Abstract
Mozambique is poised to become a major exporter of liquified natural gas (LNG), with significant impact on transforming its economy. If appropriate policies are put in place, LNG may generate long lasting gains, potentially lifting millions out of poverty. This paper addresses the challenges of transforming natural gas resource wealth underground into financial flows to support sustained development while, at the same time, avoiding Dutch disease and boom-bust cycles that are common to many natural resource rich economies. It applies the Debt, Investment, Growth and Natural Resources (DIGNAR) model to analyze the macroeconomic effects of alternative scenarios of scaling-up public investment in a volatile and exhaustible resource revenue environment to meet the country’s development needs. The model results indicate that prudent and gradual investment scaling-up is preferable to aggressive, front-loaded investments given, inter alia, absorptive capacity constraints and private sector crowding-out effects. It also shows that external savings—perhaps put in a sovereign wealth fund—would mitigate Dutch disease effects and serve as much needed fiscal buffer.
Mozambique is poised to become a major exporter of liquified natural gas (LNG), with significant impact on transforming its economy. If appropriate policies are put in place, LNG may generate long lasting gains, potentially lifting millions out of poverty. This paper addresses the challenges of transforming natural gas resource wealth underground into financial flows to support sustained development while, at the same time, avoiding Dutch disease and boom-bust cycles that are common to many natural resource rich economies. It applies the Debt, Investment, Growth and Natural Resources (DIGNAR) model to analyze the macroeconomic effects of alternative scenarios of scaling-up public investment in a volatile and exhaustible resource revenue environment to meet the country’s development needs. The model results indicate that prudent and gradual investment scaling-up is preferable to aggressive, front-loaded investments given, inter alia, absorptive capacity constraints and private sector crowding-out effects. It also shows that external savings—perhaps put in a sovereign wealth fund—would mitigate Dutch disease effects and serve as much needed fiscal buffer.
A. Background
1. Significant natural gas resources were discovered in Mozambique in 2010. Around 150 trillion cubic feet (Tcf) of proven natural gas reserves were found in two adjacent blocks, Area 1 and Area 4, in the offshore Rovuma Basin off Mozambique’s northern Cabo Delgado province (Figure 1). With these discoveries Mozambique is the third largest holder of natural gas reserves in Africa (after Nigeria and Algeria) and twelfth globally.2
Global Natural Gas Reserves, Producers, and Mozambique Production Areas
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Sources: US Energy Information Administration; companies’ statements; and IMF staff schematic, not drawn to scale.Global Natural Gas Reserves, Producers, and Mozambique Production Areas
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Sources: US Energy Information Administration; companies’ statements; and IMF staff schematic, not drawn to scale.Global Natural Gas Reserves, Producers, and Mozambique Production Areas
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Sources: US Energy Information Administration; companies’ statements; and IMF staff schematic, not drawn to scale.2. Natural gas will be a significant source of foreign exchange for Mozambique. Large foreign exchange proceeds would come from natural gas exports and/or increased domestic consumption of natural gas in substitution for imported sources of energy. However, exports and domestic consumption of natural gas would require setting up a large network of pipelines for delivery. Alternatively, natural gas can be exported over long distances in liquid form as LNG (Box 1).
3. Mozambique is in an ideal location for LNG trade, especially in relation to the fastest growing consumption markets. Natural gas consumption is expected to continue to grow strongly over the coming 20 years, backed by demand for industrial production and power generation (BP Energy Outlook, 2018). Currently, the bulk of LNG exports are destined for the Asian market, with Japan and China being the largest importers, and Qatar and Australia being the largest exporters (Figure 2). In 2018, global trade in LNG increased by 3.2 billion cubic feet per day (Bcf/d) to 41.3 Bcf/d, an 8 percent increase from the previous year, backed by strong demand growth in China, following government policies to promote coal-to-natural gas switching.
LNG Exporting and Importing Countries, 2017
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: US Energy Information Administration and IMF Staff Schematic (illustration, not drawn to scale).LNG Exporting and Importing Countries, 2017
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: US Energy Information Administration and IMF Staff Schematic (illustration, not drawn to scale).LNG Exporting and Importing Countries, 2017
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: US Energy Information Administration and IMF Staff Schematic (illustration, not drawn to scale).4. Developing Mozambique’s natural gas infrastructure will take time and require large investments. This process includes a drilling stage (upstream process) to recover raw gas from underground, and the construction of liquification plants (trains). The latter takes around 4–5 years to build and involves large investments. Reaching a final investment decision (FID) on whether to develop a project often requires securing long-term sales and purchase agreements (SPAs) that ensure that project revenues are enough to meet project financing obligations. In the case of Mozambique, it is estimated that the five trains that are being developed will require total investments of around US$55 billion (the equivalent to four-times Mozambique’s 2018 GDP).
5. Considerable progress has taken place toward building Mozambique’s liquification capacity. The five trains that are being developed will have a total production capacity of around 30 million tons per annum (MTPA) (2.4 Tcf)—equivalent to 17 percent of total 2017 LNG trade or 7.5 percent of projected LNG trade by 2026 (Figure 3). Initial production of LNG is expected in late 2023 and for the five trains to reach full capacity by 2026. These trains are linked to the following fields:
Offshore Area 1 Golfinho/Atum gas field: This project is led by Anadarko Petroleum Corporation (Anadarko) and is managed by its Mozambique subsidiary and partners in Area 1.3 The project involves the development of two onshore trains with a liquification capacity of 6.44 MTPA per train, with total investment estimated at US$22 billion. An FID for the project is expected by mid-2019 and production of LNG from the first train is expected by end-2024, followed by production from the second train by mid-2025. So far, SPAs that have been secured by Anadarko amount to 9.5 MTPA (Reuters; EIM LNG Intelligence Daily; and company statements).
Area 4 Coral South field: This project is led by Eni and consists of developing an offshore floating train with a liquification capacity of 3.4 MTPA.4 The FID was reached in June 2017, with total investment estimated at around US$8 billion. Production of LNG is expected by late 2023, with the entire LNG production sold to BP over 20 years. (Global Energy Research, February 2019).
Area 4 Mamba field: This project is jointly led by ExxonMobil and Eni and involves the development of two onshore trains with a liquification capacity of 7.6 MTPA per train.5 An FID for the project is planned by mid-2019, with total investment estimated at around US$25 billion. Production of LNG from the first train is expected by August 2024, followed by production from the second train by mid-2025. Area 4 co-venture participants have secured LNG sale commitments from affiliated buyer entities of the partners.
The onshore liquefication trains will be in the Afungi LNG park in Cabo Delgado province. Anadarko and Eni are jointly developing the park. The park is also expected to host additional trains; future investments have the potential of adding another 27 MTPA by 2032, with 12 MTPA from Area 1 (Prosperidade field) and 15.2 MTPA from the overlapping fields between Area 1 and Area 4.
Liquified Natural Gas 101 (LNG)
Natural gas can be exported over long distances in liquid form as LNG. To liquefy natural gas, it must be cooled to -160 degrees Celsius (about -260° Fahrenheit). The liquification plant, known as train, involves a process consisting of a progression of connected steps where the raw gas is purified first, then cooled to the required temperature. During the process, the volume of natural gas in its liquid state is compressed (about 600 times smaller than its volume in its gaseous state), which facilitates its shipping and storage.
The liquefication process makes it possible to transport natural gas to places when a pipeline infrastructure is not available and/or feasible. Markets that are too far away from producing regions have access to natural gas because of LNG. However, this requires (i) special ships equipped with insulated and pressurized tanks to transport the LNG to its destination; and (ii) at the receiving end, importing countries need to be equipped with terminals to regasify (return LNG to its gaseous state) where it is transported by a pipeline to distribution companies, industrial consumers, and power plants.
B. Macroeconomic Impact of Natural Gas Production
6. A Fiscal Analysis of Resource Industries (FARI) methodology is used to quantify the macroeconomic impact of natural gas development on the economy.6 The FARI model is particularly useful for the case of Mozambique because the natural gas resources are concentrated around a few large-scale projects. Relevant information at the project level is aggregated in a consistent framework that can be easily integrated into Mozambique’s macroeconomic framework to arrive at resource revenues as well as production value added and exports (Figure 3).
Real GDP growth is projected to increase in 2023–24 in line with the onset of production from the first LNG plants (trains) and related exports. Other economic activity (non-LNG) is conservatively assumed to continue to grow at a steady rate of 4 percent per year over the long-term.
Fiscal. As fiscal revenue from LNG production starts to flow in by 2023, the primary fiscal balance would improve and turn into surplus reaching around 13¼ percent of GDP by 2038. Underlying the path of improvement of the primary fiscal balance is the assumption that (i) the recovery of LNG development costs would take place over the initial four-to-six years of production, after which LNG related fiscal revenue would pick up significantly and account for almost half of total fiscal revenue, and (ii) all LNG fiscal revenue would be saved.
External sector. As LNG exports pick up in line with the production of LNG, the current account deficits would turn into surpluses by 2027.
Mozambique Selected Macroeconomic Indicators
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Sources: Mozambican authorities and IMF staff estimates and projections.Mozambique Selected Macroeconomic Indicators
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Sources: Mozambican authorities and IMF staff estimates and projections.Mozambique Selected Macroeconomic Indicators
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Sources: Mozambican authorities and IMF staff estimates and projections.C. The DIGNAR Model and Alternative Public Investment Approaches
7. This paper employs the DIGNAR model to analyze the fiscal and macroeconomic implications of savings / investment scaling up under different fiscal rule scenarios. This model provides a way to determine paths for the non-resource fiscal variables that are sustainable in the long run, using a macroeconomic framework that links fiscal variables, investment, growth and the real exchange rate. For a given investment scaling up path and envelope of resource revenues, the framework assesses the long-term sustainability of the fiscal path based on the magnitude of fiscal adjustments in current government spending or tax rates required to ensure debt sustainability.
8. The DIGNAR model consists of three production sectors, two types of households and the public sector. The production sectors consist of an exogenous LNG production sector and of firms that produce tradeables and non-tradeables goods through a Cobb-Douglas production function combining private and public capital and labor. The consumption side comprises optimizing and non-optimizing households, that is, includes those households that use financial markets to smooth consumption (optimizing households) and those without access or do not use financial markets to smooth consumption (non-optimizing households). In the public sector, the government raises taxes from the private sector, including on the LNG sector, to finance recurrent expenditures, investments on infrastructures and to service debt.
9. There are two main public investment approaches, namely: Spend-As-You-Go (SAYG) and Delinked. The choice of approach will affect how LNG price shocks will affect in turn macroeconomic performance.
Under the SAYG rule, LNG fiscal revenues in each period are used to fund public infrastructure investment, keeping recurrent spending fixed. This approach therefore does not allow an accumulation of resources in a stabilization fund, so the initial endowment of the stabilization fund, if there is one, remains at its initial level and taxes adjust endogenously to keep debt on a sustainable trajectory. The SAYG approach is thus highly procyclical, with the dynamics of public investment and other macroeconomic variables following the dynamics of LNG fiscal revenues.
Under the Delinked approach, public investment is scaled up gradually and its path is determined exogenously, that is, it is delinked from LNG revenues. Public investment is financed by a combination of LNG fiscal revenues, debt issuance and non-LNG fiscal revenues while allowing the buildup of savings in a stabilization fund. A fiscal surplus (deficit) would lead to an accumulation (reduction) of resources in the stabilization fund and, when there is a revenue shortfall, the stabilization fund would be drawn down to maintain investment commensurate with the planned path. A gradual scaling up of investment would give the authorities time to improve absorptive capacity and public investment efficiency and build fiscal buffers to prevent a disruption to public investment in the case of a negative LNG price shock.
D. LNG Production and Price Scenarios
10. LNG production, prices and revenues in the baseline and adverse scenarios are shown in Figure 4:
LNG Production follows the FARI model as discussed above. LNG production is assumed to start in 2023 and peak in 2032 based on production from a total of ten LNG trains.
LNG prices under the baseline scenario are generated by random simulations ranging between US$2 to US$9 per million cubic feet (MCF), falling within the price range observed over the last 20 years. The idea is to capture volatility of LNG prices that is common in all commodity markets. In addition, we assume an adverse scenario under which a 20 percent negative LNG price shock is applied to the baseline scenario prices.7
LNG Production and Price Scenarios (2023–2063)
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Sources: Mozambican authorities and IMF staff calculations.LNG Production and Price Scenarios (2023–2063)
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Sources: Mozambican authorities and IMF staff calculations.LNG Production and Price Scenarios (2023–2063)
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Sources: Mozambican authorities and IMF staff calculations.E. Results
11. Figures 5 through 9 show the impact of the SAYG and Delinked approaches on macroeconomic performance under the baseline and adverse scenarios using calibrated parameters for Mozambique (Appendix).
Resource Revenues and Stabilization Fund
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Resource Revenues and Stabilization Fund
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Resource Revenues and Stabilization Fund
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.12. A stabilization fund allows for the accumulation of buffers that can be used in the event of a drop in LNG prices. Figure 5 shows that, overtime, LNG production will decrease, and LNG prices can be volatile and lower than expected, leading to volatile LNG fiscal revenues which, in turn, would generate macroeconomic volatility. The SAYG approach is thus more susceptible to macroeconomic volatility, as no savings are accumulated, while under the Delinked approach a gradual scaling up of investment would allow for the accumulation of fiscal buffers through savings in a stabilization fund.
13. Excessively quick public investment scaling up would lead to investment inefficiencies. Under SAYG, public investment follows a volatile trend as it is linked to LNG revenues. Figure 6 shows that the aggressive investment scaling up under SAYG leads to a huge decline in investment efficiency, relative to the Delinked approach of gradual scaling up, mainly due to absorptive capacity constraints (lack of planning and coordination, and lower capital budget execution ratios). Under an adverse scenario, public investment efficiency improves in relative terms under SAYG, but inefficiencies remain higher than in the Delinked approach. In addition, Figure 6 shows that in the event of a negative price shock, the government would be able to maintain the same public investment path as under the gradual scaling up because this path is delinked from LNG revenues. Conversely, under SAYG, the government would be forced to implement public investment cuts as LNG revenues decline. In addition, since there are no accumulated savings under SAYG, the government must resort to additional taxes and/or debt financing to satisfy its budget constraint.
Public Investment and Investment Efficiency
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Public Investment and Investment Efficiency
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Public Investment and Investment Efficiency
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.14. Gradual scaling up allows private investment, private consumption and non-LNG output to follow a more stable path. According to the model, LNG revenues lead to other taxes to be lowered which, in turn, stimulate private investment, private consumption and non-LNG output. In addition, higher LNG revenues and a higher GDP (taxation base) result in lower tax rates that are needed to satisfy the budget constraint (Figure 7). Over the medium term, lower tax rates stimulate private consumption, private investment and non-LNG output. Under SAYG, however, the tax rate is initially lower, but this is reversed over the long term, as LNG revenues decline, other taxes need to increase to satisfy the budget constraint. As a result, private investment, private consumption and non-LNG output become higher under the Delinked approach over the long term.
Tax Rate, Private Consumption, Investment, and Non-Resource Output
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Tax Rate, Private Consumption, Investment, and Non-Resource Output
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Tax Rate, Private Consumption, Investment, and Non-Resource Output
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.15. LNG production and exports will lead to real exchange rate appreciation (Figure 8). However, this is more pronounced under SAYG because all foreign exchange proceeds from LNG exports are immediately channeled to the economy. Under the Delinked approach, accumulation of resources in a stabilization fund mitigates the Dutch disease effects, by containing to some extent real exchange rate appreciation pressures.
Real Exchange Rate
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Real Exchange Rate
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Real Exchange Rate
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.16. LNG fiscal revenues can contribute to a more sustainable debt path (Figure 9). As LNG fiscal revenues increase, the government needs to resort less to debt accumulation, leading to a decline of public debt as a share of GDP over the medium term. However, in the long-term, due to decreasing LNG fiscal revenues, public debt-to-GDP ratios would tend to increase as additional borrowing would be required. Under the Delinked approach, in both the baseline and adverse scenarios, debt levels are lower than under the SAYG approach, and the government’s ability to service its debt obligations is relatively high due to accumulated savings.
Total Public Debt
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Total Public Debt
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.Total Public Debt
Citation: IMF Staff Country Reports 2019, 167; 10.5089/9781498320009.002.A001
Source: IMF staff calculations.F. Main Findings
17. Mozambique is poised to become one of the world’s largest LNG exporters over the medium term. LNG exports will generate significant fiscal revenues that the government can use to address infrastructure gaps and other social needs, fostering economic development and significantly reducing poverty. However, volatility in LNG fiscal revenues and absorptive capacity constraints create challenges to macroeconomic management, requiring the government to find the right balance between public investment, investment efficiency and macroeconomic stability.
18. The DIGNAR model offers an assessment of alternative options for scaling up of public investment. It shows that gradually scaling up investment due to higher LNG fiscal revenues would give Mozambique time to improve absorptive capacity and public investment efficiency while building fiscal buffers to prevent disruptions to investment plans when a negative LNG price shock occurs (accumulation of savings in an actual or virtual stabilization fund would prevent the need for sizable investment cuts). Moreover, gradually scaling up investment due to higher LNG fiscal revenues would contain macroeconomic volatility, including to output, and real exchange rate appreciation pressures. It would therefore be more conducive to private sector led economic diversification.
Appendix I. Calibration: Parameters and Assumptions
1. To run the simulations a set of assumptions and parameters were defined which include the national accounting, fiscal policy, interest rates, structural parameters, natural resources and, public investment. The summary of the calibration parameters is presented on Table 1.
Mozambique: Calibrated Parameters
Mozambique: Calibrated Parameters
| Sector | Indicators | Parameters |
|---|---|---|
| National Accounts | Long-run GDP growth rate (in percent) | 6.00 |
| Exports/GDP*100 (in percent) | 33.00 | |
| Imports/GDP*100 (in percent) | 65.00 | |
| Government consumption/GDP*100 (in percent) | 21.00 | |
| Government investment expenditures/GDP*100 (in percent) | 7.50 | |
| Private investment/GDP*100 (in percent) | 17.00 | |
| Mining value added (natural resource production)/GDP*100 (in percent) | 3.00 | |
| Government wealth fund/GDP*100 (in percent)’ (external savings) | 1.00 | |
| Share of tradables in government expenditures (in percent) | 60.00 | |
| Share of tradables in private consumption (in percent) | 60.00 | |
| Government domestic debt / GDP*100 (in percent) | 8.81 | |
| Private foreign debt/GDP*100 (in percent) | 49.18 | |
| Concessional debt/GDP*100 (in percent) | 4.71 | |
| Government external commercial debt/GDP*100 (in percent) | 48.13 | |
| Grants/GDP*100 (in percent) | 5.09 | |
| Interest rates | Annualized domestic net real interest rate | 10.00 |
| Annualized foreign net real interest rate earned by the stabilization fund | 2.70 | |
| Annualized net real interest rate paid on concessional debt | 0.00 | |
| Annualized net real risk-free rate | 2.40 | |
| Annualized net real interest rate paid on government external commercial debt | 6.00 | |
| Structural parameters | Labor income share in non-traded sector (in percent) | 45.00 |
| Labor income share in traded sector (in percent) | 60.00 | |
| Elasticity of output wrt public capital | 0.25 | |
| Capital depreciation rate in non-tradable sector (in percent) | 10.00 | |
| Capital depreciation rate in traded sector (in percent) | 10.00 | |
| Depreciation rate of public capital (in percent) | 7.00 | |
| Learning by doing externality in the traded sector | 0.10 | |
| Persistence in TFP in traded sector | 0.10 | |
| Investment adjustment cost parameter in the non-traded sector | 25.00 | |
| Investment adjustment cost parameter in the traded sector | 25.00 | |
| Steady-state efficiency of public investment (share of investment turned into actual capital) (in percent) | 50.00 | |
| Inverse of the Frisch elasticity of labor supply for optimizers | 10.00 | |
| Inverse of the Frisch elasticity of labor supply for rule of thumb consumers | 10.00 | |
| Inverse of the intertemporal elasticity of consumption | 2.00 | |
| Elasticity of substitution between the two types of labor (in tradables and nontradables) | 1.00 | |
| Measure of optimizers in the economy in percent (non-credit-constrained households) | 0.60 | |
| Elasticity of substitution between traded and non-traded goods | 0.44 | |
| Home bias for additional government spending | 0.50 | |
| Elasticity of portfolio adiustment costs | 0.00 | |
| Natural resource sector | Royalty tax rate on natural resources (in percent) | 20.00 |
| Fiscal policy | User fees of public infrastructure (in percent of recurrent costs) | 50.00 |
| Labor income tax rate | 2.51 | |
| Consumption tax rate | 7.90 | |
| Tax rate on the return on capital | 5.86 | |
| Elasticity of sovereign risk | 0.00 | |
| Public investment | Severity of public capital depreciation when not maintained | 1 .00 |
| Severity of absorptive capacity constraints | 20.00 | |
| Thresholds of investment scaling up beyond which absorptive capacity constraints start binding (in percent) | 50.00 | |
| Persistence of efficiency of public investment | 0.80 | |
| Floor for the sovereign wealth fund (in percent of GDP) | 0.00 |
Mozambique: Calibrated Parameters
| Sector | Indicators | Parameters |
|---|---|---|
| National Accounts | Long-run GDP growth rate (in percent) | 6.00 |
| Exports/GDP*100 (in percent) | 33.00 | |
| Imports/GDP*100 (in percent) | 65.00 | |
| Government consumption/GDP*100 (in percent) | 21.00 | |
| Government investment expenditures/GDP*100 (in percent) | 7.50 | |
| Private investment/GDP*100 (in percent) | 17.00 | |
| Mining value added (natural resource production)/GDP*100 (in percent) | 3.00 | |
| Government wealth fund/GDP*100 (in percent)’ (external savings) | 1.00 | |
| Share of tradables in government expenditures (in percent) | 60.00 | |
| Share of tradables in private consumption (in percent) | 60.00 | |
| Government domestic debt / GDP*100 (in percent) | 8.81 | |
| Private foreign debt/GDP*100 (in percent) | 49.18 | |
| Concessional debt/GDP*100 (in percent) | 4.71 | |
| Government external commercial debt/GDP*100 (in percent) | 48.13 | |
| Grants/GDP*100 (in percent) | 5.09 | |
| Interest rates | Annualized domestic net real interest rate | 10.00 |
| Annualized foreign net real interest rate earned by the stabilization fund | 2.70 | |
| Annualized net real interest rate paid on concessional debt | 0.00 | |
| Annualized net real risk-free rate | 2.40 | |
| Annualized net real interest rate paid on government external commercial debt | 6.00 | |
| Structural parameters | Labor income share in non-traded sector (in percent) | 45.00 |
| Labor income share in traded sector (in percent) | 60.00 | |
| Elasticity of output wrt public capital | 0.25 | |
| Capital depreciation rate in non-tradable sector (in percent) | 10.00 | |
| Capital depreciation rate in traded sector (in percent) | 10.00 | |
| Depreciation rate of public capital (in percent) | 7.00 | |
| Learning by doing externality in the traded sector | 0.10 | |
| Persistence in TFP in traded sector | 0.10 | |
| Investment adjustment cost parameter in the non-traded sector | 25.00 | |
| Investment adjustment cost parameter in the traded sector | 25.00 | |
| Steady-state efficiency of public investment (share of investment turned into actual capital) (in percent) | 50.00 | |
| Inverse of the Frisch elasticity of labor supply for optimizers | 10.00 | |
| Inverse of the Frisch elasticity of labor supply for rule of thumb consumers | 10.00 | |
| Inverse of the intertemporal elasticity of consumption | 2.00 | |
| Elasticity of substitution between the two types of labor (in tradables and nontradables) | 1.00 | |
| Measure of optimizers in the economy in percent (non-credit-constrained households) | 0.60 | |
| Elasticity of substitution between traded and non-traded goods | 0.44 | |
| Home bias for additional government spending | 0.50 | |
| Elasticity of portfolio adiustment costs | 0.00 | |
| Natural resource sector | Royalty tax rate on natural resources (in percent) | 20.00 |
| Fiscal policy | User fees of public infrastructure (in percent of recurrent costs) | 50.00 |
| Labor income tax rate | 2.51 | |
| Consumption tax rate | 7.90 | |
| Tax rate on the return on capital | 5.86 | |
| Elasticity of sovereign risk | 0.00 | |
| Public investment | Severity of public capital depreciation when not maintained | 1 .00 |
| Severity of absorptive capacity constraints | 20.00 | |
| Thresholds of investment scaling up beyond which absorptive capacity constraints start binding (in percent) | 50.00 | |
| Persistence of efficiency of public investment | 0.80 | |
| Floor for the sovereign wealth fund (in percent of GDP) | 0.00 |
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Prepared by Ari Aisen, Naly Carvalho, Moataz El Said, and Edson Manguinhane (all AFR).
As of January 1, 2017, global natural gas reserves were estimated at 6,923 Tcf, with Russia, Iran, and Qatar accounting for around 50 percent of the total (US EIA).
Co-ventures include Anadarko (26.5 percent), Mozambique’s state-owned energy company ENH (15 percent), Mitsui E&P (20 percent), ONGC Videsh Ltd. (16 percent), India Bharat Petroleum (10 percent), Thailand PTTEP (8.5 percent), and Oil India limited (4 percent).
Co-ventures are ExxonMobil (25 percent), Eni (25 percent), China National Petroleum Corporation (CNPC) (20 percent), ENH (10 percent), South Korean public national gas company KOGAS (10 percent), and Portuguese group Galp (10 percent).
Eni will lead the construction and operation of the upstream facilities, while ExxonMobil will lead the midstream natural gas liquefication.
FARI is an Excel-based model that builds up from the project-level estimates of key macro indicators; including real GDP, fiscal revenue, and exports. A technical exposition of the FARI modeling is available in IMF (2016).
This choice was based on the observation that in the last 45 years there were 16 negative variations of LNG prices (36% of total observations) of which 11 (69% of total negative variations) were up to 20 percent.
References
Amato, J., 2005, “The Role of the Natural Rate of Interest in Monetary Policy,” BIS Working Paper 171, (Basle: Bank for International Settlements).
Aisen, A and F., Simione, 2018, “Towards a New Monetary Policy Regime in Mozambique”, IMF Country Report No. 18/66, pp.4–15.
Barro, R., and X., Sala-i-Martin, 2003, Economic Growth, The MIT Press, 2nd ed.
Barsky, R., A., Justiniano, and L., Melosi, 2014, “The natural rate of interest and its usefulness for monetary policy,” The American Economic Review 104 (5), 37–43.
Basdevant, O., N., Bjorksten, and O., Karagedikli, 2004, “Estimating a Time Variant Neutral Real Interest Rate for New Zealand,” Discussion Paper No.2004/01, (Wellington: Reserve Bank of New Zealand).
Bernhardsen, T., and K., Gerdrup, 2007, “The Neutral Real Interest Rate,” Economic Bulletin 2/07, (Oslo: Norges Bank).
Blinder, A., 1998, Central Banking in Theory and Practice, (Cambridge, Massachusetts: MIT Press).
Campbell, J., and J., Cochrane, 1999, “By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior,” Journal of Political Economy, Vol. 107, No. 2, pp. 205–51.
Campbell, J., A., Lo., and C., MacKinlay, 1997, The Econometrics of Financial Markets, (Princeton University Press).
Cochrane, J., 2001, Asset Pricing (New Jersey: Princeton University Press).
Fuentes, R., and F. Gredig, 2007, “Estimating the Chilean Natural Rate of Interest,” Working Paper No. 448 (Santiago: Central Bank of Chile).
Giammarioli, N., and N. Valla, 2004, “The Natural Real Interest Rate and Monetary Policy: A Review,” Journal of Policy Modeling, Vol. 26, pp. 641–60.
Hodrick, R. J. and E. C. Prescott, 1997, “Postwar us business cycles: an empirical investigation,” Journal of Money, credit, and Banking, 1–16.
Holston, K., T. Laubach, and J. Williams., 2017, “Measuring the Natural Rate of Interest: International Trends and Determinants,” Journal of International Economics 108, supplement 1 (May): S39–S75.
Kara, H., F. Ogunc, U. Ozlale, and C. Sarikaya, 2007, “Estimating the Output Gap in a Changing Economy,” Southern Economic Journal, Vol. 74, pp. 269–89.
Khun, L., F. Ruch., and R. Steinbach, 2017, “Reaching for the (r)-stars: estimating South Africa’s neutral interest rate,” Working Paper 9097, South African Reserve Bank.
Laubach, T. and J., Williams, 2003, “Measuring the natural rate of interest,” Review of Economics and Statistics 85 (4), 1063–1070.
Laubach, T. and J., Williams, 2016, “Measuring the natural rate of interest redux,” Business Economics 51 (2), 57–67.
Magud, N and E., Tsounta, 2012, “To cut or not to cut? That is the (Central Bank’s) Question: In search of the Neutral Interest Rate in Latin America,” IMF Working Paper WP/12/243
Peiris, J. and M. Saxegaard, 2008, “Monetary Policy in Sub-Saharan Africa: Lessons from a Dynamic Stochastic General Equilibrium Model Applied to Mozambique,” pp. 163–204, in Post-Stabilization Economics in Sub-Saharan Africa Policies: Lessons from Mozambique, ed.by J. Clements and S. Peiris (Washington: IMF).
Phelps, E., 1962, Golden Rules of Economic Growth, (New York: Norton).
Wicksell, K., 1907, “The Influence of the Rate of Interest on Prices,” Economic Journal, Vol. 17, June.
Wicksell, K., 1898, Geldzins und Guterpreise, translated by R.F. Kahn, 1936, as “Interest and Prices.”
Woodford, M., 2003, Interest and Prices: Foundations of a Theory of Monetary Policy, (Princeton University Press).
Prepared by Mokhtar Benlamine (AFR), Pedro Munguambe, Pinho Ribeiro (all BM), and Harold Zavarce (AFR). The authors thank Naly Carvalho (AFR) for excellent research assistance.
In 2015–16, macroeconomic stability was affected by lower commodity prices, adverse weather conditions, renewed civil strife, and the disclosure of large, previously hidden SOE loans in April 2016 that subsequently led to a halt of donor budget support.
The BM increased its lending rate by 600 basis points to 23¼ percent.
The metical has appreciated by almost 24 percent against the U.S. dollar since October 2016.
By end-March 2019, international reserves stood at 5¾ months of next year’s non-megaproject imports.
Wicksell’s (1898, p.102 and p.120) seminal work introduced the concept stating that “[there] is a certain rate of interest on loans which is neutral in respect to commodity prices and tends neither to rise nor to lower them. This is necessarily the same as the rate of interest which would be determined by supply and demand if no use were made of money and all lending were affected in the form of real capital goods. It comes to much the same thing to describe it as the current value of the natural rate of interest on capital”. Woodford (2003) presents a contemporary theoretical elaboration. Blinder (1998), Amato (2005) and Barsky et al. (2014) discuss the role of the indicator for monetary policy.
See Giammarioli and Valla (2004) for a review.
Magud and Tsounda (2012) applies the literature to a group of emerging and low-income countries in Latin America under inflation targeting or with evolving monetary regimes. Fuentes and Gredig (2007) presents results for Chile. More recently Kuhn et al. (2017) applies Laubach and Williams (2003, 2016) to South Africa.
The consumption smoothing model has been applied by Campbell and Cochrane (1999) and Cochrane (2001). The uncovered interest parity condition approach is challenging to apply for countries with thinner and less liquid financial markets with structural breaks in the risk premium. Barro (2003, p.20) elaborates on the optimal level of real interest rate by equaling the long term-growth rate in the context of Phelps (1966, pp.10–18 and fn.12) the golden rule for capital accumulation.
They entail a maximum likelihood estimation in conjunction with filtering techniques.
For the case of Mozambique Perris and Saxegaard (2008) estimates a DSGE. The calibrated steady state implies a nominal neutral interest rate of 12.3 percent.
As argued in Magud and Tsounda (2013, p.17) static approaches could be more appropriated for economies with thinner and less liquid financial markets while dynamic estimates may represent better most advanced economies.
The dynamic estimations were performed with the pre-cyclone baseline. Since the supply shock is transitory and the estimation methods used medium-term forecast to eliminate end of sample bias, the average results are not expected to be affected.
See Cochrane (2001) and Campbell, Lo and MacKinlay (1997) for a summary of this discussion.
The CRRA utility function is given by
See Fuentes and Gredig (2007) for a derivation.
The NRIR is not equal to the long run growth rate of output, implied under the golden rule in Phelps (1962), commonly used as rule of thumb to produce neutral rates estimates.
In this case the utility function is given by u(ct) = [(ct – xt)-1]/(1-γ) γ > 0,γ ≠ 1 where xt characterizes the level of habit persistence and it is assumed exogenous to simplify the analysis.
φ is the weight of past consumption in the degree of habit persistence.
This rate results from taking 7.27 percent for non-mining potential output growth proxied by 2004–15 average growth rate and 2.9 percent population growth rate.
We assume that the Mozambican economy in 2013 (a period of relatively low policy rate, output growth around potential and low and stable inflation) had zero output gap and interest rate close to the neutral level. Using a potential per capita output growth of 4.37 percent, a real interest rate of 6.39 percent, with β = 0.978 and γ = 1.5 , we obtain φ = 0.973 .
The introduction of habit persistence yields reasonable results for economies in process of development with relative capital scarcity and on a gradual path to financial opening with thin and less liquid domestic financial markets.
The equilibrium level for the nominal interest rate correcting the international interest rate, i*,with the expected depreciation of the nominal exchange rate, Ê, and the risk premium, ρ; is given by i = i* + Ê + ρ.
The expected nominal exchange rate depreciation could be derived taking the first difference from the real exchange rate definition in log terms yielding
Current model estimates from Holston, Laubach and Williams (2017) are available at https://www.newyorkfed.org/research/policy/rstar.
See Damodaran (2018) calculations at http://pages.stern.nyu.edu/~adamodar/New Home Page/datafile/ctryprem.html.
For the period before the MIMO rate we use BM lending facility rate.
This long forecast horizon is required to absorb the risk premium shock.
Census X-13 and SARIMA process are used to adjust form outliers and any seasonality.
Statistical filters tend to be bias by putting more weight on the most recent observations of the data series.
See Kara et. all (2007) for details.
We also estimate a simpler model with neutral rates following a random walk process. The results were robust to this change of specification.
The results are robust to forecast windows between 12 and 28 quarters.
The results are robust using core inflation rate excluding fruits, vegetables and administered prices.
See Aisen and Simone (2018, pp.4–15) for an analysis of the transition to a new monetary policy regime.
Adding dynamic approaches would contribute to better understand the NRIR dynamics. Estimating a dynamic stochastic general equilibrium model (DSGE) or calibrating a QPM where NRIR is interpreted as the real interest rate in a model with sticky prices based on New-Keynesian theory are avenues to be explored in further work.
The BM is enhancing its capacities for forward-looking monetary policy formulation decision making. Since 2008, the IMF is providing TA to develop a full-fledged Forecasting and Policy Analysis System (FPAS). The BM’s Department of Economic Studies has a well-established core modeling team and a QPM core model used to provide inputs to the monetary policy committee. The QPM calibration contains a convergent time varying path for the NRIR based on a version of the uncovered interest parity condition approach considering the effect of indebtedness on the country risk premium.
Aisen and Simione (2018, p.5 and Box 1, p. 9) consider the current monetary regime evolving towards inflation targeting where the “medium-term inflation objective remains 5–6 percent as set by the Government”. In the process of strengthening governance and modernizing operations, the BM may set an explicit and publicly known numerical inflation objective for the medium-term narrowing its current commitment to keep inflation low within one single digit and strengthening the expectational channel.
