Business Cycles in Developing Countries:
Are They Different?
Rand, John and Tarp, Finn
December 2001
Online at https://mpra.ub.uni-muenchen.de/62445/
MPRA Paper No. 62445, posted 03 Mar 2015 11:27 UTC
by
John Rand and Finn Tarp
Abstract
According to Lucas (1981) understanding business cycles is the first step in designing appropriate stabilization policies. In this paper, we demonstrate a series of ways in which developing countries differ from their developed counterparts when focus is on the nature and characteristics of macroeconomic fluctuations. Cycles are shorter, making it necessary to modify the filtering procedures normally applied for industrialized countries. This leads to different stylized facts of the business cycle across countries and regions, and the developing countries are more diverse than the rather uniform industrialized countries. Great care is therefore needed when the causal mechanisms in economic models are specified. A “one-size fits all” approach is unlikely to be appropriate.
Outline
1. Introduction
2. Business Cycle Duration and De-Trending
3. Business Cycle Dates and Duration in Developing Countries 4. Stylized Facts Revised
5. Discussion and Conclusion
I. INTRODUCTION
The widespread use of traditional Keynesian models in combination with the Phillips- curve to study business cycle fluctuations was severely challenged in the early 1970s. The new classical school pointed repeatedly to the missing microeconomic foundation.
Subsequent critique of the new classical theories was, in turn, focused on the fact that they were unable to satisfactorily explain observed fluctuations in the industrialized economies. Nevertheless, the debate about the new classical revival helped resurrect business cycle analysis and stimulate the development of both the new Keynesian school and the Real Business Cycle (RBC) theory.
In recent years, focus has been on how well the new Keynesian and RBC models explain the so-called stylized facts of business cycles. Yet, existing literature is almost exclusively concerned with developed countries. Only scant attention has so far been paid to macroeconomic fluctuations in developing countries, the notable exceptions being Agénor, McDermott and Prasad (2000) and Pallage and Robe (2001).1 In these contributions, it is assumed that the length of the cycles is comparable to the duration in developed countries. In this paper, we investigate whether this assumption is valid based on a sample of 15 developing countries. Verifying the correct duration of macroeconomic fluctuations is critical. The stylized facts that emerge from simple business cycle analysis are very sensitive to the chosen distinction between business cycles and the underlying growth performance.
Analyzing business cycles is useful for a variety of reasons. Canova (1998a, 1998b) highlights that such insights may guide researchers in choosing leading indicators for economic activity, and provide a set of “regularities” which macroeconomists can use as a benchmark to examine the validity of numerical versions of theoretical models. Burnside (1998) agrees with Canova on this point, and furthermore discusses the importance of applying more than one filter when de-trending is undertaken. When data are de-trended information is lost, and the nature of the information lost depends on the filter used. Any
1 Agénor, McDermott and Prasad (2000) have 12 developing countries (mainly middle-income countries) in their sample from which stylized facts are derived for 14 indicators. Pallage and Robe (2001) have 63 countries in their sample but only consider stylized facts related to foreign aid, including multilateral and bilateral aid and commitments as well as disbursements.
filter has the potential of masking differences between models and data. In this paper, we therefore apply both the Hodrick-Prescott (HP) and the Band-Pass (BP) filter. Burnside and Canova do not agree, however, on the existence of a single set of stylized facts about business cycles. We do not pretend to enter this long-standing controversy. We adopt instead the taxonomy proposed by the National Bureau of Economic Research (NBER) and derive a set of stylized facts covering 15 indicators for 50 developing countries. They turn out to be clearly different from those of industrialized countries.
The paper is organized in five sections. Following this introduction, Section 2 provides an overview of the methodology used to estimate the duration of the business cycles. The de- trending procedures are also described in some detail. Section 3 goes on to document our estimates of the duration and turning points of the business cycles in developing countries, and in Section 4 we derive the implications hereof for the stylized facts. Section 5 concludes and discusses the implications for future research.
II. BUSINESS CYCLE DURATION AND DE-TRENDING
In their seminal contribution to the so-called classical business cycle literature, Burns and Mitchell (1946) define business cycles as follows:
Business cycles are a type of fluctuations found in the aggregate economic activity of nations that organize their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions, and revivals which merge into the expansion phase of the next cycle; this sequence of changes is recurrent but not periodic; in duration business cycles vary from more than one year to ten or twelve years; they are not divisible into shorter cycles of similar character with amplitudes approximating their own (Burns and Mitchell, 1946, p. 3).
Based on this general approach, researchers at the National Bureau of Economic Research (NBER) have for some 75 years worked on the identification of business cycle turning points in a model free environment.2 Using monthly series on output, income, employment and trade for an increasing number of sectors, cyclical peaks and troughs have been estimated for each series using a variety of estimation techniques.
2 See http://www.nber.org/cycles.html and Mitchell (1927).
Supplementing all this with qualitative judgments on the persistence and seriousness of cyclical movements across sectors has formed the basis for the identification of common turning points, including their dates.3 It is the latter summary information on the aggregate business cycles that is made publicly available.
The classical methodology of Burns and Mitchell (1946) and the NBER is complex and demanding in terms of analytical capacity. Bry and Boschan (1971) therefore simplified it, and the proposed Bry and Boschan (BB) procedure is based on a single reference series (typically real GDP). The adherent analytical steps and set of decision rules for selecting turning points in the business cycles are summarized in Table 1.
Table 1. Bry and Boschan (BB) procedure for programmed determination of turning points 1. Determination of extremes and substitution of values
2. Determination of cycles in twelve month moving average (extremes replaced).
A: Identification of higher (or lower) than five months on either side.
B: Enforcement of alternation of turns by selecting highest of multiple peaks (or lowest of multiple troughs).
3. Determination of corresponding turns in Spencer curve (extremes replaced).
A: Identification of highest (or lowest) value within +/- five months of selected turn in twelve month moving average.
B: Enforcement of minimum cycle duration of fifteen months by eliminating lower peaks and higher troughs of shorter cycles.
4. Determination of corresponding turns in short-term moving average of three to six months, depending on months of cyclical dominance (MCD).
A: Identification of highest (or lowest) value within +/- five months of selected turn in Spencer curve.
5. Determination of turning points in unsmoothed series.
A: Identification of highest (or lowest) value within +/- four months, or MCD term, whichever is larger, of selected turn in short term moving average.
B: Elimination of turns within six months of beginning and end of series.
C: Elimination of peaks (or troughs) at both ends of series which are lower (or higher) than values closer to the end.
D: Elimination of cycles whose duration is less than fifteen months.
E: Elimination of phases whose duration is less than five months.
6. Statement of final turning points.
Source: Bry and Boshan (1971, p. 21).
3 A contraction period is defined as the time from peak to trough of a cycle. Similarly, an expansion period is defined as the time between trough and peak.
All classical views of macroeconomic fluctuations involve an analysis of total increases/declines in output and/or other indicators over a given time period independent of the underlying nature of the change. In contrast, a competing approach in the business cycle literature, which we will tentatively refer to as the modern approach, has focused on the cyclical fluctuations in economic time series data around their long run trends. These short-term fluctuations are often referred to as growth cycles, and they are identified through the application of a trend adjustment procedure. Burns and Mitchell (1946) argue against the use of such trend adjusted data. De-trending may involve the loss of critical information. Stock and Watson (1999) document that the focus on growth cycles (i.e., the cyclical part of macroeconomic changes over time) has both advantages and disadvantages as compared to the classical attention to aggregate changes. They recognize that ignoring the trend (or the cyclical component) is inconsistent with various economic models. For example, in traditional growth models productivity shocks determine both the long run economic path and cycles around this trend. On the other hand, growth cycle analysis may well be more robust (and useful for policy purposes) when the underlying trend growth rate in the economy is separated out.4
Modern studies of the properties of business cycles have generally relied on linear filters to separate trend and cyclical components. The standard procedure is therefore to de-trend the data series using some approximation to an ideal filter and subsequently compute sample second moments based on the cyclical component. Most researchers have used either the Hodrick-Prescott (HP) filter (Hodrick and Prescott, 1997) or the Band-Pass (BP) filter (Baxter and King, 1998). As compared to a standard first differencing filter, the more complex HP-filter has the advantage that it does not amplify high frequency noise.
Nevertheless, a drawback is that the HP-filter at the same time allows much of the high frequency noise to be left outside the business cycle frequency band. The low pass BP- filter has been adjusted to take account of this problem,5 but it has a tendency to underestimate the cyclical component. In our analysis we therefore use both the HP and
4 Stock and Watson (1999, p. 9) illustrate this with reference to post-war Japan, which has experienced very high growth rates and few absolute declines (and thus few classical business cycles). Nevertheless, Japan has experienced various policy relevant growth cycles.
5 This is done using a twelve quarter centered moving average, where weights are chosen so as to minimize the squared difference between the optimal and the approximate filters, subject to the constraint that the filter has zero gain at frequency zero. See Stock and Watson (1999, p. 12) for a good illustrative description of how the different filters work.
the BP filters to accommodate the debate between Canova (1998a, 1998b) and Burnside (1998) on appropriate filters.
After the revival of interest in business cycle research following Kydland and Prescott (1982) an enormous amount of research has been based on an eight-year distinction between business cycles and growth. Moreover, both the HP and the BP filters are designed to cut off low frequency cycles of more than 32 quarters duration. This implies that a smoothing parameter (λ) is chosen for the HP-filter so λ = 1600 and λ = 100 when seasonally adjusted quarterly and annual data are used, respectively. While it is common to define modern business cycles as fluctuations in economic time series with a periodicity of eight years or less, there is limited empirical evidence for this practice when it comes to industrialized countries. While the choice of eight years may be appropriate in the case of the US, studies concerning OECD countries suggest that six years is likely to be a more appropriate duration of the business cycles (Pedersen, 1998). Different smoothing parameters are therefore called for.
For developing countries, we know of no study that has tried to estimate the duration of the business cycles, and they may well be different from those of developed countries.
Relying on the above smoothing parameters when studying poor countries is therefore at best ad hoc, and may lead to inappropriate conclusions as regards the summary statistics (or stylized facts) that characterize macroeconomic fluctuations.6 In the extreme, inappropriate numerical models might be validated and vice versa, depending on the choice of smoothing parameter. We therefore move on to estimate the duration of the business cycles in 15 developing countries.
III. BUSINESS CYCLE DATES AND DURATION IN DEVELOPING COUNTRIES
To estimate the duration of business cycles, their turning points must be identified. For this we apply the BB-procedure, programmed in MATLAB,7 on the 15 countries in our
6Choosing a smaller value of the smoothing parameter removes a larger part of the variance of the series since more low frequency movements are filtered away. As a consequence, the standard deviation can be significantly affected. The smoothing parameter also affects the computed second moments, implying that it may be important whether business cycles are defined as cycles with a duration of less than eight years or less than six or seven years.
7The computer code can be obtained from the authors on request.
sample. They include five Sub-Saharan African countries, five from Latin America, and five from Asia and North Africa as shown in Table 2.8 Because of the difficulty of obtaining reliable quarterly GDP data for all of the countries in the sample, we use indexes of industrial production as a proxy for the aggregate business cycle. We therefore follow Agénor, McDermott and Prasad (2000), who argue that because output in the industrial sector corresponds roughly to output in the traded goods sector and is closely related to business cycle shocks for the countries analyzed, this variable is a reasonable proxy for measuring the aggregate business cycle. The primary data source is the International Monetary Fund (IMF) International Financial Statistics (IFS), where real output data are approximated by either the industrial production or the manufacturing production index. Data are available for varying time periods in the 15 countries, but the period 1980-99 is well covered across countries. Results are summarized in Table 2.
Table 2. Duration of the business cycle for 15 developing countries (in quarters)
Region Country Period
(Q=quarter)
Average expansion length
Average contraction length
Average length of the business cycle
Sub-Sah. Africa South Africa 61,Q1-99,Q4 5.8 5.9 11.8
Malawi 70,Q1-99,Q4 5.9 5.4 12.0
Nigeria 70,Q1-99,Q4 4.0 5.5 9.5
Cote d´Ivoire 68,Q1-99,Q4 4.8 4.8 9.7
Zimbabwe 78,Q1-99,Q4 5.1 5.3 10.4
Latin America Uruguay 79,Q1-99,Q4 4.9 4.3 9.1
Columbia 80,Q1-98,Q4 5.0 4.7 9.7
Peru 79,Q1-99,Q4 4.6 4.3 9.4
Chile 60,Q1-99,Q4 3.7 3.8 7.8
Mexico 60,Q1-99,Q3 4.8 4.7 9.5
Asia and N. Africa India 60,Q1-99,Q4 3.1 4.7 8.1
Korea 60,Q1-99,Q4 6.3 10.4 18.1
Morocco 60,Q1-99,Q4 3.7 4.0 7.7
Pakistan 70,Q3-99,Q4 5.4 5.8 11.2
Malaysia 70,Q1-99,Q4 4.2 4.9 9.6
All Countries All 4.8 5.2 10.2
Notes: Because of missing data for some quarters for Zimbabwe and Cote d´Ivoire, some adjustments had to be made for these two countries in order to estimate the duration of the business cycle using the Bry and Boschan procedure.
8 The countries from North Africa should clearly not be grouped with Sub-Saharan Africa due to major differences in economic indicators. To facilitate the presentation of our results they have been grouped under the heading of Asia and North Africa.
For Latin American countries the average length of the expansion periods is longer than the contraction period, whereas the opposite is characteristic for Asian and North African countries in the sample. It is more difficult to find a pattern in the business cycle duration for Sub-Saharan African countries. Yet, it does appear that the average duration of the business cycle is longer than in the other regions. Generally, it is clear from this analysis that the average length of the business cycle for all developing countries is only between seven and 18 quarters, equivalent to no more than four and a half years. While some variation exists, a period of up to eight years duration cannot be justified. Taking account of the standard deviation of the results (no more than five quarters), six years is a more appropriate choice as upper limit.
Following Pedersen (1998) this has two important implications. When the cyclical component has cycles with less than six years duration and when the near integrated time series are filtered, the optimal value of the smoothing parameter (λ) for the HP-filter is between 310 and 340. Setting λ = 1600 will lead to distorted results. Similarly, also the BP-filter should be configured differently to reflect the appropriate cycle duration.
Next, consider the actual peaks and troughs for the 15 developing countries in our sample as reported in Table 3-5. The interesting questions in the present context are whether (i) the timing of recessions and booms are independent across the 15 countries in the sample (i.e., whether there is a common business cycle), and (ii) how business cycles in developing countries are related to cycles in the industrialized countries. Artis, Kontolemis and Osborn (1997) find relatively synchronous peaks/troughs in the years 1973-74, 1979-80 and 1989-90 for G7 and European countries. It is evident that the first two of these turning points reflect the two international oil crises, and the last episode seems correlated with the collapse of Eastern Europe. Besides these three events not much is apparent in terms of common business cycle features in the industrialized countries.
Table 3 documents the peaks and troughs during the period 1980-98 for the five Sub- Saharan African countries. It appears that the second oil crisis and related events affected these countries with a lag as compared to the trough in the industrialized countries.
Nevertheless, country specific circumstances appear to have played some role in the more specific timing of the beginning of the recession that is not quite as regular as in the Latin
American sub-sample, as discussed below.9 The turning points of the business cycles in Sub-Saharan African countries vary considerably, though a common trough is evident in 1985, reflecting the general economic depression in Africa during the 1980s. In South Africa recessions got shorter during the period 1980-98, but business cycle features for Nigeria, Zimbabwe and Cote d’Ivoire did not change much during the sample period.
Thus, no improvement took place, and in the case of Malawi, the duration of recessions even increased, confirming the troubling difficulties experienced by Malawi (see IMF, 2001b, and Mosley, Harrigan and Toye, 1991).
Table 3. Peaks and troughs for Sub-Saharan African countries 1980-98
South Africa Malawi Nigeria Cote d´Ivoire Zimbabwe
Peak/Trough 81,Q4 - 83,Q1 80,Q3 - 82,Q1 81,Q1 - 83,Q1 81,Q1 - 82,Q4 82,Q2 - 83,Q1 Peak/Trough 84,Q2 - 85,Q3 83,Q3 - 85,Q1 84,Q1 - 85,Q2 84,Q1 - 85,Q3 84,Q1 - 85,Q4 Peak/Trough 86,Q3 - 87,Q2 86,Q3 - 88,Q1 86,Q1 - 86,Q4 86,Q2 - 87,Q3 86,Q3 - 88,Q1 Peak/Trough 88,Q1 - 89,Q1 89,Q3 - 91,Q1 87,Q4 - 90,Q2 89,Q1 - 90,Q3 89,Q1 - 90,Q1 Peak/Trough 90,Q1 - 91,Q1 92,Q3 - 94,Q2 91,Q2 - 92,Q3 92,Q1 - 93,Q3 90,Q4 - 93,Q1 Peak/Trough 92,Q4 - 94,Q2 95,Q3 - 97,Q2 93,Q4 - 94,Q4 94,Q4 - 95,Q3 93,Q4 - 95,Q1
Peak/Trough 95,Q3 - 96,Q4 95,Q4 - 96,Q3 96,Q2 - 97,Q3
Turning now to the Latin American countries in Table 4, they also experienced a common, lagged trough following the second oil crisis as compared to the industrialized countries. The synchronized trough in the Latin American countries took place in 1982.
But otherwise the turning points for the individual countries seem country specific.
Consistent with the average results in Table 2 the expansion periods are longer for Uruguay, Peru and Mexico during 1980-98 than the contraction periods. However the recessions clearly got shorter in Mexico during the 1980s and 1990s as compared with recessions in the 1960s and 1970s. Whether this is due to improved economic policy, exogenous factors or some combination hereof is an issue we will not pursue further here, but see for example Giugale, Lafourcade and Nguyen (2001) and Lustig and Ros (1993).
Columbia experienced recessions and expansions during 1980-98 of almost identical duration, whereas Chile had much shorter recession periods as compared with earlier decades. This fits well with prior insights about the Chilean economic performance
9 Data do not allow systematic comparison with experiences following the first oil crisis for Sub-Saharan Africa, but scattered observations not reported here seem to indicate that this variability (i.e. the timing of the onset of the recession in individual countries) was even more pronounced in the early 1970s.
discussed in Solimano (1993) and IMF (2001a). All in all, when the time period for the analysis of Latin American countries is shortened, it becomes clearer that the average expansion periods are longer than the average contraction periods, reflecting improved economic performance in more recent years.
Table 4. Peaks and troughs for Latin American countries 1980-98
Uruguay Columbia Peru Chile Mexico
Peak/Trough 80,Q3 - 82,Q1 80,Q4 - 82,Q1 80,Q4 - 82,Q1 80,Q4 - 82,Q1 81,Q3 - 83,Q1 Peak/Trough 83,Q4 - 84,Q3 83,Q4 - 85,Q1 83,Q4 - 85,Q2 83,Q2 - 84,Q1 85,Q3 - 86,Q3 Peak/Trough 85,Q3 - 87,Q1 86,Q3 - 88,Q1 86,Q4 - 89,Q1 85,Q3 - 86,Q3 87,Q2 - 88,Q1 Peak/Trough 88,Q4 - 89,Q3 89,Q3 - 90,Q3 89,Q4 - 90,Q3 87,Q2 - 88,Q1 88,Q4 - 89,Q3 Peak/Trough 90,Q4 - 92,Q1 91,Q4 - 93,Q1 91,Q2 - 92,Q3 88,Q4 - 89,Q3 90,Q4 - 91,Q3 Peak/Trough 92,Q4 - 94,Q1 93,Q1 - 95,Q1 94,Q2 - 95,Q1 90,Q2 - 91,Q1 92,Q2 - 93,Q1 Peak/Trough 94,Q4 - 95,Q3 95,Q4 - 96,Q3 95,Q4 - 96,Q3 91,Q4 - 93,Q1 93,Q4 - 94,Q3
Peak/Trough 96,Q4 - 97,Q3 94,Q2 - 95,Q3
The business cycles of Asian and North African countries included in Table 5 were influenced by the oil crisis at very different points in time. The relevant dates are almost randomly distributed. It would clearly be interesting to expand the sample to see whether this observation is robust, but the necessary data are not available. In addition, it is only in the case of Malaysia that shorter recession periods were experienced during the period 1980-98 as compared with previous decades.
Table 5. Peaks and troughs for Asian and North African countries 1980-98
India Korea Morocco Pakistan Malaysia
Peak/Trough 80,Q1 - 81,Q2 81,Q4 - 85,Q1 80,Q4 - 81,Q3 80,Q1 - 81,Q3 82,Q3 - 83,Q4 Peak/Trough 82,Q1 - 83,Q2 87,Q2 - 88,Q2 82,Q2 - 83,Q1 82,Q2 - 83,Q3 85,Q4 - 87,Q1 Peak/Trough 84,Q1 - 85,Q2 90,Q3 - 92,Q3 83,Q4 - 84,Q3 85,Q1 - 86,Q2 87,Q4 - 89,Q1 Peak/Trough 86,Q1 - 87,Q2 93,Q2 - 94,Q1 85,Q2 - 86,Q1 87,Q1 - 88,Q3 89,Q4 - 91,Q2 Peak/Trough 88,Q1 - 89,Q2 94,Q4 - 98,Q2 86,Q4 - 89,Q1 90,Q1 - 91,Q3 92,Q4 - 94,Q4 Peak/Trough 90,Q1 - 91,Q2 89,Q4 - 90,Q3 93,Q1 - 94,Q3 95,Q4 - 97,Q1 Peak/Trough 92,Q1 - 93,Q2 91,Q4 - 92,Q3 96,Q1 - 97,Q3
Peak/Trough 94,Q1 - 95,Q1 93,Q4 - 95,Q1
Peak/Trough 96,Q1 - 97,Q3 95,Q4 - 97,Q1
The very frequent and long duration of recession periods in the countries in this sample may appear somewhat surprising as they are generally considered relatively well- managed economies. This highlights that business cycle analysis based on turning points
does not capture the depth and shape of the downturn, and similarly for the upturn.10 To illustrate this point consider Figure 1 where two recessions with different duration are shown. It is clear that the cumulated welfare loss shown as areas A and B are not necessarily different. In other words, it cannot (as often done) be concluded that countries experiencing long recession periods have greater output loss than countries with shorter recession periods. It may well be more critical to avoid deep recessions. This underscores the importance of distinguishing between different kinds of recessions (including both duration and amplitude) when economic policy advice is formulated.
All in all it can be concluded that the developing countries in our sample were influenced differently in terms of timing (i.e., with a lag) by the second oil crisis than the industrialized countries. This suggests that business cycles in developing countries may well be as much a result of recessions in the industrialized countries as a consequence of the original international crisis itself. This hypothesis about the vulnerability of developing countries is supported by Kouparitsas (2001). He evaluates the extent to which macroeconomic fluctuations in developing non-oil producing countries are caused by shocks originating in the industrialized countries. Based on a computable general equilibrium model he finds a strong transmission mechanism of the business cycle. His results indicate that fluctuations in output of the industrialized countries may well account for about 70% of the variation in the consumption of developing countries.
Finally, our results document that the average duration of business cycles in developing countries is shorter than in the industrialized countries. Developing countries are different, and in general, they move relatively quickly from peak to trough and vice-versa.
This is costly as documented by Ramey and Ramey (1995) and clearly reflects the insufficient capacity to counteract exogenous influences, including the limited extent of automatic stabilization. In Section 4, we move on to derive the stylized facts that emerge when the shorter business cycle duration is taken into account.
10 For an interesting study of the welfare losses incurred by 33 countries due to business cycles during the last three decades see Pallage and Robe (2000).
IV. STYLIZED FACTS REVISED
In this section we apply the de-trending procedure described in Section 2 in combination with the modified smoothing parameters, estimated from the results in Section 3. A revised set of stylized facts emerges for 50 developing countries, including both low and middle-income countries. Detailed results are presented for Latin America, Sub-Saharan Africa and Asia and North Africa in a set of standard tables, including Table 6a and 6b to 11a and 11b, where a and b refers to the use of respectively the HP and the BP filter in the de-trending procedure. Data sources include World Development Indicators (World Bank, 2000), Global Development Finance (World Bank, 2000), International Financial Statistics (IMF, 2000), International Development Statistics (OECD, 2000) and Macroeconomic Time-series from the World Bank WebPages.
(a) Sub-Saharan Africa
A key issue concerning business cycle fluctuations in developing countries is whether aggregate fluctuations in the various indicators are characterized by time series properties, such as volatility and persistence, which are similar to the characteristics observed in industrialized countries. Examining summary statistics for the filtered cyclical components, it can be seen from Table 6a and 6b that volatility in the Sub-Saharan African sample is much higher for all the 15 variables included here than the level typically observed in developed countries.11 Moreover, the volatility of the cyclical components obtained using the BP-filter is generally much lower than the standard deviations estimated when using the HP-filter. The BP-filter eliminates some of the high- frequency variation in the data, whereas the HP-filter only eliminates low-frequency variation. The estimated volatility in Table 6a and 6b is significantly lower than in an analysis where “standard” assumptions (i.e., using the eight year definition of the business cycle discussed in Sections 2 and 3) about the smoothing parameters are used. The relative volatility among the variables is more robust to changes in the smoothing parameter.12
11 See Stock and Watson (1999) for detailed stylized facts of the US economy.
12 Because the HP and BP filters used in this paper tend to eliminate more of the low-frequency variation than a first differencing procedure the standard deviations in Table 6a and 6b are generally lower than would be the case with a first differencing filter. However the ordering of countries by their cyclical volatility is similar.
During the period 1967-97, a number of empirical business cycle regularities can be identified for Sub-Saharan Africa. Output is generally much more volatile than that of industrialized countries. However the magnitude of the standard deviations of output in Sub-Saharan Africa is much less than that reported by Pallage and Robe (2001). They estimate that shocks to poor countries are about six times more severe than shocks to industrialized countries. Our result indicate that the volatility of output is only about 3-4 times that of developed countries. This highlights that the choice of smoothing parameter is indeed an important one.
Considering some of the other variables the highly volatile nature of private investment, money stock (M2), official development assistance (ODA) and credit to the private sector stand out. All of the variables mentioned have very high standard deviations relative to GDP. This reflects the evident vulnerability of African economies when it comes to exogenous factors as well as variables that can be affected more directly by policy.
Another characteristic in the data is that consumption is more volatile than output. This suggests that the consumption smoothing inherent in the permanent income hypothesis appears absent in Sub-Saharan Africa in contrast to empirical evidence available for the industrialized countries. It should be kept in mind, though, that the consumption figure documented here includes both consumption of services and consumption of durables.
The latter is typically more volatile than GDP and other consumption indicators and is therefore considered separately when data for developed countries are analyzed with reference to the permanent income hypothesis. This is not possible here due the nature of the data available.
Table 6.a. Standard deviations for Sub - Saharan Africa, HP, percent
Gdp Abs Con Pco Pub Inv Imp Exp M2 Oda Tot Rer Cpi Cre Wag
Benin 2.86 4.16 3.79 3.85 8.80 20.53 13.91 15.95 11.79 15.58 9.21 na na 17.59 na
Burkina F 2.43 3.85 3.89 4.18 8.39 14.49 10.40 12.52 14.58 14.39 9.85 na na 21.79 na Burundi 3.56 3.83 4.71 4.65 13.27 24.60 8.43 11.15 na 13.07 24.89 6.59 5.05 23.04 na Cameroon 4.28 6.44 6.65 7.27 7.65 11.43 9.41 10.71 9.41 20.18 13.69 na na 16.61 na
Congo 4.17 5.44 5.35 6.55 21.89 24.70 17.76 15.96 na 22.74 14.50 na 76.51 na na
C. dIvoire 3.73 7.37 5.92 6.00 8.04 22.11 9.28 8.86 15.76 20.37 16.99 14.04 4.80 16.69 na Gabon 10.68 13.96 6.84 9.37 12.82 30.03 17.69 10.71 17.55 24.03 16.38 12.22 7.01 17.08 na Gambia 2.57 8.20 8.16 9.17 9.33 16.51 11.93 12.76 11.05 27.13 12.18 7.55 7.57 16.89 na Ghana 3.95 5.33 5.07 5.75 9.31 16.33 13.66 10.76 10.74 26.16 11.84 28.79 13.11 20.52 na Kenya 3.94 6.00 6.50 7.85 4.19 14.97 12.11 5.67 11.95 14.59 9.88 7.14 6.93 13.92 na Madagasc. 3.01 4.66 3.57 3.61 5.04 19.19 12.77 9.08 11.50 20.54 7.75 8.30 6.90 12.33 na
Malawi 3.88 5.72 4.79 7.46 7.93 19.88 11.02 8.88 8.58 17.51 9.04 na na 20.18 na
Mali 4.15 4.22 4.47 4.65 9.97 10.98 9.81 6.86 14.51 17.86 6.81 na na 21.35 na
Niger 6.18 8.95 9.05 11.17 9.86 35.43 13.43 14.10 16.87 18.84 12.98 11.11 6.26 18.95 na Nigeria 4.41 7.62 8.33 8.92 14.69 15.57 13.53 13.75 17.17 24.56 17.51 19.02 9.13 18.17 na Rwanda 11.41 6.87 7.26 6.84 23.02 15.21 13.73 18.78 11.63 14.27 20.30 na na 20.17 na Senegal 3.38 2.28 2.34 2.60 2.63 9.50 5.16 10.13 14.12 20.08 4.84 12.28 6.52 17.71 na
S. Africa 3.16 5.01 2.04 2.55 1.97 13.27 9.10 3.46 10.67 na 6.46 8.95 1.73 na na
Zambia 2.43 6.85 7.06 13.00 18.75 12.73 11.21 8.31 na 25.90 19.04 na na na na
Zimbabwe 5.10 4.95 7.60 9.55 12.26 14.19 Na na na 41.15 na 6.46 4.79 na na
Notes: Gdp = Real gross domestic product, Abs = Real domestic absorption, Con = Real total consumption, Pco = Real private consumption, Pub = Real general government consumption, Inv = Real gross domestic investment, Imp = Real imports of goods and services, Exp = Real exports of goods and services, M2 = Nominal money and quasi money (M2), Oda = Official development assistance, Tot = Terms of trade index, Rer = Real effective exchange rate index, Cpi = Consumer price index, Cre = Private sector credit , Wag = Nominal wage index. Data sources include WDI (2000), GDF (2000), IDS (2000), IFS (2000) and Macro Time Series from www.worldbank.org/research/growth/
Table 6.b. Standard deviations for Sub - Saharan Africa, BP, percent
Gdp Abs Con Pco Pub Inv Imp Exp M2 Oda Tot Rer Cpi Cre Wag
Benin 1.79 2.46 2.75 2.94 5.24 11.55 8.83 8.77 8.78 11.24 6.62 na na 12.65 na
Burkina F 1.90 2.63 2.52 2.74 5.80 9.28 7.17 8.15 6.42 8.21 6.53 na na 8.30 na
Burundi 2.46 2.63 3.35 3.41 9.08 19.78 5.52 9.36 na 8.76 17.22 4.20 3.01 14.47 na
Cameroon 2.90 4.25 4.33 4.70 4.98 7.21 6.33 7.29 4.62 13.20 11.30 na na 9.41 na
Congo 1.75 2.73 2.28 3.25 17.16 19.20 9.98 8.47 na 11.79 9.59 na 34.03 na na
C. dIvoire 1.96 3.86 3.30 3.25 5.04 14.07 5.43 5.34 7.87 12.99 8.26 7.42 2.50 9.09 na
Gabon 5.28 7.23 5.29 7.12 7.47 17.74 9.77 5.59 7.93 18.90 9.29 7.22 4.00 9.28 na
Gambia 1.54 3.18 3.23 3.59 4.37 8.63 4.33 5.31 6.61 18.56 6.82 4.24 3.16 11.79 na
Ghana 2.33 3.88 3.40 3.71 6.46 12.80 8.61 8.16 6.63 18.91 9.12 13.93 7.73 13.71 na
Kenya 2.14 3.09 4.14 4.92 2.51 10.62 7.33 4.50 6.17 6.91 6.10 4.46 3.01 8.10 na
Madagasc. 1.89 2.87 2.11 2.22 2.69 12.95 8.09 5.51 6.59 10.51 3.51 5.85 3.07 7.36 na
Malawi 2.66 4.19 3.74 5.86 5.01 14.89 8.06 6.72 4.72 11.04 5.18 na na 8.10 na
Mali 2.56 2.62 2.91 3.02 7.16 7.00 6.53 5.11 8.02 10.65 4.29 na na 14.25 na
Niger 3.54 5.96 6.81 8.13 5.38 23.53 8.66 11.32 7.24 13.13 9.00 6.38 3.70 9.71 na
Nigeria 2.98 4.47 5.57 5.93 9.62 8.38 7.68 8.30 9.84 15.45 12.54 10.87 5.65 11.89 na
Rwanda 8.08 4.49 4.54 4.22 15.83 11.05 9.60 14.03 6.13 9.91 14.06 na na 15.33 na
Senegal 2.46 1.54 1.48 1.70 1.68 6.33 3.44 6.97 8.32 14.08 3.76 7.23 3.34 10.32 na
S. Africa 1.85 3.02 0.98 1.17 1.27 8.32 5.98 1.89 5.54 na 3.40 5.61 0.79 na na
Zambia 1.69 4.19 4.56 7.78 10.73 8.83 7.25 5.85 na 16.22 13.29 na na na na
Zimbabwe 2.41 2.32 4.16 5.40 8.43 7.79 na na na 25.78 na 3.62 2.19 na na
Notes: See Table 6.a.
Turning now to analyzing cross correlations between GDP and other variables, Agénor, McDermott and Prasad (2000) define a series as pro-cyclical, a-cyclical, or counter- cyclical when the contemporaneous correlation coefficient is respectively positive, zero, and negative. In addition, the series is thought of as significantly contemporaneously correlated when 0.26 < X < 1.00, where X represents the cross correlation coefficient between GDP and the other variable involved.
The relationship between business cycle fluctuations in aggregate output and the different components of aggregate demand is well documented for developed countries. This has not so far been the case for developing countries. From Table 7a and 7b it can be seen that there is a robust positive relationship between consumption, both total and private, and domestic output in Sub- Saharan African countries. The magnitude of the correlations is in line with that observed in industrialized countries, and there are few exceptions. Data from Gabon and Gambia point in the direction of counter-cyclical consumption, and Nigeria, Zambia and Zimbabwe show signs of a weaker relationship between consumption and output than documented for the rest of the region and the industrialized countries. The general picture is however clear.
There is also a strongly positive contemporaneous correlation between de-trended investment and GDP data in almost all the Sub-Saharan African countries, and this is independent of the type of filter used. This observation is not different from what is observed in industrialized countries, and indicates that investment and GDP are indeed positively related to each other. The only outlier is Kenya, where there is an insignificant negative correlation between investment and output when looking at the band pass filtered time series.
The relationship between government expenditure and GDP often attracts considerable attention, inter alia because of the desire to ensure that fiscal policies help stabilize the economy. We find indications of a positive relationship between government expenditure and output for most of the countries in the Sub-Saharan African sample. There is therefore no evidence of a counter-cyclical role of the government’s fiscal policy in the present data, although some countries show signs of a negative relationship between government purchases and output. In contrast to the finding of Agénor, McDermott and Prasad (2000),
we would argue that fiscal policy needs reform before it is likely to have the desired contra-cyclical and stabilizing effect in Sub-Saharan Africa.
Turning next to the relationship between domestic business cycles and fluctuations in the variables relevant to international trade, Table 7a and 7b document a strongly positive relationship between imports and output in almost all of the Sub-Saharan African countries in our sample. In contrast, exports do not appear significantly correlated with the aggregate business cycle. This implies that foreign trade on balance would appear to be counter-cyclical, a characteristic also prevalent in developed countries. Exceptions include Nigeria where there are signs of a positive correlation between the trade balance and output, in all likelihood due to the substantial significance of oil exports in GDP.
Another exception is Rwanda.
Focusing on the correlation between the terms of trade index and output, it is difficult to identify a general pattern for the countries studied here within the short-term framework of business cycle analysis. In industrialized countries it is common to find a positive correlation between lagged values of the terms of trade index and domestic output, and Agénor, McDermott and Prasad (2000) also report that the terms of trade are strongly related to output in their more limited sample, representing in particular middle-income countries. Our data do not support that terms of trade disturbances can in general explain business cycle fluctuations in output in Sub-Saharan African countries. Interestingly, for example South Africa and Nigeria are cases where a positive relationship can be identified. Yet, insignificant correlations are common and signs change when the filter applied changes. This puts the complexity of the terms of trade and output relationship in poor Sub-Saharan African countries into perspective and suggests that it is likely that quantity changes in imports and exports in response to price changes did indeed take place during the period under study. Nevertheless, responses clearly did differ from, for example, the first to the second oil crisis due to the difference in the availability of foreign exchange. All this therefore highlights that it is wise to study specific episodes and countries carefully before general conclusions are attempted, remembering that there are countervailing factors at work affecting respective the supply-side and the demand-side of the economy.
Monetary policy is often assigned a key role in stabilization programs in developing countries, and the relationship between monetary variables and the business cycle has become a topic of interest. A large literature has evolved around the question whether money causes output, and a positive correlation between money variables and output exists in industrialized countries. For Sub-Saharan African countries there are indications of this feature. Generally, the correlation between output and M2 is positive for a majority of the 20 African countries considered here, and this is so independent of the filter used.
A Granger causality test shows some indication of causality going from money to output, but this result is very dependent on the choice of lags in the Granger causality procedure.
Furthermore in a number of countries we also find evidence of the opposite causation from output to money. All in all we find little robust evidence for unidirectional Granger causality from M2 to output in the Sub-Saharan African sample. So it is difficult to say on this basis whether restrictive monetary policy may have had harmful real consequences or whether monetary policy does not seem to affect output. In any case, the pro-cyclical behavior of monetary aggregates should not be ignored as it does signal mutual interdependence.
Another monetary aggregate considered here is domestic private sector credit. Equity markets are weakly capitalized in most developing countries as compared with the industrialized countries, and this is so in particular in Sub-Saharan Africa. Private sector credit is therefore likely to play a critical role in determining investment and suggests that overall economic activity is influenced by domestic private sector credit. There is some indication of a pro-cyclical relationship between credit and output in the Sub-Saharan African region. The correlations peak as in Agénor, McDermott and Prasad (2000) at a zero lag, maybe indicating that the availability of domestic credit affects activity fairly rapidly. A Granger causality test indicates that it is very difficult to make a robust statement as regards the causality between private sector credit and output, as was the case for the other monetary aggregate M2. Regardless of the Granger causality test the positive association between private sector credit and domestic activity has important implications for the design of stabilization programs. Ignoring this link may exacerbate the output cost of a restrictive monetary policy aimed at lowering inflation.
A substantial literature documents the counter-cyclical behavior of prices in industrialized countries, and it is typically argued that this negative relationship provides support for
supply driven interpretations of the business cycle, including real business cycle models.
The correlation between the consumer price index and output in our Sub-Saharan African sample is divided into two groups. Half the countries show pro-cyclical and half counter- cyclical behavior. Thus the African sample is not in accordance with the consistent negative pattern between output and prices in industrialized countries. It therefore appears that demand driven models of output should not be ruled out in the case of at least some African countries, whereas the supply-side is critical in others. This reinforces the point already made above about the need for careful attention to country specific circumstances and to countervailing forces at work (on both the demand and the supply-side of the economy) when for example a terms of trade shock hits.
The interpretation of the unconditional correlation between output and measures of the real effective exchange rate (REER) is complicated. The short run relationship depends crucially on the sources of the macroeconomic fluctuations. Nonetheless, unconditional correlations may be useful for two reasons. First, the signs and magnitude of these correlations could give an indication of the types of shocks that have dominated fluctuations over a period of time. Second, the correlations could help in interpreting the correlation between output and other trade related variables. In our sample, a clear picture does not emerge when examining the cross correlation between REER and output. Some countries provide evidence for a positive relationship and some show a generally negative correlation. However, in many cases the correlations are not significantly different from zero. This absence of a systematic relationship between REER and the business cycle is consistent with the result obtained when analyzing industrialized and middle-income countries, and it implies that policy analysis related to business cycles should not overemphasize the effects of REER on the economy.
The correlation between Official Development Assistance (ODA) and GDP is also documented in Table 7a and 7b. Pallage and Robe (2001) show that for a majority of the Sub-Saharan African countries aid flows are pro-cyclical.13 This finding is not supported by our analysis. Pallage and Robe (2001) note the magnitude of output fluctuations
13 Pallage and Robe (2001) base their analysis on both ODA commitments and disbursements. They generally find that commitments are ”less clearly” pro-cyclical than disbursements. We find that commitments are either counter-cyclical or at least do not provide any evidence for being pro-cyclical. As regards disbursements we find that commitments and disbursements are highly correlated. Since commitment data are generally more reliable and better sourced than disbursements, we find it justified to rely on the former in the present analysis.
experienced by African countries, and this may clearly be an important handicap for economic growth. They further argue that the existence of strongly pro-cyclical aid flows underpin the suggestion that aid may be harmful to growth in the African context. The cyclical nature of aid flows is therefore of interest. From our analysis (where appropriate filters are applied) it emerges that it is only in Congo that aid has been significantly pro- cyclical. In other countries the correlation is either statistically insignificant or aid is counter-cyclical.
Table 7.a. Cross correlations for Sub - Saharan Africa, HP
Gdp Abs Con Pco Pub Inv Imp Exp M2 Oda Tot Rer Cpi Cre Wag Benin 1.00 0.75 0.67 0.61 0.41 0.26 0.52 0.56 -0.08 -0.41 -0.11 na na -0.09 na Burkina F 1.00 0.80 0.66 0.61 0.38 0.42 0.34 -0.10 0.19 0.14 0.09 na na 0.27 na Burundi 1.00 0.87 0.66 0.60 0..38 0.35 0.06 0.01 na -0.03 0.20 -0.34 -0.34 0.17 na Cameroon 1.00 0.79 0.72 0.71 0.29 0.58 0.42 0.08 -0.02 -0.29 0.10 na na -0.03 na Congo 1.00 0.94 0.87 0.81 0.01 0.37 0.82 0.59 na 0.53 0.11 na -0.58 na na
C. dIvoire 1.00 0.87 0.83 0.75 0.83 0.61 0.60 -0.47 0.58 0.02 0.49 0.36 0.32 0.35 na
Gabon 1.00 0.95 0.08 -0.27 0.70 0.88 0.84 0.72 0.47 -0.11 0.16 -0.13 0.31 0.24 na
Gambia 1.00 -0.18 -0.21 -0.18 -0.29 0.23 0.06 0.59 -0.01 -0.27 -0.39 -0.07 0.23 0.10 na
Ghana 1.00 0.87 0.90 0.87 0.25 0.34 0.52 0.23 0.35 0.17 0.05 -0.30 -0.10 0.27 na Kenya 1.00 0.72 0.83 0.86 0.03 0.19 0.24 -0.18 0.40 0.37 -0.18 0.35 -0.46 0.50 na Madagasc. 1.00 0.94 0.83 0.82 0.65 0.84 0.71 0.47 0.14 0.36 -0.15 -0.01 -0.23 0.30 na Malawi 1.00 0.74 0.82 0.75 -0.10 0.22 0.35 0.29 0.45 0.10 0.10 na na 0.18 na
Mali 1.00 0.88 0.81 0.80 0.29 0.41 -0.01 0.40 0.23 -0.27 0.62 na na -0.00 na
Niger 1.00 0.77 0.67 0.60 0.43 0.55 0.17 0.09 0.45 0.06 0.13 0.26 0.04 0.35 na
Nigeria 1.00 0.28 0.26 0.30 -0.06 0.22 0.16 0.66 0.13 0.23 0.12 -0.17 -0.16 0.07 na Rwanda 1.00 0.91 0.88 0.75 0.74 0.37 -0.24 0.77 0.54 -0.74 -0.19 na na 0.35 na Senegal 1.00 0.83 0.63 0.64 0.10 0.61 0.43 0.79 0.13 -0.28 -0.32 0.09 0.26 -0.10 na S. Africa 1.00 0.99 0.84 0.83 0.20 0.95 0.95 -0.22 0.57 na 0.24 0.28 -0.02 na na Zambia 1.00 0.41 0.36 0.34 -0.18 0.32 0.11 -0.08 na -0.26 0.07 na na na na
Zimbabwe 1.00 0.93 0.33 0.28 0.20 0.52 na na na 0.32 na 0.03 -0.52 na na
Notes: See Table 6.a.
