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Macroeconomic Models
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Macroeconomic Models
Introduction
When the impacts of EU Cohesion Policy are being investigated, either ex-ante at the design stage,
during a mid-term review, or ex-post, it is necessary to explore the extent to which the aims of the
policy are being achieved. A key aspect of the evaluation must focus on the macroeconomic
consequences of the policies, i.e., how cohesion policy affects a range of macro aggregate indicators
such as GDP, total employment, productivity, prices, wages, consumption, investment, the fiscal
balance and the trade balance. The effects cannot be simply inferred from the study of economic
data, such as contained in published national accounts, since economic performance is affected by a
wide range of internal policy actions and external developments in the global economy. To identify the
specific role of cohesion policy requires the use of special tool of analysis called macroeconomic
models, usually abbreviated to “macro models”.
Description of the technique
A macroeconomic model is a tool used to present a holistic view of the operation of an economy,
usually in the form of a computer-based system. It is a means of collating research on the economy in
a systematic and policy-relevant way, and depends on the availability of such research. The goal of a
macroeconomic model is to replicate the main mechanisms of an entire economic system, which may
consist of a region (such as the Italian Mezzogiorno), a nation state (such as Poland), or a collection of
nation states (such as the 27 members of the EU). The only requirement is that the entity being
modelled is large enough to display the distinctive properties that are the subject area of
macroeconomics.
Of course, it is also possible to construct economic models of other entities, such as a town, a
transport system, a cluster of firms, the operation of a particular policy programme, etc. However,
such models fall outside the category of macroeconomic models.
(a) Calibration to data
Macroeconomic models only become useful for empirical policy analysis when the behavioural
relationships in them (derived from theory) can be quantified in practice. For example, in the simple
Keynesian consumption function, household consumer expenditure is driven by household disposable
income. But to be useful for policy analysis we need to know by how much consumption is likely to
change for a given change in disposable income. The technical term for this is that macro models
need to be calibrated using actual data - this ensures that the properties of the calibrated model
emulate, or replicate, the properties of the real economy being studied.
Two main approaches are used to calibrate macro models. The first uses econometric techniques in
conjunction with historical data. Econometric techniques range from simple regression analysis (e.g.,
ordinary least squares) to much more sophisticated techniques. The more sophisticated methods
attempt to address the problems associated with the mathematics of behavioural relationships, i.e.,
ensuring that the calibration is capturing a “true” underlying pattern of behaviour and not just random
effects. When such techniques are used, it is necessary to have long time series of historical data
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(say, over 30 annual observations) and behavioural relationships that have fairly simple and stable
functional forms.
A second approach to calibration is to step aside from conventional econometrics on the model
behavioural equations, but to draw on existing empirical research to fix key parameters at values that
are considered to be “reasonable” on an a priori basis. This approach can usually be applied to very
large and structurally complex models and has the advantage that the key behavioural properties are
known (since they are imposed) and are consistent with collective research knowledge.
While some small-scale models can be calibrated using pure econometric techniques, in practice
policy models are calibrated using a mixture of techniques. Indeed, too rigid an application of
econometric techniques can often become an obstacle to policy modelling since they may reject all
possible theoretical formulations and result in an agnostic outcome. For example, a paper by
Summers goes so far as to claim that no substantial issue of macroeconomic research has ever been
resolved by analysing data using econometrics (Summers, 1991).
Most macro models, even when described as “econometric”, are actually calibrated using a mixture of
techniques and a variety of possible estimation methods. Of course one must take care when
imposing coefficients that the selected values are considered to be broadly consistent with any
available empirical findings and appropriate to the nature of the economy being studied. Hence, in
small open economies inside the euro zone, we expect domestic prices to be heavily conditioned by
external prices, and would impose coefficients accordingly. In models of larger, less open economies,
we would expect that domestic cost pressures would play a bigger role, and would modify any
imposed calibration accordingly.
(b) Underlying theoretical structure
Macroeconomic theory is a very active field of research, with many competing and diverse
assumptions and theories. Since macro policy models draw from this pool of research, they can differ
from each other in the manner in which they reflect some aspects of theory more than others.
Increasingly macroeconomic theory has been integrated with microeconomic theory through basing it
on solid micro-foundations (Wickens, 2008). This has led to a decline in the use of what are called
“time-series models”, i.e., models which are constructed purely from the statistical study of the interrelationships between time-series of data and generally have no micro-foundations. Modern
macroeconomics seeks to explain the aggregate economy using theories based on strong microfoundations.
So different macro models will not only reflect the different uses for which they are intended, but also
the viewpoint and experience of the modeller who constructed them on how much theory to impose.
We illustrate this point in terms of two issues that arise when models are used to examine the likely
impacts of EU Cohesion Policy: crowding out and expectation mechanisms.
An important difference between models of broadly similar theoretical foundations may lie in the
different nature and strength of crowding out mechanisms, through the labour market (Philips curve),
through fiscal tightening and through monetary tightening. For example, the assumption might be
made at the calibration stage that all increases in productivity are passed on to labour. Consequently,
none of the productivity increases caused by Cohesion Policy will have any effect in increasing cost
competitiveness in the recipient countries. On the other hand, the view might be taken that there is
likely to be a less than full pass-through of productivity changes to wages. This is known to be the
case in many of the new EU member states. For example, in countries where there is a high rate of
foreign direct investment and significant foreign ownership of firms, productivity pass-through to
domestic wages can be less than complete during extended periods where the economy is
modernising. Strong crowding-out assumptions may not be appropriate to the lagging economies of
many of the new member states. In addition, the “grant” nature of Cohesion Policy funding, with
modest local co-finance, may further lessen fiscal and monetary crowding out.
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A second difference between many modern policy models concerns the choice between modelconsistent (or forward-looking) expectations versus static (or auto-regressive) expectations. If forward
looking expectation mechanisms are assumed, then all agents have (model consistent) information
about the future consequences of cohesion policy impacts and consequences, and can react today in
light of tomorrow’s impacts. Other models may make no such assertion, and may take a more
pragmatic view that for the analysis of long term investment policies in rapidly transforming
economies, the incorporation of model consistent expectations may not be realistic in the context of
these economies. Furthermore, if the basic model set-up is inappropriate to an economy, the
incorporation of model-consistent expectations may compound the error and increases the possibility
of misinterpretation of the policy analysis. Forward-looking expectations are perhaps more appropriate
for the analysis of short-term demand and monetary shocks, where the underlying economic structure
is fairly stable and well understood. There may be less justification for their use for long-term supplyside shocks administered mainly through public investment in productive infrastructure and human
capital, in a situation where the underlying structure is not well understood, and is rapidly changing.
Why do we need macro models?
There are three main reasons for constructing a macro-policy model, each of which derive from the
needs of policy impact evaluation.
1.
Understanding
If we wish to understand how economies function, then we need models. In many cases these models
remain implicit and intangible and are not incorporated as computer-based models. Indeed, all
explanations advanced by economists are based on models, derived from the background in theory
that forms an essential element of their education and training. However, it is unsatisfactory to leave
the models implicit, since arguments cannot be focused on the validity of implicit models. We need to
build models in an explicit way, by setting out as precisely as possible a description of economic
processes, and using econometrics as a means of selecting “good” models and rejecting “bad”
models.
When it comes to evaluating the role of EU Cohesion Policy, we need to augment a basic macro
model with additional mechanisms that set out explicitly how we believe such policies affect an
economy, and we can assign values to key policy parameters that state the strength or weakness of
any policy mechanism. Of course, where there is uncertainty about the nature of the underlying
properties of the economy, and the exact way in which a policy instrument affects the economy, we
need to have different explicit models that set out our different understanding of how the world works.
2.
Forecasting
A very common use of macro models is in forecasting future performance. This is the least understood
and most abused use of models, if care is not exercised. Economic forecasts have two essential
features that are seldom emphasised or understood. First, they are “contingent”, in the sense that the
forecast is not absolute, but conditional on a range of ex-ante assumptions that may turn out to be
false ex-post. Second, the implicit and/or explicit model used to produce the “contingent” forecast may
itself be wrong.
If we were content to use macro models to examine policy impacts ex-post, the need to forecast would
vanish. All we would require of a model is that it replicates the properties of the economy, to a greater
of lesser extent. But in the case of EU Cohesion Policy impact analysis, we are often in the position of
needing to examine policy impacts ex-ante. For example, we may desire to estimate the likely impacts
of the current Cohesion Policy programme for the years 2007-2013 on the future development of the
economy out to the year 2020 (i.e., five years after all activity is likely to cease. Here, we need to be
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able to construct forecasts of future behaviour as one element in the process of policy evaluation to be
described next.
3.
Scenarios
Developing model-based scenarios allow us to ask ‘what if?’ type of questions. We are forced to use
models for this task since it is seldom feasible to carry out policy experiments using a control group.
For example, it is not possible to give cohesion funding to one set of countries and not to another, and
to sit back and analyse the impacts ten years on, as when testing a drug.
It is common to use the scenario approach by developing two scenarios. First, a without-policy
baseline, which requires a forecast to some selected time horizon. Second, a repeat of the simulation,
but this time making a specific change to a specific policy instrument or to a range of instruments.
Comparing the first with the second scenario can be used as a way of examining the effect of the
specific policy change.
Although it sounds a simple and straightforward approach, the use of model-based scenarios is the
subject of a serious caveat, referred to as the “Lucas critique”, after Lucas, 1976. In essence, the
Lucas critique states that since policy alters economic structure, models built on past data cannot be
used to analyse future policy shocks. Models that consist of reduced form specifications and complex
empirical lag structures are particularly vulnerable to the Lucas critique. Models with reasonably
coherent micro underpinnings, where structural change is explicitly modelled, are less vulnerable.
EXAMPLES OF MODELS USED FOR COHESION POLICY IMPACT ANALYSIS
Since the major reform of EU Cohesion Policy in the late 1980s, many models have been used for
policy impact analysis. We list some of these in the references below. However, current research into
the effectiveness of cohesion policy is dominated by two specific models, or model systems: the
QUEST system operated by DG-ECFIN in the Commission, and the HERMIN system currently used
within DG-REGIO, and in many of the member states. A third model, E3ME, has been used to study
the environmental aspects of cohesion policy.
Examples of models used for Cohesion Policy impact analysis
1.
HERMIN
HERMIN models were first developed in the late 1980s to analyse the Irish Cohesion Policy
programmes. Since then they have been extended to all the cohesion countries, as well as to some
regions (e.g., East Germany, the Italian Mezzogiorno and to all of the 16 Polish NUTS II regions.
Each HERMIN model has three broad components: a supply side; an absorption side; and an income
distribution side. A special characteristic of the HERMIN model is that it disaggregates GDP into five
production sectors (manufacturing, building & construction, market services, agriculture and nonmarket services). In addition, it has a fairly standard treatment of the expenditure and income measures
of GDP.
A HERMIN country model has many neoclassical features in the longer term. More technically, such
“neoclassical” features constitute what are called the “micro-foundations” of the macro structure of the
model and are an essential feature of any model intended for use over a long time horizon. Thus,
output in manufacturing is not simply driven by demand. It is also influenced by price and cost
competitiveness, where firms seek out minimum cost locations for production. In addition, factor
demands in manufacturing and market services are derived on the assumption of cost minimization,
using a CES production function, where the capital/labour ratio is sensitive to relative factor prices.
The incorporation of a structural Phillips curve mechanism in the wage bargaining mechanism
introduces further relative price effects. Most importantly, cohesion policy acts on the supply side of
the model, at least in the medium to long term. Conventional Keynesian mechanisms are incorporated
into the short-term behaviour of a HERMIN model. When subject to a demand shock, expenditure
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and income distribution sub-components generate fairly Keynesian multiplier mechanisms.
Interest and exchange rates are exogenous in the HERMIN models of euro zone states and in states
operating currency boards. Otherwise, they are endogenised, but monetary crowding out is very
modest in the context of Cohesion Policy shocks in isolation, in line with the general assumption that
the cohesion economies are ‘small’ and ‘open’.
2
QUEST II and III
QUEST was developed and is maintained by European Commission, DG-ECFIN, and is a multicountry model designed to analyse the business cycle, the long-term growth of the member states of
the EU and the interactions of these states with the rest of the world, especially with the United States
and Japan. The QUEST II version of the model identifies stock and flow equilibrium variables at
macroeconomic level including physical capital, net foreign assets, money and government debt which
are endogenously determined with wealth effects allowed to influence flows of savings, and
production and investment decisions of private households, firms and the government. The supplyside of the economy in QUEST II is modelled explicitly to conform to a neo-classical aggregate
production function setting potential capacity, with long-run growth rates of this potential determined
by the rate of (exogenous) technical progress and the growth rate of the population. Results of
simulations may be presented as deviations from a baseline scenario. The model has real interest rates
and exchange rates determined endogenously, and this does allow for the possible monetary
‘crowding-out’ effects of Structural Funds on the private sector to be taken into account.
Recently the QUEST model was reformulated as a Dynamic Stochastic General Equilibrium model (or
DSGE). Micro-founded dynamic general equilibrium models are now widely used in policy analysis.
The focus of these models is on the economy as a whole, consisting of an integrated system of
economic agents who base their decisions over a range of variables by continuously re-optimising,
subject to budgetary, technological and institutional constraints. They are forward looking and
intertemporal, i.e., current decisions are affected by expectations about the future, which are deemed
to be model-consistent. Moreover, QUEST III endogenises growth – i.e. it explicitly models the long
term growth impacts of cohesion policy investment in research, innovation and human capital.
3.
E3ME
E3ME, an energy-environment-economy model for Europe, is a multi-sectoral, regionalised, dynamic
econometric model of the EU. It is not a Computable General Equilibrium (CGE) model, but a
disaggregated time-series, cross-section econometric model, that has benefited from some of the
techniques used in CGEs relating to calibration on recent data. The model has been developed for the
European Commission under the EU JOULE/THERMIE programme by a team of partner institutes
across Europe led by Cambridge Econometrics. It is designed as a specifically forward-looking
model for assessing energy-environment-economy issues and policies. The model therefore combines
economic, energy and environment components. References to recent versions are given below.
How models are used for Cohesion Policy analysis?
The QUEST and HERMIN model systems tend to be used within the Commission at the design stage
of a new programme of Cohesion Policy (ex-ante analysis); at the time of mid-term review (a mixture
of ex-ante and ex-post); and when the programmes are completed (ex-post).
Although each cohesion expenditure programme is a complex plan consisting of hundreds of
individual projects and measures, grouped under a small number of Operational Programmes, these
need to be reclassified these for use in model-based analysis into a smaller number of economic
investment and expenditure categories: e.g., public infrastructure; human resources; and aid to private
enterprises. These expenditure flows are usually treated as additions to stocks of infrastructure,
human capital and R&D, and it is these improved stocks that generate longer term benefits to the
economy, lasting beyond the end of the programming period.
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The HERMIN models were specifically designed for use in the impact analysis of Cohesion Policy and
were the first serious attempts to look at the long-term impacts of the complex range of public
investment initiatives that together make Cohesion Policy. The design of the policy mechanisms in the
models drew on the emerging literature on “new” growth theory and the analysis of endogenous
growth. Previously, models had tended to assume that technical progress was externally determined
and not under policy control. Consequently, it was difficult to analyse the long-term impacts of policies
whose purpose was to raise the level of productivity. However, HERMIN has also been used to
analyse the combined effects of the Single European Market and Cohesion Policy in the lagging
states.
While the QUEST II model was primarily developed to simulate and analyse changing macroeconomic
financial policies associated with deepening and widening of the EU, it has been used for wider policy
analysis, such as to assess the impact of the Maastricht criteria on growth and employment; the long
run effects of fiscal consolidation and structural reforms in Europe; the impact of monetary policy on
the success of government expenditure cuts; and the macroeconomic effects of various tax reforms
and VAT harmonisation. The model has also been used to assess the employment and growth effects
of the Trans-European Transport Networks. In recent years QUEST II, and its successor, QUEST III
have been used for analysis of Cohesion Policy.
The E3ME model was designed to provide a forward-looking framework for energy and environment
policy, taking account of economic impacts, but has also been used to analyse a range of structural
and financial policy innovations. A recent example of its application (GHK et al. 2003) was in
assessing the contribution of the Structural Funds to sustainable development in the three programme
periods 1989-1993, 1994-1999 and 2000-2006. E3ME modelled demand-side and supply-side effects
by which investment and expenditure in education drive economic activity and environmental change.
The demand-side modelling was used to assess the effect that SFs have on the levels of taxes,
current government expenditure, investment and the economy. The supply-side modelling assessed
the longer-term effects of the SF on changes in productivity and changes in the accumulation of capital
and technology. These longer-term effects are incorporated in the model through changes in
productivity induced by these expenditures. The analysis compared a baseline (policy on) scenario
based on historical data, against a counterfactual scenario (policy off) without the structural funds.
The main steps involved in model-based evaluation
The planning and execution of a macroeconomic model should only be considered if the evaluators
have adequate research experience, modelling knowledge, technical computing skills and time. This is
because numerous highly technical steps are involved, as outlined below.
Step 1: The modelling context
An existing modelling unit can be used as a basis for developing and operating model-based analysis
of Cohesion Policy. If no such unit exists, then the establishment of a new unit should be undertaken
only if there are other model-related tasks that also need to be addressed, such as a need for
independent medium-term economy forecasts, analysis of routine domestic budgetary policy, etc.
Links with university-based researchers can also play a supportive role in initiating modelling activity in
a government evaluation unit.
Step 2: Locating a suitable model
It is generally rare for a model to be built from scratch for an evaluation exercise, because models are
quite resource-intensive. This often means that an existing model with broadly acceptable features
must be chosen, and adapted as necessary for the particular policy under analysis. Depending on the
scope of the analysis, e.g., a single region, a single country, or pan-European, there will be different
numbers of competing models available which will generally require input from the model proprietor.
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In the case of the HERMIN model, the Commission can supply extensive technical documentation on
each of the individual country models and can facilitate the initiation of such work in the member
states.
Step 3: Adapting the model for evaluation
Once a suitable model is selected, its structure must be analysed to see how it would cope with the
transmission mechanisms used by the policy to be evaluated. National evaluation teams would be
well advised to study the documentation of the two main model systems: QUEST and HERMIN, and to
base their local modelling research on these systems.
Step 4: Establish a baseline projection
With the model developed and tested, it is ready for use in policy analysis. The first task is to simulate
a “baseline” against which the policy impacts will be developed. For example, if one were carrying out
an ex-ante analysis of the Cohesion Policy programme 2007-2013, and doing it prior to the start-up of
the programme, the baseline scenario would probably be selected as a “no-policy” case, i.e., what
might the economic outcome be in the complete absence of Cohesion Policy. This comes down
essentially to the preparation of a medium-term forecast for the period from 2007 to well after the
termination of the programme, e.g., 2020. This is a challenging task that requires a lot of knowledge
of the economy in question as well as the longer term properties of the model.
Step 5: Prepare the policy change to be analysed
In the case of Cohesion Policy analysis, one would have to derive the magnitudes of the flows of
investment in public infrastructure, human resources and R&D that are associated with the policy that
must be analysed. In the ex-ante case, these tend to be the published “planned” expenditures. In the
mid-term case, they are a mixture of actual ex-post data and planned ex-ante data for the remainder of
the programme. In the ex-post case, they will normally consist of the actual investment expenditures.
Step 6: Running the policy scenario
To evaluate the effect of a public intervention, the modeller must introduce changes into the baseline
case that reflect the nature of the policy change, e.g., a flow of investment in public infrastructure,
human resources and R&D. The model must then be run to calculate the final effect on model output
(i.e. "endogenous") variables. In the case of an ex-ante evaluation, the impact can then be estimated
by establishing the difference between the two simulations, i.e., the “with-policy” minus the “baseline”
(or “without-policy”) case.
Step 7: Assessing the results
The last phase is that of the formulation and presentation of results. Given the complexity of the tool
and the numerous variables that can be quantified, it is necessary to take specific steps to present
results in a simple and easily accessible way, particularly as the readership may not be experts in the
field of macroeconomic modelling. This phase must therefore be considered as one of the most
important in the implementation of the tool. Thus, an important part of the results assessment is to
clearly state the assumptions and choices made in the evaluation exercise. For the more technically
inclined reader it would also be desirable to include reference to the model structure/methodology, as
this is often taken for granted but can have a big influence on the ability to interpret the results.
An additional element of the assessment should always be to examine how sensitive the findings are
to small changes in the key assumptions made. For example, are the impacts sensitive to the profile
of the baseline forecast? How do the results depend on the values selected for some of the model’s
key parameters?
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Strengths and Limitations of the approach
Strengths
Many of the goals of Cohesion Policy are defined at the macroeconomic level. The outputs from a
macroeconomic model therefore are generally consistent with requirements, meaning that this is an
important tool in establishing whether Cohesion Policy has achieved its macroeconomic goals.
A key strength of a macroeconomic model is its ability to use the data to inform the structure of the
model. If there are adequately long data time series, then econometric techniques can be used to
derive a more robust model structure. In any case, the model should reflect the main characteristics of
the data.
Models such as QUEST and HERMIN usually have dynamic properties and can track changes in
policy impacts over time, rather than just examine a notional difference between two equilibrium
points.
Weaknesses
The first practical weakness is that there is a heavy resource requirement when dealing with
macroeconomic models. Good quality data sets are required to ensure sensible coefficient values, a
factor that can restrict the scope of such models. The work involved in constructing (or adapting) such
models is technically demanding.
A more fundamental criticism is that a model is only as good as its assumptions and parameters – and
some of these are difficult to verify in practice. While some of the assumptions command a fair degree
of consensus, some do not. For example, most macroeconomic models share a broadly similar
demand-side structure based around the expenditure components of GDP, with further modules for
the traded sectors and possibly input-output tables to cope with inter-industry demand and specific
sectoral effects. But more divergence occurs with the treatment of supply-side factors, which measure
the potential output of an economic system, often through the use of a production function. Typically
technology is assumed to be exogenous, although much of the more recent analysis of convergence
and the ‘new growth’ literature emphasises the important role of endogenous effects linked to
infrastructure and human capital growth. Because potential output can never be observed, there is
more debate over how it should be measured, leading to different structures, different model
properties, and ultimately different evaluation results.
A common criticism of macroeconomic models in the past was that their structure and properties were
not consistent with what we know about the real microeconomic behaviour of economic agents.
Today, few analysts would use an old-style time-series "micro-free" model of the kind that was
common prior to the 1980s. But models tend to differ in the extent and way in which they incorporate
micro-foundations. The new QUEST III model is at one end of the spectrum of possibilities, with
comprehensive micro-foundations. HERMIN is on the other side of the spectrum, where some microfoundations are imposed, but complete optimising behaviour and forward-looking expectations are
considered to be too strong an assumption in the cohesion countries.
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References
QUEST II and III:
Roeger, W. and J. in’t Veld (1997), QUEST II – A multi-country business cycle and growth model,
Economic Papers No 123, European Commission.
In’t Veld J. (2007), "The potential impact of the fiscal transfers under the EU Cohesion Policy
Programme", European Economy Economic Paper no. 283, European Commission DirectorateGeneral
for
Economic
and
Financial
Affairs,
Brussels,
July.
http://ec.europa.eu/economy_finance/publications/publication9579_en.pdf
Roeger W., J. Varga and J. in’t Veld (2008), “Structural reforms in the EU: a simulation-based analysis
using the QUEST model with endogenous growth”, European Economy Economic Paper no.351.
http://ec.europa.eu/economy_finance/publications/publication13531_en.pdf
Varga J. and J. in’t Veld (2009), “A model-based assessment of the macroeconomic impact of EU
structural funds on the new Member States" , European Economy Economic Paper no.371.
http://ec.europa.eu/economy_finance/publications/publication14342_en.pdf
Ratto M, W. Roeger and J. in’t Veld (2009), “QUEST III: An Estimated Open-Economy DSGE Model of
the Euro Area with Fiscal and Monetary Policy”, Economic Modelling, 26 (2009), pp. 222-233.
http://dx.doi.org/10.1016/j.econmod.2008.06.014
(also published as: European Economy Economic Paper no.335 , European Commission DirectorateGeneral
for
Economic
and
Financial
Affairs,
Brussels,
July
2008.
http://ec.europa.eu/economy_finance/publications/publication_summary12920_en.htm )
Roeger W., J. Varga and J. in’t Veld (2009), “Modelling the Lisbon Strategy: Analysing policies to
promote knowledge investment with an endogenous growth model", Comparative Economic Studies
(forthcoming).
HERMIN and the COHESION System of HERMIN Models (CSHM)
Barry, F., Bradley, J., Hannan, A., McCartan, J. and Sosvilla-Rivero, S. (1997). Single Market Review
1996: aggregate and regional impact: the cases of Greece, Spain, Ireland and Portugal, Office for
Official Publications of the European Communities in association with Kogan Page, London.
Bradley, J. (2000). “Policy Design and Evaluation: EU Structural Funds and Cohesion in the European
Periphery”, in Empirical Models and Policy-Making: Interaction and Institutions, F. den Butter and M.
Morgan (eds.), London: Routledge.
Hallet, M. and G. Untiedt (2001), The potential and limitations of macroeconomic modelling for the
evaluation of EU Structural Funds illustrated by the HERMIN model for East Germany; Informationen
zur Raumentwicklung No. 4/2001.
Bradley, J. (2006). “Evaluating the Impact of European Union Cohesion Policy in Less-developed
Countries and Regions”, Regional Studies, Special Issue: The Evaluation of European Union
Cohesion Policy, (eds.) J. Bachtler and C. Wren, pp. 189-199.
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Bradley, J., Untiedt, G. (2008): Do economic models tell us anything useful about Cohesion Policy
impacts? A comparison of HERMIN, QUEST and ECOMOD. In: Stierle-von Schütz, U., M.H. Stierle,
F.B. Jennings Jr., A. Kuah (2008): Regional Economic Policy in Europe – New Challenges for Theory,
Empirics and Normative Interventions. Edgar Elgar, Cheltenham UK, pp. 159-180.
Bradley, J., Untiedt, G. (2008), "The COHESION System of HERMIN country and regional models:
Description and Operating Manual – Version 3”, GEFRA/Münster, and EMDS/Dublin, Report prepared
for DG Regional Policy.
Other models and applications
Frederick Treyz, George Treyz, (2003), Evaluating the Regional Economic Effects of Structural Funds
Programs
Using
the
REMI
Policy
insight
http://ec.europa.eu/regional_policy/sources/docconf/budapeval/index_en.htm
D’Alcantara, G., Italianer, A. 1982, A European project for a multinational macrosectoral model,
Document MS 11, DG XII, Brussels: Commission of the European Communities.
Bayar, A. (2007), Study on the Impact of Convergence Interventions 2007-2013, Working paper,
ULB/EcoMOD, May.
Allard C., Choueiri N., Schadler S. and R. van Elkan (2008), Macroeconomic Effects of EU Transfers
in New Member States, IMF Working paper 08/223.
GHK, PSI, IEEP, CE (2003) The Contribution of the Structural Funds to Sustainable Development – A
Synthesis Report (Volume 1) to DG Regio, EC, chapter 4.
Other references
Lucas, R. (1976). "Econometric Policy Evaluation: A Critique." Carnegie-Rochester Conference Series
on Public Policy 1: 19–46.
Summers, L. (1991). “The Scientific Illusion in Empirical Macroeconomics”, Scandinavian Journal of
Economics, Vol. 93, No. 2, pp. 129-148.
Wickens, M. (2008). Macroeconomic Theory: A Dynamic General Equilibrium Approach, Princeton
University Press, Princeton
Key terms
CGE - Computable General Equilibrium Models
General equilibrium analysis models an economy to present an integrated picture of the labour market
and goods market relationships Computable General Equilibrium (CGE) models look at goods and
factor markets simultaneously with wages, prices and hence incomes determined endogenously.
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Evaluating Socio Economic Development, SOURCEBOOK 2: Methods & Techniques
Macroeconomic Models
Demand-side effects
Demand-side effects measure the impact of economic change in terms of expenditure and income
effects.
Supply-side effects
Supply-side effects measure the impact of economic change on productivity, wages, and profits.
Exogenous:
Generated by factors outside those being modelled, as opposed to internal factors (endogenous).
Keynesian multiplier:
The "Keynesian multiplier” is the idea that an increase in public expenditure generates an increase in
other expenditure, with the result that the increase in national income is greater than the initial
expenditure. Concretely, governments stimulate the economy, people and businesses who receive
this money then spend some of it on consumption goods (saving the rest), and this extra spending
allows businesses to hire more people and pay them, which in turn allows a further increase in
consumer spending. This process continues, the multiplier process tapers off (each round of spending
is a fraction of the previous one), and eventually reaches an equilibrium. The multiplier is reduced the
more open the economy becomes, in the presence of taxation, and in the presence of supply-side
constraints (in which case, government spending is said to "crowd" out private spending). Increases in
imports and in tax payments at each step reduce the size of the multiplier effect. Hence, multipliers in
small open economies (such as the cohesion states) tend to be much smaller than in larger, less open
economies of the EU.
Cumulative CP multiplier:
The concept of a “cumulative multiplier” has proved to be a useful impact measure for Cohesion
Policy, and is defined as follows:
By normalising on the cumulative size of the CP investment as a share of GDP, this measure can be
used to compare and contrast CP impacts in different countries, all of whom receive different relative
shares of EU assistance and contribute different amounts of domestic co-finance. By using a
cumulative measure of the impacts on GDP for a specified numbers of years, the multiplier quantifies
the overall impact of the policy at that date.
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