The comparative-static version of the computable general equilibrium (CGE) model GTAP developed by Purdue University in 1993 is used to analyse the economic impact of the export ban. It is widely used to examine the macroeconomic impact of trade policy (Kawasaki, 2024; de Menezes, Countryman, Agerman, & de Miranda, 2024; Ban & Fujikawa, 2023; (Ha et al., 2017); (Haddad et al., 2024); (Nantembelele et al., 2023); Nurdianto & Resosudarmo, 2016 and (Qiao et al., 2023)). The GTAP model is a multi-region and multi-sector CGE model that assumes perfectly competitive markets with constant returns to scale and bilateral trade under the Armington assumption (Saini, 2012). According to (Campoamor et al., 2018), every GTAP model consists of four main components: a) a database with information on social accounting matrices, input–output, taxes and trade flows, providing the necessary input information for the subsequent impact analysis; b) a mathematical model that mimics the workings of the world economy, integrated by equations linked to producers’ cost minimisation, consumers’ utility maximisation and market clearing conditions; c) macroeconomic closure conditions, which differentiate between endogenous and exogenous variables; and d) data on elasticities of substitution among primary factors, between domestic and imported goods and between imports from different geographical sources. For more details on the GTAP model, see (Hertel & Tsigas, 1997).

The most recent GTAP version 9 database was used for the modelling simulations of the Indonesian export ban (Aguiar et al., 2016). The GTAP version 9 database has three reference years, namely, 2004, 2007 and 2011, and it already aggregates 140 regions and 57 sectors. For the new reference years, domestic databases are combined with international datasets on macroeconomic aggregates, bilateral trade, energy, agricultural input–output and protection (Aguiar et al., 2016).

We further aggregate based on the relevant region and sector, in accordance with the objective study of assessing the economic impact of Indonesia’s minerals export ban. By region, we will use aggregation data from 12 countries, where Indonesia implements a mineral export ban and is the world’s largest minerals exporter. Moreover, we aggregate 10 other countries as Indonesia’s largest minerals export destination: Japan, China, India, Korean, Malaysia, Singapore, Brazil, the United Kingdom, the United States, and the European Union. Meanwhile, the remaining countries are classified as part of the Rest of the World (ROW). Furthermore, based on research needs, the original 57 sectors were re-combined into four sectors: minerals n.e.c. containing minerals or mining of metal ores, manufacturing, transportation, and others. Table 1 provides a summary of regional and sectoral aggregation.

In this study, we adapted Burfisher’s (2016) approach to export quantity control. The export ban is represented by swapping the export tariff variable with the export quantity variable. By endogenising the export tariff and exogenising the export quantity, the model can zero out the export flow. scenarios or simulations are used in this study to assess the economic impact of Indonesia’s mineral export ban:

1. Simulation 1 (SIM1): Indonesia stopped 50% of minerals exports to Japan, China, India, Korean, Malaysia, Singapore, Brazil, UK, USA, EU-28 and ROW.

2. Simulation 2 (SIM2): Indonesia stopped 100% of minerals exports to all regions Japan, China, India, Korean, Malaysia, Singapore, Brazil, UK, USA, EU-28 and ROW.

Figure 1 depicts the simulation results of the CGE modelling of the mineral export ban on changes in Indonesia’s and other countries’ real GDP. This mineral export ban raises Indonesia’s real GDP by 0.54% and 1.07% in the 50% and 100% ban scenarios, respectively. This is consistent with the findings of Tui and Adachi (2021) from the previous IO analysis. However, this export ban resulted in a decrease in real GDP, particularly in Indonesia’s largest mineral-importing countries, such as Japan (-0.05% and −0.10%), China (−0.06% and −0.13%), India (−0.05% and −0.09 %), Korea (−0.13 and 0.27%) and Singapore (−0.01% and −0.02%).

This section examines the impact of the ban on the overall welfare of Indonesia and importing countries. In the GTAP model, welfare changes are measured using the concept of equivalent variations (EV). EV measures how much the average consumer must be compensated to be as well-off as before the trade policy measures were implemented (Kutlina-Dimitrova, 2017).

This section discusses the impact of the export ban on Indonesia’s TOT and importing countries. TOT reflects a comparison of two countries’ relative prices of goods. This TOT depicts a product’s or country’s competitiveness. The simulation results show that the export ban increases Indonesia’s competitiveness by 0.02% and 0.04% for the two SIM1 and SIM2 scenarios, respectively. The decision to prohibit mineral exports gives the Indonesian government impetus to improve and better prepare for the downstream metal industry. This enhancement is expected to boost the image of industrial metal products on the global market. Major importing countries such as China, India and Japan saw a drop in competitiveness, albeit a very small one, less than 0.01%.

Table 4 provides the simulation results of the CGE modelling of the mineral export ban on imports by sector. The impact of the import ban, particularly in the mineral and manufacturing sectors, has been significantly reduced. The mineral sector fell by −4.95% and −9.91%, whereas the manufacturing sector decreased by −0.38% and −0.75%, This demonstrates that the export ban was successful in reducing Indonesia’s import dependence, particularly on mineral and manufactured products.