RHOMOLO
Source: Commission modelling inventory and knowledge management system (MIDAS)
Date of Report Generation: Mon Jan 12 2026
Dissemination: Public
© European Union, 2026
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Overview
Acronym
RHOMOLO
Full title
Regional Holistic Model
Main purpose
RHOMOLO is a model used to simulate the impact of EU policies at the regional level (NUTS 2), providing policy support in the evaluation of investments, reforms, and structural changes in the economy.
Summary
RHOMOLO is a recursively dynamic, spatial, computable general equilibrium (CGE) model. It is used to simulate the sector-, region- and time-specific impact of EU policies, and to support policymakers in evaluating investments, reforms and structural changes to the economy.
The current version of RHOMOLO (v4) covers 235 EU27 NUTS 2 regions and one residual 'Rest of the World' region. It disaggregates their economies into ten NACE Rev. 2 sectors, which requires constant data updates and maintenance. The model includes all monetary transactions in the economy resulting from agents making optimising decisions. Goods and services are produced in markets that can be either perfectly or imperfectly competitive, and are consumed by households, governments, and firms. Spatial interactions between regions are captured through costly trade matrices of goods and services, as well as capital and labour mobility. This makes RHOMOLO particularly well suited to analysing policies related to investments in human capital, transport infrastructure and innovation, among other things.
The RHOMOLO model has been developed by the JRC in collaboration with the Directorate-General for Regional and Urban Policy (DG REGIO). The explicitly modelled spatial dimension makes it a unique tool for territorial impact assessment.
An up-to-date list of policy applications and publications of the model can be found here.
The latest RHOMOLO Newsletter containing the most recent activities of the Regional Economic Modelling group can be found here.
The RHOMOLO web application allows users to explore the results of four illustrative shocks obtained with the RHOMOLO V4 model calibrated with the data constructed by García Rodríguez et al. (2023). The aim of this simple web application is to convey the importance of using a spatial model capable of producing results at the NUTS-2 level in the European Union.
Model categories
Economy
Model keywords
endogenous growthinnovationhuman capitaleconometrically estimated parametersmacroeconomics
Model homepage
Ownership and Licence
Ownership
EU ownership (European Commission)
Ownership details
Licence type
Non-Free Software licence
The license has one or more of the following restrictions: it prohibits creation of derivative works; it prohibits commercial use; it obliges to share the licensed or derivative works on the same conditions.
Details
Structure and approach
In the tradition of computable general equilibrium (CGE) models, RHOMOLO relies on an Arrow-Debreu equilibrium framework, in which supply and demand depend on the price system. Policies are introduced as shocks. Following a shock, the system moves towards a new equilibrium, with adjustments driven by optimal supply and demand behaviours. Like all CGE models, RHOMOLO therefore provides an evaluation of the interaction effects between all agents through markets, imposing full system consistency. This type of analysis is known as scenario analysis, whereby the results of simulations including policy shocks are compared to a baseline scenario with no shocks.
Given RHOMOLO's regional focus, particular attention is devoted to explicitly modelling spatial linkages, interactions and spillovers between regional economies. For this reason, models such as RHOMOLO are referred to as spatial CGE models.
Each region is inhabited by households that are aggregated into a representative agent whose preferences are characterised by a love of variety. Households derive income from labour (in the form of wages), physical capital (profits and rents), other financial assets and government transfers. Labour mobility can be switched on or off depending on the needs of the analysis to be carried out. Household income is spent on savings, consumption and taxes.
Firms in each region produce goods that are sold and consumed in all regions by households and governments. Other firms, either in the same sector or others, can also use these goods as inputs in their production processes. Transport costs for trade between and within regions are assumed to be of the 'iceberg' type, varying according to sector and region pair. The market structures of industrial sectors in each region can be modelled as either perfectly or imperfectly competitive. The latter can be characterised as monopolistic competition, Cournot oligopoly or Bertrand oligopoly. The number of firms in each sector and region is estimated using national Herfindahl indices, under the assumption that all firms in one region use the same technology. Firms with higher market shares are able to extract higher mark-ups from consumers than their competitors because of their higher weight in the price index. Since market shares vary by destination market, mark-ups also vary by destination market.
Input and parametrization
The article by García-Rodríguez et al. (2025) illustrates the dataset underlying RHOMOLO V4. Furthermore, the model requires several calibrated inputs and exogenous parameters to function. For instance, the interest rate is set at 0.04%, while the rate of depreciation of private capital is set at 0.15%. More generally, parameters related to elasticity of substitution on both the consumer and producer sides are based on similar models or derived from econometric literature.
Further details on model parameterisation can be found in Section 3 of the article by Salotti et al. (2025).
Main output
All RHOMOLO output variables are available on a yearly basis, with the level of detail broken down by region and sector. Regional variables can be aggregated by country, EU or other geographical area.
- Households-related output variables:
- Factor supply by household; Income; Taxes paid on income; Savings; Consumption; Price of consumption; Net disposable income.
- Firms-related output variables:
- Price of exports; Lerner index of monopoly power; Market share; Average sales price; Average production cost; Profits; Fixed cost of production; Marginal cost of production; Aggregate intermediate input; Aggregate input of primary factor; Price of aggregate intermediate input; Intermediate demand for each good; Total factor productivity; Aggregate labour-factor demand; Price of aggregate labour-factor demand; Price of aggregate input of primary factor; Demand of each factor; Taxes paid on demand of each factor; Taxes paid on sales.
- Investment-related output variables:
- Investment; User cost of capital; Price of investment.
- Government-related output variables:
- Factor supply by Government; Income of Government; Current expenditure; Public investment; Price of consumption of Government; Transfers from Government to household; Savings.
- Import-related output variables:
- Demand for composite of each good; Price of each composite good’s demand; Exports; Net exports.
- Other variables:
- Price of each factor; Unemployment rates; Number of firms in each sector.
Spatial & Temporal extent
The output has the following spatial-temporal resolution and extent:
| Parameter | Description |
|---|---|
| Spatial extent / country coverage | EU Member states 27 |
| Spatial resolution | NationalSub-national (NUTS2) |
| NUTS2 (NUTS1, country-level and EU27-level results are also available depending on the type of analysis) | |
| Temporal extent | Very short-term (less than 1 year)Short-term (from 1 to 5 years)Medium-term (5 to 15 years)Long-term (more than 15 years) |
| up to 2060 | |
| Temporal resolution | Years |
Quality & Transparency
Quality
Model uncertainties
Models are by definition affected by uncertainties (in input data, input parameters, scenario definitions, etc.). Have the model uncertainties been quantified? Are uncertainties accounted for in your simulations?
- response
- not applicable
- details
- The type of analysis carried out with RHOMOLO, that is scenario analysis, can take care of uncertainties by simulating several alternative scenarios to be compared with the baseline one.
- url
Sensitivity analysis
Sensitivity analysis helps identifying the uncertain inputs mostly responsible for the uncertainty in the model responses. Has the model undergone sensitivity analysis?
- response
- yes
- details
- Sensitivity analysis is an integral part of our modelling approach and is systematically performed with every model analysis. During each evaluation, we examine how variations in key parameters influence the model outputs by adjusting these critical inputs systematically and observing the resulting changes in responses. For example, when analysing labour market policies using RHOMOLO, we modify the labour substitution elasticities in the production function to evaluate their effect on employment outcomes, wage adjustments and overall economic efficiency.
- url
Have model results been published in peer-reviewed articles?
- response
- yes
- details
- The RHOMOLO model and its various iterations have been extensively validated and recognised within the academic community through regular publication in prestigious, peer-reviewed journals. It has appeared in numerous scholarly articles in top-tier journals, including Regional Studies, Spatial Economic Analysis and the European Journal of Political Economy, thereby demonstrating its robustness and relevance in addressing contemporary challenges in economic and spatial analysis.
- url
Has the model formally undergone scientific review by a panel of international experts?
Please note that this does not refer to the cases when model results were validated by stakeholders.
- response
- yes
- details
- url
Model validation
Has model validation been done? Have model predictions been confronted with observed data (ex-post)?
- response
- not applicable
- details
- Model projections cannot and should not be confronted with observed data because RHOMOLO is not a forecast model.
- url
Transparency
To what extent do input data come from publicly available sources?
This may include sources accessible upon subscription and/or payment
- response
- Based on both publicly available and restricted-access sources
Is the full model database as such available to external users?
Whether or not it implies a specific procedure or a fee
- response
- no
- details
- The main dataset used to calibrate the model (with 2017 data) is publicly available.
- url
Have model results been presented in publicly available reports?
Note this excludes IA reports.
- response
- yes
- details
- documents
For details please refer to the 'peer review for model validation' documents in the bibliographic references
Have output datasets been made publicly available?
Note this could also imply a specific procedure or a fee.
- response
- no
- details
- Model outputs are publicly available through the publications made by the members of the TEDAM team. Full results are available upon request.
- url
Is there any user friendly interface presenting model results that is accessible to the public?
For instance: Dashboard, interactive interfaces...
- response
- yes
Has the model been documented in a publicly available dedicated report or a manual?
Note this excludes IA reports.
- response
- yes
- details
- All the model equations are transparently documented in Salotti et al. (2025) and other recent articles.
Is there a dedicated public website where information about the model is provided?
- response
- yes
Is the model code open-source?
- response
- no
- details
Can the code be accessed upon request?
- response
- no
- details
The model’s policy relevance and intended role in the policy cycle
The model is designed to contribute to the following policy areas
- Business and industry
- Digital economy and society
- Economy, finance and the euro
- Education and training
- Employment and social affairs
- Regional policy
- Research and innovation
- Single market
- Taxation
- Trade
- Transport
The model is designed to contribute to the following phases of the policy cycle
- Evaluation – such as ex-post evaluation
- Formulation – such as ex-ante Impact Assessments
The model’s potential
The RHOMOLO model is designed for policy impact assessment. The explicitly modelled spatial dimension at the NUTS2 regional level makes it a unique tool for territorial impact assessment. Spatial interactions between regions are captured through trade of goods and services (which is subject to trade costs), income flows, factor mobility and knowledge spillovers, making RHOMOLO particularly well suited for simulating human capital, transport infrastructure, R&D and innovation policies.
RHOMOLO has been used for the impact assessment of the European Regional Development Fund (ERDF), the Cohesion Fund, Horizon Europe, the European Social Fund (ESF),the European Competitiveness Fund, the portfolio of the European Investment Bank, and the European Defence Fund, among others.
Moreover, RHOMOLO is used for the evaluation of specific investment projects and other reforms depending on the requests made by Member States and regional authorities.
Previous use of the model in ex-ante impact assessments of the European Commission
Use of the model in ex-ante impact assessments since July 2017.
2025SWD/2025/555 final
Impact Assessment on the European Competitiveness Fund Accompanying the documents Proposal for a Regulation of the European Parliament and of the Council on establishing the European Competitiveness Fund ('ECF’), including the specific programme for defence research and innovation activities, repealing Regulations (EU) 2021/522, (EU) 2021/694, , (EU) 2021/697, (EU) 2021/783, repealing provisions of Regulations (EU) 2021/696, (EU) 2023/588, and amending Regulation (EU) [EDIP] Proposal for a Regulation of the European Parliament and of the Council establishing Horizon Europe, the Framework Programme for Research and Innovation, for the period 2028-2034 laying down its rules for participation and dissemination, and repealing Regulation (EU) 2021/695 Proposal for a Council Decision on establishing the Specific Programme implementing Horizon Europe - the Framework Programme for Research and Innovation for the period 2028-2034, laying down the rules for participation and dissemination under that Programme, and repealing Decision (EU) 2021/764
- Lead by
- GROW
- Run by
- European Commission
- Contribution role
- baseline and assessment of policy options
- Contribution details
The JRC's quantitative impact analysis was used by the Secretariat-General (SecGen), DG BUDG, DG GROW and DG RTD to develop and justify the ex-ante impact assessment of the European Competitiveness Fund, which underpinned its legislative proposal. The JRC’s work was included in the official impact assessment document SWD(2025) 555 final, where it supported the argument for the Fund’s design, by providing evidence-based projections of GDP, employment, and competitiveness outcomes. The sensitivity analysis, requested by the Regulatory Scrutiny Board, strengthened the robustness of the assessment by addressing uncertainties in budget allocation and implementation timelines, enhancing transparency and compliance with EU regulatory standards. The RHOMOLO CGE model’s outputs enabled the beneficiaries to quantify trade-offs and synergies across sectors and Member States, informing decisions on the Fund’s size, eligibility criteria, and expected returns. The use of a validated model (previously applied for InvestEU) added credibility to the Fund’s economic rationale, facilitating stakeholder buy-in and alignment with broader EU policy goals. The JRC’s contribution was critical in enabling evidence-based policy design, as the impact assessment became a foundational document for the Fund’s approval and implementation, demonstrating its potential to deliver measurable economic benefits while adhering to rigorous scrutiny. The uptake is evidenced by the direct integration of the JRC’s analysis into the SWD(2025) 555 final and its role in addressing the Regulatory Scrutiny Board’s requirements, which are prerequisites for legislative adoption.
2025SWD/2025/565 final
Impact Assessment Accompanying the document Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL establishing the European Fund for economic, social and territorial cohesion, agriculture and rural, maritime, prosperity and security for the period 2028-2034 and amending Regulation (EU) 2023/955 and Regulation (EU, Euratom) 2024/2509
- Lead by
- REGIO
- Run by
- European Commission
- Contribution role
- baseline and assessment of policy options
- Contribution details
The JRC conducted an ex-ante impact assessment of the European Fund for economic, social and territorial cohesion, agriculture and rural, maritime, prosperity and security for the period 2028-2034 at the request of the SecGen and DG BUDG. Using the RHOMOLO CGE model —previously applied for Cohesion policy and European Social Fund analysis, among other things— the JRC designed and estimated scenarios based on the Fund’s qualitative policy framework available (no quantitative inputs were received specific to the next MFF budget). Through iterative discussions with stakeholders, the JRC refined these scenarios to simulate economic impacts on GDP, employment, and competitiveness. Key deliverables included technical input for the official impact assessment (SWD(2025) 565 final), testing assumptions on budget scale and implementation timelines. The work involved model-based quantification, scenario design, and policy-aligned interpretation of results.
2018SWD/2018/307 final
Impact assessment accompanying the document Proposal for a Regulation of the European Parliament and the Council on: establishing Horizon Europe - the Framework Programme for Research and Innovation, laying down its rules for participation and dissemination and; Proposal for a Decision of the European Parliament and the Council on: establishing the specific programme implementing Horizon Europe - the Framework Programme for Research and Innovation and; Proposal for a Regulation of the European Parliament and the Council on: establishing the Research and Training Programme of the European Atomic Energy Community for the period 2021-2025 complementing Horizon Europe - the Framework Programme for Research and Innovation
- Lead by
- RTD
- Run by
- European Commission
- Contribution role
- baseline and assessment of policy options
- Contribution details
The model helped to assess the following impacts:
- Investment cycle
- Equal treatment of products and businesses
- Stimulation of research and development
- Innovation for productivity/resource efficiency
- Economic growth and employment
2018SWD/2018/289 final
Impact assessment accompanying the document Proposal for a Regulation of the European Parliament and the Council on: the European Social Fund Plus (ESF+) and; Proposal for a Regulation of the European Parliament and the Council on: the European Globalisation Adjustment Fund (EGF)
- Lead by
- EMPL
- Run by
- European Commission
- Contribution role
- baseline and assessment of policy options
- Contribution details
The model helped to assess the following impacts:
- Innovation for productivity/resource efficiency
- Investments and functioning of markets
- Impact on jobs
- Impact on jobs in specific sectors, professions, regions or countries
- Indirect effects on employment levels
- Factors preventing or enhancing the potential to create jobs or prevent job losses
- Level of education and training outcomes
2018SWD/2018/282 final
Impact assessment accompanying the document Proposals for a Regulation of the European Parliament and of the Council on: the European Regional Development Fund and on the Cohesion Fund and; Proposal for a Regulation of the European Parliament and the Council on: a mechanism to resolve legal and administrative obstacles in a cross-border context and; Proposal for a Regulation of the European Parliament and the Council on: specific provisions for the European territorial cooperation goal (Interreg) supported by the European Regional Development Fund and external financing instruments
- Lead by
- REGIO
- Run by
- European Commission
- Contribution role
- baseline and assessment of policy options
- Contribution details
The model helped to assess the following impacts:
- Investment cycle
- Equal treatment of products and businesses
- Stimulation of research and development
- Innovation for productivity/resource efficiency
- Economic growth and employment