Sources of Business Cycles in a Low Income Country

• Author: Christopher Otrok,Romain Houssa,Jolan Mohimont
• Published date: 01 February 2015
• Date: 01 February 2015
• DOI: http://doi.org/10.1111/1468-0106.12097

SOURCES OF BUSINESS CYCLES IN A LOW

INCOME COUNTRY

ROMAIN HOUSSA*University of Namur; University of Leuven

JOLAN MOHIMONT University of Namur

CHRISTOPHER OTROK University of Missouri and Federal Reserve Bank of St

Louis

Abstract. We examine the role of global and domestic shocks in driving macroeconomic fluctua-

tions for Ghana. We are able to study the impact of exogenous shocks, including productivity, credit

supply and commodity price shocks. We identify the shocks using a combination of sign and

recursive restrictions within Bayesian vector autoregressive models. As a benchmark we provide

results for South Africa to document the difference between two economies with similar structures

but at different stages of development. We find that global shocks play a more dominant role in

South Africa than in Ghana. These shocks operate through three channels: trade, credit and

commodity prices.

1. INTRODUCTION

Developing policies for stabilizing macroeconomic fluctuations has been the

subject of many papers in both advanced and emerging markets. A prerequisite

for building the structural models to develop these policies is knowledge of the

main sources of fluctuations in these economies. In this paper we take the first

step towards developing stabilization policies in a low-income country by docu-

menting the driving forces of fluctuations in this type of economy.

Empirical work for low-income countries has unique challenges. First, data is

typically of poor quality and the time-series length is short relative to those

available for more advanced economies. Second, the economic structure of these

economies is typically quite different from more advanced economies. In this

paper we address the data issue by constructing our own time series for GDP.

This allows us to create longer time series than is available historically. We take

a first pass at issue two by studying a more advanced economy with a similar

production structure.

In our case, we study Ghana. Ghana is a good test case because it is the only

low-income country that is currently operating with an explicit inflation target-

ing framework in Sub-Saharan Africa. Moreover, the Bank of Ghana has been

*Address for Correspondence: Department of Economics, Centre of Research in the Economics of

Development, Center for Research in Finance and Management, University of Namur, Rempart de

la Vierge 8, 5000 Namur, Belgium. E-mail: romain.houssa@Unamur.be. This paper is part of a

research project on macroeconomic policy in low-income countries supported by the UK’s Depart-

ment for International Development (DFID). The paper was presented at the Conference on

‘Macroeconomic Challenges Facing Low-Income Countries: New Perspectives’ (Washington, DC,

30–31 January 2014). The views expressed herein are those of the authors and should not be

attributed to the IMF, its Executive Board, or its management, or to DFID, or to the Federal

Reserve Bank of St Louis.

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Pacific Economic Review, 20: 1 (2015) pp. 125–148

doi: 10.1111/1468-0106.12097

© 2015 Wiley Publishing Asia Pty Ltd

granted full independence since the bill was passed in December 2001 and the

Monetary Policy Committee was established in September 2002.

Our objective is to develop a set of stylized empirical facts about Ghana in

terms of the shocks that drive fluctuations. Our purpose is to investigate the type

of shocks and model features that one would want to consider in building a

structural dynamic stochastic general equilibrium (DSGE) model. The empirical

facts here can then be used in such a model building exercise. This objective

guides us in the type of shocks we consider. We are interested in the extent to

which Ghana is exposed to international versus domestic shocks. This will

motivate us to consider global counterparts to all of our shocks. We study a

sequence of shocks that may be of importance to a country such as Ghana. First,

we consider productivity shocks to measure the extent to which business cycles

are driven by real factors. Second, we study credit market shocks to understand

the importance of financial sector disruptions. Third, we consider commodity

price shocks to measure the importance of such price changes of a dominant

primary goods sector. We do not consider monetary policy shocks due to

changes in monetary policy over the sample period. We leave this latter issue to

a more structural investigation.

To identify shocks we use a standard macroeconomic tool: the vector

autoregression (VAR), which we estimate using Bayesian methods. Identifica-

tion of shocks is through a set of sign restrictions in the spirit of Uhlig (2005) for

both the credit and productivity shocks. These use minimal but robust implica-

tions of structural models to impose restrictions on impulse response functions.

For commodity price shocks we exploit the exogeneity of commodity prices for

a small economy such as Ghana and use a recursive setup, with commodity

prices ordered first. Global shocks are estimated using a similar set of restric-

tions on data constructed using the first principal component of G7 country-

level data.

In addition to studying the impact of shocks in Ghana we also study the

impact of the same set of shocks in South Africa. The important question here

is whether or not Ghana, which has a similar production structure and a mon-

etary policy framework as South Africa, is fundamentally different. That is, in

building a structural model for Ghana, how much of our experience with models

from more advanced and stable economies can we import?

This paper is related to the literature on the sources of macro-economic

fluctuations in developing countries. One strand of this literature employs

univariate methods to estimate cyclical variations in macroeconomic series (e.g.

Agenor et al., 2000; Cashin, 2004; du Plessis, 2006; Male, 2011). For instance,

Agenor et al. (2000) employ de-trended methods to a set of macro-aggregates

for 12 developing countries and find procyclical real wages, suggesting that

productivity shocks play a dominant role in the macroeconomic fluctuations of

these economies. They also find countercyclical government expenditure, which

implies that the government plays a stabilizing role in these economies. Finally,

their analysis shows that the business cycle of advanced countries has a signifi-

cantly positive impact on the economic activity of developing countries.

However, Male (2011) recently challenged this finding. She applies the classical

R. HOUSSA ET AL.

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