17 Monetary Policy in Low-Income Countries in the Face of the Global Crisis: A Structural Analysis

Andrew Berg, and Rafael Portillo
Published Date:
April 2018
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Alfredo Baldini, Jaromir Benes, Andrew Berg, Mai C. Dao and Portillo Rafeal 

1 Introduction

Understanding the impact of the global financial crisis in low-income countries (LICs) is an important task for national authorities and international organizations.1 Beyond its intrinsic importance, the crisis provides a relatively clean ‘experiment’: it can be interpreted as an exogenous event for most LICs, while its magnitude facilitates tracing its effects. As such, it provides insights about the structure of these economies and their exposure to external factors. It also allows central banks to assess—and learn from—past decisions.

In this chapter we develop a quantitative model—adapted to the specific characteristics of LICs—to analyse the impact of the financial crisis on Zambia, and the role that monetary policy played in the transmission of the crisis. We compare the predictions of the model to a dataset of Zambian macroeconomic and financial variables.

Zambia is in many ways a representative low-income country. It is dependent on commodity exports (copper). It is financially underdeveloped, with foreign-owned banks playing the central role, along with the exchange rate, in the transmission of monetary policy. Its monetary policy framework is also fairly representative. The Bank of Zambia targets monetary aggregates under a floating exchange rate regime. As in other LICs, fiscal developments can pose a challenge for monetary policy through their effect on aggregate demand and the allocation of credit.

The design of our model explicitly incorporates these features. We model banks’ various assets and liabilities and their respective interest rates, and assume that the private sector is unable to obtain financing beyond the banking system. We allow for the possibility that shocks to the banking system may be reflected in binding credit constraints in addition to higher interest rates. We also model fiscal developments and their implications for the transmission of external shocks. Our model is otherwise standard, i.e., it conforms to the typical structure of DSGEs.2

From Zambia’s perspective—and that of low-income countries in general—we view the global financial crisis in terms of three related shocks. The first was a large deterioration in Zambia’s terms of trade, associated with the collapse in copper prices during 2008 and 2009. The second was an increase in the country’s external risk premium, as foreign investors’ demand for Zambian assets decreased. The third shock was a decrease in Zambian banks’ risk appetite in response to the crisis, which we define as a shift away from (risky) lending toward safe assets. Specifically, banks increased lending rates (relative to interest rates on safer assets), reduced their overall lending to the domestic private sector, and increased their demand for liquidity and government bonds. We view these shocks as reflecting a single underlying event—the global financial crisis—though we do not undertake here to model this relationship.

The combination of these shocks led to a large nominal and real depreciation, a reversal in current account dynamics—from large deficits to balance—a decline in domestic demand, and a temporary decrease in inflationary pressures. On the fiscal front, government revenues declined and debt issuance increased. In the banking sector, the reallocation of assets away from loans to the private sector and toward government securities and liquidity, together with a steep slowdown in the growth of broad money, contributed to a decrease in the money multiplier (or alternatively, an increase in measures of banks’ liquidity).

In this context, the actual response of monetary policy can be characterized as ‘stop and go’. The T-bill rate (the preferred instrument for open market operations in Zambia) initially increased by 400 basis points between mid-2008 and mid-2009. As the crisis worsened, the policy stance was later reversed, allowing T-bill rates to fall by more than 1,000 basis points in the second half of 2009, and liquidity increased substantially.

We reproduce the crisis in our model by picking a combination of the afore-mentioned shocks that help match the exact path of key external variables (the terms of trade, the nominal exchange rate, and the current account).3 We then compare the model’s output with data on ten macroeconomic and financial variables, conditional on the ‘stop and go’ policy pattern, i.e., on a sequence of monetary policy shocks that replicates the large swings in T-bill rates.

Our main results are the following. First, we find that the model broadly reproduces the path of most variables, with the notable exception of GDP. This relative success suggests that DSGE models can contribute to the quantitative analysis of macroeconomic developments and policy in Zambia and low-income countries more generally, although more work is needed to understand the behaviour of GDP and the macro-financial-balance of payment linkages in these countries.

Second, we find that all three real shocks—terms of trade, external risk premium, and change in banks’ appetite for risk—are necessary to help match the data. The first two shocks tend to generate the desired nominal depreciation and a subsequent decrease in imports, but they have counterfactual implications for the current account and the volume of credit, as consumers would smooth the temporary decrease in income through an increase in externally financed credit and a higher current account deficit. Meanwhile, the decrease in banks’ risk appetite helps match the current account reversal and the contraction in credit but by itself would result in an appreciation of the currency, as relative demand for foreign goods would decrease. It is only by combining the three shocks that the model can reproduce the stylized facts.

Third, our modelling exercise shows that developments in the banking sector were an important part of the transmission of the crisis to the domestic economy. In our model, the contraction in credit induced by banks is required to generate the right current account reversal, while its impact on aggregate demand helps generate the decline in inflation observed during the crisis. The increase in lending premia is also helpful to understand the impact on aggregate demand, although by itself it would not generate a current account reversal. Moreover, banks demand for liquid and safe assets helped shape the monetary policy stance, given the money-targeting regime in place.

Finally, our model shows that the ‘stop and go’ policy response was counter-productive, in that it may have contributed initially to the contraction in aggregate demand. A more accommodating policy would have helped stabilize the economy earlier, albeit at the cost of higher nominal depreciation and inflation. While the effect would have been limited in absolute terms, given the magnitude of the real shocks hitting the economy, such a policy would have reduced the decline in private spending in 2009 by 3 to 6 per cent, depending on the specification. Policy rules that respond to various developments in the banking system (changes in the growth rate of credit or deposits) would have also helped stabilize the economy.

In light of the last result, we also discuss the determinants of the initial ‘stop’ response of monetary policy. We find that the policy response appears to have been driven by ‘rear-view’ or ‘side-view’ issues, not all of them directly related to the crisis. First, authorities were concerned with inflationary pressures at the time, mostly associated with the food and fuel price shock of 2007 and early 2008. Second, authorities may have also been responding to the large nominal depreciation induced by the crisis. Third, authorities may have been reluctant to loosen policy at a time of incipient increases in measures of ‘excess liquidity’. Policy makers were also likely influenced by the overshooting of reserve money targets during 2008, which may have led to a view that monetary policy was loose.

Our chapter is related to the large and growing literature on the impact of the recent financial crisis.4 Relative to previous work on the credit channel, which focused on the role of borrowers’ financial conditions on the amplification of shocks, recent work has emphasized developments in the financial system itself as the source of the crisis.5 Our work has elements of both, giving importance to both systemic and counterparty-specific risks. Unlike most of these recent contributions, however, we limit ourselves to a relatively simple treatment of the banking sector in an open economy, since our goal is to provide a coherent story for Zambia’s experience during the crisis.

Our chapter is also related to the literature on financial crises in emerging markets, especially on the role of monetary policy.6 We differ in that our focus is on a combination of external shocks—rather than just the current account reversal—and we pay special attention to developments in the banking/monetary system. Also, the relatively low degree of financial dollarization in Zambia (less than 30 per cent of loans and deposits) allows us to abstract from currency mismatches—a central theme in that literature. Finally, our work is also related to Agenor and Montiel (2006, 2008) who emphasize—in a static small open economy framework—the role of the domestic banking system in monetary policy in developing countries.

The chapter is organized as follows. Section 2 briefly reviews relevant aspects of the structure of the Zambian economy and sets the stage. Section 3 introduces the structure of the model and the shocks we consider. Section 4 discusses the Zambia data and the calibration, and applies the model to Zambia under the actual path of monetary policy and under alternative policy responses. Section 5 discusses the factors behind the initial monetary policy response. Section 6 derives some policy implications for low-income countries and concludes.

2 The Economy of Zambia and the Global Financial Crisis

Zambia is in many ways a typical low-income country and faced a typical set of shocks, for such countries, during the global financial crisis.7 It exports mainly commodities, mostly copper. It is relatively—and typically—closed to trade, aid-dependent, and rural (Table 17.1). Agriculture, mining, manufacturing, and construction each make up roughly a tenth of GDP, again broadly like other low-income countries (Figure 17.1). Employment is much more skewed towards agriculture at about 70 per cent of the total, reflecting the typically large agriculture productivity gap.8

Table 17.1.Selected Economic Indicators
GroupsExportsImportsAidRural PopulationCommodity Exports
(% of GDP)(% of GDP)(% of GDP)(% of Total)(% of Exports)
Low-Income Countries28.348.413.169.264.0
Emerging Economies42.241.41.834.527.9
Advanced Economies65.561.00.020.519.5

Figure 17.1.GDP Composition by Sector, 2011

Source: WDI and dxTime database.

Its financial sector contains a number of features of particular importance for the transmission mechanism of external financial shocks and monetary policy, again typical of most low-income countries. The financial sector is underdeveloped and fairly concentrated, with foreign-owned banks playing the central role. It is moderately dollarized, and the capital account is fairly open (Table 17.2).

Table 17.2.Financial Depth Indicators
GroupsDomestic Credit to Private Sector (% of GDP)Private Bank Credit (% of GDP)5-Bank Asset Concentration (%) (% of GDP)Stocks Traded Total Value (% of GDP)Credit to GovernmentDollarizationChinn-Ito Financial Openness Index
Low-Income Countries19.618.880.
Emerging Economies60.949.169.626.612.04.00.3
Advanced Economies145.3133.784.470.
Data are for 2011, except for Kenya (2010). Commodity exports include food, agricultural raw materials, and ore and minerals.
Data are for 2011, except for Kenya (2010). Commodity exports include food, agricultural raw materials, and ore and minerals.

Its monetary policy framework is also fairly representative of many low-income countries, particularly in sub-Saharan Africa. The Bank of Zambia targets monetary aggregates under a managed floating exchange rate regime. Perhaps some what more unusually, its float is quite ‘pure’, with the central bank engaging minimal foreign exchange intervention (at least during the period in question).

Like many such countries, including in sub-Saharan Africa, Zambia has enjoyed strong growth for many years, with real per capita growth averaging about 2.3 per cent during 2000–07, compared to an average of 2.7 per cent for low-income countries. Zambia also had reasonable—and typical—success with inflation stabilization, with inflation coming down from 76 per cent during 1990–99 to 18 per cent during 2000–07 (Figure 17.2).

Figure 17.2.Real Per Capital GDP Growth and inflation (%)

Source: WDI.

The global financial crisis hit countries such as Zambia fairly hard along several dimensions, with large declines in terms of trade as commodity prices collapsed and the sharp increase in global risk aversion and reductions in capital inflows (Figure 17.3). With these shocks, the real exchange rate depreciated sharply, while inflation—already high with the rise in food and fuel prices in 2008—ticked up. According to official GDP statistics, Zambia had no noticeable dip in growth during the 2008–09 period, quite unusually for sub-Saharan African LICs, and a point to which we return below.

Figure 17.3.Zambia During the Crisis

Source: WDI.

After detailing the model structure in the next section, we will confront the data much more closely in Section 3 to understand these events and in particular the role of monetary policy.

3 Core Model Structure

The model is made up of the following six blocks: households, firms, the banking system, the monetary authority, the government, and the rest of the world. The flow chart in Figure 17.4 visualizes the links and feedback relations between these blocks.

Figure 17.4.Model Blocks

For each block we present the equations that describe behaviour. See Appendix A in Baldini et al. (2015) for a derivation from utility and profit maximization. Note that in some cases we relax some of the restrictions imposed by optimization to allow for greater flexibility in the dynamics of the model. This greater flexibility helps match the specific path of real macro and financial variables during the crisis, without forsaking the logic of first principles or diluting the mechanisms of interest.9

3.1 Households

Our modelling of households has the following features. First, households’ inter-temporal decisions are influenced by the domestic lending rate (RtL), reflecting the dominance of banks in financial systems in LICs. Second, consumers may be constrained in their ability to borrow at the lending rate offered by banks. These features are reflected in our Euler equation for consumption:

where πc,t is CPI inflation, λt is the marginal value of wealth, and uF,1,t is the value of the multiplier associated with the borrowing constraint.10 The marginal value of consumption is given by:

The parameter χ measures the degree of backward-looking behaviour (or habit formation). We assume total consumption is spent on domestic goods and imports following a Leontieff specification, which implies the following demand for domestic goods: Cd,t = ωCt. This specification captures the view that in low-income countries imports are not close substitutes with domestically produced goods. The CPI is a weighted sum of import and domestic prices: Pc,t = ωPd,t + (1 – ω)PM,t. The demand for imports is also potentially affected by a borrowing constraint:

with uF,2,t denoting the marginal value of the constraint.11 This restriction allows us to emphasize the impact of a financial shock on the demand for imports rather than on overall consumption (more on this below). Financing for import consumption requires lenders’ acceptance of additional foreign currency exposure. It is plausible that banks may be especially unwilling to finance such exposure during the crisis.12

Consumers demand deposits from banks, which earn interest at the rate RD,t. The demand for real deposits is given implicitly by the following function:

where D(*, *,*) is continuously differentiable and homogeneous of order zero, with Di > 0 for i = 1,2 and D3 < 0. Demand for real deposits depends on consumption and the ratio of lending rates and deposit rates; lagged consumption is introduced to generate sluggishness in the demand for deposits.

The supply of labour by consumers is subject to nominal wage rigidities. This results in a Phillips curve for nominal wage inflation (πW,t=WtWt1), which depends on future and past wage growth and deviations between the marginal disutility of labour—which is constant—and the marginal value of real wages:

3.2 Firms

There are two types of firms in the economy: those that produce for domestic consumption and firms that produce export goods for the world market.

3.2.1 Domestic Firms

Domestic firms produce consumption goods using labour, capital—the stock of which has been fixed to 1—and imported inputs MY,t:

Cost minimization leads to the following equations for factor demand:

where Wt, PM,t, and Qt are factor costs and PY,t is the sector’s nominal marginal cost. The function F(*, *, *) introduces sluggish adjustment in the demand for labour and imported inputs in response to changes in (relative) factor prices; it is introduced to improve the empirical properties of the model.

Domestic inflation πd,t=Pd,tPd,t1 is given by a hybrid Phillips curve:

Finally, the nominal value of capital PK,t, which as we will see later matters for risk premia in the banking sector, is given by a standard forward-looking asset pricing equation:

where δ is the depreciation rate for physical capital and σK is the degree of forward-looking behaviour in the pricing of capital.

3.2.2 Exporting Firms

Exporting firms use domestic and imported inputs. They take prices for their output as given by world markets (PX,t). Supply of exports is given by the ratio between the price of exports and the marginal cost of firms in that sector, subject to adjustment costs:

where α is the share of domestic goods in the production of traded goods. This parsimonious specification helps capture a low elasticity of exports to relative prices, given an inelastic supply of factors and limited mobility across sectors. The price of exports PX,t is subject to shocks to the terms of trade Tt:

where S is the nominal exchange rate.

3.3 The Banking Sector

We assume financial intermediation is carried out by a perfectly competitive banking system, which consists of wholesale and retail branches. At the wholesale level the representative bank’s balance sheet is the following:

Banks’ liabilities consist of deposits by residents Dt and foreign debt Ft—denominated in foreign currency but measured here in local currency. Assets consist of loans Lt, government bonds Bbk,t, and reserves at the central bank Ht, which earn no interest but help banks manage liquidity needs associated with deposits.

Profit maximization by banks leads to several arbitrage conditions. First, arbitrage between local currency returns on domestic and foreign bonds, RB,t and Rt, respectively, lead to the following relation:

where Rt is given by the uncovered interest parity with world interest rates plus a potential shock to the country risk premium:

Arbitrage between (net) returns on loans and other assets lead to the following relation between wholesale lending rates RL,t* and interest rates on government bonds:

where we have included an exogenous component to the risk premium on loans (uF,3,t). Note that wholesale lending rates are not directly relevant for private sector decisions.

Finally, liquidity needs to manage deposit results in the following implicit demand for H:

where H(*,*) is continuously differentiable and homogeneous of degree zero, with H1 > 0 and H2 < 0. Banks demand for liquidity is also subject to a shock uF,4,t. As a result of these liquidity needs there is a negative premium on the interest rate on deposits:

with Λ1 < 0.

At the retail level, branches receive funding from wholesale branches and extend credit to households with some degree of monopoly power.13 Retail lending is risky and rates are subject to adjustment costs, all of which results in the following pricing equation for loans:

where gt is given by:

and a (*¯) on top of a variable denotes its steady state value. Three factors affect the risk premium on lending rates. The first factor is the external finance premium gt. It is usually micro-founded by assuming that returns on loans are risky, reflecting idiosyncratic risk on the borrowers part, which is costly for banks to verify and requires a compensating premium. This informational asymmetry is greatly reduced if borrowers can provide their own funds (capital in this case) to finance part of their project, which is why lowering the ratio of gross repayments to the value of capital reduces the premium. The second factor is the exogenous component uF,3,t in equation (16). Finally the dynamic path of the lending rate is also affected by the adjustment costs at the retail level.

Beyond the arbitrage conditions between different interest rates, we also allow for the possibility that banks may ration borrowers at the prevailing lending rate. The rationing is captured by the shocks uF,1,t and uF,2,t. While we do not model the rationing formally, we believe there are reasons why banks may be reluctant to raise interest rates sufficiently to eliminate excess demand for loans, because of adverse selection (as in Stiglitz and Weiss, 1981), costly state verification (as in Williamson, 1987), or moral hazard (as in Bester and Hellwig, 1987).

We model a decrease in banks’ appetite for risk as a simultaneous increase in shocks uF,i,t, for i=1,… ,4. As a result of higher aversion, banks simultaneously increase the premium on lending rates (uF,3,t in equation (16)), ration their lending to the domestic private sector, including import finance (shocks uF,1,t and uF,2,t in equations (1) and (2), respectively), and increase their demand for liquidity (uF,4,t in equation (17)). This simultaneity justifies treating these proximate shocks as coming from one single shock—the increase in banks’ appetite for risk, which we denote uF,t. We impose the following normalization:

where the μis are chosen to improve the fit of the model.

3.4 Monetary Authority

We allow for different options regarding how the monetary authority operates, i.e., what variables are targeted by the central bank, and what instruments—or combinations of instruments—are used. We allow for such flexibility in this block of the model in order to help account for systematic differences between policy choices in LICs and advanced economies, and to compare among various policy rules. Here are the policy rules we model:

  • A reserve money growth rule:

The reserve money growth rule nests various specifications: (i) an inflation targeting regime implemented using reserve money growth as the policy instrument D,H = κL,H = 0, κπ,H >0); (ii) a constant money growth rule D,H = κL,H = κπ,H = 0); (iii) a rule that combines inflation targeting with broad money targeting D,H > 0, κπ,H > 0); (iv) a rule that targets credit growth (κL,H >0). Note that rule (iii) is consistent with current practice in some LICs, where broad money is often an intermediate target whereas reserve money serves as an operational target.

  • Standard Taylor rule with the interest rate on government bonds being the main policy instrument:

Note that in both of types of rules we abstract from targeting the output gap since this variable is difficult to assess in low-income countries, with quarterly GDP often unavailable. Without loss of generality, our policy rules imply a zero inflation target: when comparing the model with the data we add a constant term—reflecting the authority’s implicit inflation target—to the inflation dynamics of the model. Depending on the rule, a tightening of monetary policy can be modelled as a positive shock uM,t to the policy rate/short-term T-bill rate RB,t or a liquidity withdrawal -uM,t on Ht.

We also keep track of the central bank balance sheet:

where Bcb,t denotes the central bank’s holdings of government debt and Ft are the central bank’s international reserves (measured in local currency). In this chapter we set Ft to zero, since intervention in the foreign exchange market did not play an important role in Zambia during the global crisis. Regardless of the policy regime, we assume the central bank implements policy by varying Bcb,t. Finally we define a measure of relative or ‘excess’ liquidity in the banking system, which is given by the inverse of the money multiplier:

3.5 The Government

The government taxes economic agents, spends on a basket of goods similar to those of consumers, issues debt, and pays interest. Its budget constraint is given by:

where Gt is real government spending, Tt is the nominal tax revenue, and Bt is the total stock of government debt (Bt = Bbk,t + Bcb,t). We assume the government only pays interest on debt held by commercial banks. Consistent with the tax structure in many low-income countries, where import duties make a large share of government revenue, we assume that tax revenue (in per cent of nominal GDP) is sensitive to the value of imports:

YtN is the level of nominal GDP and ϕ measures the sensitivity of tax revenues to imports.

Government debt is anchored by the following spending rule:

This rule ensures that government debt outstanding converges to a given long-run level BbkYN. The parameter ρG measures the sluggishness in real government spending (in per cent of GDP), while τG—together with ρG—measures the speed of adjustment to reduce debt levels.14

Note that the country’s resource constraint requires Yt = Xt + Cd,t + ωGt, while nominal GDP is defined as YtN=Pc,t(Cd,t+Gt)+PX,tXtPM,tMt (with Mt = CM,t + My,t + (1 – ω)Gt).

3.6 Relationship with the Rest of the World

We close our model by keeping track of the country’s balance of payments:

4 Applying the Model to Zambia

Having introduced the core structure of the model, we now apply it to Zambia. In this section we discuss the data, calibration, the characterization of the crisis, and the simulation results.

4.1 The Zambia Dataset

We collected data for fifteen quarterly macroeconomic and financial variables.15 On the external sector the data includes the terms of trade, imports, the current account, and the nominal exchange rate. Data on the banking/monetary sector includes reserve and broad money, credit to the private sector by the banking system, interest rates on treasury bills, and lending rates. As in our model, we use the ratio of reserve money to broad money to assess liquidity in the banking sector, rather than the measure of liquidity used by the authorities—banks’ reserves in excess of those needed to satisfy regulatory requirements.16 On the fiscal side, we collected data on total revenues, spending, and the stock of government debt. On the real sector side, we have quarterly data on GDP, interpolated from annual data.

We present the data as follows. About half of the variables (terms of trade, real credit to the private and public sector, real imports, real GDP growth, and excess liquidity) are expressed as percentage deviations from a deterministic trend or constant, which we calculate using pre-crisis data for each variable. Nominal variables are expressed in percentage points—Zambia’s inflation target during this period is assumed to be 10 per cent. Finally, to help understand the magnitude of the macroeconomic adjustment, government revenues, government spending, and the current account are measured in per cent of GDP.

4.2 Calibration and Functional Forms

Simulating the model requires specifying functional forms for functions D, F, H, and Λ. Consistent with the optimization in Appendix A in Baldini et al. (2015), the functional forms are as follows:

Regarding calibration, Table 17.3 contains all parameter values and key steady state ratios, organized by economic agent. Choosing parameter values for Zambia was a difficult exercise, and our calibration is tentative. To our knowledge, there has been little empirical work—either micro-level studies or econometric estimates of macro models—that would help inform the calibration; more work is clearly needed in this area. Our approach was the following:

  • All the relevant steady state ratios (P¯CC/D¯,D¯/H¯,F¯/Y¯N,T¯/Y¯N,P¯CG/Y¯N,B¯bk/Y¯N) are calibrated to the Zambian economy. Parameters γM , ω and α are also chosen to replicate the degree of openness of the economy.
  • Several parameters (χ,ξw, ς, γM and ξc) are set in accordance with recent empirical work on African countries (Chapters 8 and 12).
  • Some parameters (β,δ,σKy and ξR), for which there is no Zambia—or LIC—specific data, are set to standard values in the literature.
  • We set g1 to zero to explicitly remove the financial accelerator from the analysis. This transmission channel was not helping match the dynamics of the crisis. Since we believe it may be relevant in other situations and for other countries, we leave it in the model for future use.17
  • The value of ψX reflects our prior that real exports in Zambia are likely to be fairly unresponsive to movements in the real exchange rate, mainly because copper exports represent 75 per cent of total exports.
  • We choose the money growth specification for monetary policy. In our baseline we assume monetary authorities do not respond to broad money or loan growth, which is consistent with the response during the crisis. We relax that assumption in our sensitivity analysis. We have little to go on in calibrating the parameters of this rule, in part motivating the sensitivity analysis. Our view is that it is in fact difficult to characterize Zambian monetary policy as being mainly rule-based, as reflected in the discussion below and in the importance of monetary policy shocks in explaining its evolution. However, we estimated a slightly simplified version of the reaction function using single-equation GMM. From equations (18) and (20), we can drive a specification:
    where h1 is the inverse of the interest semi-elasticity of demand for reserves H and εt is an assumed shock to demand for reserves added to equation (18) for estimation purposes. Our point estimate for h1 is 0.159 (HAC standard error of 0.028) and for the coefficient on expected inflation of 0.158 (0.028), for an estimated value of kappaπ,H of 1.0. This is insignificantly different from the calibrated value of 1.5.18
  • On the government side, we choose ϕ, τG, and ρG to broadly reproduce the path of fiscal variables during the crisis.
  • The remaining parameters are chosen to improve the fit of the model. On the consumers’ side this applies to ϱ1 and ϱ2 (parameters that affect the income elasticity of broad money demand). On the banks side, the parameters chosen this way are ι (related to the interest elasticity of reserve money demand), μ2, μ3, and μ4 (related to the shock to banks’ appetite for risk). Note that ϱ1, ϱ2, and ι help shape the path of broad and narrow money during the crisis but do not alter the sign of the response. We discuss the choice of μi’s in subsection F.
Table 17.3.Calibration of Model Parameters and Steady State Ratios
Monetary Authority
The Government

As can be seen from our calibration of the last set of parameters, our approach differs somewhat from a pure calibration exercise. In particular, we use the information derived from fitting the model to the data to improve our choice of the last group of parameters, some of which do not have a specific micro-foundation to support them. This approach could be defined as informal/partial estimation. We believe our approach is justified by the purpose of our model, which is to serve as a data-consistent story-telling device.

4.3 Overview of Shocks and the Transmission Mechanism

Before analysing Zambia’s experience during the crisis, we briefly present impulse responses of key model variables to each of the four shocks in our model. This will help illustrate the underlying transmission channels. Figure 17.5 summarizes the model’s response to a terms of trade improvement of 20 per cent and to a loosening of monetary policy expressed as an increase in the growth rate of reserve money by 10 per cent, respectively. Both shocks lead to a temporary increase in domestic demand and output, as is shown in the path of real imports, money demand, and GDP growth. They differ in terms of their effect on inflation: the terms of trade improvement appreciates the exchange rate and lowers inflation, while the monetary loosening leads to higher inflation and nominal depreciation. The effects of the two shocks are also qualitatively different for the volume of credit to the private sector. While the policy loosening encourages higher borrowing and an increase in real credit, the terms of trade improvement results in a decrease in the volume of private credit, reflecting consumption smoothing.

Figure 17.5.Impulse Response Functions of Key Variables to a Terms of Trade Shock and a Monetary Policy Shock

Figure 17.6 summarizes the impulse response functions to the two risk shocks in the model: a 10 percentage-point increase in the shock to the country-wide risk premium uR, and a shock to the banking sector’s risk appetite uP, such that—all else equal—the premium on the ‘notional’ lending rate would increase by 5 percentage points. The increase in the country premium leads to a nominal depreciation, with upward pressure on inflation and an ensuing increase in policy and lending rates, which in turn leads to a contraction in domestic demand and output. The banking shock, on the other hand, leads to a squeeze in credit and a sharp fall in real imports. The fall in domestic demand triggers disinflation, lowers marginal costs for exporters and together with the drop in imports, improves the current account and contributes to higher GDP.19 Finally, the shock to banks’ risk appetite would—by itself—generate a nominal and real appreciation, as the demand for imports would fall.

Figure 17.6.Impulse Response Functions of Key Variables to a Risk Premium Shock and a Banking Shock

Overall, the transmission channels operate as one would expect from a model of this type, although it must be emphasized that some of the shocks are pushing key variables (such as exchange rates and the current account) in opposite directions. The interesting question for the remainder of the chapter is whether the model can provide us with explanations to our case study of Zambia, given the particular constellation of shocks and policy responses the country faced during the crisis.

4.4 Replicating the Crisis

Having analysed the impact of each shock separately, we now combine them together to mimic the impact of the crisis. As mentioned earlier, the aim here is to replicate Zambia’s external environment during this period. Our approach is as follows. We set the path of the terms of trade shock uT,t and the risk premium shock uT,tuRf,t such that the model’s terms of trade and nominal exchange rate exactly replicate their counterparts in the data from 2008:Q4 to 2010:Q2.

Regarding the shock to the banks’ risk appetite uF,t, we set their path so as to match the current account from 2008:Q4 to 2009:Q4. We focus on the mapping between the current account and banks’ appetite for risk for the following reasons. Given the structure of the banking sector in the model (all of the country’s financing including foreign borrowing goes through the banking sector), this shock has direct implications for the current account behaviour. This linkage is consistent with the literature on sudden stops, where the external shock enters the model in the same way as our shock μF,1,t in equation (1).20 In addition, the mapping between the banking shock and the current account reversal is also consistent with the fact that Zambia’s banking system is largely foreign-owned, so that a change in banks’ attitude toward domestic loans would likely be reflected in capital flight.21

Regarding monetary policy, we set the path of shocks uM,t, such that the model replicates the observed path of the ninety-one-day T-bill rate in Zambia during the same period. As will become evident later, we believe the behaviour of monetary authorities cannot be characterized by a systematic rule but rather as a sequence of discretionary policy measures. The use of shocks to mimic the policy response is therefore more appropriate. Finally, to ensure consistency with the standard analysis of impulse responses in this type of models, we assume the path of shocks is fully anticipated at the beginning of our simulations (which corresponds to 2008Q3 in the data).

The mapping between shocks and selected variables warrants some discussion. We use the IRIS toolbox, developed by one of our co-authors, to implement this mapping. The procedure requires (i) solving the linear approximation of the model using standard rational-expectations techniques, under the assumption that all shocks are anticipated at the beginning of the simulation; (ii) inverting the VAR representation of the model’s solution to recast the shocks as linear functions of the model variables (including leads and lags); and (iii) backing out the sequence of shocks that is necessary to reproduce the path of selected variables. The IRIS toolbox contains built-in functions to carry out this procedure.

4.5 Baseline Results

Conditional on the four ‘hard-tuned’ variables above, we simulate the model’s response and compare the remaining model variables with their counterparts in the data. The starting point of the data and the model is the same in most cases, except for inflation and the growth rate of reserve money, for which the model’s starting point is the pre-crisis average. By doing so, we are assuming that the economy was broadly at trend before the crisis hit. We return to the pre-crisis behaviour of inflation in the last section.

First, we characterize the evolution of the key observed variables during the crisis, starting in 2008-IV (Figure 17.7). Along with the terms of trade deterioration we observe an immediate depreciation of the nominal exchange rate S. The exchange rate depreciation feeds initially into higher inflation. The current account reversal is in the order of 20 percentage points of quarterly GDP, reflecting the exit of foreign investors. The capital flight is associated with a large contraction in the volume of credit issued by the domestic banking system and an increase in lending rates, which prevents private agents from borrowing abroad to smooth out the effects of the crisis. Aggregate demand contracts significantly as a result of the credit crunch and inflation subsequently declines, while real deposits in the banking sector decrease by over 15 per cent.22 Note that the behaviour of GDP, which has been interpolated to generate quarterly series, is not consistent with the overall macroeconomic picture. The fiscal outcome worsens—especially revenues—and the outstanding stock of government debt increases by about 20 per cent.

Figure 17.7.Overview of the Baseline Simulation

Baseline simulation (lighter lines) versus data (darker lines). Units are percentage or percentage point deviations from pre-crisis trend at quarterly frequency.

In this context the initial monetary policy response can be characterized as contractionary. Interest rates on treasury bills increase by about 400 basis points between July 2008 and July 2009. This is associated with a decrease in the growth rate of the monetary base and contributes to the contraction of broad money and the increase in lending rates.

Starting in July 2009, however, there is a reversal in the monetary stance in response to the slowdown. Liquidity is injected into the banking system (H increases) above the pre-crisis level and the T-bill rate drops sharply, by about 1,300 basis points by July 2010. This loosening policy drives down the lending rate and brings aggregate demand slowly back towards the baseline level. The monetary loosening coincides with a recovery of the terms of trade which appreciates the exchange rate, lowers inflation, and supports the recovery in demand.

How do the model’s predictions compare with the data? In general, the model performs well qualitatively and comes close to the data for the lending rate, inflation rate and import demand. For the other variables, the model predicts correctly the direction, although the magnitudes are, as can be expected, not always closely matched. For example, in the model, credit to the private sector contracts faster and stronger than in the data, as is also the case with real money demand, while credit to the government is predicted to surge faster than in the data. The fiscal variables (revenues and spending as share of GDP) are slightly more volatile in the data than the model predicts. Clearly, the model cannot account for all sources of rigidities or policies that may shape the path of the economy. There are more sources of government and private credit funding, such as aid donors, non-banks etc., which may generate either additional volatility or delays in some of the macro responses that the model does not capture.

Regarding GDP growth, however, the model’s predictions are completely at odds with the annual GDP data: the model predicts a large contraction in GDP while the data indicate no such contraction. A first consideration is that the only available data are annual, while the contraction in the model takes place at the end of 2008 and beginning of 2009. An estimate of the quarterly output gap extracted from available quarterly data produces a recession whose timing is quite similar to that of the simulation (Figure 17.8).

Figure 17.8.Simulated and Imputed Quarterly Real GDP

Imputed Quarterly GDP is based on a factor-augmented VAR using 12 available quarterly series (exports, imports, terms of trade, industrial production (IP) in mining, manufacturing, electricity, CPI inflation, base money, broad money, credit to the private sector, the ninety-one-day T-bill rate, and the bilateral (dollar) nominal exchange rate). We apply Chow-Lin distribution methods to estimate quarterly GDP based on official annual GDP data and the three principle factors, then apply a band-pass filter to extract the cyclical component (details available on request).

The magnitude of the recession is still much smaller in the data, however. A partial explanation could be the very poor data quality of Zambian real GDP data, probably even more than other Zambian data used here.23 That there may be something wrong with the Zambian data in this regard, in broad terms, is suggested by the fact that in general primary product-exporting LICs experienced substantial growth slowdowns in 2009 (Figure 17.3).

Zambia also benefited from some large positive supply shocks during the period in question. First, thanks to good weather, the harvest was unusually large in 2008 and 2009, such that real agricultural output grew by 2.6 per cent in 2008 and 7.2 per cent in 2009, compared to an average of 1.6 per cent over 2002–2007. Second, as new mining capacity came on stream during 2009, reflecting the lagged effect of high FDI over preceding years, real GDP in mining grew by 20.2 per cent in 2009 (despite substantial anecdotal evidence that low copper prices drove temporary mine closures during late 2008 and early 2009). Output excluding mining and agriculture grew substantially slower in 2008 and 2009 than in previous years (Figure 17.9).

Figure 17.9.Real GDP Growth Excluding Agriculture and Mining

Source: dxTime.

Finally, and more speculatively, FDI collapsed from a sharp peak of 12 per cent of GDP in 2007 to 6 per cent in 2008 and 5 per cent in 2009. This is not explicitly modelled. It is reasonable to expect, however, that much of this FDI financed imports of intermediate investment goods. By abstracting from this factor, the model requires that the fall in imports be associated with a similar fall in consumption of domestic goods, given the utility function. In fact, though, some of this import decline may have been a direct result of the decline in FDI and hence had less implication for domestic activity. An extension to consider FDI explicitly might be useful, though calibration would place strong demands on weak investment data.

4.5.1 A Digression on Fiscal Policy

Beyond its quantitative properties, the model also illustrates how fiscal policy affects the transmission of external shocks. As mentioned earlier, the fiscal outlook worsened as a result of the global crisis, and public debt increased. In normal circumstances, holding everything else constant, such an increase in debt would have been financed in part by capital inflows and in part by a crowding out of private sector credit and an increase in interest rates. In this case, however, the increase in debt coincides with a large decrease in private sector credit and a reversal of capital flows. While these two factors are pushing in opposite directions, the net effect more than outweighs the effects of fiscal policy. Holding reserve money growth constant, the T-bill rate would have decreased. However, for a given current account path, higher government debt would have resulted in an additional decline in private sector credit. Note that the impact on aggregate demand from stable government spending (financed by debt) is positive.

4.6 Shock Decomposition

It is helpful to analyse how the different shocks contributed to each variable’s dynamics.24Figure 17.10 presents the path of all three external shocks. The initial path of all three shocks is consistent with the above narrative: there is an increase in banks’ risk aversion (positive uF) through the first five quarters, a deterioration in the terms of trade (negative uT) that recovers by the end of 2009, and an increase in the country-risk premium from 2009 to 2010Q2 (positive uR).25

Figure 17.10.Tuned Paths of External Shocks

Tuned path of the banking/financial shock (uf), the terms of trade shock (ut), and the country risk premium shock (ur). Units are percentage deviations from pre-crisis steady state at quarterly frequency.

Figure 17.11 presents the shock decomposition for three nominal variables: the nominal exchange rate, the lending rate, and the inflation rate. One striking observation is that the shocks are generating opposing effects on the dynamics of these variables. The terms of trade and external risk premium shocks (uT, uR) are generating pressures for nominal exchange rates to depreciate and lending rates and inflation to increase, while the banking sector shock (uF) is having the opposite effect.

Figure 17.11.Structural Shock Decomposition 1

Baseline simulation paths for selected price variables and their decomposition to four structural shocks of the model. Units are percentage point deviations from pre-crisis trend at quarterly frequency.

More importantly, the banking shock plays an important role in the transmission of the crisis. This reflects the dominant role of the banking sector in our model. The decrease in banks’ risk appetite in the period up to the end of 2009 exerts a downward pressure on inflation and the exchange rate, as it generates a decline in consumption, including for imports. The overall effect of the banking shock on lending rates is negative, despite the appearance of the shock in equation (16): as the shock makes private demand contract and inflation fall, the endogenous response of monetary policy—combined with a contraction in the demand for money—makes T-bill (and lending) rates fall.

Figure 17.12 shows the shock decomposition for the volume of credit to the private sector, credit to the public sector and real imports. The figure reveals how strongly the credit rationing of banks affected the contraction in lending and import demand. In the absence of the banking shock, the other two shocks would have resulted in an increase in lending for smoothing purposes. In the case of government debt, all shocks are initially contributing to its increase.

Figure 17.12.Structural Shock Decomposition 2

Baseline simulation paths for selected quantity variables and their decomposition to four structural shocks of the model. Units are percentage point deviations from pre-crisis trend at quarterly frequency.

Finally, note that the tightening of monetary policy exacerbates the negative impact of the shocks in the initial quarters. The lending rate is further increased, private credit is further reduced and demand (see imports) contracts slightly more given this tightening policy. However, relative to the contribution of the external and financial shocks triggered by the crisis, the impact of policy is far less decisive for the evolution of demand and economic activity. This reflects the severity and sheer magnitude of the exogenous shocks that hit the economy during this episode. In the following section, the role of monetary policy is discussed in detail.

Having described the performance of the model and how each shock contributed to the path of key variables, we can now justify our choice for the weights on the different components of the bank risk shock (the μis). As can be seen from the shock decomposition exercise, the greatest impact of the bank risk shock is on credit volumes. This makes it natural to normalize the shock to consumers’ borrowing constraint (uF,1,t) and guided our calibration of the overall bank shock itself (μF,t). The choice of μF,2 is guided by the observation that (uF,1,t) is highly contractionary, as it has large effects on aggregate demand. Shocks to import financing (uF,2,t) do not have such large effects; a substantial weight on (uF,2,t) is thus helpful in matching the large current account reversal absent a notable output decline. The increase in lending spreads helps calibrate μF,3,t—absent such a shock, lending spreads would not increase. Finally, μF,4,t helps track the behaviour of reserve money. In its absence the model would require an implausibly large contraction in reserve money to replicate the path of the T-bill rate.

4.7 The Role of the Monetary Policy Response: Shock Counterfactuals

Recall that the monetary policy rule is specified in terms of the growth rate of reserve money, reproduced here for convenience:

with discretionary deviations from the rule captured by the shock process uM,t.

Figure 17.13 displays the decomposition of the dynamics of reserve money growth, the T-bill rate, and real broad money. Not surprisingly, the monetary shock accounts for most of the movements in reserve money growth and the T-bill rate. In other words, fluctuations in monetary policy are directly responsible for the behaviour of two key nominal variables (short rates and reserve money growth). This is not true of real money variables: most of the variance of real broad money balances is accounted for by the real shocks.

Figure 17.13.Structural Shock Decomposition: Monetary Variables

Baseline simulation paths for the monetary variables (reserve money growth, T-bill rate, broad money) and their decomposition to monetary policy shock (um) and exogenous (real) shocks (exog. shocks). Units are percentage point deviations from pre-crisis trend at quarterly frequency.

To assess the role of policy, we first simulate the model without policy shocks. Figure 17.14 compares the model dynamics with and without policy shocks. In contrast with the previous stop and go pattern, reserve money growth is now mostly flat. Given the contraction in demand for broad money, this results in an initial decline in the T-bill rate, which amplifies the nominal depreciation and raises inflation. At the same time, the increase in the lending rate is not as large, as liquidity is more abundant than under baseline. Another clear effect of the neutral monetary policy is the dampening of the increase in outstanding public debt since the lower T-bill rate implies lower debt servicing costs.

Figure 17.14.Counter-Factual Simulation 1: Flat Money Growth

Baseline simulation (darker lines) versus counter-factual (lighter lines). Units are percentage or percentage point deviations from pre-crisis trend at quarterly frequency.

In terms of real variables, the effect of the accommodating policy stance appears to be limited. The contraction on import demand is slightly smaller, as is the contraction in credit to the private sector. A closer look reveals sizable effects, however. Table 17.4 summarizes the relative performance of alternative policy responses, relative to the baseline, along a number of dimensions. The average difference between private spending (Ct) under ‘stop and go’ and under the more neutral stance, over the period 2009:I to 2009:IV, is 2.8 per cent of steady state spending. During the same period the model predicts a moderately higher inflation—3 percentage points higher—although in line with Zambia’s implied inflation target of 10 per cent, while the nominal exchange rate would have been 12 per cent more depreciated.

Table 17.4.Model Performance Across Alternative Monetary Policy Responses
Monetary PolicyNeutralLooseBroad MoneyCredit GrowthTaylor Rule
Private Spending2.
Each row indicates the average difference between the alternative monetary policy regime and the baseline, in per cent of that variable’s steady state value, in 2009.
Each row indicates the average difference between the alternative monetary policy regime and the baseline, in per cent of that variable’s steady state value, in 2009.

Figure 17.15 displays simulation results if monetary policy had actually been loosened, i.e., money growth higher than average during 2009:I to 2009:IV. The inflationary effects are now larger (see also Table 17.4). Inflation is now 6 percentage points higher than under the baseline, while the nominal exchange rate is 24 per cent more depreciated. However, the model predicts lending rates would have stayed flat and private spending would have been 5.4 per cent higher. One important observation is that, given the steady state value of the T-bill nominal interest rate, there is a lot of room for interest rates to fall—1,300 basis points—without hitting the zero lower bound. In this scenario monetary policy comes very close to hitting the zero bound but stays above it.

Figure 17.15.Counter-Factual Simulation 2: Expansionary Policy

Baseline simulation (darker lines) versus counter-factual (lighter lines). Units are percentage or percentage point deviations from pre-crisis trend at quarterly frequency.

4.8 The Role of the Monetary Policy Response: Rule Counterfactuals

We now explore the performance of the model under three alternative policy rules. In the first case, the authorities continue to implement an inflation-targeting regime by setting targets for reserve money growth, but they also respond to deviations in broad money growth from its long-run value (κD,H = 0.5). In the second case, we assume the authorities target the growth rate of loans rather than the growth rate of deposits (κL,H = 0.3). In the third case, we assume the authorities follow the interest rate rule:

with ρR = 0.5 and κπ = 3.

The results are summarized in Table 17.4. All three rules would have improved the country’s private spending performance, although again at the cost of higher inflation and nominal depreciation. The conclusion from this exercise is that policymakers were confronted to a trade-off: while a loser policy would have helped weather the external shocks, the country would have faced somewhat higher inflation as a result. This reflects the nature of the ‘global crisis’ shock, which does not easily lend itself to an aggressive monetary policy response. In this case there is no ‘divine coincidence’ (Blanchard and Gali, 2007).

4.9 The Role of the Nominal Rigidities

The assessment that monetary policy played a role during the crisis is closely related to the inclusion of nominal rigidities in our model, reflected in parameters ξc and ξw. As is well known, the latter feature is what allows monetary policy to have real effects in DSGEs. In addition to the shocks-based analysis and the impact of various policy rules, an alternative exercise to measure the importance (and the limits) of monetary policy is to compare the results of the model under sticky and flexible prices, in which case monetary policy is irrelevant for the real effects of the crisis.26 We therefore compare the baseline simulation with the flexible-price version of our model (ξc = ξw = 1). For the sake of brevity we limit the comparison to consumption, which is presented in Figure 17.16.

Figure 17.16.Counter-Factual Simulation 3: the Role of Nominal Rigidities

Baseline simulation (blue) versus flexible price simulation (green). Units are percentage or percentage point deviations from pre-crisis trend at quarterly frequency.

This exercise is in line with our previous finding. On the one hand, the presence of nominal rigidities in the baseline results in a decrease in consumption that is larger than the decline in consumption under flexible prices. This confirms our initial assessment that the initial tightness of monetary policy aggravated the impact of the crisis. On the other hand, the decrease in consumption is broadly similar across the two cases, which confirms that real (non-monetary) factors were the main factors behind the crisis.

5 Understanding the Initial Monetary Policy Response

In this section we analyse the motivation behind the initial policy response. We start by looking at the behaviour of policy before mid-2008 (see Figure 17.17). Interest rates were broadly constant prior to the crisis but then began to increase steadily around or possibly a few months before the onset of the crisis. What factors can account for such behaviour?

Figure 17.17.Key Monetary Variables Prior to and During the Crisis

We can divide the factors depending on when they occurred. Following the metaphor from the introduction, we distinguish between ‘rear-view’ factors and ‘side-view’ factors. The former refer to factors that, while having occurred in the past, were still influencing policy; the latter refer to factors that were occurring at the time of the policy decision.

The first ‘rear-view’ factor is inflation itself. When simulating the model and confronting it to the data, we made the simplifying assumption that the economy was starting from steady state. As we mentioned earlier, this assumption was more or less valid for most variables in our sample, with the notable exception of inflation. Figure 17.17, top right corner, shows the monthly behaviour of year-on-year inflation since 2006. As is clearly visible, inflation had begun in-creasing steadily since end-2007, going from 8.9 to 16.6 per cent by December 2008. The increase is mostly accounted for by food prices, which had gone from close to 0 to 21 per cent between end-2006 and end-2008. Note that non-food inflation was falling throughout most of 2008.

It was well understood at the time that food inflation was driven by the ongoing global food and fuel shocks of 2007–08, during which the price of most commodities doubled or tripled in a few months. While the policy adage is to allow for first-round (direct) effects of such shocks and to prevent second-round (indirect) effects, there was a concern at the time that inflation risked losing its anchor and that a policy tightening was needed.27

An additional and related ‘rear-view’ factor was the consistent miss of reserve money targets during 2009. Table 17.5 displays the targets and the actual levels of reserve money.28 The misses reflected an intentional accommodation of the surge in demand for nominal balances as a result of higher inflation, and were interpreted at the time as indicating neutral policy. However, the gradual increase of T-bill rates during 2008 suggests policy was not as accommodative as the target misses suggested.

Table 17.5Money Targets in Zambia (2008–2009), in bn of Kwacha
TargetRev. targetActual
Source: IMF Staff Reports.
Source: IMF Staff Reports.

The combination of target misses and high inflation in 2008 led to an effort to (further) tighten policy in 2009. This can be seen by looking at the targets set—at the end of 2008—for reserve money growth in mid-2009 (see Table 17.5), which were lower than end-2008 values.29 The targets were subsequently revised midway through 2009, coinciding with the large fall of T-bill rates. It is worth noting that the targets were missed in the opposite direction in the second half of 2009, suggesting the authorities did not anticipate the crisis-induced decline in demand for reserve money during that period.

In terms of ‘side-view’ factors, we believe the nominal depreciation—and the associated capital flight—may have made the authorities reluctant to loosen policy sooner. Indeed, during the second half of 2008, the nominal exchange rate had depreciated by 50 per cent, and foreign direct and portfolio investments had fallen by more than 30 per cent. As predicted by our model, a loosening would have accelerated capital flight and amplified the nominal depreciation, the prospects of which were likely to concern the authorities.

Finally, an additional ‘side-view’ consideration may have been the reluctance to provide the banking sector with additional liquidity as the banking system appeared to have ample liquidity. As Figure 17.17 indicates, the ratio of reserve money to broad money had been increasing since the end of 2007, by about 20 per cent by end-2008, which may have reinforced the perception that monetary policy was loose. In our model, such dynamics were driven instead by the fall in broad money and the increase in liquidity demand by banks, which indicates policy was actually tight instead.

6 Conclusion

We have shown that a DSGE model, fitted to the specifics of a low-income country, provides a reasonable characterization of Zambia’s performance during the crisis. We believe our framework is well-suited for confronting the model with data and for making forecasts conditional on various policy scenarios.

Our analysis yields several lessons for policymakers in low-income countries. First, monetary policy should be forward-looking and respond to current or expected shocks, instead of responding to the current inflationary effects of past shocks. Such a strategy of ‘driving by looking through the rear-view mirror’ exposes the central bank to potentially large policy mistakes that need to be reversed later, further contributing to economic instability. Second, central banks should avoid paying excessive attention to banks’ liquidity—or reserve money—as the exclusive indicator of the monetary policy stance—a common practice in sub-Saharan Africa. Rather than loose monetary policy, the build-up of liquidity may reflect growing risk aversion in the banking system. More generally, central banks in the region need to monitor overall developments in the banking system, including credit volumes and interest rate premia, in order to gauge the right policy stance. These lessons are well understood in developed and emerging market countries, but they have yet to take hold in low-income countries.

While reserve money targeting remains a common practice in sub-Saharan Africa, the flexibility with which it is implemented can help avoid potential policy mistakes. In addition, as central banks in the region move toward incorporating additional elements of inflation targeting in their frameworks—with its emphasis on the inflation forecast, greater policy clarity, less reliance on monetary aggregates, and a greater role for short-term rates—the response to large unexpected events should improve.

Our model has also shown, however, that—at present—monetary policy in LICs may be limited in its ability to offset large external shocks. These shocks—worsening in the terms of trades, increases in risk premia—confront policymakers with unpleasant trade-offs between output and inflation. Our results also show, however, that monetary policy errors can add to the volatility. More generally, a systematic forward-looking policy response can enhance credibility and anchor expectations in a way that should reduce over time these unpleasant trade-offs.

From an analytical perspective, we have found it important to model the crisis as a combination of shocks. In particular, we have found that the inclusion of the shock to the banking sector—itself a collection of shocks to various aspects of the banks’ profit maximization conditions—greatly improves the quantitative performance of the model. Further analysis of the mechanisms underlying these shocks is a fruitful topic for further research. Explicit treatment of FDI may also be important for some purposes.

Our framework has abstracted from other key aspects of central bank policy in low-income countries, most notably the direct intervention in foreign exchange markets. In related work, Chapter 13 analyses the interaction of monetary policy rules with rules describing foreign exchange rate interventions. We have also abstained from analysing in greater detail the challenges posed by fiscal policy in the implementation of monetary policy, even though our model explicitly incorporates the fiscal sector. Chapter 12 explores the interaction of fiscal and monetary policy in the context of aid shocks, but more work is needed in this area.

Appendix 17A: Quarterly Macroeconomic and Financial Variables
DescriptionSourceSeasonally adjustedFrequencyComments
Broad Money/Deposits (M3)IFS/Bank of ZambiaxMonthlyAggregated to quarterly frequency (averaging)
Reserve money (M1)IFS/Bank of ZambiaxMonthlyAggregated to quarterly frequency (averaging)
Commercial lending ratesBank of ZambiaMonthlyAggregated to quarterly frequency (averaging)
Headline CPIIFS/Bank of ZambiaxMonthlyAggregated to quarterly frequency (averaging)
CPI—Non-foodIFS/Bank of ZambiaxMonthlyAggregated to quarterly frequency (averaging)
CPI—FoodIFS/Bank of ZambiaxMonthlyAggregated to quarterly frequency (averaging)
Exchange rate Kwacha per USDIFSxMonthlyAggregated to quarterly frequency (averaging)
US import price indexIFSxMonthlyAggregated to quarterly frequency (averaging)
Net claims on private sector by banksIFS/Bank of ZambiaxMonthlyAggregated to quarterly frequency (averaging)
Stock of outstanding domestic debtBank of ZambiaxMonthlyAggregated to quarterly frequency (averaging)
Revenues and grantsIMF staff/Zambian Authorities (MOF)xMonthlyAggregated to quarterly frequency (averaging)
Total exportsIMF staff/Zambian Authorities (MOF)xMonthlyAggregated to quarterly frequency (averaging)
90-day Treasury bill rateIFSMonthlyAggregated to quarterly frequency (averaging)
Deposit rateIMF Staff/Bank of ZambiaMonthlyAggregated to quarterly frequency (averaging)
Price of copper (USD per metric tonne)London Metal Exchange (LME)xMonthlyAggregated to quarterly frequency (averaging)
Nominal imports—Goods and services (millions, USD)Bank of ZambiaQuarterly
Current account (millions, USD)Bank of ZambiaQuarterly
GDP at constant pricesIFSYearlyInterpolated to quarterly frequency (quadratic interpolation)
GDP at current pricesIFSYearlyInterpolated to quarterly frequency (quadratic interpolation)
GDP at current prices (USD)IFSYearlyInterpolated to quarterly frequency (quadratic interpolation)

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Printed with permission of Pacific Economic Review (2015), 20(1), 149–92.


By typical structure we mean that profit and utility maximization by agents in the model result in equations that are standard in DSGEs: New Keynesian Phillips curves for prices and wages—with both forward- and backward-looking elements—a Euler equation for consumption, various factor demand functions by firms and interest parity conditions between domestic and foreign assets. In addition the economy is subject to a resource constraint (the balance of payments).


We simulate our model using IRIS, a Matlab-based package developed by one of our co-authors (Benes). This package can be freely downloaded from


Papers on the overall impact of the crisis in low-income counties include IMF (2009b) and Berg et al. (2010).


The former literature was built on the seminal contributions of Bernanke, Gertler, and Gilchrist (1999) and Kiyotaki and Moore (1997). New work on financial intermediation includes Goodfriend and McCallum (2007), Christiano, Motto, and Rostagno (2010), Curdia and Woodford (2009), Adrian and Shin (2011), and Gertler and Kiyotaki (2011). See Woodford (2010) for a simple exposition.


Indeed, we chose to study Zambia not because of its idiosyncrasies but because of a timely request from the IMF country team.


See Erceg, Guerrieri, and Gust (2006) for a discussion of the restrictions implied by fully micro-founded models and their implications for matching short-run properties of the data.


This constraint can be microfounded by assuming that consumers pay for imports at the beginning of the period, before receiving their labour and interest income, and that such lending is subject to a borrowing constraint that may fluctuate over time. While the rate at which consumers borrow within the period would also show up in the consumer price index, we assume such rate is equal to zero.


We do not model issues related to currency risk. Efforts to microfound this risk might be instructive but are outside the scope of this chapter.


The modelling choice that banks lend to retail is partly practical and partly empirical. Because there are few reliable data on investment, we have not incorporated that variable and as a result the simplifi cation involved in assuming lending goes to households, costs little. And in fact, 46 per cent of outstanding bank lending from the formal financial sector in 2008 went directly to households (Bank of Zambia, 2011). In addition, according to survey data, credit from non-bank and informal financial institutions to individuals is substantially higher than formal bank credit, consistent with the view that some of the bank lending to enterprises may involve on-lending to individuals (Finscope Zambia, 2010).


Our modelling of fiscal and monetary policy assumes lack of fiscal dominance, i.e., monetary policy is active while fiscal policy is passive. See Baldini and Poplawski-Ribeiro (2011) for an assessment of fiscal dominance in sub-Saharan Africa.


See Appendix 17A for a description of the data.


This choice is inconsequential: the correlation between the inverse of the money multiplier and the authorities’ measure of excess liquidity, at the monthly frequency, was 0.92 during 2008–10.


In an earlier specification of our model we attempted to reproduce the crisis without the banking shock but with an active financial accelerator. This specification could generate the observed increase in lending rates but at the cost of a counterfactual increase in the current account deficit and an increase in private-sector loans. The increase in loans was required for the premium to increase endogenously, as the model could not generate a sufficient decrease in the price of domestic capital (an alternative way of generating an increase in the premium). For this reason we decided to focus on an exogenous shock to the banking sector instead.


We allowed the coefficient on the lagged interest rate to differ from 1, and indeed it was estimated to be 0.87. With only forty-five observations and the restrictive identifying assumptions required, these results need to be taken with a grain of salt. However, it is reassuring that the specification passes the usual Hansen overidentification test and weak instrument tests, and that the results are consistent with the calibration. These results are available on request.


The expansion in GDP observed for the shock to uF is a common finding in models of sudden stops, which also involve shocks to a binding collateral constraint as in equation (1). See Chari, Kehoe, and McGrattan (2005).


It is possible, however, that the contraction in credit might have been due to domestic considerations unrelated to—but coincident with—capital outflows. In this case there would be two different shocks, one accounting for the capital outflows and the other for the contraction in credit.


Part of the decline in inflation could be accounted for by the fall in the international prices of food and fuel in the second half of 2008. We do not account for such effects here.


‘There is a high degree of uncertainty attached to estimates of the level and growth rate of real GDP’ (IMF, 2014: p. 10). The only available series for this period uses 1994 as the benchmark year to estimate GDP from the supply side. Especially for services and construction, no appropriate indicators exist. On the expenditure side, no reliable indicators of household consumption exist. The above quarterly series does not address most of these weaknesses, because the Chow-Lin method used to estimate the quarterly output gap tracks the available annual data.


The IRIS toolbox can carry out this type of exercise.


The initial drop in uR results from the forward-looking behaviour of the exchange rate and is necessary to maintain consistency of the observed exchange rate with the UIP condition. Note that the size of the shocks is consistent with the magnitude of the event, e.g., the initial shock to the terms of trade corresponds to a 50 per cent decline. The shock to the financial system, holding all else equal, implies a 20% decline in quarterly consumption. Although it is difficult to compare across models, the shocks are larger in magnitude than shocks found in the sudden stop literature, e.g., Mendoza (2010).


We are grateful to Andy Levin for suggesting this approach.


‘In the second half of 2008, the monetary policy objective will be to achieve the end-year inflation target of 7.0% [from 12.1% in the 12 months through June 2008]. However, a number of factors pose challenges to the achievement of the inflation objective and money supply targets. These challenges include the cost-push effects of expected higher prices of oil on the world market; expected increases in utility charges; continued electricity outages; and the rise in world food prices.’ (Bank of Zambia, 2008). See also IMF (2009a, 2010).


These targets were set in the context of the Fund-supported Policy Reduction and Growth Facility arrangement. Targets for 2008 were set in early 2008, targets for 2009 were set in early 2009, with a revision mid-2009. See IMF (2009a).


‘The primary objective of monetary policy in the first half of 2009 is to achieve an end-year inflation target of 10.0% at end-December 2009 [from 17% at end-December 2008] … To achieve this objective, the Bank of Zambia will implement appropriate monetary policy to ensure that reserve money and consequently broad money remain within their programmed path in order to dampen inflationary pressure’ (Bank of Zambia, 2009).

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