MIRAGE-e 2 structure overview #
In a nutshell, MIRAGE-e 2 is a multi-sector multi-country CGE model which is particularly fitted to assess jointly trade policy and climate change mitigation policies
- A representative agent, which gathers both consumers and the
government, maximises a LES-CES utility function.
- The consumer emits greenhouse gases from its consumption of fossil nergy goods
- Firms in each sector maximize their profit function under the
production function described in
the supply side
- Can be one representative firm under perfect competition or N identical firms under oligopolistic competition à la Krugman
- Firms emit greenhouse gases from their production process and from consumption of fossil energy goods
- Demand for final goods and intermediate goods are aggregated in two
different Armington-like demand function as described in
the demand side page.
- Tariffs and NTMs are differentiated between final and intermediate goods, through aggregation
- Non-tariff measures can be introduced, and modelled as an iceberg cost or a rent-creating trade cost (allocated to exporter or importer)
MIRAGE-e mainly relies on GTAP data, but also depends on several sources.
GTAP data #
MIRAGE relies for SAMs on the GTAP database which currently provides data for 140 regions and 57 sectors. The model cannot run at this level of detail on a personal computer due to computation resource requirements and requires aggregation in a number of around 25 regions and 25 sectors.
GTAP also represents a strong network for research on global trade modelling. The GTAP official website constitutes an important source of information for CGE modellers.
Protection data #
Data on protection are particularly sensitive for in trade policy assessment. For simulations done with MIRAGE, we do not use GTAP aggregated tariff but compute our own tariff equivalents thanks to the database MAcMap-HS6 developed at CEPII and that represents bilateral applied tariffs for 5,113 products in the HS6 nomenclature. The tariff aggregation is done through the use of reference groups.
See section: MAcMap database
Non-tariff measures #
MIRAGE-e is able to take non-tariff measures into account. It relies on the following data:
- Trade frictions are measured as the ad-valorem equivalent for time spent un customs, provided by ImpactEcon.
- Ad-valorem equivalents for NTMs in services are built at CEPII [(:harvard:Font2016)]
- Ad-valorem equivalents for NTMs in goods are taken from [(:harvard:Kee2009)]
MIRAGE-e baseline is built using the EconMap database. It is used to calibrate the baseline trajectory of:
- Total factor productivity
- Population by education level
- Savings rate and current account
- Energy efficiency
See section: baseline
Core features #
Supply and Demand #
The strength of the CGE framework is its foundation on microeconomic equations that represent the behaviour of agents in a consistent neo-classical framework. In MIRAGE, a representative agent is maximising its utility relatively to the perceived set of prices, and the demand in countries is complemented by investment demand under budget constraint and firms maximising their profit. The supply and demand of countries are entangled through international trade relations which make them interdependent.
Production factors #
The representation of factor markets is also crucial to determine the mobility of production factors across countries and sectors, as well as their availability. There are in MIRAGE three type of factors common to all productive sectors: capital, skilled labour and unskilled labour. Additionally, primary sectors are also dependant on land (agriculture) and natural resources (for mining, forestry and fishing sectors).
- capital_and_investment_dynamics for capital market
- Labour market for skilled and unskilled labour factor
- Land supply and mobility for land
- Natural resources for natural resources issues
Energy-oriented features #
MIRAGE-e has several energy-oriented features:
- The production function is specially tailored for GHG emissions coming from production process
- Specific treatment of energy goods for final and intermediate consumption
- Specific energy efficiency dynamics in the baseline
- Ability to simulate climate change policy simulations, such as the Paris agreement