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Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles

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Sustainable Macroeconomics, Climate Risks and Energy Transitions

Abstract

In this chapter, we study the perils of non-sustainable growth, which often take the shape of a boom-bust cycle. These frequently emerge from the high volatility seen in resource production, prices, over-leveraging, and from their impact on macroeconomic stability. The evolution of external debt is often related to short-termism in economic, financial, and policy decision-making. In fact, as resource-rich countries become more globalized, the economy becomes more vulnerable through increased commodity exports and fluctuations in their prices. Inevitably, the traditional simple models of a closed economy had to give way to extended versions which would allow for dynamic and open growth, driven by foreign trade, current and capital accounts, and external finance. We focus on countries that borrowed heavily from abroad and thus became vulnerable to external shocks. When resource prices decline sharply, many developing resource-rich countries experience a debt crisis and face a jump in borrowing costs; this, in turn, may trigger internal real and financial crises. We replicate those scenarios by employing numerical solutions of some model variants and trace out the impact of commodity boom-bust cycles, trade balances, and foreign debt. We emphasize those general features of exhaustible resources and their extraction dynamics since they are also inherited, as it were, by the extraction and use of fossil fuels; these will be dealt with in the next chapter.

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Notes

  1. 1.

    U.S. hydraulic fracturing of wells led to a surge in U.S. crude oil and natural gas production: 9.7 million barrels of oil and 92 billion cubic feet per day in April 2015 according to the U.S. Energy Information Administration (EIA) (https://www.eia.gov/petroleum/production/).

  2. 2.

    Following the initial recovery from the pandemic, commodity markets showed signs of improvement.

  3. 3.

    OPEC+ consists of OPEC countries and additional oil exporting countries including Russia, Kazakhstan, and others.

  4. 4.

    Maloney (2002, p. 1) also attributed limited technological advancement to slow growth in Latin America.

  5. 5.

    Manzano and Rigobon (2001) found a negative estimated coefficient only for cross-section data.

  6. 6.

    Tradables consist of domestically produced goods that can be exported to other countries. Non-tradable goods include local services. DeGregorio et al. (1994) stated that tradable sectors should have a share of total export in total production greater than 10% (manufacturing has 45%, mining 31%, agriculture 24%, overall services 4%).

  7. 7.

    Empirical tests such as by Chen and Rogoff (2003) showed that higher commodity prices made the real foreign exchange (FX) rate appreciate. Theoretical models and empirical analysis on the real FX rate determination in terms of price levels, tradable and non-tradable goods as well as productivity in these sectors are provided in Nyambuu and Tapiero (2018).

  8. 8.

    Bank for International Settlements provides data on how much of government debt is in foreign currency.

  9. 9.

    Many resource-rich countries found it challenging to repay their high external debt before the COVID-19 hit. We expect the situation to get worse in the coming years.

  10. 10.

    For details on both positive and negative effects in the welfare function, see Chap. 9.

  11. 11.

    Extraction costs will be considered in the extended growth model with resource extraction technology in Chap. 6 together with numerical solutions.

  12. 12.

    According to Corden (1984), Dutch disease is described as “the adverse effects on Dutch manufacturing of the natural gas discoveries of the nineteen sixties, essentially through the subsequent appreciation of the Dutch real exchange rate” (p. 359).

  13. 13.

    In Mansoorian (1991, p. 1502), GDP is a function of non-traded goods’ price, labor, capital, and resource extraction.

  14. 14.

    See Eastwood and Venables (1982, pp. 289–290) for more detail.

  15. 15.

    Grüne and Pannek (2011) and Grüne (2015) show that NMPC can be used with a small number of periods; instead of computing optimal value function for all possible initial states. NMPC only calculates single optimal trajectories. The results are obtained based on a shorter decision horizon with a small number of prediction periods.

  16. 16.

    For the impact of the arctan function on the state-dependent interest rate, see Nyambuu and Semmler (2017).

  17. 17.

    In Chap. 6, backstop technology will be incorporated into the basic growth model together with exhaustible resources, thus highlighting its importance for extraction processes, and demonstrating how it can contribute to the illuminating problem of exhaustible resources. The numerical solutions with different costs will be presented showing dynamic trajectories of the state variables, including capital accumulation and extraction rates for non-renewable resources. There, the impact of these constraints on economic growth is covered in detail.

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Appendix

Appendix

Following Dasgupta and Heal (1974, p. 10), the current value Hamiltonian with \(\omega \) and \(\gamma \) as co-state variables of constraints, K and R, respectively, is shown below

$$\begin{aligned} H=U(C)+\omega (Q(K,S)-C)+\gamma (-S) \end{aligned}$$
(3.7)

The necessary optimality conditions for the optimal control are obtained as

$$\begin{aligned} U^{'}(C)&=\omega \end{aligned}$$
(3.8)
$$\begin{aligned} \omega Q_{S}&=\gamma \end{aligned}$$
(3.9)
$$\begin{aligned} \dot{\omega }&=\omega (\theta -Q_{K})\end{aligned}$$
(3.10)
$$\begin{aligned} \dot{\gamma }&=\theta \gamma \end{aligned}$$
(3.11)

with \(Q_{S}=\frac{\partial Q(K,S)}{\partial S}\) and \(Q_{K}=\frac{\partial Q(K,S)}{\partial K}\).

Rearranging gives us \(\dot{\omega }=U^{"}(C)\dot{C}\) or \(\frac{\dot{\omega }}{\omega }=\frac{U^{"}(C)\dot{C}}{U^{'}(C)}\), and \(\frac{\dot{\omega }}{\omega }=\theta -Q_{K}\). Based on these equations, Dasgupta and Heal (1974), pp. 10–11, showed the steps on how to derive the consumption: \(\frac{\dot{C}}{C}=\frac{Q_{K}-\theta }{\varepsilon (C)}\) where \(\varepsilon (C)=-\frac{CU^{"}(C)}{U^{'}(C)}\).

With \(x=K/S\), (Dasgupta and Heal 1974, p. 11) derived the following elasticity of substitution between K and S that is used in the CES production function

$$\begin{aligned} \sigma =-\frac{q^{'}(x)\left( q(x)-xq^{'}(x)\right) }{xq(x)q^{"}(x)}\epsilon \left[ 0,\infty \right] \end{aligned}$$
(3.12)

According to Solow (1973), if the share of K exceeds the share of S in Cobb-Douglas production function, sustained consumption per capita would be a feasible objective. Dasgupta and Heal (1974, pp. 14–19), analyzed the case when \(S=0\) with different values of \(\sigma \) and showed that only in the cases when \(\sigma =1\) and \(\sigma <1\), non-renewable resource’s role in production becomes essential.

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Nyambuu, U., Semmler, W. (2023). Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles. In: Sustainable Macroeconomics, Climate Risks and Energy Transitions. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-27982-9_3

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