metadata: authors: 0: author: Shaukat Khan,Tehmina
1: author: Gottschalk,Jan
count: Mongolia
docna: 0: docna: Investigating the transmission channels
            behind Dutch disease effects: lessons from Mongolia using a
            CGE model
docty: Policy Research Working Paper
subtopic: Mining & Extractive Industry (Non-Energy),Food Security,Economic Development,Macroeconomic Management
abstracts: cdata!: This paper uses a computable general
            equilibrium model -- Maquette for Millennium Development
            Goal Simulations (MAMS) -- calibrated to Mongolia to
            investigate how the development of major mining projects
            leads to Dutch disease. The simulations suggest that the
            process is complex, with the relative strength of the
            different spending and resource movement channels determined
            by structural features of the economy, such as factor input
            needs of the mining sector and substitution elasticities,
            and how mineral windfalls are eventually spent. In Mongolia,
            mining sector demand for domestic factor inputs explains
            two-thirds of the appreciation of the real exchange rate,
            with demand for labor, a "quasi-fixed" factor, the
            most potent channel for transmitting Dutch disease. The
            simulations also suggest that public policies may only play
            a limited role in limiting Dutch disease, even if growing
            fiscal revenues are channeled toward productivity-enhancing
            public investment rather than public consumption or lower
            taxes. This finding suggests that policy makers face real
            trade-offs, namely that, as an equilibrium response, Dutch
            disease is unavoidable and at odds with an export-led,
            manufacturing-oriented development strategy unless resources
            are left in the ground (or mining earnings are saved
            abroad). If the objective is to limit Dutch disease, then
            the simulations point to policies that minimize the usage of
            domestic inputs by the mining sector, or that accommodate
            the growing demand for key inputs such as labor e.g. through
            immigration. Regarding spending, policy makers should
            channel mining revenues toward public investment, to expand
            the economy's long-run supply potential. Where large
            direct income flows from the mining sector to households are
            important, monetary policy may be more useful than fiscal
            policy in constraining private spending.
geo_reg_key: 517191,119225,517198
repnme: repnme: Investigating the transmission channels
            behind Dutch disease effects: lessons from Mongolia using a
            CGE model
display_title: Investigating the transmission
            channels behind Dutch disease effects: lessons from Mongolia
            using a CGE model
listing_relative_url: /research/2017/09/27960256/investigating-transmission-channels-behind-dutch-disease-effects-lessons-mongolia-using-cge-model
topicv3: Agriculture and Food Security m1326205 435,Mining Services m1326410 1701,Mining Sector m1327113 554,Maquette for MDG Simulations m1504536 3114,Dutch Disease m1583679 4708
publishtoextweb_dt: 2017-09-06T00:00:00Z
no_of_pages: 39
author_keys: 0: author_key: 1071309
model_extractions: WPS8183 Policy Research Working Paper 8183 Investigating the Transmission Channels behind Dutch Disease Effects Lessons from Mongolia Using a CGE Model _Tehmina S. Khan Jan Gottschalk_ Macroeconomics and Fiscal Management Global Practice Group September 2017
Policy Research Working Paper 8183

# **Abstract**

This paper uses a computable general equilibrium model —Maquette for Millennium Development Goal Simulations (MAMS)—calibrated to Mongolia to investigate how the development of major mining projects leads to Dutch disease. The simulations suggest that the process is complex, with the relative strength of the different spending and resource movement channels determined by structural features of the economy, such as factor input needs of the mining sector and substitution elasticities, and how mineral windfalls are eventually spent. In Mongolia, mining sector demand for domestic factor inputs explains two-thirds of the appreciation of the real exchange rate, with demand for labor, a “quasi-fixed” factor, the most potent channel for transmitting Dutch disease. The simulations also suggest that public policies may only play a limited role in limiting Dutch disease, even if growing fiscal revenues are channeled toward productivity-enhancing public investment rather than public consumption or lower taxes. This finding suggests that policy makers face real trade-offs, namely that, as an equilibrium response, Dutch disease is unavoidable and at odds with an export-led, manufacturing-oriented development strategy unless resources are left in the ground (or mining earnings are saved abroad). If the objective is to limit Dutch disease, then the simulations point to policies that minimize the usage of domestic inputs by the mining sector, or that accommodate the growing demand for key inputs such as labor e.g. through immigration. Regarding spending, policy makers should channel mining revenues toward public investment, to expand the economy’s long-run supply potential. Where large direct income flows from the mining sector to households are important, monetary policy may be more useful than fiscal policy in constraining private spending.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tkhan@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Investigating the Transmission Channels behind Dutch Disease Effects: Lessons from Mongolia Using a CGE Model**

Tehmina S. Khan and Jan Gottschalk<sup>1</sup> JEL: D57, D58, E60, O53, Q33 Keywords: CGE, MAMS, Dutch disease, Mongolia, Natural Resources

> 1 Tehmina S. Khan is from the Macro-Fiscal Management Global Practice (MFMGP) and Jan Gottshalk is at the International Monetary Fund, Washington DC. Contact author: tkhan@worldbank. The views expressed are those of the authors and do not necessarily reflect those of the World Bank Group or the IMF. The authors are particularly  grateful to Hans Lofgren for detailed comments, and to James Anderson, Rogier Van Den Brink, Steve Barnett and Julia Bersch for many helpful comments and suggestions.
# **Contents**

|Contents ........................................................................................................................................................ 1|
|---|
|I.<br>Introduction ........................................................................................................................................... 1|
|II.<br>Model framework .................................................................................................................................. 5|
|Baseline scaling-up scenario: Mining output doubles and the mining sector is fully integrated into the<br>economy .................................................................................................................................................... 6|
|Reference scenario: Mining output doubles but mining sector is not integrated into the economy, i.e., is<br>an enclave sector ....................................................................................................................................... 7|
|III.<br>Transmission Mechanisms Leading to Dutch-Disease Effects ......................................................... 9|
|The role of domestic production factors in the scaling-up of mining production ..................................... 9|
|a)<br>Using only intermediate inputs as domestic production inputs ..................................................... 9|
|b)<br>Using only labor as domestic production factor ......................................................................... 13|
|c)<br>Using only capital as domestic input .......................................................................................... 16|
|d)<br>Combined effect of domestic inputs ........................................................................................... 18|
|The role of domestic spending of mining receipts in the scaling-up of mining production .................... 20|
|a)<br>Government spending of mining receipts ................................................................................... 20|
|Spending additional government revenues on public consumption ................................................ 22|
|Spending additional government revenues on tax reductions ......................................................... 23|
|Spending additional government revenues on public investment ................................................... 24|
|<br>b)<br>Private sector spending of dividends from the mining sector ..................................................... 25|
|IV.<br>Lessons ............................................................................................................................................ 29|
|Complexity of the transmission channels ........................................................................................... 29|
|Equilibrium outcomes ......................................................................................................................... 29|
|Policy implications .............................................................................................................................. 30|
|Appendix A: Mongolia Social Accounting Matrix ................................................................................. 34|
|Appendix B: Macro Indicators in the BASE (Business-As-Usual) case. .............................................. 35|
|Appendix C: Macro indicators in the baseline (mining sector doubles in size) ...................................... 36|

1
# **I. Introduction**

Dutch disease, a term originally used by The Economist in 1977 to describe the appreciation of the Dutch guilder after oil was after the start of North Sea oil exports, refers to the macroeconomic effects of natural resource windfalls. Following seminal papers by Corden and Neary (1982), Corden (1984), and Van Wijnbergen (1984), economists have recognized a factor of production _movement_ effect and a _spending_ effect as key elements of the disease.<sup>2</sup> With government spending of resource revenues also playing an important role in transmitting Dutch disease to the economy, a large literature focused on appropriate fiscal policies in resource rich countries has also emerged (Van der Ploeg and Venables, 2012; Barnett and Ossowski, 2002; Davis et al. 2002).

Dutch disease is potentially relevant to Mongolia, where natural resource dependence has grown fast over the past decade-and-a-half. Mineral wealth is estimated at $1 trillion to $3 trillion for a population of just 3 million (IMF, 2015). As major mining projects – notably, the giant copper-gold Oyu Tolgoi (OT) mine<sup>3</sup> – have come onstream, growth has surged, making the country one of the fastest growing in the world in recent years. Mining exports account for more than 90 percent of total exports and nearly a fifth of government revenues, leaving the economy highly vulnerable to commodity price volatility. Despite the adoption of a fiscal rule in 2010, fiscal policy has remained pro-cyclical and the public sector has become a conduit for Dutch disease to the economy in recent years. Public spending has expanded strongly, and Mongolia is perhaps the only country in the world which has employed a direct resources-to-cash scheme – operating a universal cash transfer scheme between 2010 and 2012 (Ying and Howes, 2015). Arguably, these policies came up against to domestic absorption constraints – regarded as a key factor in why developing countries experience problems with resource windfalls (Van der Ploeg, 2012; Van der Ploeg and Venables, 2010) – that are visible in the form of structurally high inflation and large (non-mining) trade deficits.

Looking beyond fiscal policy, private spending of income from the mineral sector has emerged as another channel of transmission for Dutch disease. In 2012, the government allotted a notional 20 percent of the equity of the state-owned mining company, Erdenes Tavan Tolgoi (TT),<sup>4</sup> to the population. Prior to elections in mid-2012, it monetized the value of these shares by offering the public a choice of selling their stake back to the government if they did not wish to wait for the public listing of Erdenes TT. Approximately half the population – about 1.2 million people – elected to receive cash for their shares, with the remainder opting to retain shares.<sup>5</sup> It is estimated that 1.5 million Mongolians still own shares in Erdenes TT, and in June 2016, the Mongolian PM announced plans to buy another 30 percent of the shares from the public.<sup>6</sup> This study analyzes the transmission mechanism behind Dutch disease effects within a computable general equilibrium (CGE) framework. Specifically, the study examines what are most important channels by which Dutch disease is transmitted to the Mongolian economy when the mining sector scales up in size and the

> 2 The literature has also expanded to include similar effects from other shocks such as capital inflows, aid, and remittances and tourism.  See Auty, 2001; Adams and Bevan, 2003; and Acosta et al, 2009. Magud and Sosa (2010) also provide a comprehensive overview of the analytical literature and associated empirical evidence.

> 3 The OT mine is one of the largest copper-gold mine complexes in the world. It is expected to produce 0.5 million tons of copper, 0.65 million ounces of gold, and 3 million ounces of silver a year, on average, during its first 10 years. Escondida, the world’s largest copper mine located in Chile, in comparison, produced 0.81 million tons of copper in 2011 and 1.09 million tons in 2010. 4 Erdenes Tavan Tolgoi (Erdenes TT) is the subsidiary of Erdenes MGL a government-owned company that owns Tavan Tolgoi (TT). TT is one of the world’s largest untapped coking and thermal coal deposits, and is divided into six sections the largest two of which are referred to as the Tsankhi coalfields. Erdenes TT manages the development of the East Tsankhi block.

> 5 IMF (2011) and Van den Brink et al. (2012).

> 6 http://theubpost.mn/2016/06/13/cabinet-to-buy-back-erdenes-tavan-tolgoi-shares-from-the-public/.

2
associated policy implications. It uses model-based analysis to gain conceptual clarity on these issues. The drawback is that such an analysis is necessarily highly stylized and therefore model results provide only a limited degree of realism. That is, the simulation results presented here for Mongolia should be seen as sketching out the underlying processes, with individual numerical results providing qualitative insights but not quantitative precision.

Regarding our modeling strategy, a CGE framework is preferred as it allows us to take into account complex interactions across time, across different factor inputs and a multitude of sectors in the economy. In contrast, DSGE models that are frequently used to model Dutch disease in existing literature are more limited with respect to the number of variables that can be considered and the degree to which economies/sectors can be disaggregated. The paper is accompanied by a background paper that benchmarks a CGE model for Mongolia against actual developments in the economy during the 2006-09 commodity boom-bust period (Khan and Gottschalk, 2017). That paper shows that a CGE framework is able to approximate macroeconomic impacts associated with commodity price fluctuations, but that it underestimates macroeconomic demand pressures, in part due to the inherent complexity of commodity boom-and-bust cycles and the long-term focus of MAMS simulations in which economic adjustment to shocks takes time.

The paper proceeds as follows. It calibrates the World Bank’s CGE model MAMS (“Maquette for Millennium Development Goal Simulations”) to Mongolia. The model provides an economy-wide multisectoral framework for medium-to-long-run analysis.<sup>7</sup> Baseline simulations are set up to benchmark the impact of a doubling of mining sector output between 2013 and 2016, with simulation results reported until 2026. In these simulations, all transmission channels for Dutch Disease are allowed to operate, i.e., the mining sector is fully integrated into the economy, drawing upon available factor inputs and generating income for households and the public sector. The simulations show that a doubling in mining production results in an appreciation of the real exchange rate of 15 percent and in a contraction of tradable sector output by approximately 20 percent.<sup>8</sup> These two variables are considered to be key indicators of Dutch disease effects.

A key issue that needs to be clarified at the outset relates to the role of foreign direct investment (FDI) for Dutch disease effects. Arguably, none of the Dutch disease effects described above would have happened if it had not been for large FDI projects that build the major mining projects in the first place. This study attempts to dig deeper and show that what really matters for Dutch disease effects is (i) to what extent these projects utilize domestic inputs, and (ii) to what extent natural resource revenues generated by them are spent domestically. To illustrate this, the study constructs a reference scenario where FDI projects bring about a major expansion in mining but the rest of the Mongolian economy remains completely insulated as long as the mining sector is a perfect enclave that utilizes no domestic inputs (i.e., all production factors and inputs are imported) and all income generated by the project accrues to foreigners and never enters the Mongolian economy. Our simulations show that Dutch disease effects arise only as the enclave character of the mining sector is loosened and spillovers to the domestic economy occur.

Finally, alternative scenarios are set up to gauge the relative importance of individual transmission channels, including the use of domestic factor inputs (intermediate, labor and capital); government spending of income from the mining sector on public consumption, investment and tax cuts; and household spending of dividend income from the mining sector.

> 7 See Lofgren et al. (2013) for a description of the basic features of MAMS. 8 Again, these results should be seen qualitatively.

3
To our knowledge, this is the first study to unpack the channels through which Dutch disease is transmitted in such extensive detail. The simulations provide the following insights:

- The use of domestic inputs (versus spending of revenues by the government and households) by the mining sector seems to explain about two-thirds of the total simulated real appreciation, and 80 percent of the shrinkage of the tradable sector that occurs in the baseline.

- Unpacking the relative importance of domestic input, the use of labor by the mining sector is the most potent channel for transmission of Dutch disease. This is reflected in a larger shrinkage of the traded sector and larger real appreciation, accounting for just over a third of the total appreciation that occurs in the baseline (capital inputs account for less than 10 percent). This reflects the fact that labor is a “quasi-fixed” factor that grows only in line with population growth. In contrast, domestic capital inputs can be “endogenously” increased over time through higher savings and investment. This points to a role for active labor force policies (immigration, or increases in labor force participation rates) in reducing Dutch disease pressures.

- Rising household incomes due to rising wages can have positive or negative effects. Higher household incomes are partly spent on goods and services (reinforcing Dutch disease), but are also partly saved, thus helping to finance investment and helping to increase the economy’s capital stock. On aggregate, though it seems they are positive in our simulations.

- The impact of government spending on Dutch disease effects depends critically on the additional revenues the government receives and how they are spent. Simulations suggest a sizable (or onefor-one) fiscal impact from spending higher mining revenues on public consumption or tax cuts. In the case of rising public consumption, simulations also point to some crowding out of private consumption. When revenues are spent on public investment, the real appreciation is minimal in the short run (due to the higher import content of public investment). In the long run, the real exchange rate depreciates because higher public investment raises productive capacity of the economy. However, when all channels are operational, government spending on public investment alone is insufficient to reverse Dutch disease impacts from the other channels.

- The impact of spending of dividend income by households on the tradable sector is similar in magnitude to those individual scenarios where rising government revenues are spent on public consumption and tax reductions. This suggests that rising spending on goods and services has approximately the same impact on tradable sector production, irrespective of the source of spending.

The results imply a number of policy lessons. First, Dutch disease is complex even in a highly stylized model setting: myriad prices change in the economy, and multiple channels are at work when mining output is scaled up. Which channel of transmission dominates depends critically on the structural features of the economy including factor elasticities, the intensity by which mining uses certain inputs and their import shares, and the size of revenues flowing to the government.

The results also suggest that Dutch disease is unavoidable and inherently at odds with an export-led development strategy. On the production side, limiting Dutch disease impacts would require minimizing domestic inputs into the mining sector as it scales up production, or encouraging faster accumulation of the needed inputs. As discussed above, “labor” is arguably the most potent channel in the simulation: unlike other factor inputs that can be endogenously increased over time (for instance capital inputs through greater savings), labor can only expand in line with population growth. Accordingly, it remains a bottleneck over the long term, driving appreciation of the real exchange rate and causing the non-resource traded sector to shrink as the latter donates labor to the other sectors. This suggests a role for labor force policies that encourage greater labor force participation or immigration.

4
On the spending side, limiting Dutch disease impacts will require focusing on activities that have a high import content and that boost the overall supply potential of the economy, especially that of the tradable sector. This likely means focusing spending on public investment. If household spending of mining income is also an important channel of transmission, then there may be a role for tighter monetary policy to reduce the potency of this channel.

The rest of the paper is organized as follows. Section II summarizes the key features of MAMS and the calibration strategy for Mongolia with the underlying structure of the economy. Section III analyzes the results for a set of simulations designed to explore the relative importance of the spending and resource movement channels through which Dutch disease occurs in Mongolia. Section IV discusses the main policy conclusions.

# **II. Model framework**

MAMS is a recursive dynamic CGE model developed at the World Bank to analyze strategies for achieving MDGs, and for undertaking country-level, medium-to-long-run policy analysis (Lofgren et al., 2013). The model is dynamic recursive in that the solution in any time period depends on current and past periods, not the future. Only relative prices matter and these help to clear the markets for factors, goods, and services. As part of the micro foundations of the model, producers maximize profits and consumers maximize utility.

The calibration strategy for Mongolia is detailed in the companion paper (Khan et al., 2017) and this section only describes the broad framework. The underlying Social Accounting Matrix (SAM) that sets out the basic relationships and income flows in the economy was calibrated using publicly available 2005 data from the National Statistical Office (NSO), notably input-output tables for that year. These data were supplemented with other NSO data on employment, balance of payments, and public finances.

In the Mongolia CGE model, there are seven sectors (i.e. activities, each of which produces a specific good or service), five of which are in the private sector (Table 1). In addition, there ae three institutions: a single representative household, the government and the “rest of the world” (ROW). None owns a sector or industry, but rather earns income from a share of the relevant production factors (labor, capital and mining capital). For the household, the income sources also include transfers from the government and remittances from overseas, while the government also earns revenues from indirect and direct taxes. The SAM is set out in Appendix A.

Production technologies are nested. At the top level, output for each sector is generated by aggregates of value added and intermediates. At the lower level, these aggregates are produced by a CES aggregation of production factors and a Leontief aggregation of intermediate inputs, respectively. The mining sector is a special case in that factor use also is determined by a Leontief formulation. Factor inputs other than the mining factor are paid market wages; the mining factor (“mining capital”) is paid the residual. Overall labor supply is determined by exogenous population growth, so that labor is a relatively scarce factor. Finally, the selection of production, trade, input and demand elasticities is set to match actual economic developments as closely as possible (see Khan et al. 2017).

The choice of mechanisms for maintaining internal and external balances are as follows. With respect to the budget, spending adjusts in response to revenues, with additional revenues spent on government consumption (unless specified otherwise as is the case with some individual simulations that explored later). Private investment is kept constant as a share of total absorption while private saving adjusts to provide needed financing. A flexible real exchange rate clears the balance of payments. The numéraire is the CPI so that nominal values like wages, household income, household expenditures are CPI-indexed.

5
**Table 1: Model Disaggregation**

|**Sectors (Activities and**<br>**Commodities**|Non-government (5)<br>Government (2)|Agriculture<br>Construction<br>Private Traded Sector<br>Private Non-traded<br>Mineral<br>Public Infrastructure services<br>Other government services|
|---|---|---|
|**Factor Inputs**|Primary Factors (3)<br>Wholly Imported factors (2)|Labor<br>Private Capital<br>Mining Capital<br>Fuel<br>Intermediate Mining Capital|
|**Institutions**|Institutions (3)|Household<br>Government<br>Rest of the World (ROW)|

# **Calibration strategy**

To analyze the channels by which a scaling up of the mineral sector causes Dutch disease, three key scenarios are developed.

- **“BASE” (business-as-usual** ). This simulation assumes that, in the absence of a structural expansion in the mining sector, the economy grows at an annual rate of 5 percent. Selected results for key macroeconomic indicators are shown in Appendix B and show a continuation of long run trends.  The GDP shares of key macroeconomic variables remain broadly constant over time. The long run growth rate for government consumption is the same as government investment. The real exchange rate is broadly stable. As the economy grows, factor payments received by each of the factor inputs increase over time, but wages increase sharply over the simulation period (2005-2026), reflecting the fact that it is a relatively scarce factor. Other details of the calibration strategy are outlined in Khan et al. (2017).

- **“Baseline scaling up” simulations:** In this scenario, the OT mine starts to produce in 2013 with mining output doubling by 2016. In this scenario, the mining sector is fully integrated into the economy, using labor, capital and intermediate inputs, and generating income for the government and households.

- **“Reference” scenario:** In this scenario, mining becomes **_a perfect enclave sector_** , so that even as mining output doubles, there is **_no impact on the economy_** at all **.** With this scenario in hand, the different channels through which Dutch disease occurs are switched on, one-by-one, in order to examine their effects in isolation.

Since the main scenarios of interest are the scaling up of the mining sector, and the individual reference scenarios, the following sub-section sets out how these are calibrated, and their impacts.

# **Baseline scaling-up scenario: Mining output doubles and the mining sector is fully**

# **integrated into the economy**

The scenario assumes that OT production starts in 2013, with the mine reaching full capacity by 2016. Production increases are gradual between 2013 and 2016. By 2016, production doubles relative to 6
<!-- Start of picture text --> 120% —Baseline scaling up<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 5% —Tradeable sector-<br>0% baseline scaling up<br>—Real exchange rate -<br>5% baseline scaling up<br>-10%<br>15915%<br>-20%<br>-25%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
- _Use of domestic production factors_ : scaling up mining production will require domestic production inputs such as domestically-produced intermediate inputs, domestic labor, or domestic capital, which lead to Dutch-disease effects.

- _Domestic spending of mining receipts_ : scaled-up mining production will lead to higher incomes for the government and domestic households, and part of this additional income will be spent; the resulting increase in domestic demand could be another source of Dutch-disease effects.

Isolating these two effects requires a modification of the SAM such that: (i) no domestic factor inputs are used when mining production is scaled up; and (ii) neither households nor the government receive any additional income when mining output increases. This means that mining production would: (i) only use imported inputs; (ii) the mining sector would be fully foreign-owned; and (iii) it would pay no taxes or import duties. Beginning with the production side, the following changes are made to the SAM. First, usage of domestic intermediate inputs, labor and capital used in the mining sector is set to zero. Second, domestic production factors are replaced with imported intermediate, mining-specific production factors. Apart from imported intermediate inputs, the only remaining production factor is mining-specific capital, which is assumed to be fully foreign owned.

On the income side, the production-side modifications rule out any increase in income for households: neither labor nor domestic capital (from which households earn income) are employed in the mining sector. This leaves only the government via higher tax collection as a potential beneficiary from scaled-up mining production. To switch this channel off, import duty, export taxes, production or sales taxes, and dividends from this sector accruing to the government are set to zero.<sup>11</sup> However, these modifications reduce household income in the steady state while at the same time creating a current account imbalance due to higher demand for imported intermediate inputs. To rebalance the SAM, transfers from ROW to households are increased, which also compensates for the rising current account deficit. On the fiscal side, lost revenue from the mining sector is replaced via higher budget support from abroad.

As Figure 2 shows, even though mining output is doubled in both the reference and baseline scenarios, the effects on the real exchange rate and tradable sector output are negligible in the former. Higher mining production yields higher income for the mining sector and higher foreign exchange earnings for the economy at large in both scenarios. However, in the **reference** scenario, the extra FX earnings flow immediately out of the domestic economy as payment for imported intermediate inputs and increased dividend payments for foreign owners of the mining-specific production factor. Accordingly, there is no need for relative price changes, a real appreciation or changes in the structure of the domestic economy; rather, the domestic economy is fully insulated from the mining sector. This corresponds with the mining sector being a perfect enclave sector.

> 11 There was no need to set income taxes to zero as well because income taxes related to the mining sector accrue to the government only through the involvement of domestic households with this sector. Foreign ownership is proxied via their ownership of the mining sector specific production factor, which is not subject to income taxes. In the simulations, the role of income taxes paid by foreign companies operating in the mining sector is instead simulated via (i) dividend income to the government (reflecting that the foreign investors in the OT project offered the government an ownership share in the project) and the imposition of a mining-specific production tax.

8
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<!-- Start of picture text --> 20<br>15 __———_———<br>10 —Domestic demand demand<br>5 —Domestic procurement procurement<br>0 — Imports<br>5 ——<br>-10<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>8 ——<br>6 —Domestic demand demand<br>4<br>5 —Domestic procurement procurement<br>—Imports<br>0 ee<br>-2<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 20 10<br>15 __———_——— 8 ——<br>10 —Domestic demand demand 6 —Domestic demand demand<br>5 —Domestic procurement procurement 4<br>0 — Imports 5 —Domestic procurement procurement<br>—Imports<br>5 —— 0 ee<br>-10 -2<br>2012 2015 2018 2021 2024 2012 2015 2018 2021 2024<br>4 9<br>Zz —Domestic production production 8<br>QO +}. atsat—Domesticat—Domestic—Domestic procurement 766<br>2 —Exports 5<br>4 —Domestic production<br>4<br>6 ——— 4 23 —Domestic procurement procurement<br>B 1 Exports<br>01|+--7T ' ' 4 '<br>><br>2<br>2012 2015 2018 2021 2024 2012 2015 2018 = 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> 4<br>Zz —Domestic production production<br>QO +}. atsat—Domesticat—Domestic—Domestic procurement<br>2 —Exports<br>4<br>6 ——— 4<br>B<br>-10<br>-12<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 9<br>8<br>766<br>5<br>4 —Domestic production<br>323 —Domestic procurement procurement<br>1 Exports<br>01|+--7T ' ' 4 '<br>><br>2<br><!-- End of picture text -->
<!-- Start of picture text --> 3%<br>—Labor wage rates<br>2%<br>2% —Capital wage rates<br>1%<br>1%<br>0%<br>.1%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 2%<br>1%<br>0%<br>.<br>\ —Domestic producer prices<br>-1% \ —Domestic sales prices of<br>‘ domestically-produced goods<br>2% —Imports & exports prices<br>__ ee<br>-3% 1 1 4<br><!-- End of picture text --> <!-- Start of picture text -->
1.0%<br>0.8%<br>0.6% —Household income<br>0.4% —Household consumption<br>expenditures<br>0.2%Oy<br>0.0%<br>-0.2%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> 3.0% —Revenues<br>2.5%<br>2.0% —Govgrnmentexpenditures consumption<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>0.5%<br>2012 2015 «2018 «92021 2024<br><!-- End of picture text -->
<!-- Start of picture text --> 5%<br>0%<br>5% —Baseline scaliing-up scenario<br>Scenario with only labor inputs<br>“10% nario with only intermediary inputs<br>-15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 5%<br>O%<br>5%<br>“10%°<br>-15%<br>-20% —Baseline’Scaling up scenario<br>—Scenario with only labor inputs<br>-25% —Scenario with only intermediary inputs<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
<!-- Start of picture text --> 0.8<br>0.6 agriculture<br>construction<br>0.4 + pvt. traded<br>—pvt. non-traded<br>0.2 —mineral—other govt.<br>=-total<br>0.0<br>02<br>-0.4<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 2.0%<br>—Scenario with only labor input<br>1.5%<br>—Scenario with only inte: lary<br>inputs<br>0.5%<br>0.0%<br>-0.5%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 3% * + agriculture<br>construction<br>2% =e vadable bie<br>—other govt.<br>1% —total (ex Specific inputs)<br>0%<br>-1% prrcaaeee<br>-2%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —Scenario with only labor inputs<br>7% , —Scenario with only intermediary inputs<br>6%<br>5%<br>4%<br>3%<br>2%<br>0%<br>-1%<br>2012 2015 942018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 2%<br>0%<br>-2%<br>agriculture<br>4%<br>6% pvt. non-traded<br>8% —other govt.<br>-10%<br>-12%<br>-14%<br>-16%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 4% +» agriculture —construction<br>—pvt. tradable —>pvt. non-tradable<br>2% —other govt. —total<br>0% t T ee<br>-2%<br>0%<br>-8%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
<!-- Start of picture text --> 40 —Domestic demand<br>30 —p tic ren rae<br>20 ~-Imports---""~<br>10 “<br>0 -—*<br>“10<br>-20<br>-30<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>0<br>10<br>~<br>-20 =><br>-30<br>—Domestic production<br>-40 —Domestic procurement<br>-50 -- Exports<br><!-- End of picture text --> <!-- Start of picture text --> 3% |) agriculture construction<br>2% —pwt. tradable _non-tradabale<br>—other govt.<br>1%<br>0% = 7<br>1%<br>2<br>-3%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> 4%<br>3% =<br>2%<br>0%<br>4%<br>agriculture construction<br>“2% | _ pvt tradable —pit. non-tradabale<br>-3% | —other govt<br><!-- End of picture text -->
<!-- Start of picture text --> —Baseline scaling-up scenario<br>~Scenario with only capital inputs<br>5% , —Scenario with only labor inputs<br>—Scenario with only intermediary input<br>0%<br>5%<br>-10%<br>-15%<br>20%<br><!-- End of picture text --> <!-- Start of picture text --> 5%<br>0%<br>-5%<br>—Bakeline scaling up scenario<br>-10% |—Scekario with only capital inputs<br>—Scenario with only labor inputs<br>-15% |—Scenario with only int ry inputs<br>-20%<br>-25%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
<!-- Start of picture text --> agriculture —construction<br>1% , “Pvt tradable —pvt. non-tradable<br>we — other govt.<br>0% eetcssc<br>"<br>-1% Y<br>2% | \x<br>3% ‘ any<br>onan?<br>4% New =e<br>-5%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> 2.0%<br>1.5%<br>1.0% —Labor wage rates<br>.<br>0.5% —Capital wage rates<br>0.0%<br>-0.5%<br>-1.0%<br>-1.5%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 7%<br>6%<br>5%<br>4%<br>3%<br>2% —Scenario—Scenario with with only only capital labor inputs inputs<br>1% —Scenario with only intermediary inp.<br>0%<br>1%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 3%<br>ox ett teenneneansanenseneanannennen snes ene teet<br>° ’<br>“<br>é<br>0%<br>1%<br>‘agri —construction<br>-2% tradable —pvt.non-tradab<br>-3% ~-total<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 12%<br>10%<br>8%<br>—Scenario with only capital inputs<br>6% —Scenariowith only intermediary inputs<br>4% —Scenario with only labor inputs:<br>2%<br>0%<br>-2%<br>4%<br>2012 2015 82018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 0.5%<br>0.5%0.0% | :\ _.<br>1.0% YS —<br>1.5% (reese<br>2.0%<br>-2. 5% —agriculture construction<br>-3.0%° —pmt.other tradablegovt. —= pvto t al. nontradable<br>-3.5%<br>2012 2015 «2018 «2021 2024<br><!-- End of picture text -->
<!-- Start of picture text --> 0.05%<br>0.00%<br>-0.05% hold<br>-0.10% —Housel income<br>-0.15%<br>-0.20% —Household consumption<br>-0.25% expenditures<br>-0.30%<br>0.35%<br>-0.40%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 3.5%<br>3.0% _<br>2.5% a with only captaliputs<br>2.0% —ScenarScenar io with ‘ o nlynly capitalintermediaryi inputs<br>1.5% —Scenario with only labor inputs<br>0.5%<br>1.0%<br>0.0%<br>von<br>1.0<br><!-- End of picture text --> <!-- Start of picture text -->
1.5%<br>ss<br>1.0%<br>0.5%<br>0.0% : . . .<br>-0.5% ‘only capital inputs<br>—Svcenario with only labor inputs<br>1.0% —~ Scenario with only inlermediary inputs<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> 2.0% —Scenario with only capital<br>15% See ario with only oe<br>: --Scenanotermediary wihi only labor“<br>1.0% input Pea“<br>0.5% ’<br>uw<br>0.0% wl<br>0.5%<br><!-- End of picture text -->
<!-- Start of picture text --> —Baseline scaling-up scenario<br>—Scenario with only capital inputs<br>Scenario with only labor inputs<br>5% —Scenario with only intermediary inputs<br>-- Scenario with all domestic inputs<br>5%0% He ~* —————er<br>-10% ss,“Nene ee cnet renee ee eee_.<br>-15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —Baseline scaling up scenario<br>—Scenario with only capital inputs<br>—Scenario with only labor inputs<br>—Scenario with only intermediary inputs<br>5% )~~Scenario with all domestic inputs<br>70%5% es= ceS — ————___———<br>710%=15% SS,alata<br>~20%<br>-25%<br>2012 2015 «2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 2% —Scenario with all domestic<br>0% inputsPp<br>—Sum of domestic<br>-2%4% intermediary,capitalital iinputs labor and<br>6%<br>-B%<br>~10%<br>-12%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 2% —Scenario with all domestic<br>0% input:<br>-2% —Sum of domestic<br>-4%6% playaCapital inputs labor and<br>8%Oy<br>-10%<br>12%<br>-14%<br>-16%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
<!-- Start of picture text --> 10% —Revenues<br>8%<br>6%<br>4%<br>2%<br>0%<br>-2%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> = Direct taxes<br>= Mining sector dividends<br>= Export taxes<br>25 ® Other indirect taxes<br>20 = Import taxes<br>16<br>1.0<br>0.50.0 - 1<br>0.5<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text -->
<!-- Start of picture text --> —Baseline<br>—Govt revenue spent on public consumption<br>5%) Govt revenue spent on public investment<br>—Govt revenue spent on tax reduction<br>0% SS<br>5%<br>10%<br>=15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —Govt revenue spent on earedconsumption<br>5%—GovGov t revenue enespent on asnesjuctiormivestmens<br>0% | ' ' ' ' 1<br>5%<br>-10%<br>15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> —Baseline ; .<br>—Govt revenue spent on Public consumption<br>Govtrevenuesperon em patent<br>10% Govt spe ul<br>5% _—<br>0% a 1<br>$%<br>“10%<br>15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —Baseline: revenues spent on public consumption<br>—Baseline: revenues spent on publictax cut investment<br>0%<br>-5%<br>-10%<br>-15%<br>-20%<br>2012 2015 «892018 2021 2024<br><!-- End of picture text -->
<!-- Start of picture text --> 15%<br>10% —aarGutture<br>’(construction(construction —pvt. tradable tradable<br>{ —pvt.non-tradable<br>5% ra =-govl. consumption<br>0% < =—total (ex.—total (ex. (ex. mining specific—inputs)—inputs)inputs)<br>-5%<br>2012 2015 «82018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 15%<br>7 agriculture<br>10% aooo —constructionpvt,pvt, tradable<br>ra —pvt. non-tradable<br>5% ra govt. consumption<br>0% Zov Zovv —iotal<br>— oe<br>-5%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 15% 15%<br>10% —aarGutture 7 agriculture<br>’(construction(construction —pvt. tradable tradable 10% aooo —constructionpvt,pvt, tradable<br>{ —pvt.non-tradable ra —pvt. non-tradable<br>5% ra =-govl. consumption 5% ra govt. consumption<br>0% < =—total (ex.—total (ex. (ex. mining specific—inputs)—inputs)inputs) 0% Zov Zovv —iotal<br>— oe<br>-5% -5%<br>2012 2015 «82018 2021 2024 2012 2015 2018 2021 2024<br>—agriculture —construction__ 50 agriculture—construction<br>15% , _ rit tadable tadable pvt. non-tradable non-tradable — 40 ~ -pvt. tradable<br>--mineral vt. consumption —pvt. non-tradable ae<br>10% ---total 30 --mineral a<br>—govt. consu a<br>5% 20 ~~ total aa“<br>10 a<br>0%<br>“5% SS 0<br>— 10 eeeeee<br>-10% -20<br>2012 2015 2018 2021 2024 2012 2015 2018 2021 2024<br>12 —Domestic demand demand —agriculture —construction<br>10 —Domestic procurement procurement —pvt. tradable —pvt. non-tradable<br>8 —Imports 0.8% .<br>--govt. consumption<br>6 0.6% ee<br>4 0.4% Sorte<br>2 02% f .<br>2 0.0%<br>4 0.2% NN<br>6 0.4%<br>2012 2015 2018 2021 2024 2012 2015 2018 2021 2024<br>12 —Domestic demand demand agriculture —construction<br>10 —Domestic procurement procurement —pvt. tradable —pvt. non-tradable<br>8 —Imports 0.8% .<br>--govt. consumption<br>6 0.6% oe<br>4 0.4% ee<br>2 0.2% Li .<br>° -&<br>0.0%<br>2 :<br>40.2%<br>6 0.4%<br>2012 2015 2018 2021 2024 2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —agriculture —construction__<br>15% , _ rit tadable tadable pvt. non-tradable non-tradable<br>--mineral vt. consumption<br>10% ---total<br>5%<br>0%<br>“5% SS<br>—<br>-10%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 12 —Domestic demand demand<br>10 —Domestic procurement procurement<br>8 —Imports<br>6<br>4<br>2<br>2<br>4<br>6<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 12 —Domestic demand demand<br>10 —Domestic procurement procurement<br>8 —Imports<br>6<br>4<br>2<br>°<br>2<br>40.2%<br>6<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> agriculture —construction<br>—pvt. tradable —pvt. non-tradable<br>0.8% .<br>--govt. consumption<br>0.6% oe<br>0.4% ee<br>0.2% Li .<br>-&<br>0.0%<br>:<br>0.2%<br>0.4%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
<!-- Start of picture text --> 3.5% —Household income<br>3.0%<br>25% Household consumptio<br>. expenditures<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —agriculture —construction<br>008 —pvt. tradable —pvt. non-tradabl<br>0.06 me : consumptionP os _<br>0.04<br>;<br>0.02<br>0.00 Ce ee ele ee<br>-0.02 =<br>-0.04 <====2=-<br>-0.06<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text -->
1.5%<br>—Labor wage<br>1.0%<br>0.5% —Capital wage rates<br>0.0%<br>0.5%<br>-1.0%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> —total ex. mining = —agriculture<br>—construction —pvti. tradable<br>2.0% pvt. non-tradable —govt. consumption<br>———<br>1.5% =ES<br>1.0%<br>0.5%<br>ee<br>0.5%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 50 —agriculture<br>construction ’<br>“0 —pvt.non-tradablepvt. tradable =”a<br>”<br>30 --govt. consumption ,--*<br>--total a<br>20 a<br>40 a<br>: res<br>0 es7 a 7<br>-10<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> agriculture —construction<br>—pvt. tradable —pvt. non-tradable<br>0.6% )--govt. consumption<br>0.4%<br>0.2% mane<br>f ee<br>0.0% | : 1 1 re<br>-0.2% ~__—— —<br>0.4%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
<!-- Start of picture text --> 16<br>14 --Domestic demand .<br>12 —Domestic procurement «a<br>++ Imports an<br>10 oa<br>8 —<br>6 VA<br>4 /<br>2 ,/<br>o 4 EEE<br>2<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> 6%<br>5% --Labor wage rates i<br>-c ; rae<br>4% ‘apital wage ratesod”+<br>3% -“-<br>2% “<br>1% aca<br>2<br>Oo”<br>-1%<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> . .<br>~-agriculture construction<br>—pvt. —pvt.non- I<br>0.04 govt.pvt. tradablecons. —govt.put noi n v. tradable<br>0.02 ee ee —_<br>0.00<br>0.02<br>0.04<br>0.06<br>-0.08<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> 9%<br>8% =-agriculture construction<br>—pvt. tradable —pvt. non-tr; le<br>7% govt. cons —to “ a<br>6% 4-*<br>5% A<br>4% atos<br>3% a<br>2%1% -a -<br>0% od— a-- eee errre<br>-1%<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> 4035 —Domestic. production.<br>—Domestic procurement<br>30 _|<br>25 Exports<br>20 a<br>is “—<br>10 x —"<br>5 -*<br>0<br>5<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text -->

<!-- Start of picture text --> 60 =-agriculture construction<br>50 —pvt. tradable —pvt.non-tradablewe,<br>40 - — govt. cons. —govt.inv. _,¢<br>20 ae oa<br>20 ea<br>10 a<br>—— =<br>10 -——=—. -<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text -->
leads to increased demand for labor and capital. The latter in turn bids up wage rates for labor and capital (Figure 18C) – with investment rising over time, the supply of capital increases in the long run, which dampens the increase in the wage rate for capital. The increase in private investment itself is a result of the surge in public investment, because the last increases economy-wide absorption and private investment is programmed in the simulations to grow in line with absorption. With wage rates increasing, household income rises, which leads to both higher savings and household consumption expenditures. The increase in household income and the resulting rise in household consumption expenditures boosts overall demand, broadly aligning demand with the rise in the production potential stemming from higher total factor productivity.

The net effect is to boost tradable sector output – a key departure from previous scenarios. The challenge for the tradable sector is to find outlets for its higher production. One outlet is higher domestic sales, driven by the overall increase in demand; in contrast to the other scenarios, domestic tradables retain their competitiveness because there is no substantial real appreciation in this scenario, allowing domestic tradables to participate in the general increase in demand. The other outlet is an increase in exports; to facilitate this, the real depreciation is critical to increase price competitiveness and to create export markets for tradables (Figure 18D). Finally, just as in other scenarios imports for tradables increases as a result of the general increase in domestic demand, notwithstanding the real depreciation.

Another effect is that higher private investment leads to higher demand for agricultural products and construction services the two sectors with the strongest increase in both demand and production; consequently, agriculture does not fulfill its customary role observed in previous scenarios of releasing labor while stocking up on capital.

With the tradable sector for once unavailable to supply the rest of the economy with labor and capital, the burden falls mainly on the sectors supplying non-tradables and public consumption goods. _D_ emand for these is relatively weak, so that TFP gains in these two sectors can be used for releasing production factors instead of boosting production levels (Figures 18E,F). In addition, labor is substituted with capital in the public consumption goods sector—the elasticity of substitution between factors is calibrated to be relatively high in this sector, based on historical experience<sup>22</sup> —which explains why the release of labor from the public consumption goods sector is relatively much larger than the release of capital. For non-tradables, demand is also relatively weak, which means that part of the total factor productivity gains in this sector can be used as well to release production factors.

# **_b)_** **_<u>Private sector spending of dividends from the mining sector</u>_**

In this scenario, the only transmission channel that is switched on is a rise in private sector household income stemming from the receipt of dividends from the mining sector.<sup>23</sup> In the baseline calibration, domestic private ownership of the mining sector has been set to about 30 percent, compared to 10 percent for the government and 60 percent for foreigners. Household dividend collection exceeds total government revenue collection from the mining sector by approximately 50 percent, which implies that dividends are a very substantial income source for households.

A caveat is that typically households in natural resource rich economies are likely to own small scale mines whereas large-scale new mining projects are likely to be owned mainly by foreign companies. Hence, dividend payments to households from the new large-scale mining project simulated here are likely

> 22 See companion paper Khan et al (2017). 23 As before, payments from the mining-specific production factor to households are interpreted as dividend payments. 25
<!-- Start of picture text --> A%<br>0%<br>‘<br>4% \‘;<br>\--Baseline scenario<br>-8% \‘ —Scenario where mining sector<br>\, revenues flow only to hhs<br>12% \ .<br>‘\ wee<br>716% Neen<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text -->
-2%om = f _ ‘<br>“A%F Baseline scaling-up scenario<br>ai<br>—Scenario with all domestic<br>“8% inputs & spending of mining<br>-10% receipts<br>-12% 2<br>“14%<br>“16%<br>“18%<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text -->

<!-- Start of picture text --> —Pvt sector spends mining dividends (re-scaled impact)<br>+++ Scenario with only intermediary inputs<br>--Scenario with only capital inputs<br>—Scenario with only labor inputs<br>0%<br>“1% woooes<br>2%-3% ana arenn stn IT ETSTSETI TIT<br>4%<br>5%<br>on 2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> Bit—ScenarioScenario Sector withwith spendsonlyonly capital minngdvdedsintermediary inputsinputs. (scaled impac)<br>a ° celiaid with only labor inputs<br>“2%<br>4%<br>6%<br>7%<br>“10%<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text -->
<!-- Start of picture text --> 0%<br>-2% ~-Baseline scaling-up scenario<br>4% WJ"i<br>6% ‘Scenario with all domestic inputs &<br>si \ spending of mining receipts<br>-10% ‘\<br>-12% ie geen<br>-14% a<br>-16% raed<br>-18%<br>-20%<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text -->
<!-- Start of picture text --> TM%<br>o% postr tren nnn<br>S% ‘<br>4<br>4% 44<br>% Wy =<br>2% 4 @ Household income<br>———— Household consumption expenditures<br>1% +<br>1% 2012 2014 216 218 220 222 wee 228<br><!-- End of picture text --> <!-- Start of picture text --> 8.0% Revenues<br>7.0% —Government consumption expenditures<br>4.0%3.0%<br>2.0%<br>10%<br>0.0%<br>-1,0% 2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> 4.0%<br>3.0% Labor wage rates, —Caphewage ra es l<br>2.0%<br>1.0%<br>0.0%<br>-1.0%<br>-2.0%<br>2012) 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 8% - —agriculture<br>7% construction—pvt tradable<br>6% — —pyt nontradable<br>5% —govt cons.<br>4% total ex. mini ed inputs<br>2%om<br>1%<br>“1% -<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> 6% —agriculture - construction<br>4% —pvtSMINCTA tradableoreo —pvtGO nontradableconsumption<br>0% |<<br>2%<br>-a% _—<br>6%<br>2012) 2015 82018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> agriculture +—construction —pvt trad<br>1.2% pyt nontrad —govt cons<br>0.8%<br>0.4%<br>0.0%<br>0.4%<br>0.8%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
# **IV. Lessons**

This section briefly summarizes the three main lessons that emerged from the simulations in this paper.

## **Complexity of the transmission channels**

_The economic effects of scaled-up mining production are complex_ . Dutch-disease effects, which typically focus on the real exchange rate and tradable sector response, capture only part of the transmission mechanism. Arguably, all sectors of the economy are affected by a scaling up of mining production—and not only the tradable sector—and many relative prices change, not only the real exchange rate.

Having said that, the focus on the tradable sector in the discussion of Dutch disease effects is not misplaced because in most of the scenarios considered here it does play a special role: the shrinkage in the tradable sector releases production factors that are in demand elsewhere in the economy. Unwrapping this a bit more leads to the core of the transmission mechanism: scaling up mining production raises the demand for domestic production factors, be it directly through the mining sector requiring additional domestic labor and capital inputs as it scales up production or indirectly through higher demand for domestically-produced goods and services by the mining, government, and household sectors as their incomes rise.

At least in the short term, the supply of domestic production factors can be considered as more-or-less fixed; the only sector where domestic production can easily be replaced through imports is the tradable sector, and doing so makes it possible to shrink the sector while still meeting the demand for tradables, which in turn releases the production factors that are in demand in other sectors. In this sense the shrinkage of the tradable sector plays a crucial role in facilitating the short-run supply response of the economy to meet the demand for non-tradable goods and services. In the absence of a central planner who decrees the shrinkage of the tradable sector, it is the real appreciation that makes this happen.

But there are also other processes at work. For example, the rise in labor wage rates that took place in many scenarios reinforces the effect of the real appreciation on profitability of the tradable sector, thereby contributing to its shrinkage. Changes in relative prices on product markets—especially price increases of non-tradables relative to tradables—lead to expenditure-switching effects that ultimately channel demand towards imports. Looking beyond the tradable sector, in many scenarios agriculture substituted labor with capital, thereby increasing the labor supply available for other sectors.

The strength of these different channels depends on structural characteristics of the economy such as factor input requirements of the mining sector, elasticities of substitution, revenue take of the government from scaled-up mining production, etc. As a result, this paper can sketch the main transmission channels at work but it cannot yield universally applicable rules of how scaling-up of mining production will affect individual sectors and relative prices; the process is too complex for this to be feasible.

## **Equilibrium outcomes**

_Dutch disease effects such as the real appreciation and shrinkage of the tradable sector are equilibrium outcomes_ . In a sense, this result was predetermined because all the simulations in this paper took place in a Computable General Equilibrium (CGE) model, which means any real appreciation and tradable sector 29
response generated in this model has to be an equilibrium outcome. Hence, the extent to which this captures Mongolia’s reality depends on the deeper question to what extent a CGE model is relevant for Mongolia. To this end, the companion paper has shown that the CGE model used here can reproduce many of the salient features of the boom-and-bust cycle Mongolia experienced in the second half of the 2000 (Khan et al., 2017). With this background, the finding that the scaling-up simulations yield sizeable real appreciations and shrinkages of the tradable sector as equilibrium outcomes should be taken seriously.

If the real appreciation and shrinkage of tradable sector production are equilibrium outcomes, the question arises why they should be considered as a disease. Arguably, simulation results showed substantial reallocations of demand, factor allocations, and production across different sectors, but none of these seemed to make the economy at large worse off. In fact, scenarios such as tax reductions and household spending of mining dividends included large income gains of households and increases in household consumption, features that usually would be considered as positive. As noted at the outset of this paper, to introduce disease it is necessary to add additional features that give the tradable sector a special role for economic development; for example, it might be the source of TFP growth, which implies that shrinkage of the sector would have a permanent negative effect on the growth outlook of the economy that outweighs any short-term income gains from the mining sector. This is a separate question that has not been addressed in this paper. The answer to this question, which should be considered on a country-by-country basis, determines whether the real appreciation and shrinkage of the tradable sector is indeed a problem.

A related issue is the development strategy for a country: if this strategy is predicated on building up an export-oriented manufacturing industry following the example of many Asian countries, then the real appreciation and shrinkage of the tradable sector are clearly harmful to that strategy, regardless whether these are equilibrium outcomes or not. This leads to the final question of how to avoid Dutch disease effects. This issue is taken up in the final lesson.

# **Policy implications**

_If avoidance of Dutch disease effects is a policy objective, for example because the country is committed to an export-led, manufacturing-oriented development strategy, this can only be achieved through trade-offs that relinquish aspects of scaling-up mining production that typically would be considered as beneficial:_ First, if avoiding Dutch disease effects is the overriding policy objective, an assumption that will be made for the remainder of the discussion in this section, the most certain way of accomplishing this is to leave the minerals in the ground. While radical, this proposition captures the notion that an export-led, manufacturing-oriented development strategy is fundamentally at odds with scaling up of mining production because the last is bound to lead to some Dutch disease effects that will undermine the first. After all, as argued above, Dutch disease effects are equilibrium outcomes that are embedded into the very structure of the economy and cannot be simply switched off through policy choices or clever management of the scaling-up process. The last two can have some impact, at a cost to other policy objectives, but are unlikely to avoid Dutch disease effects entirely.

Second, limiting Dutch disease effects from the production side means minimizing domestic inputs into the mining sector as it scales up production. If the goal is to build an export-oriented manufacturing industry, production factors have to be steered into this direction and not into mining. The trade-off here is that policy makers view jobs that are directly generated by the scaling up of the mining sector as a benefit—this job creation can take place either directly through increased employment in the mining industry (which leads to particularly strong Dutch disease effects) or indirectly through the demand for goods and services by this sector. But heralding jobs created by the scaling up of mining industry while simultaneously professing 30
adherence to an export-led, manufacturing oriented development strategy is internally inconsistent. Something has to give: either the scaling up of the mining sector has to be structured as much as possible as an enclave sector with minimal linkages to the rest of the economy, or the development strategy has to move away from the focus on exports and manufacturing, which in any event is not suitable for all countries.

Policy makers may also consider increasing domestic labor force participation rates – one possibility for Mongolia where female labor force participation, although not low, is concentrated in the informal/unremunerated part of the economy and is particularly limited in the mining sector (Khan, 2013 et al., 2013, a,b). Immigration may also be an option for relaxing supply constraints. Such policies have been employed in GCC countries, with evidence indicating that large inflows of foreign migrants and the associated extremely large remittance outflows have helped to dampen real exchange rate pressures associated with commodity booms (Espinoza et al. 2013). However there too tensions/trade-offs may exist, in the form of social (e.g., migrants substantially outnumbering locals as in Qatar) or long term economic consequences (low skill base of migrants that affects productivity and diversification efforts) and it is not clear to what degree these can be sustained.

_Limiting Dutch disease effects from the spending of mining receipts means saving these receipts or focusing spending on areas that minimize Dutch disease effects_ . The use of mining receipts and the associated policy options differ for the public and private sectors, as we explain below.

Beginning with the public sector, the simulations above suggest that using additional government revenues for boosting government consumption or lowering taxes should be minimized if avoiding Dutch disease effects is the overriding policy objective. Again, achieving this objective entails a real trade-off because it means that worthwhile alternative policy objectives such as raising the level of public service delivery or sharing the mineral wealth with the (current) population cannot be achieved. In fact, experience in countries like Mongolia and Timor-Leste makes it doubtful that a strategy of full saving/deferred benefits of the mining boom is politically sustainable. Yet another alternative is to focus spending on activities that (i) have a high import content and (ii) that boost the overall supply potential of the economy, especially that of the tradable sector. This likely means focusing spending on public investment. The simulations above have shown that this can avoid Dutch disease effects, even though this spending strategy was not sufficient to offset Dutch disease effects from other transmission channels. In practice, it will also be necessary to consider the sustainability of ramped up public investment spending, given that this will entail permanently higher maintenance costs that can become fiscally challenging once the new mining projects have been exhausted and mining production recedes.

Turning to the private sector, to the extent that there are large income effects, and it is not possible to direct these resources through structural policies to areas that benefit the export-led, manufacturing-oriented development strategy, a tight monetary stance could constrain private consumption and investment and thereby counteract the increase in household expenditures that is at the heart of this Dutch disease transmission channel. The drawback is that this policy has distributive implications: not all households will benefit equally from mining sector dividends, but a tight monetary stance will affect all households, including those that do not benefit from the mining sector boom.

The bottom line is that the transmission channels behind the real appreciation and the decline in tradable sector production are complex and represent equilibrium outcomes; as such, they are not necessarily a disease. However, these effects do pose a challenge for the development strategy of a country, especially if this strategy aims for export-led, manufacturing-oriented growth. In this case, it will either be necessary to forfeit (or postpone) many of the benefits that are normally associated with a booming mining sector in order to avoid Dutch disease effects, or the growth strategy will have to be changed to allow for a real appreciation and shrinkage of the tradable sector to take place.

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