At steady-state, we find that:
Combining (23) and (24):
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We thank Bernardin Akitoby, Michal Andrle, Alberto Behar, Andy Berg, Ed Buffie, Raphael Espinoza, Fuad Hasanov, Gohar Minasyan, Dragana Ostojic, Cathy Pattillo, Rick van der Ploeg, Rafael Portillo, Alex Segura-Ubiergo, Abdel Senhadji, Abdoulaye Tall, Susan Yang and participants of the 2012 CSAE conference held in Oxford for their valuable comments. We are also grateful to Manzoor Gill, Pranav Gupta, Nancy Tinoza, and Yorbol Yakhshilikov for excellent research assistance and Bernardin Akitoby for allowing us to use the dataset of Akitoby and Stratmann (2008). This paper is part of a research project on macroeconomic policy in low-income countries supported by U.K.’s Department for International Development (DFID). The views expressed herein are those of the authors and should not be attributed to the IMF, its Executive Board, its management, or to DFID.
In this paper resource-rich developing countries refers to low- and lower-middle income countries with exhaustible natural resources (e.g., oil and minerals).
A comprehensive review of the Fund’s surveillance activities supported the need for renewed emphasis on external stability (See IMF, 2011a).
For the CGER methodology, see Lee et al. (2008) and for EBA see IMF (2012b). Bems and Carvalho (2009) extend the CGER external sustainability approach—which determines the external sector balance that would bring the net foreign asset position (NFA) of a country to a desired level—allowing for a long-term trend in NFA in order to accommodate the temporary nature of exhaustible resources.
In Chart B, boom periods are indentified in two stages. First, we find periods of expansions and recessions (cycles) using the Bry and Boscham (1971) algorithm (see also Harding and Pagan, 2002). Such a methodology searches for local maxima (peaks) and minima (droughts) in the data for each country. The algorithm is calibrated to identify cycles in resource exports of a country that last at least 4 years, with a phase of at least 2 years, and a grid (window) to find local peaks and troughs of 5 years. Once the cycles are obtained, the booms are finally identified for those expansion periods in which the cumulative change in resource exports is above the 90th percentile of the entire period of expansion for a particular country.
See also the econometric evidence provided by Beidas-Strom and Cashin (2011), who find no link between relative income levels and current account balances for a group of emerging market and developing countries.
Even if a resource abundant LIC gets enough resources to invest, these frictions can hamper the process from translating investment into growth-inducing capital accumulation, becoming a potential explanation for the natural resource “curse” discussed by Sachs and Warner (1995)—a negative relationship between resource abundance and growth. Another explanation, which we do not explore in this paper, relies on strong Dutch disease effects—the atrophy of traded economic sectors which have positive spillovers to growth. For a detailed investigation of these issues in the context oil windfalls and aid surges see also Berg et al. (2010) and van der Ploeg and Venables (2010). Despite many studies supporting the existence of this “curse”, overall the empirical literature points to mixed results, suggesting also the possibility of a natural resource “blessing” (see van der Ploeg, 2011b).
Berg et al. (2013) show, for example, that gradually investing a windfall and making non-resource revenues available to cover recurrent costs of public capital can alleviate absorptive capacity constraints, help preserve resource wealth, and address concerns about growth sustainability, macroeconomic stability, and Dutch disease. Baunsgaard et al. (2012), van der Ploeg and Venables (2011b) and van der Ploeg (2011a) advocate for the so called parking strategy of postponing domestic spending until the economy is ready to implement efficient spending choices.
Interestingly, Manzano and Rigobon (2007) argue that during the 1970s, when commodities’ prices were high, natural resource abundant countries used them as collateral for debt. As the 1980’s witnessed an important fall in the prices, these countries faced debt crises. In this regard, the previously mentioned natural resource curse might be related to a debt overhang.
The one-sector model is suitable to analyze current account adjustments due to non-optimal private and public sector behavior. However, the framework does not directly imply the economy’s equilibrium real exchange rate.
Some of our results have the flavor of the conclusions of Blanchard and Giavazzi (2002), who show that when a poor and faster-growing country achieves greater integration with a richer country, larger current account deficits are somewhat appropriate. They consider a reduction in the interest rate wedge facing the poorer country as a measure of capital account integration, which is analogous along some dimensions to an increase in natural resource rents. In addition, we have built several extra features into our framework that can be used to substantiate and manifest the caution of running persistent current account deficits for some countries. Most notably, the absorptive capacity and inefficiency considerations greatly mitigate the implications of a pure opening. Moreover, we consider finite increases in resource rents, rather than the permanent and in effect infinite scope for capital inflows implied by greater financial integration in their framework.
Bems and Carvalho (2011) find that external savings are dominated by consumption smoothing motive. The results suggest that the precautionary motive in the stochastic version of the model contributes only marginally in improving the mean squared error statistics relatively to the deterministic setting.
The oil sector here represents any exhautible resource sector.
Development agencies report that cost overruns of 35% and more are common for new projects in Africa. The most important factor by far is inadequate competitive bidding for tendered contracts. See Foster and Briceno-Garmendia (2010) and Lledo and Poplawski-Ribeiro (2013), among others.
This specification is borrowed from Kim and Ruge-Murcia (2009), who use this functional form to model asymmetric nominal wage adjusment costs.
This is in the same spirit of the recent literature on incomplete markets and heterogenous agents models. In this literature, the problem of maximizing an objective function subject to an inequality constraint is replaced with an unconstrained maximization problem, whose objective function or budget constraint include a penalty function that tries to capture the effects of the inequality constraint. This approach allows the use of perturbation methods to simulate these models. See Preston and Roca (2007) and Algan et al. (2010), among others.
Since we want to derive current account benchmarks with optimality content, we focus on the social planner problem, where the government takes optimally both private and public decisions. By doing this, the government internalizes and, therefore, mitigates the negative effect that fiscal policies may have on the private sector. To some extent, this explains why domestic borrowing is ruled out: although domestic borrowing can be a financing source for public investment, it can substantially crowd out the private sector and, as a result, is dominated by external borrowing (see Buffie et al. 2012).
Transversality conditions on st, kt, and dt are also imposed.
This implies a return on public capital of 23 percent. Foster and Briceño-Garmendia (2010) estimate returns for electricity, water and sanitation, irrigation, and roads range from 17% to 24% in low-income countries. Similarly, the macro-based estimates in Dalgaard and Hansen (2005) cluster between 15% and 30% for a wide array of different estimators.
Since in the model, adjustment costs are only present off steady state, then (ϕκ and ϕs are picked to match a specific ratio—in this case 25%—of average adjustment costs to total investment outlays. To do so, we also need values of private and public capital stocks, in percentage of GDP. As we explain below in our application to CEMAC, we use the average estimates for Cameroon and Congo by Cubas (2011) of 100 and 36 percent of GDP, respectively.
For further discussion on public investment and a proposed index for efficiency see Dabla-Norris et al. (2011).
By running linear regressions, Akitoby and Stratmann (2008) and Van der Ploeg and Venables (2011a) find empirical evidence of a positive link between interest rate spreads and the debt to gross national income ratios for a subset of developing economies. From a theoretical perspective, Bardhan (1967) was the first one to postulate an upward-sloping supply schedule, where the cost of debt increases with the absolute level of foreign debt. More recently, this assumption has been used by Agenor (1997), Schmitt-Grohe and Uribe (2003), and Turnovsky (1997), among others. By expressing the supply curve in terms of debt-to-gdp ratios, in our model a country that adopts growth-oriented policies can shift the supply curve downward, so that at each level of debt the country faces a lower risk premium, as proposed by Sachs (1984).
In the analytical experiments that follow, we assume that there is full private and public investment efficiency, i.e., ek=es=1. Of course, one could also vary these parameters over time, but as discussed below, the effect would be very similar to that of varying the degree of absorptive capacity constraints.
Development considerations can suggest starting simulations off steady state. Our results, which are available from the authors upon request, do not change to a great extent. When we apply the model to derive a current account benchmark for CEMAC, we start simulations off steady state.
Negative values of debt denote accumulation of foreign assets.
These two scenarios have different steady states and represent different economies. In the imperfect capital mobility case, the high country risk premium implies a lower discount factor reflecting very impatient agents. This explains in part the consumption tilting. In contrast, in the case of almost perfect international mobility, the country risk premium is significantly lower and therefore the discount factor much higher. Hence agents in this economy are more patient. The steady-state value of debt, which is given exogenously to the model, has also implications for the discount factor. At the steady state, a lower debt value would imply a higher discount factor and more patient agents, leading to higher current account balances in the dynamics.
For instance, lower efficiencies will call for higher current account balances.
In the analysis we include the oil-producing CEMAC countries: Republic of Congo, Chad, Cameroon, Gabon, and Equatorial Guinea.
Though the focus here is on oil production, other natural resources (e.g. natural gas) could also be taken into account.
External positions have been further strengthened by HIPC and MDRI debt relief in Cameroon, Central African Republic and Republic of Congo.
The underlying current account is defined here as the projected current account balance by the IMF desks taking into account the countries macroeconomic frameworks and authorities plans. Alternatively, the underlying current account could also be defined as the current account balance that would emerge at a zero output gap.
The “current account norm” is defined as the equilibrium current account that is in line with macroeconomic fundamentals.
In this application, we have chosen the same values for the initial and the steady-state debt to GDP ratios. Of course, it is possible to pick a different steady-state value depending on the user’s view of the state to which the economy is converging.
The dynamics of the macroeconomic variables, including the current account, are driven by both the projected oil path and the inherent dynamics associated with starting the economy off steady state. This also reveals the need of having a stand about the steady state to which the economy is converging. For simplicity in the analysis, in these simulations the steady state is still determined by the structural parameter values described in the calibration subsection.
By the same token, this also means that lowering the efficiencies of public and private investment would induce higher current account balances.