the nemesis reference manual - seureco-erasmeon the supply side, nemesis distinguishes 32 production...

The NEMESIS Reference Manual

CoordinationErasme Team,FranceProf. P. ZagaméB. Boitier,A. Fougeyrollas,P. Le Mouël

Core TeamsNational Technical University of Athens, GreeceProf. P. Capros,N. Kouvaritakis

Federal Planning Bureau, BelgiumF. Bossier,F. Thierry, A. MelonThe 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 15I.1. Current version of the endogenous technical change module . . . . . . . . 15

I.1.1. The stock of knowledge . . . . . . . . . . . . . . . . . . . . . . . . 16I.1.2. From stock of knowledge to innovation . . . . . . . . . . . . . . . 17I.1.3. innovation to economic performance . . . . . . . . . . . . . . . . . 18I.1.4. Calibration in the model . . . . . . . . . . . . . . . . . . . . . . . . 21

I.2. New production functions with embodied endogenous technical changeand skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21I.2.1. Multi-level, putty-semi-putty CES production functions . . . . . . 22I.2.2. The Endogeneization of Technical Change . . . . . . . . . . . . . . 26I.2.3. Estimation results . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

I.3. Households’ final consumption . . . . . . . . . . . . . . . . . . . . . . . . 47I.3.1. Aggregate consumption . . . . . . . . . . . . . . . . . . . . . . . . 47I.3.2. Allocation of aggregate Consumption . . . . . . . . . . . . . . . . . 49

I.4. External trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54I.4.1. Intra-European trade . . . . . . . . . . . . . . . . . . . . . . . . . . 54I.4.2. Extra European Trade . . . . . . . . . . . . . . . . . . . . . . . . . 57I.4.3. Imports and Exports prices . . . . . . . . . . . . . . . . . . . . . . 59

I.5. Wage setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61I.5.1. Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3

Contents

I.5.2. Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63I.5.3. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64I.5.4. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

I.6. Labour supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75I.6.1. The data on participation rates of working-age population . . . . . 76I.6.2. Determinants of participation rates . . . . . . . . . . . . . . . . . . 78I.6.3. Calibration of labour supply . . . . . . . . . . . . . . . . . . . . . 83

I.7. Taxation and subsidies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87I.7.1. Institutional sectors accounts . . . . . . . . . . . . . . . . . . . . . 87I.7.2. Public finances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87I.7.3. Focus on most important taxations system . . . . . . . . . . . . . 89

I.8. Sectoral Interdependencies . . . . . . . . . . . . . . . . . . . . . . . . . . . 92I.8.1. Demand flows to products . . . . . . . . . . . . . . . . . . . . . . . 92I.8.2. technological progress interactions. . . . . . . . . . . . . . . . . . . 95

I.9. housing investments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98I.9.1. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99I.9.2. The data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102I.9.3. Model estimate and results . . . . . . . . . . . . . . . . . . . . . . 103I.9.4. Sensibility analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 106I.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 . . . . . . . . . . . . . . . . . 17I.2. The stock of knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17I.3. Two types of innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18I.4. Process innovation and economic performance . . . . . . . . . . . . . . . . 18I.5. Product innovation and economic performance . . . . . . . . . . . . . . . 19I.6. CES nesting structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23I.7. Ex-ante and ex-post isoquants . . . . . . . . . . . . . . . . . . . . . . . . . 25I.8. EU National share of high skill on total employment, in 2005 (source:

Eurostat) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36I.9. European high skill share in total employment for NEMESIS sectors,

(source EU-KLEMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38I.10. European high skill share in total employment for NEMESIS sectors . . . 39I.11. Sectoral illustration of the final results . . . . . . . . . . . . . . . . . . . . 40I.12. Ratio of European employee unit cost between high and low skills at

sectoral level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41I.13. Corrected European share of compensation of employees for high skill at

sectoral level in 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43I.14. Allocation of Durable Goods . . . . . . . . . . . . . . . . . . . . . . . . . 50I.15. Allocation of Non Durable Goods . . . . . . . . . . . . . . . . . . . . . . . 50

5

List of Figures

I.16. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70I.17. results whole model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71I.18. Sectoral results P1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73I.19. sectoral results P2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74I.20. Participation rates to labour market of men and women aged 25 to 64,

EU27 + Norway, 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76I.21. Participation rates to labour market of women aged 50 and 64 by skill,

EU27 + Norway, 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77I.22. Social Contribution paid . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91I.23. Social contribution received . . . . . . . . . . . . . . . . . . . . . . . . . . 92I.24. Sectoral interdependencies in NEMESIS . . . . . . . . . . . . . . . . . . . 94I.25. Knowledge spillovers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97I.26. Rent Spillovers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98I.27. Sensibility analysis with common adjustment coefficient . . . . . . . . . . 107I.28. Model response to 1% shock on households real disposable income: Com-

parison according to adjustment coefficients . . . . . . . . . . . . . . . . . 108

6

List of Tables

I.1. Labour compensation growth, period 1998-2005 . . . . . . . . . . . . . . . 65I.2. Unemployment rate , period 1998-2005 . . . . . . . . . . . . . . . . . . . . 66I.3. Price growth and high skill share , period 1998-2005 . . . . . . . . . . . . 67I.4. Labour productivty growth, period 1998-2005 . . . . . . . . . . . . . . . . 68I.5. Coefficients summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69I.6. Estimation results of participation rates . . . . . . . . . . . . . . . . . . . 82I.7. Elasticities of activity rates in NEMESIS in 2008 . . . . . . . . . . . . . . 85I.8. Estimates results of households gross fixed capital formation error correc-

tion model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105I.9. Estimates results for short term model with individualised adjustment

coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

7

Introduction to NEMESIS

The NEMESIS model (New Econometric Model of Evaluation by Sectoral Interdepen-dency and Supply), has been partialy funded under the fifth and sixth RTD FrameworkPrograms of European Commission General Directorate of Research1. It is a systemof economic models for every European country (EU27 less Bulgaria and Cyprus, plusNorway), USA and Japan, devoted to study issues that link economic development,competitiveness, employment and public accounts to economic policies, and notably allstructural policies that involve long term effects: RTD, environment and energy reg-ulation, general fiscal reform, etc. The essential purpose of the model is to provide aframework for making forecasts, or ‘Business As Usual’ (BAU) scenarios, up to 25 to30 years, and to assess for the implementation of all extra policies not already involvedin the BAU. NEMESIS uses as main data source EUROSTAT, and specific databasesfor external trade (OECD, New CRONOS), technology (OECD and EPO) and land use(CORINE 2000). NEMESIS is recursive dynamic, with annual steps, and includes morethan 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 mechanismsare based on econometrics works.

1The 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 ofstrong interdependencies between sectoral activities that are very heterogeneous from adynamic point of view, with leading activities grounded on Research and Development,and from environment and sustainable development with a huge concentration of pol-lutants on few activities. These interdependencies are exchanges of goods and serviceson markets but also of external effects, as positive technological spillovers and negativeenvironmental 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 thedifferent available modules). Although econometrics, the model cannot be classified asa neo-keynesian model, in the new version that built-in the new theories of growth; itescapes also to the classification of general equilibrium model, as it incorporates originalmechanisms that do not refer to the strict orthodoxy of the mainstream neo-classicalapproach, 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 Agri-culture, Forestry, Fisheries, Transportations (4), Energy (6), Intermediate Goods (5)Capital goods (5), Final Consumption Goods (3), Private (5) and Public Services. Eachsector is modeled with a representative firm that takes its production decisions givenits expectations on production capacity expansion and input prices. Firms behaviourincludes very innovative features grounded on new growth theories, principally endoge-neous R&D decisions that allow firms improving their process productivity and productquality. Production in sectors is in this way represented with CES production functions(with the exception of Agriculture which uses Translog functions, and Forestry and Fish-eries where technology is represented with Leontief functions) with 5 production factors: capital, unskilled labour, skilled labour, energy and intermediate consumption, wherealso endogenous innovations of firms come modify the efficiency of the different inputs(biased technical change) and the quality of output (Hicks neutral technical change). Theproduction function was estimated by the dual approach and estimation and calibrationof links between R&D expenditures, innovations and economic performance were pickedup 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 pricingby mark-up (due to innovation that creates monopoly situations). Interdependenciesbetween sectors and countries are finally caught up by a collection of convert matricesdescribing the exchanges of intermediary goods, of capital goods and of knowledge interms of technological spillovers, and the description of substitutions between consump-tion 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 mea-sure of wealth for the households, interest rates and inflation. Variables covering childand old-age dependency rates are also included in an attempt to capture any changein consumption patterns caused by an ageing population. The unemployment rate isused, in the short-term equation (only), as a proxy for the degree of uncertainty in theeconomy. Consistent with the other behavioural equations, the disaggregated consump-tion module is based on the assumption that there exists a long-run equilibrium butrigidities are present which prevent immediate adjustment to that long-term solution.Altogether, the total households aggregated consumption is indirectly affected by 27different consumption sub-functions through their impact on relative prices and totalincome, 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 factorin this choice – it allowed an emphasis to be put on intra-EU trade flows, which are alarge portion of the total trade in the EU. The intra- and extra-EU export equations canbe separated into two components, income and prices. The income effect is captured bya variable representing economic activity in the rest of the EU for intra-EU trade, and avariable representing economic activity in the rest of the world for extra-EU trade. Pricesare split into two sources of impacts in each of the two equations (intra- and extra-EUtrade). For intra-EU trade, they are the price of exports for the exporting country andthe price of exports in other EU countries. For extra-EU trade, prices impacts comethrough the price of exports for the exporting country, and a rest-of-the-world pricevariable. The stock of innovations in a country (which, in NEMESIS, is taken relative tothe total innovation stock in Europe in a particular sector) is also included in the exportequations in order to capture the role of innovations in trade performance and structuralcompetitiveness. For imports, equations are identical for both intra- and extra-EU trade.The income effect is captured through domestic sales by domestic producers, while theprice effects are represented in both the import price, as well as the price of domesticsales by domestic producers. The stock of innovations is again included to account forthe effects of innovations on trade performance.

The wage equations, which determine in NEMESIS the dynamics of prices and in-comes, are based on a theory of wage-setting decisions made by utility maximisingunions. The unions calculate utility from higher levels of employment and from higherreal wages (relative to wages outside the sector) in the sector, subject to the labour-demand constraint imposed by firms’ profit-maximisation. The implication of this formof wage equation is that conditions in the labour market are important for determiningwage 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 ex-tended the field of policies the model could explore to the areas of Agriculture, Forestry,Bio-energies, Tourism, Transportations, Urbanization and Nature Conservation, throughtheir implications on land-use. These six sectors are actually of significant importancefor land use; they are at the origin of all possible land claims which modelling in NEME-SIS is cross sectoral to this extent that all sectors are competing for land. Land claimby 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 exogenousvariables concerning word assumptions including interest rates, exchange rates, activityproxies for the rest of the world, prices of wholesales commodities and specially oil; de-mographic assumptions by country such total Population, population and participationrates to labour force by gender per 5 years cohorts; national policies assumptions and no-tably fiscal policies (indirect and direct taxes, social security benefits and contributions)and government expenditures (defence, health, education, infrastructures, others expen-ditures) and investments; and energy and environment assumptions as excises duties andother 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 aremacro-economic, as GDP (European, National or Regional) and its counterparts (finalconsumption, investment, exports, imports, etc.), sectoral, as production, value addedand employment per NACE economic sector or sector clusters, or agent based (Gov-ernement, Non Financial Corporations, Financial Corporations, Households includingNPIH, and outside).Beyond economic indicators as GDP, prices and competitiveness, employment and

revenues, financial balances for the main agents, etc., NEMESIS Energy EnvironmentModule (NEEM) gives detailed results on energy supply and demand by fuel type andtechnology, 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 carbonconstraint). The inclusion in the model of detailed data on population and working force,allows also the model delivering many social indicators as employment, unemploymentand labour force participation rates by gender, GINI coefficient for wages and earnings,and a set of indicators dedicated to measure inequalities between European countries andregions for key variables as GDP and final consumption per capita. Additional originalindicators 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, whichimpact 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 usedfor many purposes as short and medium-term economic and industrial “forecasts” forbusiness, government and local authorities; analysing Business As Usual (BAU) scenar-ios and economy long-term structural change, energy supply and demand, environment,land-use and more generally sustainable development; revealing the long term chal-lenges of Europe and identifying issues of central importance for all European, national,regional scale structural policies; assessing for most of Lisbon agenda related policiesand especially knowledge (RTD and human capital) policies; emphasizing the RTD as-pect of structural policies that allows new assessments (founded on endogenous technicalchange) for policies, and new policy design based on knowledge: Education, Skill andHuman Capital and RTD.NEMESIS has notably been used to study BAU scenarios for European Union and re-

veal the implication for European growth, competitiveness and sustainable developmentof the Barcelona 3% GDP RTD objective, of the 7th Research Framework Program ofEuropean Commission, of National RTD Action Plans of European countries, of Euro-pean Kyoto and post-Kyoto policies, of increase in oil price, of European action plan forRenewable Energies, of European Nuclear Phasing in/out, etc. NEMESIS is currentlyused to assess for European Action plan for Environmental and energy technologies, forEuropean financial perspective (CAP reform) and for Lisbon agenda, with in deep de-velopment on the modelling of RTD, Human Capital and labour market and Europeanregions.

14

CHAPTER I

The core economic Model

We will present in this section two versions of the supply side and of the endogenoustechnical 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 secondone is currently tested and enhance the previous formulation.

Section I.1Current 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 fromthe work carried out by Arrow [17]. Here, the rate of technical progress is linked toexpertise or experience, measured by gross accumulated investment. They thereforetake up a similar viewpoint to the AK model in which the capital K variable containsinformation relating to the state of the technology. This approach has been adopted in

15

I.1. CURRENT VERSION OF THE ENDOGENOUS TECHNICALCHANGE 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 toexperience curves. Making technical progress endogenous provides a more effective,immediate implementation of policies to fight against greenhouse gases as a result of theexperience acquired in the field.Other overall balance models use R&D expenditure to make technical progress en-

dogenous. 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]) andin models applied to the environment and climate change (such as Nordhaus’ RDICEmodel [254] or Fougeyrollas et alii’s GEM-E3 model [147]). Endogenisation throughR&D expenditure is not easy. The first difficulty comes in calculating the relationshipbetween R&D and process or product innovations. The second comes from the possibil-ity that there may be a number of balances. The third stems from the diversity of R&Dlevels of intensity and results in the sectors. Only a few sectors, such as those linkedto ICT and the pharmaceutical sector, are R&D intensive. It is therefore necessary toadopt a detailed sector-based approach so that the endogenisation of technical progressis 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 modeltakes it place in this new family of macro-econometric models with endogenous technicalprogress. The special feature in Nemesis is the endogenisation of technical progressacross three phases: from R&D to the stock of knowledge, from the stock of knowledgeto innovation and from innovation to economic performance, the second feature is thelevel of disagregation.

I.1.1 The stock of knowledge

The variable that plays a vital role in the endogenisation of technical progress in Neme-sis is the variable “knowledge” (KNOW ) that arises out of the R&D stock. A sector’sR&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 ofknowledge (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 matri-ces. These matrices, which are differentiated by sector and by country, are constructedaccording to the methodology developed by Johnson for the OECD (Johnson, 2002).This consists of identifying, for every patent registered at the European Office, the sec-tors producing and using the innovation described in the patent. This is then used todetermine the proportion in which the knowledge accumulated in a sector will benefitothers, by calculating knowledge transfer coefficients, the knowledge being, by assump-tion, borne by the patents. This work is done in great detail (over 100 sectors) andthe results are re-agglomerated in Nemesis’ sector-based nomenclature in the form oftechnological flow matrices. “Knowledge” also feeds on the R&D stock in foreign sectorsand on the public sector R&D stock.�

R&D Stock of the Sector

R&D Stock of Other Sectors

R&D Stocks of Foreign Sectors Public R&D Stock

KNOW

Technology Flow Matrices

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). Thetwo types of innovation are considered here:

• process innovations that increase the global productivity of factors in the specifi-cation that we have chosen;

• product innovations, which, in the fixed nomenclature of national accounting that

17


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. Processinnovation increases the global productivity of factors, thus increasing product supplyand reducing the unit production cost, and therefore the price. This price reductionleads to increased demand, which is dependent on demand price elasticity (figure I.4).�

Supply Side

Demand Side

Productivity Growth Increase in Supply

Process InnovationPrice Fall

Demand Price Elasticity εIncrease in Demand

Figure I.4.: Process innovation and economic performance

Growth in demand helps to absorb extra supply (at a constant usage level) if demandprice elasticity is higher than or equal to one. However, econometric estimates in chrono-

1National accounts already take, partially, the increasing quality of goods and services into accountin their calculus, however this accounting is relatively rough. In the NEMESIS model we considerthe quality improvements that are additional what are very important whenever we increase R&Defforts.

18


logical series reveal an elasticity generally lower than one for each sector, and thus forthe whole economy. This result comes from the assumption of a representative firm persector: we do not consider the innovative firm in competition with the other companiesin its activity sector. This amounts to assuming that all firms in the sector innovateand reduce their prices. Increased demand then depends on the capacity for absorptionrepresented by elasticity lower than one. In this case process innovation reduces the useof factors as the effects of supply outweigh the effects of demand.

Product innovation acts like an increase in efficiency per volume unit and increasesdemand for units of efficiency (figure I.5). Volume production is only maintained if theincrease in demand for the new efficiency is just equal to the increase in efficiency dueto innovation. Generally, product innovation does more than compensate for the fallin factor usage due to process innovation. R&D therefore leads simultaneously to anincrease in GDP and in the use of factors.

�Supply Side

Demand Side

Product Innovation

Increase in efficiency per volume

Fall in price of efficiency unit

variation of demand volumeIncrease 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 inknowledge 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 inno-vation and quality innovation respectively (box 1).

19


Box 1. The effects of innovation on economic performanceProcess innovation: the accumulation of knowledge (KNOW ) generates an increase inthe global productivity of factors (TFP ).

∆TFPTFP

= α∆KNOWKNOW

Product innovation: the accumulation of knowledge (KNOW ) leads to an improvementin quality (QUAL).

∆QUALQUAL

= α′∆KNOWKNOW

Economic performance: increased production (Y ) depends on increased demand due toinnovation depending of two elasticities ε and ε′.

∆YY

= ε∆TFPTFP

+ ε′∆QUALQUAL

i.e.

∆YY

=(εα+ ε′α′

) ∆KNOWKNOW

= β∆KNOWKNOW

Finally, economic performance, measured by increased production due to increasedknowledge, is written as follows:

∆YY

= β∆KNOWKNOW

Most of the available econometric studies link increased production with an increasein R&D stock (SRD) using the following formula :

∆YY

= α∆SRDSRD

The difference between these two approaches is an explicit integration of all thespillovers in the first and an implicit or nil integration in the second. Econometric stud-ies (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 resultsare independent of the methods chosen. However, where β is estimated using instantcross-section series (inter-companies), it is higher than when chronological estimates areused.

I.1.4 Calibration in the model

20


In the NEMESIS model the α and α′ had been calibrated to reproduce the desiredvalues of the mean β parameter. This had been done by making sensitivity analysis overthe historical data in order to reproduce past trends, in order to be as close as possibleof the historical facts. After that, the βparameters are differentiated using sectoral R&Dintensities.

Section I.2New production functions with embodied endogenous technicalchange 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 beenoften adopted by applied modellers for representing the links existing between technicalchange and economic growth. These models gave important insights regarding the com-plementarities between productivity growth on one hand and investment on the otherone, through the technical progress embodied in the new vintages. The developmentof new growth theories from the Eighties stated furthermore that technical change wasitself an endogenous process based on R&D and innovations decisions of private andpublic actors. We therefore adopted for NEMESIS an embodiment approach, close fromthe one already implemented in GEM-E3 model, and where technical change resultsfrom 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 thedirection of technical change. The modelling of production technologies was inspired bythe approach that was developed by Adriaan Van Zon [324] and Huub Meijers & Adri-aan 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 produc-tion factor demands from a set of simple recursive update rules. This model is basedon a putty-semi-putty vintage production structure, and it is precisely the possibility oflimited 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 isendogenised, as for the Putty-Clay and the Clay-Clay vintage production models as theywere first introduced by Johansen [196] and Salter [280]. RUM makes positive use of thefact that it is often not necessary to know all the details of every individual vintage: from

21

I.2. NEW PRODUCTION FUNCTIONS WITH EMBODIEDENDOGENOUS TECHNICAL CHANGE AND SKILLS

a macro-economic point of view, only the average characteristics of the capital stock areimportant.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 sub-section, we describes then the modelling of technical change, endogenized on R&D, andincorporated in production vintages. The resolution of firms’ optimization program, andof the main equations that where introduced in NEMESIS for R&D, innovation and pro-duction factor demands, are available in NEMESIS reference manual, the presentationhere focusing on methodological issues.

I.2.1 Multi-level, putty-semi-putty CES produc-

tion functions

The nested CES framework

The multi-level nested CES production functions, pioneered by Sato [283], have recentlybeen widely used in macroeconomics. Its flexibility, and its usefulness to implementand analyse endogenous growth makes it an attractive choice for many applications ineconomic theory, applied modelling and empirics (cf. Acemoglu (2002), Papageorgiouand 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 four-level nested CES production functions that differ only from the values of substitutionelasticities, and of share parameters.

NEMESIS production functions

NEMESIS production functions were extended to include low skilled and high skilledlabour, and 5 productive inputs : CapitalK, Low Skilled LabourLL, High Skilled LabourLH , 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 productionfactors. At a second stage, Low Skilled Labour was separated from Capital, High Skilled

22


Figure I.6.: CES nesting structure�

K E

KELHS

LLS KELHS

KELHSLLSM

Y

Labour and Energy, that we supposed to be gross complements. High Skilled labouris separated from the bundle formed by Capital and Energy at a third level, and thenCapital 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 formedby other inputs, are identical. Partial substitution elasticities are noted:

• σ1for substitutions between M and K, LH LL, E;

• σ2for substitutions between LH and K, LLE ;

• σ3 for substitutions between LL and KE ;

• 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 possibilitiesbetween production factors are greater ex-ante than ex-post, that is to say, they aregreater at the moment of the investment in the new vintage than when the marginalproduction capacity was already installed. We have then to distinguish the ex-ante fromthe ex-post production functions.

23


The Ex-Ante Production Function

To begin with the production function for the first level of the nesting, that combinesmaterials and the bundle of all other inputs to produce the output in volume QY , letsconsider the following production function with constant returns to scale:

QYt,t+1 =[δaMt·M−ρ

a1

t,t+1 + δaKLEt ·KLE−ρa1t,t+1

]− 1ρa1 (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+1is the amount of the bundle formed by Capital, Energy and High skilledLabour associated to Low skilled labour on vintage t at date t+ 1;

• δaMtand δaKLEt are the ex-ante distribution parameters;

• σa1 is the ex-ante partial elasticity of substitution between M and KLE withσa1 = 1

(1+ρa1) .

By assumption, the capital associated to the new vintage, installed at date t, needs oneyear to be productive: date t+ 1.

The Ex-Post Production Function

The ex-post production function has the same CES, constant returns to scale, speci-fication that the ex-ante function (I.4):

QYt,t+1 =[δpMt·M−ρ

p1

t,t+1 + δpKLEt,t+1·KLEt,t+1

]− 1ρp1 (I.2)

where δpMt, δpKLEt and σ

p1 are the ex-post parameters of the CES function, and with

σp1 = 1(1+ρp1) . By assumption, ex-post substitution possibilities between KLE andM are

limited and we have σp1 < σa1 .

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 onecan express the ex-ante and ex-post production functions in term of factor coefficients,respectively:

1 =[δaMt·m−ρ

a1

t,t+1 + δaKLEt · v−ρa1t,t+1

]− 1ρa1 (I.3)

and

1 =[δpMt·m−ρ

p1

t,t+1 + δpKLEt · v−ρp1t,t+1

]− 1ρa1 (I.4)

with :

• v = KLEQY

, the coefficient for the factors inside the bundle and

• m = MQY

the factor coefficient for Materials.

Figure I.7 shows that ex-post isoquants (e.p), associated with certain ex-ante technologieson the curve (e.a), have a stronger curvature than ex-ante isoquants, reflecting that thesubstitutions 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 ex-

post production functions are equal and there is only the technique (m, ν), on the ex-ante isoquant, in common with the ex-post isoquants. This technique, defined as thetangential technique, allows determining the exact position of the ex-post isoquant onfigure I.7.

25


One can also express the ex-post parameters in terms of ex-ante parameters and ofthe tangential technique (m, ν), by identifying equations I.3 and I.4 above :

δpMt= δaMt

[mt]ρp−ρa (I.5)

and

δpKLEt = δaKLEt [νt]ρp−ρa (I.6)

Equations I.3 and I.4, that characterize the ex-ante and ex-post production technolo-gies can also be re-expressed in terms of the tangential technique (m, ν) and of theex-ante parameters :

gt (mt, νt) =[δaMt

m−ρa

t + δaKLEtν−ρat

]− 1ρa = 1 (I.7)

with gt (mt, νt) the ex-ante production function, in terms of factor coefficients, asso-ciated with the vintage t;and :

ft,t+1 (mt,t+1, νt,t+1,mt, νt) =[δaMt

m(ρp−ρa)t m−ρ

p

t,t+1 + δaKLEtν(ρp−ρa)t ν−ρ

p

t,t+1

]− 1ρa = 1 (I.8)

with ft,t+1 (mt,t+1, νt,t+1,mt, νt) the ex-post production function, in terms of factorcoefficients, 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 thequality of their products, and the productivity of their inputs, by investing in R&Dactivities and by buying certain amounts of innovations. The underlying idea is, asfor the optimal choice of production factors, that substitutions possibilities are greaterex-ante than ex-post. The representative fim invests ex-ante in R&D activities to im-prove the quality of its products, and the productivity of its inputs, on the marginalproduction capacity. Ex-post, once the marginal production capacity installed, there are

26


no possibilities for modifying the quality of products or the productivity of productionfactors.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 inother production sectors, but also from other countries and from public laboratories.They have also negative knowledge spillovers from their past innovations (fishing-outeffect), as in Jones (1995). These knowledge externalities, by modifying the productivityof R&D, give the possibility of increasing returns to scale and endogenous growth at theindustry level, even if each representative firm operates with constant returns to scaletechnologies. We have therefore six different sources of endogenous technical change inNEMESIS. One is Hicks-neutral, with the improvement of products quality, and the otherare biased with the endogenous improvement of individual factors productivity (Capital,, High Skilled Labour, Low Skilled Labour, Energy and Materials). This general settingallows furthermore taking into account the possible crowding-in or crowding-out effectsbetween the different innovation activities. We describe first how the quality of productsis combined with the volume produced to form the output Y , and, similarly, how theinput-specific innovations are combined to the volumes of inputs used to form the efficientinputs. We then present the innovation functions of firms and the formation of knowledgespillovers.

The incorporation of innovations in output

In every production sectors, product innovations are incorporated in the new productionvintage. The characteristics of products, measured by the ‘marginal quality index’ , arechosen ex-ante, and once again, we must make the distinction between the ex-ante andthe 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, andit is a CES function of the production volume, QY , and of product innovations, IY :

Yt,t+1 =(δaQYt

Q−ρa0Yt,t+1

+ δaIYtI−ρa0Yt

)− 1ρa0 (I.9)

where δaQYt and δaIYt

are the distribution parameters of respectively product innovations

27


and output in volume, and σa0 = 1(1+ρa0) .

By convention, IY is set to 1 for the base year of NEMESIS (2000), where the levelsof 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 productiveone 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, andproducts innovations are embodied in the new production vintages from ex-ante optimalproduction 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 = apYtQYt,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.9and I.10 above, in terms of factor coefficients, respectively:

1 =[δaQYt

q−ρa0yt,t+1 + δaIYt

i−ρa0yt

]− 1ρa0 (I.11)

and

1 = apYt .qyt,t+1 (I.12)

with qy = QYy and iy = IY

y successively the factor coefficients for marginal output involume 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 (qyt , iyt)and of the ex-ante parameters:

apYt(qyt , iyt) =[δaQYt

+ δaIYt.qytiyt

−ρa0]− 1

ρa0(I.13)

28


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 physicalvolumes, X, and in efficient units QX , with X = K, LL, LH , E and M respectively theCapital, 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:

QYt,t+1 =[δiMt·M−ρ

i1

t,t+1 + δiKELt ·KLE−ρi1t,,t+1

]− 1ρi1 (I.14)

KLEt,t+1 =[δiLLt

· L−ρi2

Lt,t+1+ δiKLHEt ·KLHE

−ρi2t,,t+1

]− 1ρi2 (I.15)

KLHEt,t+1 =[δiLHS,t · L

−ρi3Ht,t+1 + δiKEt ·KE

−ρi3t,t+1

]− 1ρi3 (I.16)

KEt,t+1 =[δiEt · E

−ρi4t,t+1 + δiKt ·K

−ρi4t

]− 1ρi4 (I.17)

with i = a, p for , respectively , ex-ante and ex-post production technologies. Thecapital used on the new vintage, Kt, is a fixed factor that become productive after oneyear (intallation delay).We then have ex-ante, for ,X = K, LL , LH ,M, E:

Xt,t+1 =[δaQXt

Q−ρxXt,t+1+ δaIXt

I−ρxXt

]− 1ρx . (I.18)

The ex-ante efficient units of inputs are CES combinations of the volume of the factorused 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 ofone 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 = apxtQXt,t+1 . (I.19)

29


Finally, the ex-post productivity parameters of inputs, apXt , can, , similarly than for theex-post marginal quality index of output, be expressed in terms of the ex-ante parametersand of the tangential techniques (qxt , ixt):

apxt(qxt , ixt) =(δaQXt

q−ρxxt + δaIXti−ρxxt

)− 1ρx (I.20)

with qx = QXX and ix = IX

X respectively the factor coefficients for volume of inputsand 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 mod-elled 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 pro-duced at date t.The flow of innovations innovj,t, is produced with the following constant returns in-

novation function:

innovj,t = αj,t ·RDj,t (I.22)

where RDj,t and αj,tare respectively the R&D expenditure at constant prices of therepresentative 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 = αjKNOWj,t

NEj,t(I.23)

with αja constant and positive parameter, KNOWj,t the knowledge stock of the sectorfor innovation type j, and NEj,tthe ‘Research Difficulty’ index that is a positive func-tion of all past successful innovations realized by the sector (Jones (1995), ’Fishing-out’effect):

30


NEj,t = (Ij,t−1)βj

with βj a positive parameter.The knowledge externality, KNOWj,t, reduces R&D costs by innovation, and reflects

the fact that if innovations are specific to sectors who produce it, the technologicalknowledge 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, KNOWk,j,t grows faster than the ResearchDifficulty index, NEj,t, research productivity will increase in time. We will then haveincreasing returns to scale, at the global level, while representative firms are supposed tooperate with constant return to scale production and innovation functions, compatiblewith pure and perfect competition on all product markets. In this case, every policiesstimulating R&D expenditures will have long term positive impacts on the growth rateof the economy, while these impacts will stay limited in time in knowledge externalitiesdo 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 fol-lowing 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 > 0measuring the delay for R&D expenditures to transform into formal knowledge that willinfluence 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 publicR&D in EU-27. These two sources of research externalities have distinct impacts oneconomic performance of European firms, private R&D being more oriented toward in-dustrial applications of inventions, and public R&D toward basic research. Econometricstudies reveal to that extent a greater contribution to economic growth of basic researchcompared to applied research, but also greater maturation delays. In NEMESIS, τ wasset to one year for private R&D expenditures, which implies that, if one takes also into ac-count 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 lagof two years. From knowledge externalities coming from the public sector, τ was set to3 and it needs four years for public research to influence economic performance.

Knowledge spillovers from private R&D expenditures are measured in NEMESIS withJohnson (2002) OECD Technology Concordance (OTC) that transforms patent applica-tions data, based on the International Patent Classification (IPC), into patent counts bysector of the economy. OTC is a matrix that is used for dispatching the R&D performedby the industrial sectors (’Industries of Manufacture’) in the sectors that will the mostlikely use the process or product innovations that they realize (’Sectors of Use’). Fromthis methodology, knowledge externalities flow from industrial sectors to industrial sec-tors themselves and toward service sectors, but there are no externalities from servicesectors toward industrial sectors. This is a limitation due to IPC definitions that donot include innovations in softwares and in services. OTC matrices were calculated forNEMESIS 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 totheir share in total private R&D expenditure. This approach was retained in NEMESISfor the reason that there do not exist precise information in existing databases on howthe public R&D contribute to sectoral innovation performance. There exist data inEUROSTAT on the repartition of public R&D by socio-economic objectives, but thisrepartion doesn’t help much for retrieving the amount of spillovers that flow to economicsectors. Our assumption of grandfathering follows then the idea that these spillovers atsectoral as important that the specific sectors are engaged in R&D activities; also, wedid supposed in NEMESIS that research

For knowledge externalities from foreign sources, NEMESIS uses trade flows of goodsand services. The assumption is that knowledge transfers between countries are bareby traded goods, that is to say by the imports realized by the country that receives theexternality. For illustration, the intra-sectoral knowledge spillover for a country i and asector s (source 1) is measured in the following way:

SRDsi,s,j,t = PRODi,s,t

PRODi,s,t + IMPi,s,t.SRDi,s,j,t +

∑c6=i

IMP i,c,s,tPRODi,s,t+IMPi,s,t

.SRDc,s,j,t

,

where PRODi,s,t is the production of good s in country i, IMPi,s,tis the total importsof good s by country i, IMP i,c,s,tis 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 stockin 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:

KNOWc,s,j,t =(SRDI

c,s,j,t + SRD−Ic,s,j,t + SRDPNc,s,j,t + SRDPF

c,s,j,t

)where:

1. SRDIc,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;

2. SRD−Ic,s,j,t represents the past R&D efforts realized in the other production sectors.It is the inter-sectoral knowledge spillover;

3. SRDPNc,s,j,t represents R&D externalities coming from the public laboratories in the

country, that beneficiate to the sector;

4. SRDPFc,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 Industryof 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 NEMESISmodel. We estimated the aggregated ex-post production function for output and inpursmeasured in efficiency units. We start this section by presenting the econometricalspecifications of the production block and the construction of data we used. In the thirdpart we present the econometric results. The fourth implements shock price simulationsin order to test the robustness of the estimations and to obtain the direct elasticities ofsubstitution between factors of different bundle. Finally, we discuss issues concerningthe ex-ante estimates.

33


Estimation specifications for the efficient form

In this part we estimate the production block only for the specification of input andoutput in efficient units. The determination of the whole block with biased technicalchange will be realized using calibration methods. We estimate the five factor demandequations with the FIML method (Full information maximum likehood). FIML is theasymptotically efficient estimator for linear and nonlinear simultaneous models, underthe assumption that the disturbances are multivariate normal.Bundle prices and production prices used in the regression are the unit costs of pro-

duction. For instance, for the last level, we consider:

PKE =[P 1−σ4K δσ4

K + P 1−σ4E δσ4

E

] 11−σ4

We define the distribution parameter with the share value of the input, for example,if we consider the materials:

δM = PM ·MPM ·M + PKELLSLHS ·KELLSLHS

To avoid problems of endogeneity, we use lagged values for input and prices in thedistribution 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 anexogenous form, which takes a deterministic linear trend, i.e. A = A0i,cet where A0i,cthe 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

(∑c

dcA0s,c

)− αs · t+ σ1,s log

(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 param-eter. Thus,

(1−ρρ

)is the time necessary for an adjustment at a 50% level.

Finally, we consider a sector for all countries, with the underlying assumption thatthe elasticities of substitution are common to all countries, but not between sectors. Inorder 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 dataon labour demand by skill. Specifically we construct two types of data: one related tothe share of employees or total employment and the other one on the labour remuner-ations. In addition to being used for the production block estimation, the data will beincorporated into the NEMESIS model. As already mentioned, we define two categoriesof 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 informationon employment by skills at the sectoral level, and there are few complete data sourceson this subject. According to our knowledge, only two databases provide such dataset:

• The Eurostat database which provides skilled labour data by age, gender, occupa-tional jobs, etc . . . but only at the national level

• The EU-KLEMS database in which we can find the share of labour employmentand 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 differentbetween each country, leading to a consistency problem that could imply surprisingresults in the model. The Eurostat database is homogenous but provides data at thenational level only. As a consequence, we choose to use the sectoral information providedby 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 shareof low and high skilled labour on total labour compensation at the sectoral level, andwe 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)�

0%

5%

10%

15%

20%

25%

30%

35%

40%

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)

Starting from the Eurostat database, we calculate the share of high skilled (SeurostatX,HS,C )and low skilled (SeurostatX,LS,C ) labour demand at national levels as follows:

SeurostatX,HS,C = HS′c

HS′c +MS′c + LS′c(I.26)

SeurostatX,LS,C = 1− SeurostatX,HS,C (I.27)

Where c is the country index, X = EMP,SAL the total employment or the totalemployees and HS

′ , MS′ and LS

′ are respectively the INSEAD1-2, INSEAD3-4 andINSEAD5-6 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 thelowest 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 jobsin the total employment is about 25% in 2005.

Sectoral levels (EU-KLEMS)

36


We also calculate the total high skilled (XEUKLEMSHS,C,S ) and low skilled (XEUKLEMS

LS,C,S )labour and the total high skilled and low skilled number employees, at the sectoral levelusing EU-KLEMS database.

XEUKLEMSHS,C,S = SEUKLEMS

HS,C,S ·XNEMESISC,S (I.28)

XEUKLEMSLS,C,S =

(1− SEUKLEMS

HS,C,S

)·XNEMESIS

C,S (I.29)

Where s is the sectoral index, SEUKLEMSHS,C,S the share of hours worked by high skilled

workers in the EU-KLEMS database once converted to the NEMESIS sectoral nomen-clature and XNEMESIS

C,S , the total employment/employees from the NEMESIS database(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. Thisrepresents of course a relatively important hypothesis, but the lack of information anddata 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 wherethe share can reach between 25% and 30% of the total labour demand. Although thesectoral 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 (δEUKLEMSX,HS,C,S , δEUKLEMS

X,LS,C,S ) in the national total employment and in the total number ofemployees using the EU-KLEMS database:

δEUKLEMSX,HS,C,S =

XEUKLEMSHS,C,S∑

C XEUKLEMSHS,C,S

(I.30)

δEUKLEMSX,LS,C,S =

XEUKLEMSLS,C,S∑

C XEUKLEMSLS,C,S

(I.31)

We then compute the national levels for high skilled and low skilled labour (XNEWHS,C , X

NEWLS,C

) using the Eurostat national share (SEUROSTATX,HS,C ) and taking the national labour and

37


Figure I.9.: European high skill share in total employment for NEMESIS sectors, (sourceEU-KLEMS)�

0%

5%

10%

15%

20%

25%

30%

35%

Agri.

Coal a

nd C

oke

Oil & G

as E

xt.

Gas D

ist.

Refine

d Oil

Elec.

Wat

er S

up.

Ferr.

& non

Fer

r. M

etal

s

Non M

et. M

in. P

rod.

Chem

icals

Met

al Pro

d.

Agr. &

Ind.

Mac

h.

Office

Mac

h.

Elect.

Goo

ds

Trans

p. E

quip.

Food,

Drin

k & T

ob.

Tex.,

Cloth.

& F

ootw

.

Pap. &

Prin

t. Pro

d.

Rubbe

r & P

lastic

Other

Man

uf.

Const

ructi

on

Distrib

ution

Lodg

. & C

ater

.

Inlan

d Tra

nsp.

Sea &

Air

Trans

p.

Other

Tra

nsp.

Ser

v.

Comm

unica

tion

Bank,

Fin

. & In

s.

Other

Mar

ket S

erv.

Non M

arke

t Ser

v.

number of employees (XNEMESISC ) of the NEMESIS model.

XNEWHS,C = SEUROSTATX,HS,C ·XNEMESIS

C (I.32)

XNEWLS,C =

(1− SEUROSTATX,HS,C

)·XNEMESIS

C (I.33)

Using these national levels (XNEWHS,C and XNEW

LS,C ) and the share of high skilled and lowskilled labour (δEUKLEMS

X,HS,C,S and δEUKLEMSX,LS,C,S ), we can calculate the new sectoral levels for

high skilled (XNEWHS,C,S) and low skilled (XNEW

LS,C,S) labour:

XNEWHS,C,S = δEUKLEMS

X,HS,C,S ·XNEWHS,C (I.34)

XNEWLS,C,S = δEUKLEMS

X,LS,C,S ·XNEWLS,C (I.35)

Final correction

Finally, to achieve the consistency between countries, we compute the new shares byskills (δNEWHS,C,S and δNEWLS,C,S) at the sectoral level, using the employment and the numberof employees previously calculated (XNEW

HS,C,S and XNEWLS,C,S). These shares will be used to

38


Figure I.10.: European high skill share in total employment for NEMESIS sectors�

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Agri.

Coal a

nd C

oke

Oil & G

as E

xt.

Gas D

ist.

Refine

d Oil

Elec.

Wat

er S

up.

Ferr.

& non

Fer

r. M

etals

Non M

et. M

in. P

rod.

Chem

icals

Met

al Pro

d.

Agr. &

Ind.

Mac

h.

Office M

ach.

Elect.

Goods

Trans

p. E

quip.

Food,

Drin

k & T

ob.

Tex.,

Cloth.

& F

ootw

.

Pap. &

Prin

t. Pro

d.

Rubbe

r & P

lastic

Other

Man

uf.

Const

ructi

on

Distrib

ution

Lodg

. & C

ater

.

Inlan

d Tra

nsp.

Sea &

Air

Trans

p.

Other

Tra

nsp.

Ser

v.

Commun

icatio

n

Bank,

Fin

. & In

s.

Other

Mar

ket S

erv.

Non M

arke

t Ser

v.

compute data on high skilled and low skilled labour at the sectoral level.

δNEWHS,C,S =XNEWHS,C,S(

XNEWHS,C,S +XNEW

LS,C,S

) (I.36)

δNEWLS,C,S =(1− δNEWHS,C,S

)(I.37)

Figure I.10 shows the corrected European high skilled labour share in total employ-ment at the sectoral level for the year 2005. We can see that once corrected the servicessector has a high skilled labour share between 35% and 45%, whereas it was only about25% 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 theEU countries. Looking at “Agriculture”, the highest share of high skilled labour is inEstonia with 27.3% followed by Finland, Norway and Latvia with 25.4%, 23.2% and22.6% respectively. We find the lowest share for agriculture in Italy and Austria with1.7%. If, we look at the “Bank, Finance and Insurance” sector, the highest shares arein 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�

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

AT BE DE DK ES FI FR GR IE IT LU NL PT SE UK CZ EE HU LT LV MT PL RO SI SK NO

Agriculture Chemicals 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 andlow skill in the total compensation of employees (θEUKLEMS

HS,C,S ). We start by com-puting EU-KLEMS compensation of employees for high skilled and low skilled labour(COMPNEMESIS

HS,C,S and COMPNEMESISLS,C,S ) at the sectoral level, using the EU-KLEMS

shares and the NEMESIS sectoral compensation of employees (COMPNEMESISC,S - Eu-

rostat being the original source).

COMPEUKLEMSHS,C,S = COMPNEMESIS

C,S · θEUKLEMSHS,C,S (I.38)

COMPEUKLEMSLS,C,S = COMPNEMESIS

C,S ·(1− θEUKLEMS

HS,C,S

)(I.39)

Thus, using the EU-KLEMS data on high skilled and low skilled employees previouslycomputed (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 sectorallevel�

0

0.25

0.5

0.75

1

1.25

1.5

1.75

2

2.25

Agri.

Coal a

nd C

oke

Oil & G

as E

xt.

Gas D

ist.

Refine

d Oil

Elec.

Wate

r Sup

.

Ferr.

& non

Fer

r. M

etals

Non M

et. M

in. P

rod.

Chem

icals

Met

al Pro

d.

Agr. &

Ind.

Mac

h.

Office M

ach.

Elect. G

oods

Trans

p. E

quip.

Food,

Drink &

Tob

.

Tex.,

Cloth.

& F

ootw

.

Pap. &

Prin

t. Pro

d.

Rubbe

r & P

lastic

Other

Man

uf.

Constr

uctio

n

Distrib

ution

Lodg

. & C

ater.

Inlan

d Tra

nsp.

Sea &

Air T

rans

p.

Other

Tra

nsp.

Ser

v.

Commun

icatio

n

Bank,

Fin. &

Ins.

Other

Mar

ket S

erv.

Non M

arke

t Ser

v.

UCEUKLEMSHS,C,S =

COMPEUKLEMSHS,C,S

SALEUKLEMSHS,C,S

(I.40)

UCEUKLEMSLS,C,S =

COMPEUKLEMSLS,C,S

SALEUKLEMSLS,C,S

(I.41)

The unit costs by skill at the sectoral level allow us to compute the ratio between highand low skills compensations at the sectoral level for each country.

µEUKLEMSC,S =

UCEUKLEMSHS,C,S

UCEUKLEMSLS,C,S

(I.42)

Figure I.12 presents the ratio of European employee’s unit cost between high skill andlow skill at the sectoral level. In average, we can see that high skilled workers are paidbetween 50% and 100% more than low skilled ones. For instance, we can observe thatin the “Electrical Goods” sector, a high skilled employee cost 81% more in Europe thana low skill employee.

Final correction

41


Now, we compute the corrected compensation of employees for the high (COMPNEWHS,C,S)and low (COMPNEWLS,C,S) skills with the corrected shares of high and low skilledemployees,SALNEWHS,C,S and SALNEWLS,C,S(see equations I.34 and I.35), the NEMESIS com-pensation of employees (COMPNEMESIS

C,S ) and the cost ratios computed just before.

COMPNEWHS,C,S =SALNEWHS,C,S(

SALNEWHS,C,S + SALNEWLS,C,S

) · COMPNEMESISC,S · µEUKLEMS

C,S (I.43)

COMPNEWLS,C,S =SALNEWLS,C,S(

SALNEWHS,C,S + SALNEWLS,C,S

) · COMPNEMESISC,S (I.44)

Finally, using both last results we compute the corrected share of high and low skillin the total compensation of employees that will be used for rest of the study

θNEWHS,C,S =COMPNEWHS,C,S(

COMPNEWHS,C,S + COMPNEWLS,C,S

) (I.45)

θNEWLS,C,S =(1− θNEWHS,C,S

)(I.46)

Figure I.13 displays the corrected European share of employees’ compensation for highskill at the sectoral level. Thus in average, the share of high skills’ cost in the total costof 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 totalemployees and the number of hours worked:

LLS,C,S,t = HEMPEC,S,t · δemployeesLS,C,S,t

LHS,C,S,t = HEMPEC,S,t · δemployeesHS,C,S,t

42


Figure I.13.: Corrected European share of compensation of employees for high skill atsectoral level in 2005�

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

Agri.

Coal a

nd C

oke

Oil & G

as E

xt.

Gas D

ist.

Refine

d Oil

Elec.

Wate

r Sup

.

Ferr.

& non

Ferr.

Met

als

Non M

et. M

in. P

rod.

Chem

icals

Met

al Pro

d.

Agr. &

Ind.

Mac

h.

Office M

ach.

Elect.

Goods

Trans

p. Equ

ip.

Food,

Drin

k & T

ob.

Tex., C

loth.

& F

ootw

.

Pap. &

Prin

t. Pro

d.

Rubbe

r & P

lastic

Other

Man

uf.

Const

ructi

on

Distrib

ution

Lodg

. & C

ater.

Inlan

d Tra

nsp.

Sea &

Air

Trans

p.

Other

Tra

nsp.

Ser

v.

Commun

icatio

n

Bank,

Fin.

& In

s.

Other

Mar

ket S

erv.

Non M

arke

t Ser

v.

Where HEMPE is the total number of hours worked by employees (millions) or theprevious XNEMESIS

C,S, and δemployeesHS,C,S,t is the corrected share of high-skilled persons engaged(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 forthe labour input:

PLHS,C,S,t = COMPC,S,t · θHS,C,S,t

PLLS,C,S,t = COMPC,S,t · θLS,C,S,t

Where COMP is the Compensation of employees (in millions of Euros) or the previousCOMPNEMESIS

C,S and θHS,,C,S is the high-skilled labour compensation (share in totallabour 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 ofNEMESIS’ 30 sectors. The sample of countries is restricted to 11 due to the lack ofdata over a long period for most European countries. Countries included in the sampleare: 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 obser-

vations per sector. As mentioned previously, we use a pooled panel estimation methodwith the FIML estimator.Estimations are made sector by sector. The small sample size imposes that the param-

eters are common to all countries (but can vary by sector) with the exception of the scaleparameters. We estimate four elasticities of substitution, five delays, one technologicaltrend 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 thesecond step, we relax the constraints on elasticities. Finally, we relax all constraints onthe 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 itcorresponds to our limit value, allowing some substitution between factors. The mostconstrained parameters appear for the delay parameter on capital. In this case, the delayseems to be infinite. The constrained value corresponds to a median delay of 20 years.Table 18??? reports estimates of the four substitution elasticities. Constrained pa-

rameters take the value n.a. for their P-Value. All elasticities are positive, highlightinga positive substitution effect between factors for all sectors. All sector elasticities liebetween 0 and 1, meaning that factors and factor bundle on the same level, are grosscomplements (for elasticity values higher than 1 we talk about gross substitutes). Re-sults differ between sectors but some trends are well identified. The first and fourthlevels present lower elasticities values than intermediate levels. Labour (both skills) ismore 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%). Onthe 15 significant values, 9 exhibit positive estimated parameters. Since technological

44


advances are very different between countries, common technological trends are neitherrelevant nor significant. Delays are highly significant and there is a common trendbetween sectors. Materials and Labour have a short adjustment delay (a mean of 0.9years for Materials, 0.7 for low skilled and 1 for high skilled Labour). Energy presents amedium delay of adjustment due to its high complementarity with Capital (a mean of1.8 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 thiscase an important part of the variability between observations is explained by these fixedeffects.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 esti-mations and are not a necessary condition for the implementation of the 27 countriesinto the model. Since the scale parameters are sectoral, they are substituted with thecalibrated variables (which only need data for one year).

Independent Simulations

In order to assess the robustness of our estimations and to determine the direct elasticitiesof substitution between all factors, we simulate the demand factor evolutions in responseto different price shocks. Table 21 to Table 26 present these simulations, which areindependent 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: directprice elasticity is negative and the sign of indirect price elasticity depend of the synergybetween factors. An increase of a factor price will induce a decrease in the demand ofthis factor, an increase in subsidiary factors and a decrease in complementary factors.All factors are subsidiary (negative indirect elasticities), except for energy and capitalwhich 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 method-ology 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 theparameters of the factors’ (or bundle of factor) efficiency demands. So we could use theprevious values of substitution elasticities σp. Moreover the ex-post parameters δpi areendogenously calculated with the tangential technique as describe previously. We obtainthe following parameters, for example, in the first level of the nested CES:

δpM,t+1 = δaM [At+1 ·mt+1]ρp−ρa

and

δpKELHSLLS ,t+1 = δaKELHSLLS [At+1 · νt+1]ρp−ρa

In the ex-ante side we have to determine also the substitution elasticities σa that aresuperior to the σp previously estimated. Referring to Meijers and Van Zon (1994), wecould find σa ≈ 1.5 · σp. As achieved in the article by Meijers and Van Zon, we could bemore accurate and determine the ratio σa/σp by sectors.

Furthermore, the ex-ante parameters δai are defined like in the example correspondingto the first level of factor efficiency demand of the nested CES:

δaM,t = PM,t−t ·Mt−tPM,t−t ·Mt−t + PKELHSLLS ,t−t ·KELHSLLS,t−t

Finally, contrarily to the ex-post functions, we need to determine the parametersrelative to the biased technical change. That is to say the parameters of the followingfunction for each factor and for the global production:

It =(δQI,t ·Q

−ρII,t + δIt · I

−ρIt

)− 1ρI

Physical volume I and Technological level QI equation parameters could be calibratedusing patent data for the level of productivity and with the European Community In-novation Survey (CIS). Using the amount of R&D expenditures and the CIS or patentdata, we could split these expenditures between factors and global productivity or prod-uct. After that, using the innovation function, we can determine the parameters δQIthat represent the weight in the cost of the quality or productivity improvement. Theparameters σ should be determined the same way as describe above.

46


Section I.3Households’ final consumption

The consumption behaviour is divided in two stages. The first is the agregate consump-tion that splits households’ incomes in consumption and global saving. At the secondstage, 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 David-son et al. [92] in which the consumption is linked to income and wealth by an ErrorCorrection Model. The econometrics estimate at first the long term relationship, thenthe 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 cumulatedinvestment in dwelling was used as a proxy for the housing stock of households, and wasadded to the wealth effect.Other significant variables on the link between wealth on different support and con-

sumption are interest rates and inflationary pressures. The unemployment rate is usedas 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 genuinewealth variable in a forward looking module that would be isolated from the rest ofthe model ; aggregate consumption function could the be the result of two type ofbehaviours ; that of “liquidity constrained” households which is founded on the currentrevenue ; that of “neoclassical households” which can borrow or lend liquidities withoutrestrictions 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 increasingwith liberalization the past of “neoclassical households” SEFTON and VELD[288].

Co-Integrating Long term equation

2The long run elasticity of consumption in relation to incomes has been set to one to ensure that thelifecycle theory is fulfilled

47

I.3. HOUSEHOLDS’ FINAL CONSUMPTION

ln(CONSNATNQc

POPc

)= lrscnn0c

+ lrscnn1 · ln( INCGDISPcPCONSNATTOTc

POPc

)+ lrscnn2 · ln

(POPRETc

POPc

)+ lrscnn3 · ln

(POPCHIc

POPc

)+ lrscnn4 · ln(RRLRc)

+ lrscnn5 ·DUM97

Dynamic equation

∆ ln(CONSNATNQc

POPc

)= crscnn0c

+ crscnn1 ·∆ ln( INCGDISPcPCONSNATTOTc

POPc

)+ crscnn2 ·∆ ln

(POPRETc

POPc

)+ crscnn3 ·∆ ln

(POPCHIc

POPc

)+ crscnn4 ·∆ ln(RRLRc)

+ crscnn5 ·∆ ln(

PCONSNATTOTc

PCONSNATTOT−1c

)+ crscnn6 ·∆ ln

(CONSNATNQ−1

c

POP−1c

)+ crscnn7 · ERR−1

+ crscnn8 ·DUM97

with:

• POPc, Population

• INCGDISPc, Gross Disposable Income

• PCONSNATTOTc, Consumers’ Price

• POPRETc Retired Population

• POPCHIc Child Population

• RRLRc Interest Rate

• ERR, the Error Term

• DUM97, a dummy variable

48


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 systemthat will allow to disaggregate the macroeonomic consumption determined above. Thebasic The presentation thereof are from Bracke I. and Meyermans E. [38]. The onlydifference 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. Theeconometric allocation system is derived from the theory of rational consumer and re-strictions imposed by it are implemented in a flexible way thanks to a CBS version of thesystem. The total aggregate consumption is therefore divided into 27 components as afunction of relative prices and total income (to which are added demographic changes).Furthermore, that allocation module assumes groupwise separability, meaning that theconsumer faces a decision problem in several stages. In the particular, the representativeconsumer decides, in a first stage, how much he will spend on "durable and complemen-tary non-durable goods" on the one hand and on "other non-durable goods" on the otherhand. In a second stage, he decides how to spend the money allocated in the first stagewithin the group i.e. how much of the amount dedicated to the durable goods will beallocated to clothing, household utilities and transportation. Transportation includespublic transportation, equipment (such as cars) and energy, divided into petrol, heavyfuel and oil. A further decision stage takes place in the non-durable goods group. Itconsists of the choice between "necessities" (including food, beverages, tobacco, edu-cation, rent, health, electricity and other expenditure items) and "luxuries"(includingcommunication, tourism and domestic services).

Based on the CBS parametrization, the long-run equilibrium relationship is:

49


Figure I.14.: Allocation of Durable Goods

D Durable goods

04 Clothing and footwear

FUR Furniture and equip.

T Transport

17 Cars etc

18 Petrol etc

OT Purchased Transport

10 Furnitures etc11 Households textile12 Major appliance13 Hardware14 Household operation15 Domestic services

19 Rail Transports20 Buses and coaches21 Air Transports22 Other Transports

Figure I.15.: Allocation of Non Durable Goods

ND Non Durable goods

NEC Necessities

LUX Luxuries

FB Food Bev. and tob.

05 Gross rent and water

FU Fuel and power

16 Medical care

01 Food02 Beverages03 Tobacco

06 Electricity07 Gas08 Liquid Fuels09 Other Fuels

23 Communication24 Equipment and accessories incl repair25 Recreation26 Hotel and restaurant27 Misc. Goods and Services

wc,i ln(

CONSc,iINCRDISPc

)= cc,i + bi ln(INCRDISPc) +

27∑j=1

si,j · ln (PCONSc,j)

+ g1,i ln(DEMPc) + g2,i ln(DEMWc) + ϑc,i

50


and the short-run one is:

wc,i∆ ln(

CONSc,iINCRDISPc

)= bsi∆ ln(INCRDISPc) +

27∑j=1

ssi,j ·∆ ln (PCONSc,j) +26∑j=1

fsi,jϑ−1c,i

+ hs1,i∆ ln(DEMPc) + hs2,i∆ ln(DEMWc) + uc,i

where :

• i, j = 1 to 27 consumption categories

• c = 1 to 26 countries

• CONS consumption of commodity (1995m euros)

• INCRDISP regional real personal disposable income (1995m euros)

• PCONS commodity price

• DEMW share of people of working age in total population

• DEMP 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 com-modities.Within a group I, the long-run equilibrium relationship is:

wc,i ln(CONSc,i

QIc

)= cIc,i + bIi ln(QIc) +

27∑j=1

sIi,j · ln (PCONSc,j) + gI1,i ln(DEMPc)

+ gI2,i ln(DEMWc) + ϑIc,i

for i ∈ I and where

• the scale effect of group I is defined by ln(QIc) =∑i∈I w

Ic,i ln(CONSc,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

• wIc,i : the budget share of commodity i in group I,

51



wIc,i∆ ln(CONSc,i

QIc

)= bs,Ii ∆ ln(QIc) +

27∑j=1

ss,Ii,j ·∆ ln (PCONSc,j) +26∑j=1

fs,Ii,j (ϑIc,i)−1

+ hs,I1,i∆ ln(DEMPc) + hs,I2,i∆ ln(DEMWc) + uc,i

for i ∈ IBetween groups of commodities, the long-run equilibrium relationship is:

wI ln(

QIcINCRDISPc

)= cIc + bI ln(INCRDISPc) +

k∑J=1

sIJ · ln (PCONSc,J)

+ gI1 ln(DEMPc) + gI2 ln(DEMWc) + ϑIc

for I = 1, ..., k groups and where

• ln(QIc) =∑i∈I w

Ic,i ln(CONSc,i)

• ln(PCRIc) =∑i∈I w

Ic,i ln(PCONSc,i)

• wI : the budget share of group I


wI∆ ln(

QIcINCRDISPc

)= bs,I∆ ln(Qc) +

k∑J=1

ss,IJ ·∆ ln (PCONSc,J) +k−1∑J=1

fs,IJ (ϑIc)−1

+ hs,I1 ∆ ln(DEMPc) + hs,I2 ∆ ln(DEMWc) + 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 · SIJ ·mJ

j ∀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 theCBS parametrization, the marginal propensity to consume is defined asmi = bi+wi)

• sIi,j : the compensated price effect of commodity j on i in group I (non zero onlyif 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 overallcoefficients.Restrictions

• Summability :∑ni=1 cc,i = 0,

∑ni=1 bi = 0,

∑ni=1 si,j = 0,

∑ni=1 b

si =

0,∑ni=1 s

si,j = 0

• Homogeneity :∑nj=1 si,j = 0,

∑nj=1 s

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 con-sumption transition matrix (mcons) with fixed coefficient.

ADDCONSQc,s =27∑co=1

(mconsc,co,s · CONSc,co)

This transition matrix is also used for calculating consumption price per category usingsectoral production and import prices prices to which VAT taxes and Excises duties areadded:

PCONSc,co =

30∑s=01

(mconsc,co,s · CONSc,co · PADDDEMc,s) + V ATCPc,co + EXCIPAHc,co

CONSc,co + V ATCP 1995c,co + EXCIPAH1995

c,co

53

I.4. EXTERNAL TRADE

Section I.4External 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 themodel goes through trade in goods and services. This matter of fact is reinforced by thestrong European integration that as led to an increasing degree of openness, resultingin a increasing share of external trade ratio to the final demand. External trade ismodelised 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 yetpossible for prices, that’s the reason why no distinction is made between intra and nonprices modelisation, except the fact that rest of the world trade prices includes tradebarriers 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 “tradepool”, i.e. into the same distribution network, that is to say that all European countriesexports to this pool and imports from it. One of the major drawback of this kind ofmodelisation is that as exports and imports are both econometrically estimated, nothinginsure 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 withoutbilateral data, that follows straightforwardly this constraint. However, this “adding up”problem was solved by modifying the exports equations in order to insure the equilibrium

3The modelling of bilateral trade flows insure this “adding up” constraint, we are currently studyingthe possibility to modelise bilateral trade flows, at least for goods, as bilateral trade flows of servicesdata are too weak for the moment.

54


between the sums of exports and the sums of imports per sector, this implicitly signifythat imports equations are better modelised than exports ones.Numerous attempt had be made for integrating in external trade equations (particu-

larly in exports equation) the so called non price competitiveness, one convicing attemptwas 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 modeli-sed 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 etalii [86] not exactly the same content as quality indices, and may focus on a particulardimension of quality, technological differentiation. Moreover, empirical testing showsthat bilateral trade flows are more suitable for estimating such quality effects, as thisallows for changes in the direction of this trade (see Hallak [171]), that one of the rea-son why the possibility for implementing bilateral trade flows in NEMESIS is currentlystudied.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 thatbig countries have an advantage for specialising their production to increasing return toscale sectors, and on the contrary, small countries are more focused on constant returnto scale production. However, this effect refers largely to world trade, and the EuropeanIntegration 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 thatthe 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 effectsand non prices effects. For imports equations, these effetcs are taken into account withthe 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 domesticprice.

55

I.4. EXTERNAL TRADE

• the non price effect is taken into account through national knowledge stock toEuropean knowledge stock ratio

ln(IMPEUQc,s) = limpeu0c,s+ limpeu1s · ln(ADDDEMQc,s)

+ limpeu2s · ln(PIMPc,sPPRODc,s

)

+ limpeu3s · ln(KNOWc,s

KNOWeu,s

)

with :

• ADDDEMQc,s Total domestic Demand by products

• PIMPc,s The Price of Imports

• PPRODc,s The production Price

• KNOWc,s the national knowledge Stock

• KNOWeu,s the European knowledge Stock


limpeu1s > 0limpeu2s < 0limpeu3s < 0

Exports Equations

For exports equations, the incomes and prices effetcs are taken into account with thefollowing 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 priceindex, which is a weighted variable of other EU countries export prices.

56


ln(EXPEUQc, s) = lexpeu0c,s+ lexpeu1s · ln(INDACTEUc,s)

+ lexpeu2s · ln(

PEXPc,sPINDICEXPEUc,s

)

+ lexpeu3s · ln(KNOWc,s

KNOWeu,s

)

• INDACTEU c,s, indicator of activity

• PEXPc,s, the Export Price

• PINDICEXPEUc,s, Indicator of competing Prices


• KNOWeu,s the Global European knowledge Stock


lexpeu1s > 0lexpeu2s < 0lexpeu3s > 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 thereforethe same effects as described above.

Imports equations

The three main effects integrated in the trade equations are income and prices effectsand non prices effects. For imports equations, these effetcs are taken into account withthe 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 domesticprice.

• the non price effect is taken into account through national R&D stock to the extraEuropean zone R&D stock ratio

ln(IMPROWQc,s) = limprow0c,s+ limprow1s · ln(ADDDEMQc,s)

+ limprow2s · ln(PIMPROWc,s

PPRODc,s

)

+ limprow3s · ln(KNOWc,s

KNOWz,s

)

with :

• ADDDEMQc,s Total domestic Demand by products

• PIMPROWc,s The Price of Imports for extra European imports

• PPRODc,s The production Price


• KNOWz,s the extra European zone knowledge Stock


limprow1s > 0limprow2s < 0limprow3s < 0

Exports Equations

For exports equations, the incomes and prices effetcs are taken into account with thefollowing 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 Europeanprice index, which is a weighted variable of other extra European zone exportprices.

ln(EXPROWQc, s) = lexprow0c,s+ lexprow1s · ln(INDACTROWc,s)

+ lexprow2s · ln(

PEXPROWc,s

PINDICEXPROWc,s

)

+ lexprow3s · ln(KNOWc,s

KNOWz,s

)

• INDACTROW c,s, indicator of activity

• PEXPROWc,s, the Export Price

• PINDICEXPROWc,s, Indicator of competing Prices


• KNOWz,s the extra European zone knowledge Stock


lexprow1s > 0lexprow2s < 0lexprow3s > 0

I.4.3 Imports and Exports prices

Exports and imports prices play a large role in determining trade volumes. the basicfeature of trade prices in NEMESIS assume that European countries operate in oligo-polictic markets, following this assumption, importers and exporters sets mark-ups ontheir 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 thedistinction 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 therest of the world, we sets two different prices, the sole difference between the two priceslay precisely in the existing trade barriers (import duties...) that multiplies the globalimport and exports prices. Other partners prices are weighted in the same manner thanfor 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(PEXPc, s) = lpexp0c,s+ lpexp1s · ln(PINDICEXPEUc,s)

+ lpexp2s · ln(PINDICEXPRWc,s)

+ lpexp3s · ln(PPRODc,s)

• PINDICEXPEUc,s, Price Index for competing Exports in Europe

• PINDICEXPRWc,s, Price Index for competing Exports in the Rest of the World

• PPRODc,s, Production Price


lpexp1s + lpexp2s + lexp3s = 1

Import prices

60


ln(PIMPc, s) = lpimp0c,s+ lpimp1s · ln(PINDICIMPEUc,s)

+ lpimp2s · ln(PINDICIMPRWc,s)

+ lpimp3s · ln(PPRODc,s)

• PINDICIMPEUc,s, Price Index for competing Imports in Europe

• PINDICIMPRWc,s, Price Index for competing Imports in the Rest of the World

• PPROD, Production Price


lpimp1s + lpimp2s + lpimp3s = 1

Section I.5Wage setting

In section we will present the specification, the estimation and the implementation ofthis modified labour market for the Nemesis model. Indeed, the integration of differentlabour skills in the model implies to reformulate and to extend the labour market ofNEMESIS. This will allows us to revisit the latest theoretical developments and toproceed to an econometric analysis at a disaggregated level. This section is organizedas follows, we first analyse the theoretical issues and the consensus that has emerged inthe last years, and then in a second part we will define the formalization that will beimplemented in the model. The third part is dedicated to the presentation of the dataused in the econometric estimation that will be presented in the fourth part. Finally, wewill 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 longand stormy history. Main empirical and theoretical controversy opposes proponents ofthe Philips curve to those of the WS-PS model. Philips curve is an empirical relationthat highlights the negative relation between nominal wage and unemployment. It couldbe well represented by :

4w = c+4pe − bU (I.47)

All variables are expressed in logarithm except U which is express in level. 4w isthe variation of nominal wage (w − w−1), 4pe is the expected inflation (it is equal to4p if expectations are perfect) and is U the unemployment rate. Whereas Philips curveestimates well wage formation over the course of a business cycle, it suffers from a lackof 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 assump-tion that real wage fluctuates around a reservation wage which representes the incomeopportunity of employees outside the firm. Main models explain wage reservation byunemployment benefit, labor productivity, positive trend or lagged real wage. Recentlitterature (Chagny and ali (2002)[58] and Reynes (2006)[134]) rejects the theoreticalunderpinning of the first three explanations and retains the latter, leading to a Philipscurve specification. Almost all theoretical models based of bargain model or efficiencywage can be represented as:

∼w = w − p = ∼

wr

+ Z − bU (I.48)

∼w is the real wage, ∼w

ris 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 laggerreal wage (∼w

r= ∼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 theoreticalunderpinnings are as valid as those of the WS setting. Chagny and ali (2002) andReynes (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

4For instance efficiency wage model, matching model or competitive wage competition...

62


unemployment (ERU) and the long run ERU” which the the key difference betweenPhilips curve and WS-PS models. Let’s assume that the medium run wage formation isdirected by :

4w = Z + a4pcons − b1U − b2(U − U−1) + d4π − f4tcs (I.50)

In this specification, wage formations may be: indexed on consumer price pcons, hys-theris or not b2, depend of labour productivity π, employer’s social contribution tcs andinfluenced 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 themedium run (assume that b2 = 0) by:

UEMR = UELR + (w − wd)/b1T (I.52)

where T is the number of quarter during which authorities are implicitly assumed tocorrect the unemployment gap.

I.5.2 Model

We extend the model developped by Chagny and ali (2002) and Reynes (2006) inorder to estimates the wage formation. We transform equation I.56 to take into accountNemesis 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 − UTl,c,t) (I.53)

+dc,l(L)4πi,l,c,t + εi,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.5. WAGE SETTING

• l corresponds to the labour qualification

• i is a sector index, i = 2, ..., 29

• Zi,l,c represents institutional variables.

UT correspond to the tendential unemployment rate. Due to lack in data we limitour 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 (or4wi,l,c), which make the distinctionbetween different kinds of labour. By definition the variation of wage equal the variationof 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 that4tcs,i,l,c = 0, wage variation equal labour compensation variation, which is available inthe EUKLEMS[309] database. Compi,l,c is built as follows:

CompHS =COMP ∗ LABHS100HEMPE ∗ HHS100

Where HEMPE is the Total hours worked by employees (millions), COMP theCompensation of employees (in millions of Euros), LABHS the High-skilled labourcompensation (share in total labour compensation) and HHS the Hours worked byhigh-skilled persons engaged (share in total hours). CompHS is thus the hours labourcompensation 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 thatworkers are mobile through sectors, unemployment rate is not defined by sector. Data

64


Low skil High Skillmean standard error mean standard error

AT 2.07 0.84 1.79 0.74BE 2.35 1.23 2.49 1.57DK 3.34 0.70 3.70 0.75GE 1.45 1.13 2.01 1.55FI 3.04 1.60 3.85 1.37FR 3.63 1.91 1.50 2.10GR 5.27 1.23 3.66 2.28IR 5.49 1.64 5.70 2.73IT 1.76 1.86 3.42 3.07NL 3.75 1.32 4.30 2.40PT 3.38 2.62 3.68 3.31SP 2.59 0.81 2.80 0.94SW 3.65 2.18 2.87 2.08UK 5.19 1.67 3.05 4.00CZ 6.80 2.80 7.00 3.60HU 10.40 5.71 11.33 5.34PL 7.28 5.66 6.89 4.72SN 7.92 2.62 6.99 4.89SK 8.72 3.51 8.20 3.39

Table I.1.: Labour compensation growth, period 1998-2005

65

I.5. WAGE SETTING



Table I.2.: Unemployment rate , period 1998-2005

are taken from Eurostat, we use set called “Unemployment rates by sex, age groups andhighest level of education attained (%)”. We retain as “age groups”, the 15-64 years old.We convert ISCE classification (International Standard Classification of Education) intolow and high skill classification.

Price

pcl,c is taken from the OECD database: “Consumer price indices (MEI)” for all countriesexcept for Slovenia. Since database not available for Slovenia we use Eurostat price (withlower time coverage).

66


HHS share P CONSmean standard error mean standard error


Table I.3.: Price growth and high skill share , period 1998-2005

67

I.5. WAGE SETTING


AT 3.21 1.20 -0.57 1.94BE 1.71 1.68 -0.23 2.03DK 2.16 1.09 -1.56 2.12GE 2.31 1.01 0.26 4.85FI 2.62 1.21 1.47 2.46FR 2.68 1.60 -0.83 2.27GR 1.68 2.56 -2.07 5.11IR 3.69 1.99 -1.84 4.21IT 1.08 1.06 -4.61 1.14NL 1.97 1.84 -2.85 8.89PT 1.37 1.40 -2.47 7.49SP 2.05 1.01 -1.89 1.37SW 3.44 1.85 -3.35 5.65UK 2.72 1.33 -2.35 1.78CZ 6.09 2.96 2.80 3.38HU 5.69 1.87 1.02 5.53PL 6.50 2.21 -0.61 4.96SN 4.17 2.68 -1.61 9.68SK 4.67 3.51 0.55 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 dividedby the number of hours worked (πHs = GOQI

HEMPE∗HHS100for unskilled labour and πLs =

GOQI

HEMPE∗(1−HHS100 ) 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, weimplement two set of regression. First set is made at the macro level. Second set ismade with sectorial specifications. To deal with error autocorrelation we use an order-1

68


Macro b2=0 Sec b2=0Price Unemp Productivity Price Unemp Productivity

LS coef 0.42 -0.46 0.41 0.76 -0.58 0.24se -0.32 0.33 0.19 0.46 0.52 0.17

HS coef 0.69 -1.34 0.29 1.05 -0.52 0.15se 0.43 1.09 0.37 0.54 0.50 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 equationI.53:

4wl,c,t = Zl,c + ac,l4pcl,c,t − b1,c,lU − b2,c,l(Ul,c,t − UTl,c,t) + dc,l4πl,c,t + εl,c,t (I.54)

In a first time we presume the non existence of hysteresis phenomena. In that case weconstraint 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 Figure2. Results of first estimation present expected coefficient sign for 20 specifications on38 (sample include 19 countries with two labour market that give 38 specifications). 6countries: GE,FR,IR,NL,HU and SN present expected sign for both markets. In somecase coefficient value exceed the unity, it’s arise only on the high skill market (exceptfor Hungary). Table I.5 gives aggregated results from regressions which present valueof the expected sign and lower than 2. Price and unemployment seem to play a moreimportant role for high skill wage, at the opposite productivity has a stronger effect onlow skill wage. Figure I.17 presents coefficient for the whole model. Most of the resultsare 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,l4pcl,c,t−b1,c,lUl,c,t−b2,c,l(Ul,c,t−UTl,c,t)+dc,l4πi,l,c,t+εi,l,c,t (I.55)

Main difference with equation I.54 is the sectorial specification of labour compensationand labour productivity. Results of sectorial regression are given in Figure I.18 andin Figure I.19. We proceed in the same way than in macro specification, in the firststep we estimate equation I.55 with b2 = 0 then we relax the constraint. First setof results (Figure I.18) present expected coefficient sign for 24 specifications on 38. 8equations, 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 regressionsrelated 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 strongereffect on low skill wage. In comparison with macro results coefficient value are higherfor price and unemployment coefficient but lower on productivity coefficient. Figure I.19present coefficient for the whole model. Most of the results are in the opposite sign thanexpected 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 − b1U − b2(U − U−1) + d4π − f4tcs (I.56)

Consider 4p0 is the constraint inflation target of the monetary authorities. In thelong 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 − b1U + d4π

The unemployment rate consistent with the target inflation rate and growth rate oflabour productivitty is:

4w −4p0 = 4π = Z + (a− 1)4p0 − b1U + 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.6Labour supply

This section presents the modelling of labour supply in NEMESIS. The decision byindividuals to participate or not to labour market relies on many socio-economic andinstitutional 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 Statesfor participation rates of working age population by categories. Then a second sub-section presents the econometric works realized for MODELS for endogenizing the laboursupply for the different population categories. The data sets that were used to modelparticipation 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 gendercategories. The results allowed nevertheless giving parameter values to calibrate thelabour 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�

78%

80%

82%

84%

86%

88%

90%

92%

94%

96%

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. TheFigure I.20displays the participation rate of the high skilled women and men between 25and 64 years old in 2005. One can observe on this figure an important diversity betweencountries especially for women. The participation rate is about 80% in Malta and CzechRepublic whereas it is more than 90% in Sweden and Portugal. The males’ participationrates differ less between countries and range between 89% in Austria and Romania and94% 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, Swedenand Romania.

Figure I.21 shows the activity rates for women aged from 50 to 65 and for low skilledand high skilled populations in 2005. For this elder population category, participationrates are always superior for high skilled than for low skilled women, of up to about 8 to10% 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 fewerdiscrepancies among EU countries for participation rates of the high skilled women thatfor 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 tryidentifying the influence of key socio-economic indicators on the decision by individu-als to participate to labour market. In economic theory, individuals realize a trade-offbetween work that provides income for consumption and leisure. Another important de-terminant of the labour supply behaviour, linked to this arbitrage between consumptionand leisure, is the value of the reservation wage that one can proxy by the amount ofsocial transfers per head received by individuals. Also, a dynamic labour market, withgrowing employment, will encourages individuals to provide their labour force, while,on the contrary, high unemployment rates will discourage them to enter on it. Thereexists also strong trends affecting the behaviour of labour supply reflecting structuralchanges occurring in European societies, that occur for example in the context of labourmarket regulation, in retirement policies and in social and professional aspirations of theyoung generations. The use of exogenous time trends, and of proxy variables such as theshare of population aged 55-64 in total working-age population, to catch the effects ofretirement policies, and of school enrolment ratios, to catch changes in youngest popu-lation social aspirations, may help us to control the influence of these additional factorsaffecting 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 tofuture works.

I.6.2 Determinants of participation rates

The goal of this sub-section was to find functional forms for participation rates tolabour market of the different population categories that were introduced in the modelNEMESIS. As it was underlined just above, the skill dimension was leaved away, inreason 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 usedand 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 werebased 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 EU27countries plus Norway, have an annual periodicity, and range from 1998 to 2004 . Theparticipation rates by sex and age groups (TxS,Ai,t ) were modelled by using the followingset of explanatory variables:

• Gwi,t = wi,t/pi,twi,t−1/pi,t−1

that represents the change in real wage between t− 1 and t,

• GLSi,t = LSi,tLi,t−1

, the increase in jobs creation between t− 1 and t,

• GSBYi,t = (SBUni,t +SBpoori,t )/pi,tUi,t

(SBUni,t−1+SBpoori,t−1)/pi,t−1Ui,t−1, GSBM

i,t =(SBUni,t +SBpoori,t +SBfarmi,t

)/pi,tUi,t(

SBUni,t−1+SBpoori,t−1+SBfarmi,t−1

)/pi,t−1Ui,t−1

and GSBOi,t = (SBUni,t +SBpoori,t +SBoldi,t )/pi,tUi,t

(SBUni,t−1+SBpoori,t−1+SBoldi,t−1)/pi,t−1Ui,t−1, that are indicators of change in

social benefits between t− 1 and t,

• SSTSi,t = STSi,t

POPS,Yi,t−1, the share of student in ISCED 5 and ISCED 6 in the 15 to 24

years old population,

• and SPOPSi,t = POPYi,t+POPMi,t

POPYi,t+POPMi,t+POPOi,t

, the share of the young and medium agegroups 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 between15 and 24 years; the class M regroups population between 25 and 64 years and theclass 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,

• POPS,Ai,t , the total population by sex and age groups,

• SBUni,t ,SBPoor

i,t ,SBfarmi,t ,SBOld

i,t , the social transfers for, respectively, unemployment,poverty:, family: and oldness,

• wi,t, the nominal wage rate,

• pi,t, the price index of final consumption,

79

I.6. LABOUR SUPPLY

• STSi,t, the total number of student in ISCED 5 and 6 by sex,

• USi,t, the total number of unemployed by sex.

Among the explanatory variables, the change in real wages will capture the importanceof the trade-off between work and leisure or between consumption and leisure. The wagerate used is identical for men and women and all age groups. The change in employmentwill 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 reliesby construction on the participation rate to labour market; the use of unemploymentrate in the econometric specification could consequently lead to biased estimators. Forsocial benefits, an indicator was constructed for each cohort, even if data on socialbenefits do not cover age groups, nor population genders. We constructed neverthelessindicators oriented toward particular age groups, by normalizing the social benefits bythe respective unemployment rates of the different age groups. This allows taking intoaccount the impact of changes in unemployment onto the evolution of social benefits perhead in volume (deflated with final consumption price). For population aged 15 to 24,social benefits include unemployment allowances and poverty assistance, for populationaged 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 24engaged 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 theinfluence 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 takethe form of logistic curves, and the set of indicators that was described above. Thelogistic curves have the following form:

ln

TxS,Ai,t − µλ− TxS,Ai,t

= σ · time+ ρ (I.58)

with µ and λ respectively the limit activity rates high and low, σ the diffusion speedof activity behaviours and − (ρ/σ) the year of inflexion of the activity rate. We obtainby developing:

80


TxS,Ai,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 wereset respectively to 0 and 1, and the parameters σ and ρ were individualised by country.We have:

TxS,Ai,t = e(σS,Ai ·time+ρS,Ai )

1 + e(σS,Ai ·time+ρS,Ai ) (I.60)

By introducing the other determinants of participation rates, we get finally the ex-pressions that were estimated:

TxS,Ai,t = βS,Aw ·GWi,t + βS,AL ·GLSi,t + βS,ASB ·GSBAi,t + βS,AST · SST

S,Ai,t

+ βS,APOP · SPOPS,Ai,t + e(σ

S,Ai ·time+ρS,Ai )

1 + e(σS,Ai ·time+ρS,Ai ) + εS,Ai,t (I.61)

The elasticity parameters βX are common to all countries and εi are the i.i.d. errorterms. Parameter restrictions were imposed with respect to the population groups thatare considered:

• βS,AST = 0 when A = Y,M i.e. we suppose a null effect of the student share (SST )on superior age groups.

• βS,APOP = 0 when A = Y,M , i.e. we suppose a null effect of the share of 15 to 64years old population (SPOP ) 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 ofthe population category, 3 to 4 elasticity parameters βX and 21 country specific con-stant ρS,Ai and time trends σS,Ai to estimate.

Estimation Results

Table I.6 displays the estimation results for the parameters , which measure the shortterm impacts of changes in economic conditions, in population structure and in educa-

81

I.6. LABOUR SUPPLY

Table I.6.: Estimation results of participation ratesGender age 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) (0.2899)

-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) (0.0540)

Males

Females

15-24

25-64

65-max

15-24

25-64

65-max

*,**,***: 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 vari-

ations 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, anincrease 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 ameliora-

tion, or a deterioration of labour market, the second column of the table indicates thatthe parameters βLare significant at 1% level for every categories, except persons aged 65and over, with anyway very low participation rates to labour market. The parametersvalues are important and range between 0.25, for women aged between 25 and 64, and0.37 for those aged between 15 and 24. These values mean that in average, an increaseof 1% of employment will lead to a rise of about 0.3 point of activity rate of populationin working-age, that is to say to an increase of about 0.35% of the labour supply. Theseresults indicate, in other words, that, in average, if 3 new jobs are created, it will reducethe 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 themarket, 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 withparameter values that range between -0.0136 and -0.0285, for women and men aged 25

5For information, Table I.6 shows also adjusted R-squared, that exhibit values superior to 0.97, andDurbin-Watson statistics that do not reveal serious autocorrelation of error terms, with values closefrom 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 popu-lation category, for both men and women, while the share of working –age populationin population aged 15 and over reduces also significantly, and mechanically, the laboursupply 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 thedifferent 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 socialbenefits and the variation in employment over two periods:

TxS,Ai,t = βS,Aw ·GWi,t + βS,AL ·GLSi,t + βS,ASB ·GSBAi,t + βS,AST · SST

S,Ai,t (I.62)

+ βS,APOP · SPOPS,Ai,t + e(σ

S,Ai ·time+ρS,Ai )

1 + e(σS,Ai ·time+ρS,Ai ) (I.63)

As we did not dispose of data by skill to perform the econometric estimations, we thenused, for the different age-groups, the same value of elasticity parameters for the twoskill categories. The value of elasticity parameters were retrieved from the estimationresults presented in Table I.6. Also, for the age groups of NEMESIS 25-54 and 55-64, thesame elasticity parameters were used, that correspond to the econometric estimationsrealized 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 theelasticity parameter estimated for men. For the categories aged between 15 and 24, wekept the assumption, revealed by econometric estimations, that changes in real wagesare 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 homogenousbetween 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. Forwomen, an increase of one point of school enrolment ratio, decreases of 0.94 point theparticipation rate, and of 0.77 point for men. The impact is less than proportional forthe 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 notmodelled, and were kept them exogenous at this stage in NEMESIS.The other determinant of the participation rates are finally the logistic trends, tra-

ducing 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 activityrate, that is to say of labour supply, of the category, with respect to a 1% increase in thegrowth rate of the explanatory variables. For the share of students and the categoriesaged between 15 and 24, the values are semi-elasticities measuring the percentage changeof activity rates with respect to a one point variation of school enrolment ratio of thecategory.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, theelasticities of labour supply are inversely proportional to the initial value of the activityrates. There are consequently in every country stronger for women, who have activityrates inferior to men, and for low skilled persons, that have participation rates inferiorto high skilled ones. We’ll have also superior values of elasticities for the age categories15-24 and 54-65, the youngest and eldest populations having inferior activity rates thanpopulation 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 majorpart 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 increaseby less that 0.1% the labour supply of the medium aged population, confirming the veryweak impact of wages onto the labour supply behaviour. There is no impact at all forpopulation aged between 15 and 24. For the eldest category, the impact of wages rangebetween 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�Low High Low High

Wages:15-24 - - - -

- - - -25-54 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)

55-64 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)

Social benefits:15-24 -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 -0.03

(CZ: -0.02; PT: -0.02) (RO: -0.02; BE: -0.02)) (IE: -0.03; LU: -0.04) (GR: -0.03; FI: -0.03)55-64 -0.05 -0.03 -0.06 -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 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)25-54 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)55-64 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)Share students15-24 -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)

Female Male

Values are non weighted European averagesIn 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 increaseof 1% of the growth rate of social benefits will reduce labour supply between 0.02% forhigh skilled women aged between 25 and 54 and 0.08% for low skilled women agedbetween 15 and 24.

The main endogenous determinant of participation rates to labour market are finallyjobs creation. A 1% increase of jobs creations raises labour supply of 0.25% for highskilled women aged between 25 and 54 and of 1.12% for low skilled women aged between15 and 24. This last figure underlines the difficulty of reducing unemployment for youngwomen with low school attainment level and for which an increase of 1% of job opportu-nities could provoke an augmentation more than proportional of their labour supply inthe short term. It it nevertheless not the case for every country, the impact depending onthe initial participation rate to labour market of this category of women. The smallestvalue of the flexion coefficient of labour supply to job opportutnities is found for CzechRepublic, with 0.5 and the highest value for Luxembourg where it reaches 2.29. We findalso 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 inAustria. For the greater population category, aged between 25 and 54, the value is 0.28in 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 alsothan school enrolment ratios, than can be endogenized on expenditures in eductionin NEMESIS, could influence very importantly the activity rates, and therefore theunemployment rates of both women and men, and of both low skilled and high skilledpersons. In Europe, in average, an increase of 1 point of the school enrolment ratio oflow skilled women reduces their labour supply of 2.87%, with a minimum of 1.27% inCzech republic and a maximum of 5.88% in Luxembourg. We have the more limitedimpacts of changes in school enrolment ratios on activity rates for high skilled men, withan elasticity of 1.2% in Europe in average, a minimum value of 0.82 in slovakia and amaximum value of 2.57 in France.

86


Section I.7Taxation and subsidies

I.7.1 Institutional sectors accounts

The main data source was the Eurostat database, completed if necessary by nationalsources (mainly for Luxemburg, Denmark and Norway). The data availability for somecountries (Ireland, Luxemburg, Hungary, Malta,Slovenia and Romania) were too weakfor building agents account for them, but main taxes and subsidies were integrated. Alldata 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 Cor-porations (FC), Non-Financial Corporations (NFC), all of which are of course linked tothe 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 datawill 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)6ESA: 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 maindifficulties in sectoral applied modelling is to apply the right taxation rate and/or subsidyto the right sector. For a part of the taxation system, some information are availlablein 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 asNEMESIS. Firstly if we consider the datasets needed the available information on VATparticularities is often too detailed for modeling as on one side the sectoral disaggregationof the model allow a strict differentiation of the different VAT rates applicable to thedifferent products and services, but on the other side, calculating VAT rates applicable toone sector based on actual rates is fastidious and need numerous assumptions concerningthe sharing of each rate in the same sector, taking into account existing exemptionsand therefore complicate more the linking of the sectoral taxation system up to themacro-econoomic one. Secondly, considering the formalisation in itself, the traditionalframework in applied modeling for integrating VAT, is to calculate implicit tax rates foreach sector, with the drawback that for analysing the consequences of the modificationof one VAT rate, the implicit rate has to be recalculated ex-ante with all errors that itmay 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, reducedrate 1, reduced rate 2, normal rate and the parking rate) and of the different shares ofeach products in the consumption.The main information sources for building the data neede are furnished by the com-

mission and the taxation and customs DG.For calculating final consumption VAT rates as precisely as possible, the most dis-

agregated 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 tothe NEMESIS one

Hence our final implicit VAT rate is the linear combination of these three datasets (theexemple shown below is for the NEMESIS Medical Care category):

TV AIMPmedcar = shpmedc ·∑T

αpmedT,c · T

+ sheconsc ·∑T

αeconsT,c · T

+ shhospc ·∑T

αhospT,c · 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 = T0, TSR, TR1, TR2, TN, TP , the differnet existing rates, 0 rate, super re-duced rate, reduced rate(s) (sometimes two rates), normal rate and the parkingrate.

• αpmedT,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. Thedistinction between the different excises duties and there allocation between the sectorswere made possible using DG taxation and custom Union informations (see [122]) , thesame database was used for allocating the rest of Taxes on products, except VAT andimport taxes. Hence, aside the three main Excises duties (alcoholic beverages, tobaccoand mineral oil), some countries have other excises duties (electricity, non alcoholicbeverage...), all of which had been incorporated in the model.

Social contributions

Employers’ social contribution (D6111) are splited into sectors using the sectoral dataon D11 wage and salaries and D1 Compensation of employees that are available onEurostat, employees’ social contribution (D6112) as well as imputed social contribution(D612) are splited between sectors depending on relative compensation of employeesas no other data were available, while Social contributions by self and non-employedpersons (D6113) are calculated only at the macroeconomic level, the figures I.22 andI.23 sums up the functiuning of the social contribution bloc. Then each type of socialcontribution is allocated to institutional sectors account ( Gov: general governement,FC: financial corporations, NFC, Non Financial corporations, H&NPISH: Householdsand 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.8Sectoral Interdependencies

In sectorally detailled models, macroeconomic dynamics is driven by sectoral ones, themix of the 32 sectors evolutions will descibe the strength and weaknesses of each europeaneconomy modeled, and hence describe their respective macoeconomic results in termsof economic growth, employment,etc.... Therefore, interlinkages between sectors arethus an important part of the model scheme as they will reflect the different sectoraltendencies 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 behomogenous), needs production factors: the five factors described in NEMESIS areemployment, intermediate energy demands, final energy demands, materials demandsand investments. A sixth factor could be added, even if it is not directly treatenedas a pure production factor, this is the research and development expenditures. Inthe NEMESIS model, sectoral interdependencies are handled through energy demands(intermediate and final), materials demands, investment demands, and through R&Drent 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.24below.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 matricesare considered to be fixed and the demands are formulated as:

DEM jC,i = βjc,i · FACTDC,i (I.64)

With this formulation a sector can not shift from a product to an other. If the sectorthat produces product j improve its productivity (that is produce the same product butwith a lower price), every sectors i that uses the product j will face a lower investmentprice (ceteris paribus). By using¯fixed coefficient matrices, this will only leed for the ito a smaller global investment price, but this sector can not choose to buy more of thej’s good instead of other ones. Consequently, we can easily see that the j has no gainto 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 fixed coefficient matrices are thereforenot 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 jin the total factor demand of sector i as a cost minimisation on a CES function. Firmsdetermine their global factor demand using its production function, then minimisesthe cost of approvisioning this global demand from a C.E.S function of elasticity ofsubstitution εi.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 moreas possible quick shifting between the different sources of supply, we add in this derivedshares adjustment delays, the formulation we choose for each type of firms matrices(Intermediate Consumption, final Energy demand and Investment) is the following:

COEF jC,i = λi · coefmatj

C,i ·(PDC,i

PVj

C

)εi+ (1− λi) · coefmat

j

C,i (I.65)

with

93


Figure I.24.: Sectoral interdependencies in NEMESIS

94


• λi the adjustment delay,

• PDC,i the global factor demand price of sector i

• PV jC the sale price of the product j and

• εi the price elasticity.

Using this formulation, if the sector j decrease its price (other sectors unchanged) theshare of demand of the sector i asked to sector j will increase depending on the priceelasticity 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 explicitatethe products components of investment, intermediate consumption and energy sec-toral demands and is moreover fully coherent with the framework we choose forclosing- up the NEMESIS supply side to grounded microeconomic behaviour. Nev-ertheless, coefficients so calculated are not those determined by national accountsstatisticians.

2. But in the baseline projections, coefficients evolution must be exogeneised, theendogenous determination of thousands of coefficients complicate the model reso-lution.

This formalisation of matrices’ coefficients had been tested using several economic andenvironmental policies and is operational.

I.8.2 technological progress interactions.

Endogenous technical change in NEMESIS needs to takes into account technologicalsinteractions 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 activi-ties of another sector without pay monetary compensation. An example of knowledgespillovers is when one invention might lead to a new ideas for different inventor. InNemesis model knowledge spillover relies positively R&D expenditure of one sector tothe knowledge stock of another. We distinguish national and international knowledgespillover.

Knows,c,t = f(RDs,c,t′ , SKNs,c,t′′ , SKIs,c,t′′ , 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&Dspillovers. This concept of technological link is called technological proximity becauseit is derived from the relative position of sector in a technological space. Concretelytechnological proximity matrix assume that the main IPC code into which a patent isclassified provides a good proxy of the producing sector of the knowledge, and the listedsupplementary IPC codes given an indication for technology spillovers to other industrialsectors. The more two sector are close to each other, the higher is the effect of R&Dexpenditure.For national knowledge spillover we assume:

SKNi,c,t =∑j 6=i

θij ·R&Dj,c,t′

For international knowledge spillover we assume :

SKIi,c,t =∑d

∑j 6=i

βcdθij ·R&Dj,d,t′ ·

96


Figure I.25.: Knowledge spillovers

where θij is the technology proximity between sector i and j and βcdis the economydistance between c and dMatrix used is Verspagen matrix transformed to Nemesis sectorial classification.

Rent Spillovers

Second kind of technological interaction is rent spillovers. Rent spillovers refer to thecase where R&D intensive input are purshased from other industries at less than theirfully adjusted price. This failure to embody correctly a higher quality into output priceis the consequence of imperfectly monopolistic pricing arising from competitive pressureon innovating industry. For Griliches [159] rent spillover is a problem of measuringcapital equipement, materials and their price correctly and not a case of pure knowledgespillovers. 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 thedegree of competition. Low degree induce more importance on rent spillovers effect thanon 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 flowsbetween sectors. This level is simply measured by Input-Output matrices. It reslutthat factor productivity is not only affected by its own R&D but also by productivityimprovment in another sector to the extend of its purchase.

97

I.9. HOUSING INVESTMENTS

Figure I.26.: Rent Spillovers

RentSi,t =∑j 6=i

δijInnovj,c,t′

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 implementedin another. Innovation which improve productivity could be made in own sector orpurchase to another throught patent transaction. To link sector innovation in user-producer principle we use the so-called “Yale matrices”. This matrix is constructed onthe 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 byJohnson [197] and extent it to other country.

Section I.9housing investments

I.9.1 Methodology7

98


Households investment was already modelled in the NEMESIS model but in a veryroughly way, and the implementation of the land use module implies a better modellingof it. We will present in this section the new formalisation and estimate of householdsinvestment in the NEMESIS model.Either at theoretical or empirical point of view, interactions between residential mar-

ket and macroeconomic are not very analysed (Leung, 2004[230]), this explains that themodelling of households investment in large applied economic model is not very devel-oped, or at least is not highlighted compared to others macroeconomic variables. Thisfact is reinforced by the lack of consensus regarding households investments formali-sation, 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 viewas a consumption and the second one concerns the wealth effect related with the own-ership of housing8. We analyse how some large applied economic models and especiallyeconometrics 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 astock adjustment model. In their modelling, the variation of the housing stock is afunction of the households real disposable income per capita, the real interest rates (inmoving average), the housing relative price (either relative price of housing services orrelative price of housing investment), the existing housing stock per capita at the previousperiod, the variation of unemployment rate and finally a partial adjustment term10.For the MIMOSA model11, Chauffour and Fourmann (1990[59]) formalise the invest-

ment rate (i.e. the ratio between housing investment and housing stock) as a func-tion of households income per capita (smoothed variable), real housing investment price(smoothed variable), real interest rate, previous housing stock per capita and unemploy-ment rate change. A very similar version of housing investment model is developed for thefrench 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

7The section depends for a part on a study realised in the ERASME laboratory (Lécina, 2008[224])and especially for the literature survey.

8We do not treat the trade-off between buying a housing or renting it (see e.g. Arrondel and Lefebvre2001[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 consideredeven if it follows the new housing purchase market, as demonstrated by Demers (2005[98]) for Canada.

9We do not present the estimate results of these studies but we will use them to compare our estimateresults in the following section.

10All variables are expressed in logarithm expect for real interest rate and unemployment rate.11See 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, Ire-land, Italy and United-Kingdom. Only four models introduce the housing investment intheir 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 onthe desired housing stock adjusted with the help of an error correction model (Engle andGranger 1987[119]). This desired housing stock depends on households income, relativeprice, 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 thatis a linear function of real per capita personal disposable income, government transfersfor housing, interest rates and inflation12. Finally, Bosi et al. (1993[35]) use similarhousing investment model for the Italian HERMES model. They relate, in logarithmicform, housing investment per capita with income per capita and relative price of housinginvestment 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 hous-ing investment and households real disposable income) with real disposable income,unemployment rate and interest rate as explanatory variables. In this case, the use ofhousing investment rates, in a error correction model, imposes a long term elasticitybetween 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 whichthey link for short term, housing investment variation with previous variation, housinginvestment price variation, real short term interest rate variation (3 month) and unem-ployment rate variation while in the long term equation, they only keep the link betweenreal 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 endogenousvariable 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 ability12All variables are expressed in logarithm.13We will specify these properties in the following section.14All variables are expressed in logarithm except interest rates and unemployment rate.

100


as well as the borrowing power of households.

• The purchase of housing, in most case, requires a long term loan. This aspectis included with the help of interest rates, the payback power being reduce wheninterest rates increase.

• In addition, socio-economic aspect can also act on the housing investment, andparticularly, the demography can play a non negligible role, it is why some modelsuse per capita variables as explanatory variables.

• The relative housing investment price, generally the ratio between housing invest-ment price and consumption price, allow the modelling of the traditional substi-tution effect. But, in the case of housing, which is a asset for the households, theinvestment price acts also on the expected wealth - insomuch as it follows housingstock price - and this wealth effect can be effectively very important as illustratedby the recent real estate bubble. Thus, housing investment price plays a doublerole, it increases purchase cost but it also raises housing expected value.

• The general economic context is generally represented through unemployment rateor employment which are relatively important for the households expectations oneconomic futures and then for their confidence on their payback capacities.

• Finally, other variables such as government subsidies for housing, like in IrishHERMES model (Bradley et al. 1993[39]), can act on households investmentdecision. Some of them are already included in the real disposable income, this isthe case for instance of transfers. It could also be interesting to include specificvariables that could reflect change in national regular regimes but as we use a panelof 12 countries, the time required to get good and reliable information constrainsus 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 alsochoose for NEMESIS because error correction model allow the distinction between twomodels: one for short term and a second for long term (equilibrium). Nevertheless, theerror correcting model requires a deep examination of variables with numerous economet-ric 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 rootsand cointegration tests, following by the error correction model estimate and finally wedisplay 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-ECOnomicdatabase (AMECO 2008[13]) of the European Commission’s Directorate General forEconomic and Financial Affairs (DG ECFIN) which provides structured and coherentdata on national account and especially times series for prices. Population data comefrom Eurostat Population database (Eurostat 2008[130]). Thus we have the followingvariables for 12 European countries15 from 1995 to 2008:

• Households and Non-Profit Organisation (NPO) real gross fixed capital formation16

(GFCF ) which is the GFCF in value divided by the total economy gross fixedcapital formation price (PGFCF )17,

• The real total economy gross fixed capital formation price (PRGFCF ) which is theratio between PGFCF and the consumption price (PCONS)

• The households real disposable income (REV Q) which is the ratio between house-holds disposable income and consumption price,

• The long term and short term real interest rates (TXLT and TXCT ) which are theratio between interest rates and consumption price,

• The number of unemployed persons (UNEMP ),

• And the population divided in 5 age groups, the “very young” (POP Y Y ) between0 and 4 years, the “young” (POP Y ) between 0 and 19 years old, the “medium”(POPM ) between 20 and 39 years, the “medium-old” (POPMO) between 40 and59 years old and the “old” (POPO) more than 60 years.

All these variables are transformed in logarithm except for the real interest rates. Wepresent in the following section the unit root tests and cointegration tests realised onthese variables.15Only EU-15 countries: Belgium, Denmark, Germany, Spain, France, Italy, Netherlands, Austria,

Portugal, Finland, Sweden and United-Kingdom.16Households gross fixed capital formation and households and NPO gross fixed capital formation in

residential are unavailable.17Price 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 coin-tegrated variables presented above. Thus, we have two models, one for long term rela-tionship and a second for short term relationship. The long term relationship is definedby the general model I.66 whereas short term equation is defined by general model I.67.

gfcfi,t = αi + δiti + βrevQ

i revQi,t + βpRgfcfi pRgfcf,i,t + βpopi popi,t (I.66)

+ βunempi unempi,t + βTXLT

i TXLTi,t + εi,t

4gfcfi,t = µi + θrevQ

i 4revQi,t + θpRgfcfi 4pRgfcf,i,t + θpopi 4popi,t (I.67)

+ θunempi 4unempi,t + θTXLT

i 4TXLTi,t + θresi εi,t−1 + ui,t

As, we use panel data, we impose identical parameters for each country for long termand short term equations (see equation I.68) except for intercept (αi and µi) and trend(δi). ε are estimated residuals from long term relationship.

βUi = βU ∀iθZi = θZ ∀i

(I.68)

Where U = revQ, pRgfcf , pop, unemp, TXLT and Z = revQ, pRgfcf , pop, unemp, TX

LT , ε.We also estimate the models I.66 and I.67, either keeping free the long term relation-

ship between households gross fixed capital formation and households real disposableincome (βrevQ) and gross fixed capital formation real price (βp

Rgfcf ) or constraining these

relationships. Table I.8 displays the estimated results of both models, with and withoutconstrained 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 elasticitiesof households gross fixed capital formation (GFCF ) with respect to households realdisposable 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 investmentin level and their income in level i.e. the share of households investment in their budgettends 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 forhouseholds real income.In addition, the elasticity of households gross fixed capital formation with respect to

households gross fixed capital formation real price (PRgfcf ) is estimated to 1.47. Thispositive 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, asa consequence, an increase of investment price increases purchase cost but raises alsothe anticipated value of the asset. Thus, as our data cover the 1995 to 2008 period, itincludes the recent real estates bubbles that occurs in most of the EU-15 countries, andan elasticity of 1.47 can traduce the bubble effect of the housing price. Nevertheless, theintroduction of such a parameter value in a economic model such as NEMESIS would leadto misleading results, therefore we constrained this elasticity at -0.5%, value in adequacywith those estimated in the literature. For instance, Egebo and Lienert (1988[117]) findelasticities of -0.45% for France, -0.56% for United Kingdom and -0.44 for Italy whileChauffour and Fourmann (1990[59]) find elasticities about -0.4% for France, -0.4% forItaly and -0.3% for West Germany. More recent studies, imposed an elasticity or excludethe 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 populationin the long run one. Regarding the effect of the long term interest rate, the null parameterseems not so surprising, and even using the short term interest rate does not providebetter parameters estimates, consequently we keep the hypothesis that long term interestrate has no impact at short term. The parameter estimates for the population appearsto be strong in the short run (+3%)18, but does not influence households investmentin the long run. The unemployed persons elasticity is negatively related to housinginvestment with a short term elasticity of -0.28% and a long term elasticity a little bitlower 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 valueof their assets, while in the long run the more traditional behaviour dominates andexplains the negative parameter value (-0.5%). Finally, an increase of 1% of householdsreal 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

18We will limit the short term effect at 1.5% in the implemented version of housing investment inNEMESIS to keep a certain stability even at short term.

104


Table I.8.: Estimates results of households gross fixed capital formation error correctionmodel

Model I.66 (Long Term) Model I.67 (Short Term)

Unconstrained Constrained Unconstrained Constrained

βrevQ 0.5196∗∗∗ 1(a) θrev

Q 0.7575∗∗∗ 0.6633∗∗

(0.3574) – (0.2896) (0.2809)

βpRgfcf 1.4731∗∗∗ -0.5(a) θp

Rgfcf 1.6091∗∗∗ 1.2401∗∗∗

(0.3465) – (0.3707) (0.3523)

βpop -0.2841 -0.4065 θpop 3.0925∗∗ 3.2721∗∗∗

(0.5338) (0.5736) (1.2115) (1.1696)

βunemp -0.1398∗∗∗ -0.1309∗∗∗ θunemp -0.2502∗∗∗ -0.2849∗∗∗

(0.0475) (0.0411) (0.0502) (0.0494)

βTXLT -0.0178∗∗∗ -0.0143∗ θTX

LT -0.0074 -0.0067

(0.0068) (0.0074) (0.005) (0.0048)

θres -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 parameterproviding relatively good results when individualised, it is the adjustment parameter(θresi ), 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 adjustmentparameters. The short term elasticity is a little bit lower for real disposable incomeand unemployed persons, stronger for population and still not significant for long terminterest rate. Now looking at the individualised adjustment coefficients, we first see thatall are negative. But the coefficients for Germany, France, Italy, Austria and Portugal arenot significant at 10% level. And we can also see that the range of significant coefficientsis 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 coef-ficients

Individualised adjustment parameter: θresi

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

θrevQ 0.523∗

θpRgfcf 1.2497∗∗∗

θpop 4.0173∗∗∗

θunemp -0.228∗∗∗

θTXLT -0.006

∗, ∗∗, ∗ ∗ ∗: parameter significantly different to zero at 10%, 5%, 1% respectively.

term parameters, we realise a sensibility analysis by introducing standard stocks in theerror correcting model.

I.9.4 Sensibility analysis

We make a sensibility analysis of housing investment error correction model estimatedin the previous section by introducing shocks on one variable and keeping the otherones fixed. Figure I.27 and I.28 display the model responses to a permanent shock of1% on each variable with the exception of long term interest rates that had been raisedby 1 point of percentage permanently. Figure I.27 presents the responses for commonadjustment parameters (-0.53) whereas figure I.28 compares results with individualisedadjustment 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

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

1 2 3 4 5 6 7 8 9 10

Rev P Tx U Pop

With Rev = revQ, P = PRgfcf , T x = T XLT , U = unemp and P op = pop.

We can see in figure I.27, that, in accordance with estimate results, the short termeffect of population is very strong but decreases progressively to reach zero at longterm. Thus, population rise has only a transitory effect on households gross fixed capitalformation. At the opposite, the real disposable income shows a moderate short runeffect on housing investment, the first year, households investment increases of about0.6% and tends to 1% (as imposed) at long term. Furthermore, the real price of housinginvestment has a particular dynamic. In a first time, an raise of 1% of investment pricepushes 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 termequilibrium of -0.5% (as constrained). Thus a perpetual shock on housing investmentreal price plays, at short term, as a bubble effect but this bubble effect progressivelydeclines to finally reduces housing investment. Looking at unemployment effect, weobserve a bigger short term shock (-0.3%) than the long term with -0.13%. Finally, asdemonstrated 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: Com-parison according to adjustment coefficients

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

0.9%

1.0%

1.1%

1 2 3 4 5 6 7 8 9 10

Denmark Austria Common

Figure I.28 shows the effect of different adjustment parameter on the response dy-namic. Denmark, where the adjustment parameter is the higher with -0.77, tends morerapidly to its long term equilibrium, therefore the Danish average adjustment decay isabout 0.3 years i.e. 50% of the adjustment to the long term equilibrium is done in 4months. At the opposite, the average adjustment is about 9 years for Austria, where theadjustment 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. Wekeep, for the implementation in the NEMESIS model, the estimated coefficients exceptfor the short term effect of population. In fact, the estimate value of this parameterseems to strong and we decide to reduce it at 1.5%, i.e. a little bit higher than the unityand we still suppose that its long term elasticity is null. Similarly, we impose a null shortterm effect of long term interest rate and finally we take the individualised adjustmentparameters 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 errorcorrecting model that determine housing investment in each European countries ac-cording to its prices, households real disposable income, total population, unemployedpersons and long term interest rate. With the housing investment for each Europeancountry, we can calculate their housing stock using perpetual inventory method. Andfinally, we can determine the national land used by housing by using the density coeffi-cients that link national housing stock with its land use.

109

Bibliography

[1] Global mitigation of non-co2 greenhouse gases. Technical report, United StatesEnvironmental Protection Agency, 2006.

[2] Technical guidelines voluntary reporting of greenhouse gases (1605(b)) program.Technical report, US Energy Information Administration, 2007.

[3] Greenhouse gas emission trends and projections in europe 2009. Technical report,European Environment Agency, 2009.

[4] Manning A. Wage bargaining and the phillips curve: The identification and spec-ification of aggregate wage equation. The economic journal, 103, 1993.

[5] Daron Acemoglu. Directed technical change. Review of Economic Studies, 69:781–809, 2002.

[6] Daron Acemoglu and Fabrizio Zilibotti. Productivity differences. Quarterly Jour-nal of Economics, 116:563–606, 2001.

[7] P. Aghion and P. Howitt. "A Model of Growth Through Creative Destruction".Econometrica, 60(2):323–351, 1992.

[8] P. Aghion and P. Howitt. Capital accumulation and innovations as complementaryfactors in long-run growth. Journal of Economic Growth, 3:111, 1998.

[9] P. Aghion and P. Howitt. Endogenous Growth Theory. MIT Press, Cambridge,1998.

110

Bibliography

[10] AGRESTE - Ministère de l’alimentation, de l’agriculture et de la pêche. AGRESTE- Enquêtes sur la valeur vénale des terres agricoles, 2007.

[11] H. Akaike. A new look at the statistical model identification. IEEE Transactionson Automatic Control, 19(6):716–723, 1974.

[12] W. Alonso. Location and Land Use. Harvard University Press, Cambridge, USA,1964.

[13] AMECO. Annual Macro-ECOnomic database. Database, 2008.

[14] J.R. Anderson, E.E. Hardy, J.T. Roach, and R.E. Witmer. A Land Use and LandCover Classification System for Use with Remote Sensor Data. Geological SurveyProfessional Paper 964, United States Department of the Interior, 1976.

[15] P.. Armington. A Theory of Demand for Products Distinguished by Place ofProduction. Staff paper, International Monetary Fund, 1969.

[16] L. Arrondel and B. Lefebvre. Consumption and Investment Motives in HousingWealth Accumulation: A French Study. Journal of Urban Economics, 50(1):112–137, 2001.

[17] K. Arrow. The economic implications of learning-by-doing. Review of EconomicStudies, 26:155–173, 1962.

[18] K. J. Arrow, H. B. Chenery, B. S. Minhas, and R. M. Solow. Capital-labor substi-tution and economic efficiency. The Review of Economics and Statistics, 43(3):225–250, 1961.

[19] K.J. Arrow, H.B. Chenery, B.S. Minhas, and R.M. Solow. Capital-Labor Substitu-tion and Economic Efficiency. The Review of Economics and Statistics, 43(3):225–250, Aug. 1961.

[20] M. Assouline and A. Epaulard. HERMES: A Macrosectoral Model for the FrenchEconomy, chapter 6, pages 205–276. In Commission of the European Communities[191], 1993.

[21] T.O. Awokuse and J.M. Duke. The Causal Structure of Land Price Determinants.Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie,54(2):227–245, 06 2006.

111

Bibliography

[22] P. Bagnoli. Developments in growth literature and its relevance for simulationmodels : annex 1. Technical report, Working Party on Global and Structuralpolicies, OCDE, ENV/EPOC/GSP(2002)7/ANN1, 2002.

[23] M. Bahgli, V. Brunhes-Lesage, O. De Brandt, H. Fraisse, and J.P. Villetelle. MAS-COTTE : Modèle d’AnalySe et de prévision de la COnjoncture TrimesTriElle.Notes d’Etudes et de Recherche 106, Banque de France, Fev. 2004.

[24] R. Baldwin and R. Forslid. Dynamic Issues in Applied Commercial Policy Analysis,chapter Putting growth effects in computable equilibrium trade models, pages 44–85. Cambridge University Press., 1999.

[25] Almut Balleer, Ramon Gomez-Salvador, and Jarkko Turunen. Labour force par-ticipation in the euro area - a cohort based analysis. Working Paper Series 1049,European Central Bank, May 2009.

[26] R. J. Barro and X. Sala-I-Martin. Economic Growth. MCGraw-Hill, inc, 1995.

[27] C. Bean. Endogenous growth and the procyclical behaviour of productivity. Eu-ropean Economic Review, 34:355–363, 1990.

[28] Gary S. Becker and Barry R. Chiswick. Education and the distribution of earnings.The American Economic Review, 56(1/2):358–369, 1966.

[29] Jess Benhabib and Mark Spiegel. Human capital and technology diffusion. Pro-ceedings, (Nov), 2002.

[30] E.R. Berndt and L.R. Christensen.

[31] Ernst R. Berndt and Jack E. Triplett. Fifty Years of Economic Measurement:The Jubilee of the Conference on Research in Income and Wealth, volume 24. TheNational Bureau of Economic Research, The University of Chicago Press, Chicago,1991.

[32] Olivier Jean Blanchard and Lawrence Katz. Wage dynamics: Reconciling the-ory and evidence. NBER Working Papers 6924, National Bureau of EconomicResearch, Inc, February 1999.

[33] X. Bonnet, G. Forgeot, and Maire P. L’investissement logement des ménages dansle modèle AMADEUS. Notes de Conjonctures, INSEE - Institut National de laStatistique et des Etudes Economiques, Dec. 1994.

112

Bibliography

[34] Valentina Bosetti, Carlo Carraro, Marzio Galeotti, Emanuele Massetti, and Mas-simo Tavoni. A world induced technical change hybrid model. The Energy Journal,0(Special I):13–38, 2006.

[35] P. Bosi, R. Golinelli, and A. Stagni. HERMES: A Macrosectoral Model for theItalian Economy, chapter 9, pages 453–531. In Commission of the European Com-munities [191], 1993.

[36] A.F. Bouwman, T. Kram, and K.K. Goldewijk. Integrated Modelling of GlobalEnvironmental Change. Netherlands Environmental Assessment Agency, 2006.

[37] R. Boyer and M. Didier. Innovation et croissance. Rapport du conseil d’analyseÉconomique, La documentation Française, 1998.

[38] I. Bracke and E. Meyermans. Specification and estimation of an allocation systemfor private consumption in europe. Working-Paper 3-97, Federal Planing Bureau,Belgium., 1997.

[39] J. Bradley, J. Fitz Gerald, D. Hurley, L. O’Sullivan, and A. Storey. HERMES: AMacrosectoral Model for the Irish Economy, chapter 8, pages 327–452. In Com-mission of the European Communities [191], 1993.

[40] D. Brécard, C. Chevalier, A. Fougeyrollas, P. Le Mouël, L. Lemiale, and P. Zagamé."3 l’aide du modèle Némésis". Rapport, Commission Européene, 2004.

[41] D. Brécard, A. Fougeyrollas, P. Le Mouël, L. Lemiale, and P. Zagamé. Evalua-tion pour la France des conséquences de l’augmentation de l’effort de R&D. InIncidences économiques d?une augmentation des dépenses de recherche en Europe,number 391 in Rapport d’information du Sénat, 2004.

[42] D. Brécard, F. Fougeyrollas, P. Le Mouël, L. Lemiale, and P. Zagamé. Macro-economic consequences of European research policy: Prospects of the Nemesismodel in the year 2030. Research Policy, 35(7):910–924, September 2006.

[43] Dorothee Brecard, Arnaud Fougeyrollas, Pierre Le Mouel, Lionel Lemiale,and Paul Zagame. Macro-economic consequences of European research policy:Prospects of the Nemesis model in the year 2030. Research Policy, 35:910–924,2006.

[44] Paolo Buonanno, Carlo Carraro, and Marzio Galeotti. Endogenous induced tech-nical change and the costs of Kyoto. Resource and Energy Economics, 25:11–34,2003.

113

Bibliography

[45] J.-M. Burniaux and H.-L. Lee. Modelling Land Use Changes in GTAP. Presentedat the 6th Annual Conference on Global Economic Analysis, The Hague, TheNetherlands, 2003.

[46] J-M Burniaux and T. Truong. GTAP-E: An Energy-Environmental Version ofthe GTAP Model. GTAP Technical Papers 16, Center for Global Trade Analysis,Department of Agricultural Economics, Purdue University, 2002.

[47] O.R. Burt. Econometric Modeling of the Capitalization Formula for FarmlandPrices. American Journal of Agricultural Economics, 68(1):10–26, 1986.

[48] Ricardo J. Caballero and Adam B. Jaffe. How high are the giants’ shoulders: Anempirical assessment of knowledge spillovers and creative destruction in a modelof economic growth. NBER Working Papers 4370, National Bureau of EconomicResearch, Inc, May 1993.

[49] G. Cameron. Innovation and growth: a survey of the empirical evidence. NuffieldCollege, Working Paper, 1998.

[50] J.B. Campbell. Spatial correlation-effects upon accuracy of supervised classifica-tion of land cover. Photogrametric Engineering And Remote Sensing, 47(3):355–363, 1981.

[51] D.R. Capozza and R.W. Helsley. The Fundamentals of Land Prices and UrbanGrowth. Journal of Urban Economics, 26(3):295–306, Nov. 1989.

[52] D.R. Capozza and R.W. Helsley. The Stochastic City. Journal of Urban Eco-nomics, 28(2):187–203, Sep. 1990.

[53] C. Carraro. Climate modelling and policy strategies. the role of technical changeand uncertainty. Energy Economics, 20:463–471, 1998.

[54] C. Carraro and M. Galeotti. Economic growth, international competitiveness andenvironmental protection: R&d and innovation strategies with the warm model.Energy Economics, 9:2.28, 1997.

[55] Alan Carruth and Andrew Henley. Housing assets and consumer spending: A re-gional analysis. Regional Studies: The Journal of the Regional Studies Association,27(11):611–621, 1993.

[56] J. Cavailhès and P. Wavresky. Urban Influences on Periurban Farmland Prices.European Review of Agricultural Economics, 30(3):333–357, Sept. 2003.

114

Bibliography

[57] Odile Chagny, Frédéric Reynès, and Henri Sterdyniak. Le taux de chômaged’équilibre. discussion théorique et évaluation empirique. Revue de l’OFCE,81(2):205–244, 2002.

[58] Reynes F. Chagny O. and Sterdyniak H. Le taux de chomage d’équilibre: Discus-sion théorique et évaluation empirique. Revue de l’OFCE, 81, 2002.

[59] J.P. Chauffour and E. Fourmann. L’Investissement-Logement des Ménages dansMIMOSA. Document de Travail 90-01, CEPII (Centre d’Etudes Prospectives etd’Informations Internationales) and OFCE (Observatoire Français des Conjonc-tures Economiques), Jan. 1990.

[60] V. Chauvin, G. Dupont, H. Heyer, M. Plane, and X. Timbeau. Le modèle Francede l’OFCE - La Nouvelle Verion: emod.fr. Revue de l’OFCE, 81(2):245–300, 2002.

[61] Valérie Chauvin, Gaël Dupont, Éric Heyer, Mathieu Plane, and Xavier Timbeau.Le modèle france de l’ofce. la nouvelle version : e-mod.fr. Revue de l’OFCE,81(2):245–300, 2002.

[62] J-P. Chavas and A. Thomas. A Dynamic Analysis of Land Prices. AmericanJournal of Agricultural Economics, 81(4):772–784, Nov. 1999.

[63] Nathalie Chen. Intra-national versus international trade in the european union:why do national borders matter. Journal of Internaional Economics, 63(1):93–118,May 2004.

[64] Carole Chevallier, Arnaud Fougeyrollas, Pierre Le Mouël, and Paul Zagamé. Atime to sow, a time to reap for the European countries : a macro-econometricglance at the RTD national action plans. Revue de l’OFCE, 97bis:235–257, 2006.

[65] D.L. Chicoine. Farmland Values at the Urban Fringe: An Analysis of Sale Prices.Land Economics, 57(3):353–362, Aug. 1981.

[66] I. Choi. Unit root tests for panel data. Manuscript, Kookmin University, Corée,1999.

[67] I. Choi. Unit root tests for panel data. Journal of International Money andFinance, 20(2):249–272, 2001.

[68] W. Christaller. Die zentralen Orte in Süddeutschland. Gustav Fisher, Jena, Ger-many, 1933.

115

Bibliography

[69] L. Christensen, D. Jorgenson, and L. Lau. Conjugate duality and the transcen-dental logarithmic functions. Econometrica, 39:255–256, 1971.

[70] L. Christensen, D. Jorgenson, and L. Lau. Transcendental logarithmic productionfrontiers. Review of Economics and Statistics, 55:28–45, 1973.

[71] L.R. Christensen and D.W. Jorgenson. The Measurement of U.S. Real CapitalInput, 1929-1967. Review of Income and Wealth, 15(4):293–320, Dec. 1969.

[72] L.R. Christensen and D.W. Jorgenson. Measuring Economic Performance in thePrivate Sector. In The Measurement of Economic and Social Performance, NBERChapters, pages 233–352. National Bureau of Economic Research, Inc, 1973.

[73] L.R. Christensen, D.W. Jorgenson, and L.J. Lau. Transcendental LogarithmicProduction Frontiers. The Review of Economics and Statistics, 55(1):28–45, Feb.1973.

[74] M. Cincera and B. van Pottelsberghe. International r&d spillovers: a survey.Cahiers Economiques de Bruxelles, 2001.

[75] Leon Clarke, John Weyant, and Alicia Birky. On the sources of technologicalchange: Assessing the evidence. Energy Economics, 28:579–595, 2006.

[76] Leon Clarke, John Weyant, and Jae Edmonds. On the sources of technologicalchange: What do the models assume. Energy Economics, 30:409–424, 2008.

[77] Wesley M Cohen and Daniel A Levinthal. Innovation and learning: The two facesof r&d. Economic Journal, 99(397):569–96, September 1989.

[78] European Commission. Vat rates applied in the member states of the europeancommunity, situation at september 2006. Document DOC/1829/2006, Taxationand custom Union, 2006.

[79] European Commission. « une politique de l’énergie pour l’europe », communicationde la commission au conseil européen et au parlement européen. COM(2007) 1Final, Janvier 2007.

[80] European Commission. Vat rates applied in the member states of the europeancommunity, situation at 1st may 2007. Document DOC/2137/2007, Taxation andcustom Union, 2007.

116

Bibliography

[81] European Commission. Directive of the european parliament and of the council,on the promotion of the use of energy from renewable sources. COM(2008) 19final, 01 2008.

[82] Juan Carlos Conesa, Carlos Díaz-Moreno, and José Enrique Galdón-Sánchez. Ex-plaining cross-country differences in participation rates and aggregate fluctuations.Journal of Economic Dynamics and Control, 26(2):333 – 345, 2002.

[83] GEM-E3 Consortium. Gem-e3 model manual. Technical report, E3M-Lab, July2006.

[84] C. Cornwell, P. Schmidt, and D. Wyhowski. Simultaneous equations and paneldata. Journal of Econometrics, 51:151–181, 1992.

[85] Giancarlo Corsetti, Philippe Martin, and Paolo Pesenti. Productivity, terms oftrade and the home market effect. Journal of International Economics, Forthcom-ing, 2007.

[86] Matthieu Crozet and Hélène Erkel Rousse. Trade performances, product qualityperceptions and the estimation of trade price-elasticities. Review of InternationalEconomics, 12(1):108–129, 2004.

[87] Matthieu Crozet and Federico Trionfetti. Trade costs and the home market effect.CEPII Working Paper N° 2007-05, March 2007.

[88] Joanne Cutler and Kenny Turnbull. A disaggregated approach to modelling uklabour force participation. Discussion Papers 04, Monetary Policy CommitteeUnit, Bank of England, 2001.

[89] P. Dasgupta and E Maskin. The simple economics of research portfolios. TheEconomic Journal, 97:581–595, 1987.

[90] P. David. Clio and the economics of qwerty. American Economic Review, 75:332–337, 1985.

[91] P. A. David, B. H. Hall, and A. A. Toole. Is public r&d complement or substituteor private r&d? a review of the econometric evidence. Research Policy, 29:497–529,2000.

[92] J. Davidson, D. Hendry, F. Srba, and S. Yeo. Econometric modelling of the aggre-gate time-series relationship between consumers’ expenditure and income in theunited-kingdom. The Economic Journal, (88):661–692, 1978.

117

Bibliography

[93] P. de Fontnouvelle and S.H. Lence. Transaction Costs and the Present Value.Southern Economic Journal, 68:549–565, 2002.

[94] Angel de la Fuente and Rafael Doménech. Human capital in growth regressions:How much difference does data quality make? an update and further results.CEPR Discussion Papers 3587, C.E.P.R. Discussion Papers, October 2002.

[95] B. Van Pottelsberghe de la potterie. Les politiques de science et technologie etl’objectif de barcelone. Université Libre de Bruxelles Working Paper WP-CEB04/11, 2004.

[96] A. Deaton and J. Muellbauer. An almost ideal demand system. American Eco-nomic Review, 70(3):312–326, 1980.

[97] H. Delessy, H. Sterdyniak, B. Coquet, P. Cour, H. Le Bihan, F. Lerais, F. Busson,J. Baude, B. Châtelain, and M. Fiole. La nouvelle version de MIMOSA, modèlede l’économie mondiale. Revue de l’OFCE, 58:103–155, July 1996.

[98] F. Demers. Modelling and Forecasting Housing Investment: The Case of Canada.Working Papers 05-41, Bank of Canada, 2005.

[99] F.A.G. den Butter and F.J. Wollmer. An empirical model for endogenous technol-ogy in the Netherlands. Economic Modelling, 13:15–40, 1996.

[100] A. Di Gregorio and L.J.M. Jansen. Land Cover Classification System (LCCS):Classification concepts and user manual. FAo Corporate Document Repository,SDRN (Environment and Natural Resources Service) - FAO (Food and AgricultureOrganization of the United Nations), 2000.

[101] X. Diao and T. Roe. Implications of the new growth theory to agricultural traderesearch and trade policy, chapter A dynamic CGE model of R&D based growthin the US economy: an experiment using the new growth theory. 1997.

[102] X. Diao, T. Roe, and E. Yeldan. Strategic policies and growth : an applied modelof r&d driven endogenous growth. Journal of Development Economics, 60:343–380,1999.

[103] Xinshen Diao, Terry Roe, and Erinc Yeldan. Strategic policies and growth: anapplied model of R&D-driven endogenous growth. Journal of Development Eco-nomics, 60:343–380, 1999.

118

Bibliography

[104] D.A. Dickey and A.W. Fuller. Distribution of the Estimators for AutoregressiveTime Series With a Unit Root. Journal of the American Statistical Association,74(366):427–431, Jun. 1979.

[105] D.A. Dickey and A.W. Fuller. Likelihood Ratio Statistics for Autoregressive TimeSeries with a Unit Root. Econometrica, 49(4):1057–1072, Jul. 1981.

[106] W. Diewert. An application of the shephard duality theorem: A generalized leontiefproduction function. Journal of Political Economy, 79:481–507, 1971.

[107] W. Diewert. Frontiers of Quantitative Economics, chapter Application of dualitytheory vol. II, pages 106–171. North Holland, Amsterdam, 1974.

[108] W. Diewert. Handbook of Mathematical Economics, chapter Duality approachesto microeconomic theory, pages 535–599. North Holland, Amsterdam, 1982.

[109] W.E. Diewert. Functional forms for profit and transformation functions. Journalof Economic Theory, 6(3):284–316, June 1973.

[110] W.E. Diewert. Applications of Duality Theory, chapter 3, pages 106–171. Volume IIof D. A. Kendrick and M. D. Intriligator [187], 1974.

[111] W.E. Diewert. Intertemporal Consumer Theory and the Demand for Durables.Econometrica, 42(3):497–516, May 1974.

[112] W.E. Diewert and T.J. Wales. Flexible functional forms and global curvatureconditions. Econometrica, 55:43–68, 1987.

[113] E. Duguet. Les subventions à la recherché et développement : complement ousubstitut au financement privé ? Etude pour le Ministère de l’industrie (DIGITIP-SESSI), 2002.

[114] EC - European Commission. FADN - Farm Accounting Data Network, 2009.

[115] EEA - European Environmetal Agency. Corine Land Cover 2000 - Mapping adecade of Change, 2004.

[116] EEA - European Environmetal Agency. Urban sprawl in Europe: The ignoredchallenge. Report 10/2006, EEA, 2006.

[117] T. Egebo and I. Lienert. Modelling Housing Investment for Seven Major OECDCountries. OECD Economics Department Working Papers 63, OECD, EconomicsDepartment, Dec. 1988.

119

Bibliography

[118] W. Enders. RATS Programming Manual. Department of Economics, Finance &Legal Studies, University of Alabama, US, 2003.

[119] R.F. Engle and C.W.J. Granger. Co-Integration and Error Correction: Represen-tation, Estimation, and Testing. Econometrica, 55(2):251–276, March 1987.

[120] T. Engsted. Do Farmland Prices Reflect Rationally Expected Future Rents? Ap-plied Economics Letters, 5(2):75–79, February 1998.

[121] ESPON Project 2.3.2. Governance of Territorial and Urban Policies from EU toLocal Level. Final report, ESPON Project, 2006.

[122] European Commission, taxation and custom Union. Taxes in europe database.

[123] Commission Européenne. Gouvernance européenne : Un livre blanc. TechnicalReport COM(2001) 428 final, Commission Européenne, 2001.

[124] Commission Européenne. Mieux légiférer à l’aide d’analyses d’impact de meilleurequalité. SEC(2007) 926, 2007.

[125] EuroRAPE - European Road Assessment Programme. European Road Assess-ment Programme: Pilot phase Technical Report. Pilot phase technical report,EuroRAPE, 2003.

[126] Eurostat. European system of accounts ESA 1995.

[127] Eurostat. Trends in households in the European Union: 1995-2025. Statistics inFocus 24/2003, Eurostat - Statistical Office of the European Communities, 2003.

[128] Eurostat. Common Questionnaire on Transport Statistics. Database, September2004.

[129] Eurostat. Land Use Statistics. Database, September 2004.

[130] Eurostat. Population. Database, July 2008.

[131] Eurostat. Selling Prices of Agricultural Products (absolute prices), Land pricesand Rents. Database, 2008.

[132] Eurostat. Annual National Accounts. Database, February 2009.

[133] Eurostat. Structure of Agricultural Holdings. Database, July 2009.

120

Bibliography

[134] Reynes F. La formation des salaires et le concept de taux de chomage d’equilibre:Une tentative de réconciliation des courbes Wage Setting et de Phillips. PhD thesis,Institut d’Etudes Politiques de Paris, 2006.

[135] D. Mc Fadden. Production Economics: A Dual Approach to Theory and Appli-cation, chapter Cost, revenue and profit functions, VOL I, pages 3–109. NorthHolland, New York, 1978.

[136] B. Falk. Formally testing the present value model of farmland prices. AmericanJournal of Agricultural Economics, 73:1–10, 1991.

[137] B. Falk and B.S. Lee. Fads versus Fundamentals in Farmland Prices. AmericanJournal of Agricultural Economics, 80(4):696 – 707, 1998.

[138] F.A.O. - Food and Agriculture Organization. Global Forest Ressources Assessment2000. FAO Forestry Paper 140, FAO, 2001.

[139] F.A.O. - Food and Agriculture Organization. FAOSTAT - Land Database, 2008.

[140] A.M. Featherstone and T.G. Baker. An Examination of Farm Sector Real AssetDynamics: 1910-85. American Journal of Agricultural Economics, 69(3):532–546,August 1987.

[141] Franklin M Fisher, Robert M Solow, and James M Kearl. Aggregate productionfunctions: Some ces experiments. Review of Economic Studies, 44(2):305–20, June1977.

[142] P. Fisher, A.J. Comber, and R. Wadsworth. Land Use and Land Cover: Contra-dition or Complement, chapter 6, pages 85–98. In Fisher and Unwin [143], 2005.

[143] P. Fisher and D. Unwin. Re-Presenting GIS. John Wiley & Sons, Ltd, 2005.

[144] R.A. Fisher. Statistical Methods for Research Workers. Oliver and Boyd, Edinburg,1932.

[145] Karen Fisher-Vanden and Ian Sue Wing. Accounting for quality: Issues with mod-eling the impact of R&D on economic growth and carbon emissions in developingeconomies. Energy Economics, (forthcoming).

[146] A. Fougeyrollas, G. Koléda, and P. Le Mouël. Quelques tests des propriétés desmodèles de croissance endogène avec micro-économie de l’innovation sur donnéesfrançaises,. contribution au 2ème programme cnrs « les enjeux économique del’innovation », CNRS, 2000.

121

Bibliography

[147] A. Fougeyrollas, P. Le Mouël, and P. Zagamé. Endogenous growth and environ-mental regulation: the case of the kyoto protocol. In 7th congress of the Societyof Computational Economics, Yale University, June 28-30 2001.

[148] A. Fougeyrollas, P. Le Mouël, and P. Zagamé. Economy wide employment impactsresulting from the implementation of the kyoto protocol, a simulation using thenemesis model. OECD, ENV/EPOC/WPNET(2003)11/FINAL, 2003.

[149] A. Fougeyrollas, P. Le Mouël, and P. Zagamé. Croissance endogène et politiquestructurelle : le cas des politiques de lutte contre le changement climatique et despolitiques de r&d. Revue Economique, 56(5), Septembre 2005.

[150] Arnaud Fougeyrollas, Pierre Le Mouël, and Paul Zagamé. Les nouvelles théoriesde la croissance en application : L’évaluation des politiques structurelles, le cas duprotocole de Kyoto. Revue économique, 56(5):1089–1105, 2005.

[151] M. Fujita. Urban Economic Theory - Land use and city size. Cambridge UniversityPress, 1989.

[152] Reyer Gerlagh and Bob van der Zwaan. Gross world product and consumptionin a global warming model with endogenous technological change. Resource andEnergy Economics, 25:35–57, 2003.

[153] A. Golub, T.W. Hertel, and B. Sohngen. Land Use Modeling in Recursively Dy-namic GTAP Framework, chapter 10, page 235. In Hertel et al. [176], 2009.

[154] A. Golub, W.T. Hertel, and H-L Lee. Modeling Land Supply and Demand in LongRun. In Presented at the Ninth Annual Conference on Global Economic Analysis,Addis Ababa, Ethipia, June 15- 17 2006.

[155] C. Gouel and T.W. Hertel. Introducing Forest Access Cost Functions into a GeneralEquilibrium Model. GTAP Research Memorandum 8, Center for Global TradeAnalysis, Department of Agricultural Economics, Purdue University, 2006.

[156] Lawrence H. Goulder and Koshy Mathai. Optimal CO2 Abatement in the Presenceof Induced Technological Change, Journal of Environmental Economics and Man-agementVolume 39, Issue 1, , January 2000, Pages 1-38. Journal of EnvironmentalEconomics and Management, 39:1–38, 2000.

[157] Lawrence H. Goulder and Stephen H. Schneider. Induced technological changeand the attractiveness of co2 abatement policies. Resource and Energy Economics,21(3-4):211–253, August 1999.

122

Bibliography

[158] Lawrence H. Goulder and Stephen H. Schneider. Induced technological change andthe attractiveness of CO2 abatement policies. Resource and Energy Economics,21:211–253, 1999.

[159] Z. Griliches. The search for r&d spillovers. NBER, 1991.

[160] Z. Griliches. The search for r&d spillovers. Scandinavian Journal of Economics,94:29–47, 1992.

[161] G. Grossman and E. Helpman. Innovation and Growth in the Global Economy.Cambridge: MIT Press, 1991.

[162] G.M. Grossman and E. Helpman. Quality ladders in the theory of growth. Reviewof Economic Studies, 58:43–61, 1991.

[163] M. Grubb. Economic dimensions of technological and global responses to the kyotoprotocol. Journal of Econonic Studies, 27:111–125, 2000.

[164] Arnulf Grubler and Sabine Messner. Technological change and the timing of mit-igation measures. Energy Economics, 20:495–512, 1998.

[165] D. Guellec and B. van Pottelsberg de la Potterie. Recherche-développement etcroissance de la productivité : analyse des données d’un panel de 16 pays del’ocde. Revue Économique de l’OCDE, 33:111–136, 2001.

[166] D. Guellec and B. van Pottelsberghe. The impact of public r&d expenditure onbusiness r&d. Economic of Innovation and New Technology, 12(3):225–244, 2003.

[167] D. Guellec and B. van Pottelsberghe de la Potterie. R&D and Productivity Growth:Panel Data Analysis of 16 OECD Countries. OECD Science, Technology andIndustry Working Papers 2001/3, OECD, Directorate for Science, Technology andIndustry, June 2001.

[168] L. Gutierrez, J. Westerlund, and K. Erickson. Farmland Prices, Structural Breaksand Panel Data. European Review of Agricultural Economics, 34(2):161–179, June2007.

[169] B.H. Hall and C. Cummins. TSP program for IPS Panel unit root test - panuit.tsp.TSP Program, May 1999.

[170] B.H. Hall and C. Cummins. TSP 5.1 - Reference Manual. TSP International,2009.

123

Bibliography

[171] Juan Carlos Hallak. Product quality and the direction of trade. Journal of Inter-national Economics, 68(1):238–265, January 2006.

[172] K. Helming, M. Pérez-Soba, and P. Tabbush. Sustainability Impact Assessment ofLand Use Changes. Springer, 2008.

[173] J.V. Henderson and Y.M. Ioannides. A Model of Housing Tenure Choice. AmericanEconomic Review, 73(1):98–113, March 1983.

[174] F. Henin. Les statuts du fermage en Europe - « Nous ne pouvons comparer quece qui est comparable », Feb. 2010.

[175] T.W. Hertel. Global Trade Analysis: modelling and Applications. CambridgeUniversity Press, 1997.

[176] T.W. Hertel, S.K. Rose, and R.S.Tol. Economic Analysis of Land Use in GlobalClimate Change Policy. Routledge, 2009.

[177] W. Hogan. A dynamic putty–semi-putty model of aggregate energy demand. En-ergy Economics, 11(1):53–69, January 1989.

[178] K. Hubacek and J.C.J.M. van den Bergh. The Role of Land in Economic Theory.Interim report IR-02-037, IIASA, May 2002.

[179] Peter Huber. Intra-national labor market adjustment in the candidate countries.Journal of Comparative Economics, 32(2):248 – 264, 2004.

[180] Barry Hughes. Direct income and substitution effects in participation decisions.The Journal of Political Economy, 80(4):793–795, 1972.

[181] Charles R. Hulten. The Measurement of Capital, chapter 4, pages 119–158. Vol-ume 24 of Berndt and Triplett [31], Jan. 1991.

[182] C. Hurlin and V. Mignon. Une synthèse des tests de racine unitaire sur donnéesde Panel. Economie & Prévision, 169(3-4-5):253–294, 2005.

[183] C. Hurlin and V. Mignon. Une synthèse des tests de cointégration sur données dePanel. Economie & Prévision, 180-181(4):241–265, 2007.

[184] K.S. Im, M.H. Pesaran, and Y. Shin. Testing for unit roots in heterogeneouspanels. Journal of Econometrics, 115(1):53–74, July 2003.

124

Bibliography

[185] INSEE - Institut National de la Statistique et des Etudes Economiques. Présenta-tion du modèle Annuel MAcroéconomique à DEUx Secteurs (AMADEUS). Sériedes documents de travail de la Direction des Etudes et Synthèses Économiques G9801, INSEE, Fev. 1998.

[186] INSEE - Institut National de la Statistique et des Etudes Economiques. Ménagesselon la structure familiale. Tableau Economique de la France (TEF), 2010 ,INSEE, 2010.

[187] M.D. Intriligator and D.A. Kendrick. Frontiers of Quantitative Economics, vol-ume 2. North-Holland Publishing Company, 1974.

[188] Ireland. The new industrial economics: recent developments in industrial organ-isation, oligopoly and game theory, chapter Product differentiations and quality,pages 84–106. 1992.

[189] W. Isard. Location and Space-economy: A General Theory Relating to Indus-trial Location, Market Areas, Land Use, Trade, and Urban Structure. MIT Press,Cambridge, 1956.

[190] K. Itakura and T. Hertel. A Note On Changes Since GTAP Book Model (Version2.2a / GTAP94 ). Technical report, GTAP, 2000.

[191] A. Italianer, G. d’Alcantara, and P. Zagamé. HERMES: Harmonised EconometricResearch for Modelling Economic Systems. North-Holland Publishing Company,1993.

[192] Joyce P. Jacobsen. Labor force participation. The Quarterly Review of Economicsand Finance, 39(5):597–610, 1999.

[193] Henry D. Jacoby, John M. Reilly, James R. McFarland, and Sergey Paltsev. Tech-nology and technical change in the MIT EPPA model. Energy Economics, 28:610–631, 2006.

[194] A. Jaffe. Technological opportunities and the spillover of r&d. American EconomicReview, 76:984–1001, 1986.

[195] T. Jansson, Bakker M., Boitier B., Fougeyrollas A., Helming J., van Meijl H., andVerkerk P. J. Cross Sector Land Use Modelling Framework, pages 159–180. InHelming et al. [172], 2008.

125

Bibliography

[196] L. Johansen. Substitution versus Fixed Production Coefficients in the Theory ofEconomic Growth. Econometrica, 27(2):157–176, 1959.

[197] D. K. N. Johnson. The oecd technology concordance (otc): Patents by industryof manufactoring and sector of use. OECD STI Working Paper 2002/5, 2002.

[198] Charles I Jones. R&d-based models of economic growth. Journal of PoliticalEconomy, 103(4):759–84, August 1995.

[199] C.I Jones. R&d based models of economic growth. Journal of Political Economy,103:759–784, 1995.

[200] C.I Jones. Times series tests of endogenous growth models. Quarterly Journal ofEconomics, 110:495–527, 1995.

[201] C.I. Jones. Théorie de la croissance endogène. De Boeck University, 2000.

[202] C.I. Jones and C. Williams. Measuring the social return to r&d. Quarterly Journalof Economics, 113:1119–1135, November 1998.

[203] D. Jorgenson. The embodiment hypothesis. Journal of Political Economy, 1(74):1–17, 1966.

[204] D.W. Jorgenson. Capital as a Factor of Production, chapter 1, pages 1–35. InJorgenson and Landau [205], 1989.

[205] D.W. Jorgenson and R. Landau. Technology and Capital Formation. MIT Press,Cambridge, MA, 1989.

[206] R.E. Just and J.A. Miranowski. Understading the Farmland Price Changes. Amer-ican Journal of Agricultural Economics, 75(1):156–168, Feb. 1993.

[207] N. Kaldor and J. Mirrlees. A new model of economic growth. Review of EconomicStudies, 29:174–192, 1962.

[208] M. Katz and C.Shapiro. Systems competition and network effects. Journal ofEconomic Perspectives, 8:93–115, 1994.

[209] M. Katz and C. Shapiro. Network externalities, competition and compatibility.American Economic Review, 75:424–440, 1985.

[210] M. Katz and C. Shapiro. Technology adoption in the presence of network exter-nalities. Journal of Political Economy, 94:822–841, 1986.

126

Bibliography

[211] Claudia Kemfert. Estimated substitution elasticities of a nested ces productionfunction approach for germany. Energy Economics, 20(3):249–264, June 1998.

[212] P. Kennedy. A Guide to Econometrics - Fifth Edition. The MIT Press, Cambridge,MA, 2003.

[213] J. Kim and L. Lau. The sources of economic growth of the east asian newlyindustrialized countries. Journal of the Japanese and International Economies,pages 235–271, 1994.

[214] C. Klein and O. Simon. Le modèle MÉSANGE nouvelle version réestimée en base2000. Document de Travail de la DGTPE 10/02, DGTPE (Direction Générale duTrésor) - MINEFE (Ministère de l’Economie de l’Industrie et de l’Emploi), March2010.

[215] Rainer Klump, Peter McAdam, and Alpo Willman. Factor substitution and factor-augmenting technical progress in the united states: A normalized supply-side sys-tem approach. The Review of Economics and Statistics, 89(1):183–192, 2007.

[216] N.R. Kocherlakota and K. Yi. Can convergence regressions distinguish betweenexogenous and endogenous growth models. Economic Letters, 49:211–215, 1996.

[217] S. Kortum. "Research, Patenting, and Technological Change". Econometrica,65(6):1389–1419, November 1997.

[218] M. Kremer. Population growth and technological change: one million b.c. to 1990.Quarterly Journal of Economics, 108(3):681–716, 1993.

[219] P. Krugman. Increasing Returns and Economic Geography. The Journal of Polit-ical Economy, 99(3):483–499, June 1991.

[220] Socrates Kypreos. A MERGE Model with Endogenous Technological Change andthe Cost of Carbon Stabilization. FEEMWorking Paper No. 123.05, October 2005.

[221] E3M Lab. General equilibrium model for economy energy environment. Technicalreport, Institute of Computers and Communications Systems-National TechnicalUniversity of Athens, 2008.

[222] L. Latruffe and C. Le Mouël. Capitalization Of Government Support In Agricul-tural Land Prices: What Do We Know? Journal of Economic Surveys, 23(4):659–691, 09 2009.

127

Bibliography

[223] L. Lau. A characterization of the normalized restricted profit function. Journal ofEconomic Theory, pages 131–163, 1976.

[224] O. Lécina. Une fonction d’investissement en logements des ménages pour le modèlesNEMESIS. Master Thesis (1st year), Université de Paris 1, July 2008.

[225] P. Le Mouël, Zagamé P., Ortiz R.A., Nielsen T.S., Kaas B.C., and Hasler B.Modelling Multi-Sectoral Land Use in Macro-Economic Model for EU-27. In Pre-sentation at IALUC Conference Berlin, April 2008.

[226] H. Lee, R.F. Owen, and D. van der Mensbrugghe. Regional integration in Asia andits effects on the EU and North America. Journal of Asian Economics, 20(3):240–254, 2009.

[227] H.-L. Lee, T.W. Hertel, S. Rose, and M. Avetisyan. An Integrated Global LandUse Database for CGE Analysis of Climate Policy Options, chapter 4, page 72. InHertel et al. [176], 2009.

[228] Jong-Wha Lee and Robert J. Barro. Schooling quality in a cross section of coun-tries. NBER Working Papers 6198, National Bureau of Economic Research, Inc,September 1997.

[229] S.H. Lence and D.J. Miller. Transaction Costs and the Present Value Model ofFarmland. American Journal of Agricultural Economics, 81:257–278, 1999.

[230] C. Leung. Macroeconomics and Housing: a review of the literature. Journal ofHousing Economics, 13(4):249–267, Dec. 2004.

[231] G. Livanis, C.B. Moss, V.E. Breneman, and R.F. Nehring. Urban Sprawl andFarmland Prices. American Journal of Agricultural Economics, 88(4):915–929,Oct. 2006.

[232] Andreas Löschel. Technological change in economic models of environmental pol-icy: a survey. Ecological Economics, 43:105–126, 2002.

[233] R.N. Lubowski. Determinants of Land-Use Transitions in the United States:Econometric Analysis of Changes among the Major Land-Use Categories. PhDthesis, Havard University, 2002.

[234] D.L. Mac Fadden. Cost, Revenue, and Profit Functions. Department of Economics,University of California, Berkeley, mimeographed, 1970.

128

Bibliography

[235] D.B. Madan and I.R. Prucha. A note on the estimation of nonsymmetric dynamicfactor demand models. Journal of Econometrics, 42(2):275–283, Octobre 1989.

[236] G.S. Maddala and S. Wu. A Comparative Study of Unit Root Tests with PanelData and a New Simple Test. Oxford Bulletin of Economics and Statistics, 61(Spe-cial I):631–52, 1999.

[237] J. Mairesse and M. Sassenou. Recherche-développement et productivité, unpanorama des études économétriques sur données d’entreprises. STI Revue, n°8,pp. 9-45, 1991.

[238] Alan Manning. Wage bargaining and the phillips curve: The identification andspecification of aggregate wage equations. The Economic Journal, 103(416):98–118, 1993.

[239] D.W. Marcouiller, J.G. Clendenning, and Kedzior R. Natural Amenity-led De-velopment and Rural Planning. Journal of Planning Literature, 16(4):515–542,2002.

[240] A. MATHIS. Vat indicators. Working-Paper 2/2004, European Commission, DGTaxation and customs.

[241] E. McGrattan. A defense of ak growth models. Federal Reserve Bank of Min-neapolis Quarterly Review, 22:13–27, 1998.

[242] H. Meijers and A. van Zon. RUM, a Recursive Update Putty-Semi-Putty VintageProduction Model : Sectoral Estimation Results For Germany and the Nether-lands. MERIT Research Memoranda 1994-010, UNU-MERIT (United NationsUniversity - Masstricht Economic and social Research and training center on In-novation and Technology), 1994.

[243] Huub Meijers and Adriaan Van Zon. Endogenous technological change in a putty-semi-putty vintage world. GEM-E3 Workin-Paper, 1999.

[244] Jacob Mincer. Labor force participation of married women. In Aspects of LaborEconomics, NBER Chapters, pages 63–106. National Bureau of Economic Re-search, Inc, February 1962.

[245] P. Mohnen. R&d and productivity growth: a survey of the literature. Universitédu Québec, Cahier de recherche n.57, 1990.

129

Bibliography

[246] C.B. Moss and A.L. Katchova. Farmland Valuation and Asset Performance. Agri-cultural Finance Review, 65(2):119–130, July 2005.

[247] E.S. Mot, P.J. van den Noord, D.D. van der Stelt-Scheele, and M.A. Koning.HERMES: A Macrosectoral Model for the Duch Economy, chapter 5, pages 113–204. In Commission of the European Communities [191], 1993.

[248] V. Mukerji. A generalized s.m.a.c. function with constant ratios of elasticity ofsubstitution. The Review of Economic Studies, 30(3):233–236, 1963.

[249] M. I. Nadiri. Innovations and technological spillovers. NBER Working Paper,n°4423, 1993.

[250] M. I. Nadiri and I.R. Prucha. Dynamic factor demand models and productivityanalysis. NBER Working Paper No. W7079, Avril 1999.

[251] Richard R. Nelson and Edmund S. Phelps. Investment in humans, technologicaldiffusion, and economic growth. The American Economic Review, 56(1/2):69–75,1966.

[252] A. Ngwa Zang. Modèle dynamique de demande de facteurs fondé sur la frontièrestochastique de coût implicite intégrant l’innovation de produit : une application àl’analyse et mesure de la productivité du secteur agricole de l’Union européenne.PhD thesis, Ecole Centrale Paris, France, December 2008.

[253] A. Ngwa Zang and P. Le Mouël. Estimation of a dynamic system of factors inputswith multiple outputs for agriculture using a dynamic panel for 9 Eu countries.In Presented at Journées INRA-IFER de recherches en sciences sociales, SociétéFrançaise d Economie Rurale, December 2007, 2007.

[254] W. D. Nordhaus. "Modeling Induced Innovation in Climate Change Policy". paperpresented at the IIASA-Yale Conference on Induced Innovation, June 1999.

[255] Blanchard O. and Katz L.F. Wage dynamics: Reconciling theory and evidence.The American Economic Review, 89, 1989.

[256] OCDE. Une nouvelle économie ? transformation du rôle de l’innovation et destechnologies de l’information dans la croissance. Technical report, Série "Sociétéde l’information". Paris, 2000.

130

Bibliography

[257] OECD - Organisation for Economic Co-operation and Development. MeasuringCapital - Measurement of Capital Stocks, Consumption of Fixed Capital and Cap-ital Services. OECD Manual, OECD, 2001.

[258] S.G. Pantula, G. Gonzalez-Farias, and W.A. Fuller. A Comparison of Unit-RootTest Criteria. Journal of Business & Economic Statistics, 12(4):449–59, October1994.

[259] Chris Papageorgiou and Marianne Saam. Two-level ces production technology inthe solow and diamond growth models. Departmental Working Papers 2005-07,Department of Economics, Louisiana State University, 2005.

[260] P. Pedroni. Critical Values for Cointegration Tests in Heterogeneous Panels withMultiple Regressors. Oxford Bulletin of Economics and Statistics, 61(SpecialIssue):653–70, 1999.

[261] P. Pedroni. Panel Cointegration: Asymptotic and Finite Sample Properties ofPooled Time Series Tests with an Application to the PPP Hypothesis. EconometricTheory, 20(3):597–625, 2004.

[262] P. Pedroni. WinRATS program for Pedroni Tests - pancoint.prg. WinRATS Pro-gram, July 2007.

[263] Patrick Perrier. La fonction de production et les données canadiennes. WorkingPapers 05-20, Bank of Canada, 2005.

[264] P.C.B. Phillips and P. Perron. Testing for a Unit Root in Time Series Regression.Biometrika, 75(2):335–346, June 1988.

[265] A.J. Plantinga, R.N. Lubowski, and R.N. Stavins. The effects of potential landdevelopment on agricultural land prices. Journal of Urban Economics, 52(3):561–581, November 2002.

[266] A.J. Plantinga and D.J. Miller. Agricultural Land Values and the Value of Rightsto Future Land Development. Land Economics, 77(1):56–67, Feb. 2001.

[267] David Popp. ENTICE: endogenous technological change in the DICE model ofglobal warming. Journal of Environmental Economics and Management, 48:742–768, 2004.

[268] David Popp. ENTICE-BR: The effects of backstop technology R&D on climatepolicy models. Energy Economics, 28:188–222, 2006.

131

Bibliography

[269] David Popp. Innovation in climate policy models: Implementing lessons from theeconomics of R&D. Energy Economics, 28:596–609, 2006.

[270] A.A. Powell and F.H.G. Gruen. The Constant Elasticity of Transformation Produc-tion Frontier and Linear Supply System. International Economic Review, 9(3):315–328, October 1968.

[271] I.R. Prucha and M.I. Nadiri. A comparison of alternative methods for the estima-tion of dynamic factor demand models under non-static expectations. Journal ofEconometrics, 33(1-2):187–211, 1986.

[272] P. Pyykkönen. Factors Affecting Farmland Prices in Finland. Adacemic disserta-tion, Faculty of Agriculture and Forestry of the University of Helsinki, May 2006.

[273] D. Ricardo. On the Principles of Political Economy and Taxation. London: JohnMurray, 1817.

[274] Gordon R. Richards. Endogenous technological advance in an econometric model:implications for productivity and potential output in the United States. EconomicModelling, 17:13–34, 2000.

[275] P. Richardson. The Structure and Simulation Properties of OECD’s InterlinkModel. OECD Economic Studies 10, OECD, 1988.

[276] Paul M Romer. Increasing returns and long-run growth. Journal of PoliticalEconomy, 94(5):1002–37, October 1986.

[277] Paul M Romer. Endogenous technological change. Journal of Political Economy,98(5):S71–102, October 1990.

[278] Paul M Romer. The origins of endogenous growth. Journal of Economic Perspec-tives, 8(1):3–22, Winter 1994.

[279] Thomas F. Rutherford and David G. Tarr. Trade Liberalization and EndogenousGrowth In a Small Open Economy: A Quantitative Assessment. FEEM WorkingPaper No. 60.98, May 1998.

[280] W. Salter. Productivity and Technical Change. Cambridge University Press., 1960.

[281] P. A. Samuelson. Prices of Factors and Good in General Equilibrium. The Reviewof Economic Studies, 21(1):1–20, 1953.

132

Bibliography

[282] Mathan Satchi. A note on adding-up restriction when modelling trade flows. Eco-nomic Modelling, 21(6):999–2002, December 2004.

[283] K. Sato. A two-level constant-elasticity-of-substitution production function. TheReview of Economic Studies, 34(2):201–218, 1967.

[284] F.M. Scherer. R&D, Patents and Productivity, chapter Using linked patent andR&D data to measure interindustry technology flows. University of Chicago Press,1984.

[285] U.A. Schneider, J. Balkovic, O. de Cara S.and Franklin, S. Fritz, P. Havlik, I. Huck,K. Jantke, A.M.I. Kallio, F. Klaxner, A. Moiseyev, M. Obersteiner, C. Ivie Ramos,C. Schleupner, E. Schmid, D. Schwab, and R. Skalsky. The European Forestand Agriculture Optimisation Model - EUFASOM. Working Papers FNU-156,Research Unit Sustainability and Global Change, Hamburg University, February2008.

[286] P. Schreyer. User costs and bubbles in land markets. Journal of Housing Eco-nomics, 18(3):267–272, Sep. 2009.

[287] Theodore W. Schultz. Investment in human capital. The American EconomicReview, 51(1):1–17, 1961.

[288] J.A. Sefton and J.W in’t Veld. Consumption and wealth: An international com-parison. Manchester School, 1998.

[289] P. Segerstrom. "Endogenous Growth Without Scale Effects". American EconomicReview, (5):1290–1310, December 1998.

[290] P. Segerstrom. "The Long Run Growth Effects of R&D Subsidies". Michigan StateUniversity and IUI Working Paper, 1998.

[291] Henry D. Jacoby Richard S. Eckaus James McFarland Marcus Sarofim Mal-colm Asadoorian Sergey Paltsev, John M. Reilly and Mustafa Babiker. The mitemissions prediction and policy analysis (eppa) model: version 4. Technical report,MIT Joint Program on the Science and Policy of Global Change Report No. 125.,2005.

[292] SETRA - Sercices d’Etudes Techniques des Routes et Autoroutes. Comprendreles principaux paramètres de conception géométrique des routes. Technical report,SETRA, January 2006.

133

Bibliography

[293] R. Shephard. Theory of Cost and Production Functions. Princeton UniversityPress, 1953.

[294] Y.J. Shi, T.T. Phipps, and D. Colyer. Agricultural Land Values under UrbanizingInfluences. Land Economics, 73(1):90–100, 1997.

[295] C. R. Shumway and A. A. Powell. A Critique of the Constant Elasticity of Transfor-mation (CET) Linear Supply System. Western Journal of Agricultural Economics,9(2), December 1984.

[296] Sjak Smulders and Michiel de Nooij. The impact of energy conservation on technol-ogy and economic growth. Resource and Energy Economics, 25(1):59–79, February2003.

[297] B. Sohngen and S. Brown. The influence of conversion of forest types on carbonsequestration and other ecosystem services in the South Central United States.Ecological Economics, 57(4):698–708, 2006.

[298] R. Solow. A contribution to the theory of economic growth. Quarterly Journal ofEconomics, 70:65–94, 1956.

[299] R. M Solow. Mathematical Methods in the Social Sciences, chapter Investment andTechnical Progress., pages 89–104. Stanford University Press, Stanford CA, 1960.

[300] R. M. Solow, J. Tobin, C. C. von Weizsacker, and M. Yaari. Neoclassical growthwith fixed factor proportions. Review of Economic Studies, 33(2), 1966.

[301] T.W. Swan. Economic growth and capital accumu. Economic Record, 32:334–361,1956.

[302] A. Tabeau, B. Eickhout, and H. von Meijl. Endogenous Agricultural Land Supply:Estimation and Implementation the GTAP model. In Presented at the NinthAnnual Conference on Global Economic Analysis, Addis Ababa, Ethipia, June 15-17 2006.

[303] A. Tegene and F. Kuchler. A Regression Test of the Present Value Model of USFarmland Prices. Journal of Agricultural Economics, 44(1):135–143, 1993.

[304] J. Temple. Robustness test of the augmented solow model. Journal of AppliedEconometrics, 13:361–375, 1998.

134

Bibliography

[305] J. Temple. New growth evidence. Journal of Economic Literature, 37(1):112–156,Mars 1999.

[306] N.E. Terleckyj. Effects of r&d on the productivity growth of industries: an ex-ploratory study. Washington: National Planning Association, 1974.

[307] Peter Thompson and Timothy G Taylor. The capital-energy substitutability de-bate: A new look. The Review of Economics and Statistics, 77(3):565–69, August1995.

[308] Marcel P. Timmer, Mary O’Mahony, and Bart van Ark. Eu klems growth andproductivity accounts: An overview. International Productivity Monitor, 14:71–85, Spring 2007.

[309] O’Mahony M. & Van Ark B. Timmer M. The eu klems growth and productivityaccounts: An overview. University of Groningen & University of Birmingham,2008.

[310] C.G. Turvey, K. Meilke, A. Weersink, S. Clark, R. Klein, and R. Sarker. Measuringthe Capitalization of Income Transfer Programs into Agricultural Asset Values.Working paper 95-465, Agricultural Canada, Policy Branch, 1995.

[311] Peter A.G. van Bergeijk, Gilbert H.A. van Hagen, Ruud A. de Mooij, and Jarig vanSinderen. Endogenizing technological progress: The MESEMET Model. EconomicModelling, 14:341–367, 1997.

[312] D. van der Mensbrugghe. LINKAGE Technical Reference Document - Version 6.0.Technical report, The World Bank - Development Prospects Group (DECPG),2005.

[313] B. C. C. van der Zwaan, R. Gerlagh, G. Klaassen, and L. Schrattenholzer. En-dogenous technological change in climate change modelling. Energy Economics,24:1–19, 2002.

[314] H. van Meijl, T. van Rheenen, A. Tabeau, and B. Eickhout. The impact of differentpolicy environments on agricultural land use in Europe. Agriculture, Ecosystems& Environment, 114(1):21 – 38, 2006.

[315] van Pottelsberghe de la Potterie. The efficiency of science and technology policiesinside the triad. PhD thesis, Université libre de Bruxelles, 1998.

135

Bibliography

[316] Jérôme Vandenbussche, Philippe Aghion, and Costas Meghir. Growth, distance tofrontier and composition of human capital. Journal of Economic Growth, 11(2):97–127, June 2006.

[317] Bart Verspagen. Measuring intersectoral technology spillovers: Estimates from theeuropean and us patent office databases. Economic Systems Research, 9(1):47–65,1997.

[318] J.H. von Thünen. Der isolierte Staat in Beziehung auf Nationalökonomie undLandwirtschaft. Gustav Fisher, Jena, Germany, 1826.

[319] I. Sue Wing. Induced Technical Change in Computable General Equilibrium Mod-els for Climate Change Policy Analysis. PhD thesis, Massachusetts Institute ofTechnology, Mai 2001.

[320] Ian Sue Wing. Representing induced technological change in models for climatepolicy analysis. Energy Economics, 28:539–562, 2006.

[321] World Bank. Global Economic Prospects - Commodities at the Crossroads - 2009.Report, The World Bank, 2009.

[322] J. Wu. Environmental Amenities, Urban Sprawl, and the Economic Landscape.In Analysis of Land Markets and the Impact of Land Market Regulation, a LincolnInstitute course, held from July 10-12, 2002, 2002.

[323] A. Zellner. An Efficient Method of Estimating Seemingly Unrelated Regressionsand Tests for Aggregation Bias. Journal of the American Statistical Association,57(298):348–368, June 1962.

[324] Adriaan van Zon. Rum: a simple recursive update model providing a condensedrepresentation of a putty-semi-putty vintage model. MERIT Research Memoranda1994-002, 1994.

[325] van Zon, Adriaan and I. Hakan Yetkiner. Endogenous energy saving technicalchange in the romer model. Research Memoranda 022, Maastricht : MERIT,Maastricht Economic Research Institute on Innovation and Technology, 1999.

136

the nemesis reference manual - seureco-erasmeon the supply side, nemesis distinguishes 32 production...

Documents