version of housing investment in
NEMESIS to keep a certain stability even at short term.
104
Table I.8.: Estimates results of households gross fixed capital formation error correction
model
Model I.66 (Long Term)
β rev
Q
R
β pgf cf
β pop
β unemp
βT X
LT
Model I.67 (Short Term)
Unconstrained
Constrained
0.5196∗∗∗
1(a)
(0.3574)
–
1.4731∗∗∗
-0.5(a)
(0.3465)
–
-0.2841
-0.4065
(0.5338)
(0.5736)
-0.1398∗∗∗
-0.1309∗∗∗
(0.0475)
(0.0411)
-0.0178∗∗∗
-0.0143∗
(0.0068)
(0.0074)
θrev
Q
R
θpgf cf
θpop
θunemp
θT X
LT
θres
Unconstrained
Constrained
0.7575∗∗∗
0.6633∗∗
(0.2896)
(0.2809)
1.6091∗∗∗
1.2401∗∗∗
(0.3707)
(0.3523)
3.0925∗∗
3.2721∗∗∗
(1.2115)
(1.1696)
-0.2502∗∗∗
-0.2849∗∗∗
(0.0502)
(0.0494)
-0.0074
-0.0067
(0.005)
(0.0048)
-0.5418∗∗∗
-0.5302∗∗∗
(0.0882)
(0.0739)
∗, ∗∗, ∗ ∗ ∗: parameter significantly different to zero at 10%, 5%, 1% respectively.
(a) : fixed parameters.
results are globally disappointing, few coefficients being significant. The only parameter
providing relatively good results when individualised, it is the adjustment parameter
(θires ), which estimates are given for short term equation in Table I.9.
We observe that the other coefficients are close to their value with common adjustment
parameters. The short term elasticity is a little bit lower for real disposable income
and unemployed persons, stronger for population and still not significant for long term
interest rate. Now looking at the individualised adjustment coefficients, we first see that
all are negative. But the coefficients for Germany, France, Italy, Austria and Portugal are
not significant at 10% level. And we can also see that the range of significant coefficients
is confined between a minimum of -0.5 in Belgium and a maximum of -0.77 in Denmark.
To analyse the effect of adjustment parameters as well as the effects of short and long
105
Table I.9.: Estimates results for short term model with individualised adjustment coefficients
Individualised adjustment parameter: θires
BE
-0.5005∗∗
DK
-0.7661∗∗∗
DE
-0.2594
ES
-0.7418∗∗
FR
-0.3308
IT
-0.3787
NL
-0.5545∗
AT
-0.1028
PT
-0.4417
FI
-0.5214∗∗
SE
-0.7269∗∗∗
UK
-0.6318∗∗∗
Fixed parameters
θrev
Q
0.523∗
θpgf cf
R
1.2497∗∗∗
θpop
4.0173∗∗∗
θunemp
-0.228∗∗∗
θT X
LT
-0.006
∗, ∗∗, ∗ ∗ ∗: parameter significantly different to zero at 10%, 5%, 1% respectively.
term parameters, we realise a sensibility analysis by introducing standard stocks in the
error correcting model.
I.9.4 Sensibility analysis
We make a sensibility analysis of housing investment error correction model estimated
in the previous section by introducing shocks on one variable and keeping the other
ones fixed. Figure I.27 and I.28 display the model responses to a permanent shock of
1% on each variable with the exception of long term interest rates that had been raised
by 1 point of percentage permanently. Figure I.27 presents the responses for common
adjustment parameters (-0.53) whereas figure I.28 compares results with individualised
adjustment parameter of Denmark (-0.77) and Austria (-0.10) with common parameters
106
case for 1% shock on real disposable income.
Figure I.27.: Sensibility analysis with common adjustment coefficient
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
-0.5%
1
2
Rev
3
4
P
5
6
Tx
7
8
9
U
10
Pop
R
LT
With Rev = rev Q , P = Pgf
, U = unemp and P op = pop.
cf , T x = T X
We can see in figure I.27, that, in accordance with estimate results, the short term
effect of population is very strong but decreases progressively to reach zero at long
term. Thus, population rise has only a transitory effect on households gross fixed capital
formation. At the opposite, the real disposable income shows a moderate short run
effect on housing investment, the first year, households investment increases of about
0.6% and tends to 1% (as imposed) at long term. Furthermore, the real price of housing
investment has a particular dynamic. In a first time, an raise of 1% of investment price
pushes housing investment up to 1.25% what can be view as a transitory bubble effect.
And in a second time, the short term positive effect declines to reach its long term
equilibrium of -0.5% (as constrained). Thus a perpetual shock on housing investment
real price plays, at short term, as a bubble effect but this bubble effect progressively
declines to finally reduces housing investment. Looking at unemployment effect, we
observe a bigger short term shock (-0.3%) than the long term with -0.13%. Finally, as
demonstrated by estimated parameters, short term effect of the long term interest rates
107
is null, and long term effect starts one year after the introduction of the shock, to reach
-0.15% at long term.
Figure I.28.: Model response to 1% shock on households real disposable income: Comparison according to adjustment coefficients
1.1%
1.0%
0.9%
0.8%
0.7%
0.6%
0.5%
0.4%
0.3%
0.2%
0.1%
0.0%
1
2
3
Denmark
4
5
6
Austria
7
8
9
10
Common
Figure I.28 shows the effect of different adjustment parameter on the response dynamic. Denmark, where the adjustment parameter is the higher with -0.77, tends more
rapidly to its long term equilibrium, therefore the Danish average adjustment decay is
about 0.3 years i.e. 50% of the adjustment to the long term equilibrium is done in 4
months. At the opposite, the average adjustment is about 9 years for Austria, where the
adjustment coefficient equals -0.1 thereby, full adjustment is not still realised at t + 10.
For the common adjustment parameter (-0.53), the average adjustment decay is 0.9 year.
We have displayed the responses of the error correction model for different variables
and for different adjustment parameters and we showed their respective importance. We
keep, for the implementation in the NEMESIS model, the estimated coefficients except
for the short term effect of population. In fact, the estimate value of this parameter
seems to strong and we decide to reduce it at 1.5%, i.e. a little bit higher than the unity
and we still suppose that its long term elasticity is null. Similarly, we impose a null short
term effect of long term interest rate and finally we take the individualised adjustment
parameters for estimated countries and we use the common adjustment parameter for
108
not estimated European countries.
I.9.5 Concluding Remarks
We have now an endogenous model for households investments. This model is error
correcting model that determine housing investment in each European countries according to its prices, households real disposable income, total population, unemployed
persons and long term interest rate. With the housing investment for each European
country, we can calculate their housing stock using perpetual inventory method. And
finally, we can determine the national land used by housing by using the density coefficients that link national housing stock with its land use.
109
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