Resilience and Growth in the Small States of the Pacific
Chapter

Chapter 13. Global Commodity Prices, Monetary Transmission, and Exchange Rate Pass-Through

Author(s):
Hoe Khor, Roger Kronenberg, and Patrizia Tumbarello
Published Date:
August 2016
Share
  • ShareShare
Show Summary Details
Author(s)
Shanaka J. Peiris and Ding Ding 

Pacific island countries (PICs) are vulnerable to commodity price shocks and this poses challenges for monetary policy. The spike in global commodity prices in 2007–08 led to a rise in headline inflation, while the global financial crisis that intensified in September 2008 put downward pressure on imported and domestic inflation through lower commodity prices and weak demand (Figure 13.1). Authorities loosened monetary policy, but reductions in bank lending rates have been limited and private credit growth anemic. These sluggish responses raise key questions about the effectiveness of monetary policy transmission mechanisms in PICs and the appropriate nominal anchor, given the high volatility in global commodity prices.

Figure 13.1Pacific Island Countries: Average Fuel, Food, and Headline Inflation

Sources: Country authorities; and IMF staff estimates.

Note: CPI = consumer price index.

The exchange rate could be used to cushion against exogenous shocks.1 With a conventional exchange rate peg, the burden of addressing the impact of exogenous shocks falls on monetary and fiscal policies. Given limited capital mobility in PICs, monetary policy enjoys a certain degree of freedom despite the pegged exchange rate regime. However, if monetary policy transmission is weak and the prospects for improving its effectiveness are limited in the short term, countercyclical policies will require an appropriate mix of monetary, fiscal, and exchange rate policies.2 Monetary policy could be closely coordinated with fiscal policy to achieve the desired impact, but that is often constrained by the lack of fiscal buffers. Therefore, pegged exchange rate regimes should not preclude considerations to introduce some flexibility to increase the role of the exchange rate in absorbing external shocks and to provide additional freedom for monetary policy.

This chapter assesses the impact of international commodity price shocks and monetary and exchange rate transmission in PICs to explore whether the exchange rate can help absorb exogenous shocks. The chapter reviews the key objectives of monetary policy and how it is conducted in PICs. This is followed by an empirical assessment of monetary and exchange transmission mechanisms in these countries, focusing on the responses of real GDP and headline inflation to external shocks, exchange rate changes, and monetary aggregates or interest rates. We then more formally consider the appropriate nominal anchor to maintain macroeconomic stability in a small island country using a model-based approach applied to Tonga. The chapter concludes with some policy implications.

The Choice of a Nominal Anchor

To ensure price stability, central banks around the world must choose between strategies that target a monetary indicator or the exchange rate.3 Analyses based on the dynamic stochastic general equilibrium (DSGE) model generally find interest rate based inflation targeting performs better than exchange rate targeting in maximizing social welfare and minimizing macroeconomic volatility (Stone and others 2009). However, McCallum (2006) compares the performances of Taylor-type interest rate rules and exchange rate based approaches to inflation targeting in an economy with varying degrees of openness. The key finding is that, as the degree of openness increases, an exchange rate based approach to inflation targeting does much better than the standard interest rate based approach in stabilizing output, with no adverse consequences for inflation variability. This is because in an interest rate based approach the variability of the interest rate is low, while that of the exchange rate based approach is high. The results suggest that in an economy with a high exchange rate pass-through to imported goods prices and low interest rate sensitivity of aggregate expenditures, smoothing the exchange rate rather than interest rates may help control inflation and reduce output volatility. Parrado (2004) shows that a trade-weighted exchange rate index explained a significant share of the variation in consumer price index (CPI) inflation in Singapore and that the Monetary Authority of Singapore has used the index to target inflation.

The high pass-through of global commodity prices and import propensity in PICs suggest a greater role for exchange rate flexibility in controlling inflation (Figure 13.2). There is a large weight of fuel and food items in the CPI basket in PICs, and thus a high pass-through of global commodity prices to headline inflation (Figures 13.3 and 13.4). More generally, the import propensity (or share of imported items in consumption and investment) is high in most PICs given the region’s small domestic manufacturing base.4 One would therefore expect the exchange rate to be an effective tool to control imported inflation without significant output costs. In fact, it appears that during 2010–11 PIC central banks appreciated their currencies in nominal and real effective terms to cushion the impact of higher global food and fuel prices. This exchange rate adjustment could be based on a view of the greater efficacy of exchange rate flexibility in managing inflation or evidence of the limited effectiveness of monetary policy transmission to commercial bank interest rates and private sector credit documented in Chapter 12.

Figure 13.2Effective Exchange Rate

(Index, 2005 = 100)

Source: IMF staff estimates.

Note: NEER = nominal effective exchange rate; PICs = Pacific island countries; REER = real effective exchange rate. Includes Marshall Islands, Papua New Guinea, Samoa, the Solomon Islands, Tonga, and Vanuatu.

Figure 13.3Pass-Through of Oil Prices to Inflation

Source: IMF staff calculations.

Figure 13.4Pass-Through of Food Prices to Inflation

Source: IMF staff calculations.

Note: Food-only consumer price inflation for Papua New Guinea, Samoa, the Solomon Islands, Tonga, and Vanuatu; and overall consumer price inflation for Fiji.

Monetary and Exchange Rate Transmission Mechanisms

The literature on the transmission mechanism of monetary policy in advanced economies and emerging markets is burgeoning, but there is a dearth of studies on the Pacific islands. Typically, this strand of research has been conducted in the context of a vector autoregression (VAR) framework pioneered by Sims (1980). Notable examples using VAR to identify the transmission of monetary policy for advanced economies include Christiano, Eichenbaum, and Evans (1999) for the United States and Kim and Roubini (2000) for the Group of Seven economies. However, there has been little analysis of the monetary transmission mechanism in the Pacific island economies, leaving a number of unanswered questions and somewhat of a vacuum of knowledge in this area.5

Chapter 12 uses single-equation econometric techniques to assess the pass-through of policy rates to lending rates and private credit growth; the authors conclude that the monetary transmission mechanism is relatively weak in the Pacific islands. However, Jayaraman and Choong (2009), using a simple VAR approach, show that money supply is the most effective channel of monetary policy transmission in Fiji. Jayaraman and Dahalan (2008) suggest that the money and exchange rate channels are important in transmitting monetary impulses to Samoa’s output, while monetary aggregates matter more for inflation. We follow a similar approach but attempt to account for the pass-through of international commodity prices and alternative channels of monetary and exchange transmission by using a more comprehensive set of external and macro-financial variables for six PICs, while controlling for endogeneity in a panel VAR framework.6

A panel VAR model is estimated for PICs to reflect the pass-through of global commodity prices and monetary and exchange rate transmission mechanisms to the real economy.7 The ordering of variables in the recursive VAR is the following: the global exogenous factors (global fuel and food prices) come first followed by the assumption that the exchange rate and monetary indicators (interest rates or monetary aggregates) respond to demand conditions (real GDP). These factors can impact headline inflation with no immediate effects and vice versa. The VAR models are estimated using annual data from 1995 to 2011 for all countries to minimize structural breaks. All variables are in log-differences, except interest rates. Standard information criteria are used to select the lag lengths of the VAR, which turn out to be two years. Results of Granger causality tests lend support for a transmission of external shocks and the exchange rate to the domestic economy.

Results

The results of the baseline panel VAR model are shown in Figures 13.5 and 13.6. The graphs display the impact on headline inflation, as measured by the impulse response, of a one standard deviation shock to global fuel prices, global food prices, output, the bilateral exchange rate against the U.S. dollar, domestic interest rates, and the headline price index itself. The exchange rate pass-through is calculated as the impact of a shock on the exchange rate (defined as an exogenous, unexpected, temporary rise at t = 0) on the headline inflation index over a specific time interval. The relative importance of the exogenous and monetary shocks for fluctuations in headline inflation and the output gap at different forecast horizons can be gauged through the forecast error variance decompositions.

Figure 13.5Impulse Response of Inflation to Shocks

Source: IMF staff estimates.

Note: Figure shows accumulated response of inflation to Cholesky one standard deviation innovations.

Figure 13.6Variance Decomposition of Real GDP and Inflation

Source: IMF staff estimates.

The transmission of external and monetary shocks provides little scope for PICs to influence the real economy through monetary management alone. The key insights are:

  • As expected, higher global food and fuel prices raise headline inflation. The impact of global food prices is larger and more persistent than global fuel prices given the greater weight of food items in the CPI basket.

  • The impact of monetary impulses on headline inflation is not as significant as the impact of exchange rate fluctuations. The pass-through of the exchange rate to headline inflation is 60 percent within one year, with a complete pass-through within the second year. On the other hand, the impact on and variation of headline inflation explained by monetary impulses is relatively small whether one considers broad money, domestic credit, reserve money, or interest rates, as in the baseline model. In fact, higher interest rates are associated with greater inflation, possibly indicating reverse causation, where exchange rate changes and inflation determine the level of interest rates.

  • Real GDP is not well explained by the shocks considered, except its own innovations. This probably reflects the importance of supply-side factors and policy variables not captured, such as fiscal policy. Interestingly, global commodity prices appear to affect economic activity more than global GDP, indicating a relatively weak impact of external demand compared to terms of trade, although none of these effects is statistically significant.8

A Forecasting and Policy Analysis System Model for Small Island Countries

While the high exchange rate pass-through and weak monetary transmission mechanism provide greater scope to use the exchange rate for controlling inflation, the appropriateness of using it as a tool of macroeconomic stabilization largely depends on the size and nature of shocks as well as the country’s economic structure. The panel VAR analysis shed light on the relative efficacy of monetary and exchange rate transmission controlling for exogenous shocks, but it did not take into account the distribution of shocks and structural economic relationships in the economy. In particular, linkages to real economic activity were not well identified, possibly owing to data limitations and country heterogeneity in the transmission of shocks. To better identify the shocks and overcome some of these limitations, we estimate and simulate a small macroeconomic model for Tonga as an illustrative PIC and small island economy using quarterly data from 1995 to 2011.

The monetary policy analysis is conducted by extending the small New Keynesian macroeconomic model of Berg, Karam, and Laxton (2006a, 2006b) to better capture factors specific to Tonga. The model is a stripped down version of a DSGE model with rational expectations. In recent years, the macroeconomic literature has used DSGE models and small New Keynesian models to analyze economic behavior and to forecast developments. The DSGE models are based on theoretical underpinnings and have been found to be useful for analyzing the effects of structural changes in the economy, as well as the effects of longer-term developments such as persistent fiscal and current account deficits. By virtue of their relatively simple structure, small New Keynesian models have also been used for forecasting and policy analysis purposes in central banks and by IMF country desks. A number of inflation-targeting central banks have used similar models as an integral part of their policy analysis (see Laxton, Rose, and Scott 2009).

Global commodity prices and the possible use of the exchange rate for domestic stabilization are incorporated in work by Carabenciov and others (2008) and Parrado (2004), respectively. To capture the commodity price dependence of Tonga and that of PICs more generally, the baseline model is extended to incorporate global oil and food prices. Moreover, to better understand the dynamics of inflation, we estimate two Phillips curves—headline inflation and domestic price inflation (rather than core inflation)—like Berg, Karam, and Laxton (2006b). Estimating domestic price inflation provides insights into the potential role of intermediate imports emphasized by McCallum (2006) for very open economies. As Tonga is a small island country, we incorporate external demand shocks from the rest of the world captured by partner country GDP growth. Output developments in the rest of the world feed directly into the small economy as they influence foreign demand for Tongan products and services (for example, remittances and tourism). To allow for the possibility of using the exchange rate as the tool of macroeconomic stabilization, we consider two closure rules to the model: a standard Taylor rule and a modified exchange rate targeting rule following Parrado (2004). Changes in foreign inflation and interest rates affect the interest rate or exchange rate depending on the closure of the model and, subsequently, demand and inflation in Tonga.

The extended Forecasting and Policy Analysis System model has four behavioral equations: (1) an aggregate demand or IS curve that relates the level of real activity to expected and past real activity, the real interest rate, the real exchange rate, foreign demand, the fiscal stance, and financial conditions; (2) a price setting or Phillips curve that relates inflation to past and expected inflation, the output gap, fuel prices, and the exchange rate; (3) an uncovered interest parity condition for the exchange rate or interest rate, with some allowance for backward-looking expectations; and (4) a modified Taylor rule for setting the policy interest rate or exchange rate as a function of the output gap and expected inflation.

The aggregate demand equation is as follows:

where ygap is the output gap; RRgap the real interest rate gap; zgap the real exchange rate gap; ygapRW the output gap in the United States; η is a measure of lending conditions specified in the aggregate demand equation, as in Carabenciov and others (2008); β a series of parameters attached to these variables; and εygap an error term that captures other temporary exogenous demand shocks. Lending conditions are proxied by real credit growth.

The Phillips curve equation is as follows:

where π4t+1 is the four-quarter-ahead inflation rate (year over year), π4t—1 the four-quarter lagged inflation rate, ygap the output gap, ztz—1 the real depreciation, α the parameters, otπ international fuel price inflation, ftπ international food price inflation, and ɛtπ an error term. Note that international fuel and food price inflation is in terms of domestic prices. For example, international fuel price inflation is expressed as:

where πtRWo is the global (Brent) oil price inflation.

As an extension, we add the following equation for domestic core inflation:

where the term π4t-1—π4c,t—1 has been added to the simple canonical inflation equation to allow for the possibility that imported goods are an important input into the production costs of many domestic goods, or if workers resist the reduction in their real wages in response to an increase in headline inflation related to higher imported prices.

The uncovered interest parity equation (UIP) is as follows:

where zt is the log of the real exchange rate (an increase represents a depreciation), RRt the real interest rate, RRtRW the U.S. real interest rate, ρ* the historical average risk premium on the domestic currency, δz the smoothness parameter, and ɛtz an error term. This equation, an uncovered interest rate parity condition, posits that the real exchange rate is a function of the expected real exchange rate (the first two terms), the real interest rate differential (the currency risk premium), and a disturbance term. Note that the above equation is equivalent to the conventional UIP condition, which is usually expressed in nominal terms. Denote the log of the nominal exchange rate as N „ then the change of the nominal exchange rate is:

Substituting this in the conventional UIP condition will derive the UIP condition in real terms. In the case of exchange rate targeting, the UIP condition is written in terms of the domestic interest rate instead.

The Taylor rule is as follows:

The Taylor rule can be modified by inverting the equation in terms of the exchange rate, as in Parrado (2004).

In the case of the exchange rate targeting regime, we replace the Taylor rule and the uncovered interest parity equation with the following equations. We follow the exchange rate targeting framework in Khor, Robinson, and Lee (2004) and Parrado (2004) in which the change of nominal effective exchange rate is a function of the inflation gap and output gap

where Δe is the long-term equilibrium change in the nominal effective exchange rate. Exchange rate targeting follows:

where the real exchange rate gap is a function of the inflation gap and output gap. It is easy to show that the above function is equivalent to the nominal exchange rate targeting rule in Khor, Robinson, and Lee (2004), as the nominal exchange rate gap depends on the real exchange rate gap and the inflation rates. For model consistency and estimation, we express the nominal exchange rate targeting rule in terms of an equivalent real exchange rate targeting rule.

Correspondingly, the real interest rate is determined by the UIP equation as:

where ρ* is the historical average risk premium on the domestic currency.

The model is estimated using Bayesian techniques based on prior distributions for the parameters from cross-country work and assumptions about the Tongan economy. Bayesian estimation in a situation of a relatively small sample size (which is almost always the case for macro time series data) helps ameliorate the problems of classical econometric estimation, which often gives macro model results that are inconsistent and faces simultaneity challenges. This is a particularly important aspect for Tonga and other PICs where data are extremely limited and other cross-country studies could help pin down structural parameters. However, to avoid imposing too much structure, we use diffused priors. All variables are seasonally adjusted using the X12 filter, with the exception of the interest rate and the exchange rate, and expressed in “gap” terms, defined as deviations from a Hodrick-Prescott time trend.

The trade-off between targeting interest rates and the exchange rate is assessed based on the historical distribution of shocks. Using the estimated parameters and distributions for the stochastic shocks, solutions are derived for the variability in inflation and the output gap under alternative policy reaction functions where the interest rate or exchange rate is the target variable in the modified Taylor rule. The rule that minimizes a loss function of inflation and output variability of a standard quadratic form given by the following equation can be used to gauge the “optimal” policy rule in terms of minimizing macroeconomic volatility:

where λπ and λy are the relative weights on inflation and output-gap variability and πT is the inflation target.

The impulse responses and variance decomposition of all stochastic shocks suggest that demand and supply shocks are nearly equally important in accounting for inflation dynamics. As far as the impulse response functions are concerned, the model shows reasonable and expected patterns. Global commodity price and demand shocks have a significant impact on output and inflation dynamics, requiring a policy response to help stabilize the economy. Exchange rate changes have a greater influence on inflation while interest rates affect aggregate demand more than exchange rate changes, albeit having a small impact. This contrasts somewhat with the perceived dominance of supply-side factors based on the results of a pure empirical panel VAR approach, highlighting the need for more systematic and country-specific analyses to better identify and interpret shocks.

An exchange rate based targeting approach is better at achieving macroeconomic stability (see Table 13.1). An exchange rate based targeting rule does much better at stabilizing both output and inflation in Tonga. The variance decompositions suggest that this result is a consequence of the greater importance of global commodity prices and external demand shocks that could be more effectively insulated through the exchange rate. The spillover of imported inflation to domestic consumption goods prices through the use of intermediate imports or wage indexation in terms of headline inflation likely makes the exchange rate a more powerful channel in controlling inflation. Moreover, the low interest rate sensitivity of aggregate demand leads to less macroeconomic volatility under an exchange rate based targeting approach than in advanced economies. The preference for targeting interest rates over exchange rates in PICs and other small island countries is thus not as clear-cut as in advanced inflation targeting economies, as suggested by McCallum (2006). Our result is also consistent with the fact that nearly all PICs except Fiji appreciated the exchange rate in response to the surge in headline inflation in 2010–11, mainly driven by higher commodity prices.

Table 13.1Macroeconomic Volatility under Alternative Policy Rules
GDP GapHeadline Inflation
Exchange rate targeting0.341.01
Interest rate targeting1.094.76
Source: IMF staff estimates.

Policy Implications

Exchange rate flexibility could be a useful tool for macroeconomic stabilization in PICs. External shocks—particularly global commodity shocks—have a significant impact on headline inflation, both directly and indirectly through intermediate imports. The high degree of exchange rate pass-through to headline inflation in PICs and the weak monetary transmission mechanism suggest a greater efficacy of exchange rate changes, as opposed to monetary policy, in affecting inflation. The impact of monetary impulses on headline inflation is weak, whether one considers broad money, reserve money, interest rates, or domestic credit. This probably reflects the excess liquidity in the banking system and structural impediments to lending such as weak contract enforcement and bankruptcy procedures.

In terms of the trade-off between exchange rate and monetary policy in macroeconomic stabilization, the empirical evidence of the model-based approach tilts in favor of exchange rate policy. This is because of the close relationship between exchange rate changes and headline inflation, and the low interest rate sensitivity of aggregate demand. That said, the use of exchange rate flexibility for short-term domestic stability objectives in the context of the de jure exchange rate basket regime will only be possible in a situation of comfortable international reserves, and should avoid significant deviation of the exchange rate from its medium-term fundamentals.

Greater exchange rate flexibility and structural reforms may help strengthen the effectiveness of the monetary transmission mechanism. While monetary policy is unlikely to be subordinate to the exchange rate regime because of limited interest-sensitive, cross-border capital flows, some private transfers such as remittances and nonresident bank deposits may respond to changes in domestic interest rates. Thus, greater exchange rate flexibility may provide greater independence to monetary policy. More important, the monetary transmission could be strengthened by enhancing the interest rate pass-through to lending rates and by addressing the excess liquidity and structural problems, such as by introducing greater competition in the financial system. The underdeveloped state of PIC financial markets also affects the interest rate and credit channel of monetary policy. Collateralized interbank lending is not widely available, and secondary markets for government and central bank paper are virtually nonexistent.

Annex 13.1. Identification of Vector Autoregression (VAR)

Structural VAR Modeling

Following Kim and Roubini (2000) and Sims and Zha (2006), we assume the economy is described by a structural-form equation:

where G(L) is an n × n matrix polynomial in the lag operator; C(L) is an n × k matrix polynomial in the lag operator; Yt is an n ×1 vector of endogenous variables; and Xt is a k x1 vector of exogenous foreign variables; ɛt is an n ×1 vector of structural disturbances, with var(ɛt) = Λ, where Λ is a diagonal matrix and the diagonal elements are the variances of structural disturbances. Therefore, structural disturbances are assumed to be mutually uncorrelated.

Corresponding with this structural model we can estimate a reduced-form VAR:

where A(L) and B(L) are matrices polynomials; μt is a vector of reduced-form disturbances, with var(μt) = Σ.

We assume the exogenous vector Xt contains IMF World Economic Outlook global fuel prices (Fuel) and food prices (Food):

These variables are included to control for changes in overall global economic conditions and fluctuations in the prices of PICs’ main imported commodities. The other endogenous variables include the real GDP (Y), exchange rate (E), a monetary aggregate or interest rate (M), and headline consumer price index (HCPI).

In the baseline model, the money policy variable is chosen to be interest rates, but other monetary aggregates are tested, and domestic credit to the private sector has the largest explanatory power, albeit still relatively small compared to the exchange rate impact on inflation.

Identification Scheme: Recursive VAR

There are many ways of recovering the parameters in the structural-form equations from the estimated parameters in the reduced form equation. A popular and convenient method is to orthogonal-ize reduced-form disturbances by Cholesky decomposition using a simple recursive VAR, as in Sims (1980).

References

    Berg, Andrew, PhilippeKaram, and DouglasLaxton.2006a. “A Practical Model-Based Approach to Monetary Policy Analysis: Overview.IMF Working Paper 06/80, International Monetary Fund, Washington.

    Berg, Andrew, PhilippeKaram, and DouglasLaxton.2006b. “Practical Model-Based Monetary Policy Analysis: A How-to Guide.IMF Working Paper 06/81, International Monetary Fund, Washington.

    Carabenciov, Ioan, IgorErmolaev, CharlesFreedman, MichaelJuillard, OndraKamenik, DmitryKorshunov, DouglasLaxton, and JaredLaxton.2008. “A Small Quarterly Multi-Country Projection Model with Financial-Real Linkages and Oil Prices.IMF Working Paper 08/280, International Monetary Fund, Washington.

    Christiano, Lawrence J., MartinEichenbaum, and Charles L.Evans.1999. “Monetary Policy Shocks: What Have We Learned and to What End?In Handbook of Macroeconomics, Edition 1, edited by J.Taylor and M.Woodford.Amsterdam: North-Holland.

    Jayaraman, T. K., and Chee-KeongChoong.2009. “How Does Monetary Policy Transmission Work in Fiji?International Review of Economics,56 (2): 14561.

    Jayaraman, T. K., and JauhariDahalan.2008. “Monetary Policy Transmission in an Undeveloped South Pacific Island Country: A Case Study of Samoa.International Journal of Monetary Economics and Finance,1 (4): 38098.

    Khor, Hoe Ee, EdwardRobinson, and JasonLee.2004. “Managed Floating and Intermediate Exchange Rate Systems: The Singapore Experience.Staff Paper 37, Monetary Authority of Singapore.

    Kim, Soyoung, and NourielRoubini.2000. “Exchange Rate Anomalies in the Industrial Countries: A Solution with a Structural VAR Approach.Journal of Monetary Economics,45 (3): 56186.

    Laxton, Douglas, DavidRose, and Alasdair M.Scott.2009. “Developing a Structured Forecasting and Policy Analysis System to Support Inflation-Forecast Targeting (IFT).IMF Working Paper 09/65, International Monetary Fund, Washington.

    McCallum, Bennett T.2006. “Singapore’s Exchange Rate-Centered Monetary Policy Regime and Its Relevance for China.Staff Paper 43, Monetary Authority of Singapore.

    Parrado, Eric.2004. “Singapore’s Unique Monetary Policy: How Does It Work?IMF Working Paper 04/10, International Monetary Fund, Washington.

    Peiris, Shanaka J., and DingDing.2012. “Global Commodity Prices, Monetary Transmission, and Exchange Rate Pass-Through in the Pacific Islands.IMF Working Paper 12/176, International Monetary Fund, Washington.

    Sims, Christopher.1980. “Macroeconomics and Reality.Econometrica,48 (1): 148.

    Sims, Christopher, and TaoZha.2006. “Does Monetary Policy Generate Recessions?Macroeconomic Dynamics,10 (2): 23172.

    Stone, Mark, ScottRoger, AnnaNordstrom, SeiichiShimizu, TurgutKisinbay, and JorgeRestrepo.2009. The Role of the Exchange Rate in Inflation-Targeting Emerging Economies. IMF Occasional Paper 267. Washington: International Monetary Fund.

This chapter is based on IMF Working Paper 12/176. We thank Patrizia Tumbarello, Hoe Ee Khor, Subir Lall, and Joyce Mafi, the former governor of the National Reserve Bank of Tonga, and members of the IMF’s Small Islands Club for their helpful comments.

De jure exchange rate regimes in Fiji, Samoa, the Solomon Islands, and Vanuatu are adjustable pegs or bands to a basket of currencies. Papua New Guinea has a de jure floating exchange rate regime. Marshall Islands, Micronesia, and Palau use the U.S. dollar as legal tender, while Kiribati and Tuvalu use the Australian dollar.

Chapter 12 shows that the monetary transmission mechanism in PICs is weak.

Exchange rate flexibility is also important to maintain external stability and thus may not be available to target domestic stability objectives. Therefore, this discussion presupposes a comfortable level of international reserves, but does not preclude the possibility of the exchange rate being subordinate to external stability concerns.

For example, the share of imported items in the CPI basket is more than 40 percent in the Solomon Islands and Tonga, two countries that disaggregate the CPI basket between domestic and imported goods.

This has been partly related to data limitations, particularly a lack of quarterly real sector aggregates.

The six countries are Fiji, Papua New Guinea, Samoa, the Solomon Islands, Tonga, and Vanuatu.

See Annex 13.1 for more details on the VAR modeling approach.

The panel VAR with global GDP instead of global commodity prices was statistically insignificant and not reported here.

    Other Resources Citing This Publication