Macroeconomic Modelling

Macroeconomic Modelling

For the purpose of Cohesion Policy Impact Assessment, BGI Consulting employs the HERMIN macroeconomic model, one of the three best-known European models (along with QUEST and ECOMOD models). Despite ongoing discussions, which one of these three models is better for analysing the impact of EU investments  and which one is better suited to the old  or the new  EU member states,, only the HERMIN model has been used in Lithuania in the past.
The HERMIN model is characterized by the fact that it incorporates both the demand side  (Keynesian) and the supply side of the economy. These mechanisms are depicted in a stylized image below. Short-term demand side effects are the result of an increase in spending and revenue policy instruments related to Cohesion Policy interventions. I.e. the impact of the demand side determines that additional GDP (additional employment, etc.) is created as a response to increased costs (or EU funded investments in the economy). Such effects occur during the year of implementation of the Program and disappear together with the end of the intervention.
Demand side effects are also important and should not be ignored. Nevertheless, while analysing theCohesion Policy, demand side effects have only a transitional importance, because from the viewpoint of economic theory, Cohesion policy interventions seek to increase the long-term economic potential (transforming and modernizing the beneficiary economy in order to become more capable of withstanding the competitive nature of  the Single Market). Hence, the most prominent effects of EU public investments are seen on the supply side, EU investments create  an effect through the following activities:
  • improves the physical infrastructure that the private sector could use in production;
  • improves human resources (for example, investing in training) that can be used by the private sector in product development;
  • directs public financial support to the private sector in order to stimulate investments and increase efficiency, thus increasing the efficiency of production factors and reducing the production and capital costs of the sector;
  • improves the R&D base that the private sector could use to increase its competitiveness in the Single and Global Markets.
Based on economic theory, two main types of impact to supply have to be analysed (modeled). The impact to supply can be attributed to improved role of physical infrastructure, R&D, education or training activities by directly increasing output. It increases the ability of indigenous producers to compete on the international market.
The second type of supply effect appears from an increase in the productivity of all or some of the production actors due to ameliorated infra structure improvements, an improved R&D base, and an increase in the level of human capital due to training and other education activities. Such an effect can be called the "external effect of productividy". The external effect of productivity has two sides - production and market services are becoming more efficient and more competitive but demand for labour power is reduced if output growth remains negligible.
The positive side of this phenomenon (the above-mentioned external effect of productivity) is that increase in the efficiency of factors simultaneously increases real income and this effect produces a chain multiplier effect and creates other benefits in the economy.



In Lithuania the HERMIN family model was used in 2009 for the first time to assess the impact of the EU Structural Funds on GDP. During this assessment, BGI Consulting together with the developer of HERMIN modeling system, EC expert, dr. John Bradley used the HERLIT-5 model, covering 5 production sectors:

  • Agriculture, Forestry and Fisheries;
  • Production;
  • Construction;
  • Market services;
Non-market services.
In the survey carried out by the Ministry of Economy in 2011, BGI Consulting has developed a version of the model (HERLIT-16), in which the supply side of the economy has been modeled for sixteen sectors. Manufacturing and market services were analysed more disaggregated by dividing into six manufacturing and four market services sub-sectors. Mining and quarrying, energy-related activities and community-based services were analysed separately.
Subsequently, BGI Consulting developed and implemented separate, more dispersed versions of the HERMIN / HERLIT model and used them to carry out  assessments at the request of the Ministries of Finance, Transport, Agriculture and Foreign Affairs of the Republic of Lithuania.
HERMIN Model System for Baltic Countries
In the long run, HERMIN's regional modeling systems have been developed and refined. The most recent exercise commissioned by the European Commission on the modeling HERMIN model was the assessment of r expected esults of Cohesion Policy in Estonia, Lithuania and Latvia for 2014-2020 (carried out at the request of the European Commission's Directorate-General for Regional and Urban Policy through Sweco International AB). During this assessment, BGI Consulting, together with the developer of HERMIN modeling system, EC expert, dr. John Bradley has developed and used the modeling system for the economies of the three Baltic States.
According to the results of the modeling, the largest return on investment of EU funds between 2014 and 2020 is expected in Estonia, in which, over a period of 18 years, the benefit generated by the investment is 2.51 times higher than the investment itself. Lithuania is expected to recieve a return of 2.33 times.
Modelling of Thematical Indicators
HERMIN / HERLIT model can be supplemented by satellite equations that allow us to model the impact of monitoring indicators when economic logic allows these indicators to be linked to the HERMIN / HERLIT system. For example, the evaluation carried out at the request of the Ministry of Economy examined the impact on monitoring indicators administered by the Ministry of Economy ( such as tourism, energy, business and business environment, R & D). For example, in order to reflect the situation in the energy field, the following monitoring indicators were modeled:
  • Energy intensity (energy per unit of GDP). The indicator was modeled in conjunction with real GDP per capita and time series.
  • Renewable energy share in total energy consumption. The indicator was tied to real GDP per capita and time series.
 It is equally possible to select and model other indicators reflecting the state of the individual public policy areas.

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