Regional Holistic Model

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.

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.

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).