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

• Author: Andrew Berg,Rafael A. Portillo,Mai C. Dao,Jaromir Benes,Alfredo Baldini
• Published date: 01 February 2015
• DOI: http://doi.org/10.1111/1468-0106.12098
• Date: 01 February 2015

MONETARY POLICY IN LOW INCOME COUNTRIES

IN THE FACE OF THE GLOBAL CRISIS:

A STRUCTURAL ANALYSIS

ALFREDO BALDINI*International Monetary Fund

JAROMIR BENES International Monetary Fund

ANDREW BERG International Monetary Fund

MAI C. DAO International Monetary Fund

RAFAEL A. PORTILLO International Monetary Fund

Abstract. We develop a dynamic stochastic general equilibrium model with a banking sector to

analyse the impact of the financial crisis in developing countries and the role of the monetary policy

response, with an application to Zambia. We view the crisis as a combination of three related shocks:

a worsening in the terms of the trade, an increase in the country’s risk premium and a decrease in the

risk appetite of local banks. Model simulations broadly match the path of the economy during this

period. We derive policy implications for central banks, and for dynamic stochastic general equi-

librium modelling of monetary policy, in low-income countries.

1. INTRODUCTION

Understanding the impact of the global financial crisis in low-income countries

(LIC) is an important task for national authorities and international organiza-

tions. Beyond its intrinsic importance, the crisis provides a relatively clean

‘experiment’: it can be interpreted as an exogenous event for most LIC, 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.

Central banks in developed and emerging markets make ample use of both

small and large quantitative structural models for this kind of exercise.1These

models have proven useful for studying shocks and monetary policy; they are

not meant to provide the ultimate answer, but rather to structure thinking and

*Address for Correspondence: International Monetary Fund, 700 19th Street, N.W., Washington,

DC 20431, USA. E-mail: rportillo@imf.org. We would like to thank Galina Hale, Andy Levin, two

anonymous referees, and participants at the conference on Macroeconomic Challenges Facing

Low-Income Countries, 30–31 January 2014 at the International Monetary Fund in Washington,

D.C. This paper is part of a research project on macroeconomic policy in low-income countries

supported by the U.K.s Department for International Development. The views expressed herein are

those of the authors and should not be attributed to the IMF, its Executive Board, or its manage-

ment, or to DFID.

1Notable examples of large models include the SYGMA model developed at the Board of the

Governors of the Federal Reserve (Erceg et al., 2006, the GEM and GIMF models developed at the

IMF (Laxton and Pesenti, 2003; Kumhoff et al. 2010) and the BEQM model developed at the Bank

of England (Harrison et al., 2005). Smaller models include the gap model described in Berg et al.

(2006).

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Pacific Economic Review, 20: 1 (2015) pp. 149–192

doi: 10.1111/1468-0106.12098

© 2015 Wiley Publishing Asia Pty Ltd

The International Monetary Fund retains copyright and all other rights in the manuscript of this

article as submitted for publication.

organize the evidence. The use of such models remains fairly limited in low-

income countries, however, for several reasons. First, many of these countries

have only recently emerged from prolonged periods of fiscal dominance and

chronic inflation, and monetary policy was primarily focused in re-anchoring

inflationary expectations rather than stabilising economic activity.2Second, it is

still an open question whether these models are useful for LIC. As several

authors have pointed out, the monetary transmission mechanism is considered

very different in these countries: a large fraction of the population does not have

access to financial services, banks dominate the financial system, secondary

markets for government securities are often missing, and interest rates may not

reflect domestic financial conditions.3Third, many of the standard models do

not explicitly incorporate various monetary aggregates, which typically serve as

operational and intermediate targets in LIC monetary policy frameworks.4

This paper provides a first attempt at filling this gap. We develop a quantita-

tive model, adapted to the specific characteristics of LIC, 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 data set

of Zambian macroeconomic and financial variables.

We believe that the objections above are not fatal, and, indeed, we hope that

this paper helps make this case. They do remind us to be modest, careful and

tailored in our application of these tools. We do not see this exercise as one of

uncovering ‘deep parameters’ but rather as organising analysis, empirical evi-

dence, and the narrative around key mechanisms and issues for a particular

question. We believe that the discipline involved in carefully specifying the key

shocks and mechanisms and comparing model predictions to outcomes can help

us sharpen our intuition and learn from experience.5

We believe that the lessons, about the modelling approach and about the

economics, are general. However, one of these lessons is that the ‘art’ lies in

picking the right model and features for the events and questions at hand. In this

light, a careful case study such as this can be more valuable, including for other

countries, than a more generic approach.

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 float-

ing exchange rate regime. As in other LIC, fiscal developments can pose a

challenge for monetary policy through their effect on aggregate demand and the

allocation of credit.

2See Adam and O’Connell (2006).

3See IMF (2008) and Mishra et al. (2010).

4See Berg et al. (2010c).

5Of course, while structural macroeconomic modelling is problematic in LIC, the paucity of data

and frequency of structural change also or even a fortiori impairs more theory-free empirical

methods. On these and other points with respect to the application of DSGE models in sub-Saharan

Africa, see Berg et al. (2015b).

A. BALDINI ET AL.

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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; that is, it conforms to the

typical structure of dynamic stochastic general equilibrium (DSGE).6

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, although 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 opera-

tions in Zambia) initially increased by 400 basis points between mid-2008 and

mid-2009. As the crisis propagated, the policy stance was later reversed, allow-

ing T-bill rates to fall by more than 1000 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).7We then

compare the model’s output with data on 10 macroeconomic and financial

6By typical structure we mean that profit and utility maximization by agents in the model result in

equations that are standard in DSGE: new Keynesian Phillips curves for prices and wages (with both

forward-looking and backward-looking elements) and the 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).

7We simulate our model using IRIS, a Matlab-based package developed by one of our coauthors

(Benes). This package is ideally suited for confronting DSGE models with data and for operating

policy analysis and forecasting systems organized around such models. It can be freely downloaded

from http://www.iristoolbox.org.

MONETARY POLICY IN LICS DURING THE CRISIS

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article as submitted for publication.