An input - output approach in analyzing Indonesia’s mineral export policy


Indonesia is a country abundant with natural resources, mainly mineral ore commodities. As of 2017, the mining sector contributes an average of 10% towards Indonesia’s GDP and constitutes around 33.2% of the export value. Evidently, mining and mineral industries play a significant role in the economic growth of the country. The Indonesian government introduced an export ban policy as a subset of the current mining law in order to boost the country’s profits arising from its mineral wealth. The policy places a ban on raw mineral ore exports, except coal, copper, iron ore, lead, and zinc. It also states that such ores must be fully processed and refined before they can be exported. It becomes imperative to understand the responsiveness of the economy as a result of changes in the trade policy. Input-output analysis, a fundamental method of quantitative economics, is applied to determine the potential economic impact of the objectives set out by the export ban. The research assesses Indonesia’s input-output data for the year 2010, focusing on 13 mining-related sectors. Analysis of the input-output table of 26 sectors justifies the Government’s decision of placing a ban on export of raw mineral ore. Application of multipliers in the analysis indicates that exclusion of certain ores from the ban should perhaps be reconsidered. The results also suggest that metals will be a major component of mineral commodities, required for the sustainable economic growth of the country.


Indonesia is endowed with natural resources such as oil, gas, and minerals. Natural resource revenues are critical to the economic growth of the country. Mining and export of mineral form a major part of such revenues. Law on Mineral and Coal Mining No. 4 of 2009 is the regulation in force governing mineral and coal mining activities in Indonesia. As per Article 103 of mining law, Production Operation Mining permit holders (IUP-OP) and Special Mining permit holders (IUPK-OP) are required to process, refine, or smelt their ore domestically (The Government of Indonesia 2009). In other words, this law prohibits exporting mining commodities in raw form. It indicates the Government’s intention to increase and optimize the value of products, supply of industrial raw materials, worker absorption, and state revenues. The export ban covers all raw mining commodities, except coal, copper, iron ore, lead, and zinc.

Article 103 is explicated by Article 170. Article 170 states that the holders of mining permits that have been engaged in production shall conduct refining or smelting activities as directed by Article 103, not exceeding 5 years from the promulgation of the Law. It means that the export ban should have been implemented latest by January 12, 2014.

Since Indonesia has a high dependency on its mining sector, any change in policy regarding mining commodities needs to be analyzed in order to estimate the potential effect on domestic economy.

Mining commodities are economically significant in Indonesia, both domestically and globally. Figure 1 shows that mining commodities constitute an average of 33.2% of the total exports during the period 2000–2017, approximately USD 52.5 billion. This figure also shows the potential economic impact of the export ban. The export value and share in total exports of mining commodities falls in 2009 when the mining law was passed, rises again as the full implementation of the ban was delayed and declines again in 2014.

Fig. 1

Export of Indonesia’s mining commodities during 2000–2017 (Center of International Development at Harvard University, n.d.)

Domestically, as shown in Fig. 2, mining commodities contributed an average of 10% towards the Indonesia’s GDP during the period 2000–2017. A trend similar to export value fluctuations can be observed in GDP contribution of the mining sector. Due to the prevalent mining law and subsequent export ban policy, GDP contribution of the mining sector falls in 2009, rises in the following years, and declines again in 2014.

Fig. 2

Contribution of the mining sector towards GDP (Badan Pusat Statistik 2019)

In a commodity-driven economy like Indonesia, an export ban can also be described as a resource nationalist policy. A resource nationalist policy is part of a protective regulatory that is enforced specifically on natural resources. Resource nationalism is defined as an occurrence when a natural resource–endowed country uses its legal jurisdiction over these resources to achieve some set of national development goals that would otherwise not be obtained if their exploitation were left to international market processes (Wilson 2009). Introduction of a regulation like the export ban emphasizes that a country has ventured into resource nationalism.


This research intends to analyze the current performance of Indonesia’s domestic sectors, particularly mining-related sectors under the light of the pre-implemented export policy. The Government of Indonesia is attempting to harness the mining sector in order to provide better benefit to the national economy. This research also aims to evaluate the potential of the mining sectors to significantly add value to the overall economy of Indonesia. The input-output (I-O) analysis is applied to corroborate the advantages of assessment to public policy along with trade policy. The export ban treads on a thin line between favorable and unfavorable consequences. Therefore, it is important that the research also provides empirical assessment of economic potential of domestic sectors, emphasizing on mining-related sectors before the export ban is fully implemented.

Literature review

An earlier research examines barriers to trade, especially export restriction and its impact on trade and global supply in selected strategic metals and minerals (Korinek and Kim 2010). As Korinek and Kim (2010) have pointed out, few expected repercussions of export restrictions are the inability to fulfill the objective of environmental protection, additional pressure on other exporters with similar export restrictions, and additional risk that could impact investment decisions of potential suppliers.

As a barrier to trade, the export restrictions come in a variety of forms, including quantitative export ban or quota, export taxes, duties and charges, and mandatory minimum export prices. There are several of researches that focus on export restrictions: export restriction as stabilization responses (Abbott 2012), export restrictions in the rare earth element market (Mancheri 2015), export restriction that poses as a barrier to trade in the form of export taxes (Parra et al. 2016). Nevertheless, this research is one of the handfuls of studies outlining export ban of raw materials, particularly focusing on raw mineral commodities.

The policy of banning the export of raw mining commodities is often construed as a resource nationalist policy. A characteristic of resource nationalism is the tendency for states to take (or seek to take) direct and increasing control of economic activity in natural resource sectors (Ward 2009).

On the other hand, resource nationalism could also be explained as the development and introduction of new policy responses by the government that are directed, among others, towards controlling natural monopolies and exerting macro-economic policy influence (Solomon 2012). Bearing this concept, many resource-rich countries implement resource nationalist policies to derive economic benefits from their natural resources and export restriction is one of the most commonly used regulations.

Resource nationalism often gains much public support as an ideological economic project, despite the potential problems and practical operational disputes that may arise (Warburton 2017). The success or failure of the ban remains unquestioned due to majority public support. The assessment of economic impact due to the export ban of raw mining commodities becomes crucial. Then, as we analyze the economic impact of the export ban, we utilize the input-output model.

Input-output (I-O) analysis is one of the most widely used tools in analyzing the economic impact of policy change. I-O analysis is an analytical framework which is based on the structure of an economic system. An I-O model maps the economic structure of a region or a country in a tabular format based on measured quantities of interconnections between economic sectors. There are several advantages of an I-O analysis: the I-O table is based on measurable quantities that can be verified empirically; Leontief multipliers along with the I-O table can analyze the potential impact of public-sector policies and private-sector decisions; I-O analysis is considered neutral, far from political influences, and can be used in any economic system; and I-O is accountable for all inputs that are used in production (Rose 1995). Considering the benefits, I-O analysis can be deemed appropriate for analyzing the impact on policy changes.

Several studies on public policy have been undertaken that use I-O analysis. For example, a novel hybrid I-O analysis is used to quantify impact of energy policies such as feed-in tariffs and power purchase agreement in Portugal (Behrens et al. 2016). Another research uses Japanese I-O table and Asian international I-O table for evaluating the Joint Crediting Mechanism (JCM) policy proposed by the Japanese government to reduce greenhouse emissions (Sugino et al. 2016). In Queensland, Australia, the GRIT technique is used in a research analyzing the impact of coal industry expansion on regional economy and smaller communities by adopting the regional I-O model to local model (Ivanova and Rolfe 2011). However, there are not many studies using the I-O approach in analyzing export policy. This research addresses the gaps observed in the previous literature: (1) lack of relevant I-O-based analysis on export ban policy, and (2) unavailable economic assessment on the export ban of the raw mining commodities.


The basic transaction table used in this research is the input-output table of Indonesia for the year 2010, which is the latest available table. This original I-O table includes 185 intermediate sectors (Badan Pusat Statistik 2015).

One of the intended purposes of this research is to determine whether mining commodities can be optimized to contribute more significantly to the overall economy of Indonesia. In order to place emphasis on the analysis of mining-related sectors, we consider it essential to aggregate the basic transaction table. An aggregation is necessary to have a reasonably comparable number of sectors to perform I-O analysis, and still be capable of comparing it with other sectors without losing the overall economic structure. Analyzing all 185 sectors will be counterproductive and can deter the intent of the research.

Out of the 185 sectors, 26 sectors are mining related. We use the similarity to classify and aggregate the intermediate sectors. In order to maintain analytical sharpness, we keep the aggregation to a minimum for mining-related sectors. As a result, the main mining commodities are coal, oil & gas, iron sand & iron ore, tin ore, bauxite ore, copper ore, nickel ore, gold ore, silver ore, other metals, and other minerals. However, we do not categorize stone as a separate commodity group as its contribution towards overall export value is relatively less; instead, it is grouped under other minerals. We also have the mining service sector which covers services particularly catering to the mining industries. Most of the aggregation of mining-related sectors occurs in the “miscellaneous processed mining products” sector—sectors that use raw minerals to produce intermediate and final goods. It consists of sectors that use raw minerals as input in their production such as metalwork, guns & ammunition, machinery, and other similar industries. Totally, we have 13 mining-related sectors.

Further, we maintain the comparability of our aggregated I-O table by including 13 non-mining-related sectors. These sectors are also classified based on similarity; conventional aggregation is followed without changing the basic structural relationships among the intermediate sectors, both mining and non-mining related. The sectors and their corresponding code numbers are stated in Table 1. Leontief inverse table is then calculated based on the I-O table for 26 sectors.

Table 1 Code of sectors for aggregated input-output table

The input-output (I-O) table is then analyzed using multiplier effects. The multipliers measure the possible effects of changes. These are output multiplier, income multiplier, and employment multiplier. These multipliers align with the objectives of the export ban. The core of these multipliers is in line with the objectives of the export ban so they can be used applied to measure potential impacts. The output multiplier (MOj) indicates total output of all the industries that is necessary to produce one additional unit of output, when there is one unit increase in final demand. This multiplier is calculated as the column sum of the Leontief inverse matrix:

$$ {\mathrm{MO}}_j={\sum}_i{L}_{ij} $$

where Lij is the rate of total gross output.

Income multiplier (MIj) shows how one monetary unit change of income from employment in each industry increases the total income from employment. This multiplier is calculated as

$$ {\mathrm{MI}}_j=\raisebox{1ex}{${\sum}_i{v}_i{L}_{ij}$}\!\left/ \!\raisebox{-1ex}{${v}_j$}\right. $$

where vi,j is the rate of labor compensation. Labor compensation indicates compensation that a certain sector uses as input in its production process. In other words, the income multiplier is calculated as the ratio of labor compensation and total output for each industry.

The last multiplier used is the employment multiplier (MEj). It is calculated based on full-time employment:

$$ {\mathrm{ME}}_j=\raisebox{1ex}{${\sum}_i{w}_i{L}_{ij}$}\!\left/ \!\raisebox{-1ex}{${w}_j$}\right. $$

where wi,j is calculated as the full-time equivalent of employment per IDR (Indonesian Rupiah) of the total output of each industry. It measures the total increase in employment when there is a unit increase in final demand. Basically, the income and employment multiplier uses a similar approach, which is measuring the economic impact on households. The difference is that the income multiplier measures the impact in monetary terms that is earnings, and the employment multiplier measures the impact in physical terms that is jobs (Miller and Blair 2009). However, unlike output and income multipliers, the employment multiplier is calculated using full-time employment data in addition to the I-O table. Since full-time employment data is not broken down corresponding to the sectors in the I-O table, the employment multiplier is calculated according to full-time employment data.

Using the I-O table, a gross value added can be calculated using a similar approach with the income multiplier. The gross value-added multiplier is calculated as

$$ {\mathrm{MG}}_j=\raisebox{1ex}{$\sum \limits_i{g}_i{L}_{ij}$}\!\left/ \!\raisebox{-1ex}{${g}_j$}\right. $$

where gi,j is the rate of gross value added. In other words, the value-added multiplier is calculated as the ratio of gross value added and total output for each industry.

Results and discussion

Analysis of the I-O table of the 26 sectors suggests that only sectors relating to the mining industry show a positive trading-balance value. As shown in Fig. 3, all the mining commodity sectors have positive trading values, with the exception of iron sand & iron ore, gold ore, and other mineral sectors which have a negative value. Positive value means that the export value of these sectors is greater than their import value. These findings justify the Indonesian Government’s disposition to maximize the benefits that can be derived from the mining industry. However, other mining-related sectors and miscellaneous processed mining products show a negative trading balance value at Rp31 trillion, and mining services have zero trading balance.

Fig. 3

Trading balance of the 26 sectors

In terms of gross value added, the manufacturing sector is the largest at Rp1,396 trillion, compared with the industry average of Rp257 trillion in value. However, the large trading values of the mining-related sectors are not occurred in their gross added value. Figure 4 shows that among mining commodity sectors, the oil & gas industry produces the highest gross added value at Rp316 trillion. However, the oil & gas industry is often excluded from the mining sector in Indonesia. Therefore, the gross added value of coal sector, although below oil & gas, is significant at Rp156 trillion in value. The low gross added value of other mining commodities indicates that currently they are being are exported in raw form, barely being processed domestically.

Fig. 4

Gross value added of the 26 sectors

The I-O model applies multipliers that can be used to estimate economy-wide effects that an initial change in economic activity has on a regional level (Bess and Ambargis 2011). According to our calculations, the average value using the output multiplier for 26 sectors is 1.71. The utility sector has the largest output multiplier value at 2.72, and the bauxite ore sector has the smallest value. However, isolating the mining sectors, the average value of output multiplier drops to 1.54. The iron sand & iron ore sector has the largest value in mining-related sectors. It indicates that one unit increase in final demand of the iron sand and iron ore sector will increase the total value of overall economic production by 184%.

The average value of 26 sectors applying the income multiplier is 0.20. The copper ore sector has the highest income multiplier value, followed closely by the governance sector. Evidently, these sectors bring more employment monetary impact. One unit increase in income from employment in the copper ore sector will increase total income of employment by 57%. On the other hand, the real estate sector has the lowest income multiplier of 0.05. In terms of mining-related sectors, the average value drops slightly to 0.19. Additionally, several mining commodity sectors have lower income multiplier values, far below the average.

Unlike output and income multipliers, the employment multiplier is calculated using full-time employment data in addition to the I-O table. However, since full-time employment data is not distributed correspondingly to the sectors of the I-O table, the employment multiplier is calculated according to the full-time employment data available for different categories of sectors. The agriculture sector has the highest employment multiplier. As shown in Table 2, the average employment multiplier value is 0.041. However, the mining sector comes second as the smallest employment multiplier at 0.001. It indicates that the mining sector does not have enough impact in terms of employment, regardless of its significant influence in other areas of Indonesia’s economy.

Table 2 Employment multiplier

Based on the value-added multiplier, the average value for the 26 sectors is 0.64. At 0.94, the bauxite ore sector has the largest value. One unit increase in final demand of the bauxite ore sector will increase the total gross value added by 94%. On the other hand, the utility sector has the smallest value-added multiplier, indicating that this sector does not affect the value of its inputs. Isolating mining-related sectors, the average value stands at 0.73.

As shown in Fig. 5, output, income, and value-added multipliers are compared with each other; however, the employment multiplier is omitted since it does not have a corresponding sector value. Overall, the mining-related sectors display better economic performance in terms of value added. However, as the Government of Indonesia intends to effectively utilize the mining sector to boost economic growth, we perform an in-depth analysis of the mining sectors. It can be observed that ore commodities have a relatively higher output multiplier, with bauxite ore being on the lower side.

Fig. 5

Comparison of multipliers for 26 sectors

We use a hypothetical situation to get a better understanding of the economic multiplier–based impact analysis. As the government implements export ban, it is mandatory to process the raw mining commodities domestically. It means that the processing sectors and the other intermediate sectors must absorb raw mining commodities, thus increasing the intermediate demand. The average GDP of Indonesia for the years 2000–2017 is USD 573 billion, and the average GDP growth is 5% (The World Bank 2018). The mining commodities contribute 10% towards the GDP. Let us suppose that the value, which is USD 3 billion, reflects the additional demand that occurs when the Government implements the export ban. The impact of the export ban on GDP consists of output impact, employment impact, and value-added impact. The additional demand is contributed through the intermediate sectors based on technical coefficients, and the related multipliers are used to calculate the impact on GDP. Therefore, when there is additional demand worth USD 3 billion resulting from the implementation of the export ban, the total output impact on GDP is USD 15 billion, the total employment impact is USD 5 billion, and the total value-added impact is USD 53 billion.


The trading balance and the gross-added value analyses further accentuate the fact that the substantial export value of the mining commodities indeed comes from raw commodities. Therefore, the Government needs to put extensive effort in improving the domestic performance of these mining commodities.

Assessing the endogenous point of view of the export ban showcases the performance and relation among the intermediate sectors of Indonesia’s economy. The multiplier analysis provides interesting findings. The iron sand & iron ore sector has the highest output multiplier value. On the other hand, though the bauxite ore sector has the smallest output multiplier value, it has the highest value-added multiplier value. Also, the nickel ore sector has one of the highest output multipliers, but it also has the lowest input multiplier value. These findings show that the commodities which are to be developed first are greatly dependent on what the government is trying to achieve. The mining commodities evidently have moderate ability to increase revenue due to change in demand, and an enormous ability to increase the value of their outputs. However, these sectors cannot be relied on to improve employment, in either physical or monetary terms.

This research further demonstrates that the input-output analysis is a useful and substantial tool in analyzing the impact of changes in export policy, especially implementation of the export ban on the raw mining commodities. The assessment of the I-O model allows us to examine the current economic performance of each intermediate sector, as well as the inter-relation among different sectors. Moreover, the main focus of our analysis is on the mining commodity sector, which enables us to determine that the export ban policy has the potential to achieve the intended purposes of increasing and optimizing the value of mining commodities. We also conclude that the exception of copper, iron ore, lead, and zinc from the export ban should be reconsidered, particularly iron ore and copper ore since these commodities have the potential to utilize change in demand resulting from the export ban.

When the Government insists on applying an export ban for other raw mining commodities, it also needs to ensure that the processing and refining of bauxite ore, nickel ore, and other metals will be able to provide to optimal added value for these commodities. Also, development of the iron sand & iron ore sector would bring more benefit to the economy since it would generate more productivity from other sectors.


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Correspondence to Rini Novrianti Sutardjo Tui.

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Tui, R.N.S., Adachi, T. An input - output approach in analyzing Indonesia’s mineral export policy. Miner Econ 34, 105–112 (2021).

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  • Input-output analysis
  • Multiplier effects
  • Economic impact assessment
  • Mineral commodities
  • Export policy