cointegration tests of purchasing power parity in africa

WorldDevelopmentVol. 31,No. 10, pp. 1673–1685, 2003� 2003 Elsevier Ltd. All rights reserved

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Cointegration Tests of Purchasing Power Parity

in Africa

JOSEPH M. KARGBO *

American Express Financial Advisors, Inc., Falls Church, Virginia, USA

Summary. — Despite two decades of implementing economic reforms, there is still a significant gapbetween official and black market exchange rates in Africa. African policy makers continue toimplement exchange rate policies based largely on the assumption that purchasing power parity(PPP) holds. This paper examines whether there is empirical support for PPP in Africa. We usedJohansen�s cointegration technique and error correction modeling on annual data for black marketexchange rates and CPI in 30 countries covering 1960–97. We found strong support for the PPPdoctrine as a useful guide for exchange rate policy reform in Africa.� 2003 Elsevier Ltd. All rights reserved.

Key words — African countries, black market exchange rates, cointegration tests, PPP

*The comments and suggestions of Professor Carlos

A. Vegh of the Economics Department at the University

of California, Los Angeles, Dr. Nomathemba Seme and

anonymous journal referees are very much appreciated.

The views expressed in this paper do not reflect the

official position of American Express Company or its

affiliates. The author accepts full responsibility for any

errors or omissions. Final revision accepted: 25 Febru-

ary 2003.

1. INTRODUCTION

The purchasing power parity (PPP) theory isan essential building block in internationalmonetary economics. As a theory of exchangerate determination, PPP asserts that the changein exchange rates between two currencies isdetermined by the relative prices of the twocountries. The contemporary treatment of PPPdates back to the works of Swedish economistGustav Cassel in the early part of the 20thcentury (see, for example, Bahmani-Oskooee,1993a, 1993b; Bigman, 1984; Cassel, 1916,1921; Dornbusch, 1988; Officer, 1976). At thesame time, our thinking about exchange ratesand the balance of payments in various coun-tries has been influenced significantly by therevolutionary developments in the globalmonetary system that started during the 1970swith the introduction of the generalized floatingexchange rate regime, to developments in in-ternational banking and information networks,coupled with rapid and sweeping changes innational monetary policies. These develop-ments revived interest in the PPP doctrine,motivated mainly by the belief that the equi-librium exchange rate is determined by the PPPrelation. Thus, over time, a flexible exchangerate will move toward that equilibrium.There has been a lot of controversy con-

cerning the usefulness of the PPP doctrine asan exchange rate determination model. Some

167

economist even questioned its validity. Forexample, Frenkel (1981, p. 145) observed:

3

During the 1970s short-run changes in exchange ratesbore little relationship to short-run differentials in na-tional inflation rates and frequently, divergences fromPPPs have been cumulative.

Other researchers, such as Baillie and Selover(1987), Kravis and Lipsey (1978), and Taylor(1988) found no support for long-run PPP.Bahmani-Oskooee (1993a) found mixed resultsabout the existence of PPP in 25 developingcountries that he studied. There was no supportfor PPP when effective exchange rates wereused in testing for PPP, but he found supportfor PPP in seven out of the 25 countries whenbilateral exchange rates were used in the ana-lysis. Research by Aggarwal and Simmons(2002), Cheung and Lai (1993), Fleissig andStrauss (2000), Kim (1990), Liu (1992), and

WORLD DEVELOPMENT1674

Salehizadeh and Taylor (1999) also providedempirical support for long-run PPP. 1 To theextent that PPP holds, it provides a link be-tween the prices of goods in various countries,which when coupled with interest rate parity,enhances our understanding of exchange ratebehavior. Studies which found prolonged de-viations from PPP cast doubt on the ability ofPPP-based models to provide satisfactory ex-planations for the behavior of exchange rates.Aizenman�s (1984) paper provided a model thatintegrated deviations from PPP with an analysisof the determinants of exchange rates, therebyproviding a link between the two strands.The common thread among all of the studies

cited above is that they used official exchangerates, and concentrated exclusively on indus-trialized countries, with the exception of Ag-garwal and Simmons (2002), Bahmani-Oskooee(1993a, 1993b), Liu (1992), and Salehizadehand Taylor (1999) who concentrated on devel-oping countries. 2 Moreover, the above studiesused a variety of measures, such as the CPI,wholesale price index (WPI), gross domesticproduct (GDP) deflator, wage rates and foodprice indexes as national prices. As a group,industrialized countries are characterized byrelatively low inflation rates, similar levels ofeconomic development and macroeconomicpolicies, high degree of political and economicinterdependence (e.g., the European Union andOrganization for Economic Cooperation andDevelopment), and the free flow of capitalgoods and services. On the other hand, themajority of developing countries generally ex-perience high inflation rates, exchange rate andtrade controls, rapid monetary growth, capitalflight, structural (real) shocks, large and per-sistent government budget deficits, lack ofhighly efficient markets, and poor data man-agement techniques, such as delays and inter-ruptions in statistical releases of national data.All of these factors could distort the properfunctioning of prices and exchange rates in theeconomy. A large number of African countrieshave experienced annual inflation rates as highas 370% during 1985–95. 3 The Communaut�eeFinanci�eere Africaine (CFA) franc zone coun-tries experienced lower inflation rates comparedto other African countries during the 1960sthrough the early 1980s. 4 At the same time,several African countries have also experiencedreal shocks, such as reductions in terms oftrade, droughts, oil shocks, civil wars and otherforms of political instability (Kargbo, 1994,2003; Krichene, 1998). The above problems

could have destabilizing effects on the PPPrelation in African countries. Research hasshown that PPP may be a more relevant theoryin higher-inflation countries than those withvery low inflation rates (see for example,Bahmani-Oskooee, 1993a, 1993b; Dornbusch,1988; Officer, 1976). It is argued that monetarygrowth in higher inflation countries couldovershadow the effects of real factors. Whetherthis is the case in our sample of countries understudy is a matter to be determined as theanalysis in this paper unfolds.Pervasive corruption and implementation of

inappropriate policies enhanced the develop-ment of parallel market for foreign exchangeand other goods, speculative activities in agri-cultural trade, and the smuggling of essentialgoods to neighboring African countries. Blackmarket exchange rates depend on underlyingsupply and demand factors for foreign ex-change, thus, to a large extent reflect the gov-ernment�s exchange rate policies in the country.Exchange rate controls and lack of access to theofficial exchange rate market could result in aburgeoning black market for foreign exchange.Over the years, the importance and coverage ofblack markets varied across African countries.In some countries, e.g., the CFA franc zone, theblack markets for foreign exchange were thin.In others, e.g., Uganda, Algeria, Ghana andSierra Leone, the black market was broad, andthe black market exchange rate was the relevantmarginal rate for a majority of transactions (seeEdwards, 1989; Nagayasu, 1998; World Bank,1986). Even though a large number of Africancountries have adopted more flexible exchangerate regimes during the past couple of years,there is still a significant gap between officialand black market exchange rates. The blackmarket premium––that is the percentage excessof the black market price of foreign currency(e.g., dollars) over the official exchange rateranged from a low of 2% in the CFA franc zoneto over 15,000% in Uganda during 1980–97.The terms black market and parallel marketconvey the same meaning in this paper, thus,they are used interchangeably.Recently, a number of economists have fo-

cused their attention on investigating whetheror not there is empirical support for long-runPPP in African countries. The interest in PPPstudies in Africa emanated from the fact thatseveral African countries have been imple-menting World Bank and IMF (InternationalMonetary Fund) supported monetary andmacroeconomic reform policies during much of

COINTEGRATION TESTS OF PPP 1675

the past two decades. The centerpiece of thereform programs is exchange rate policy reformthat is designed to reduce or eliminate distor-tions in the foreign exchange market, and im-prove the external competitiveness of Africaneconomies. As some economists (e.g., Naga-yasu, 1998; Odedokun, 2000) noted, a funda-mental assumption of the exchange rate policyreforms is that long-run PPP holds in Africancountries. Recently, Krichene (1998) usedmonthly data on bilateral official exchangerates and the CPI to examine whether or notPPP holds in five East African countries over1979–96. Krichene found support for long-runPPP. The countries included in Krichene�sstudy are Burundi, Kenya, Rwanda, Tanzaniaand Uganda. Odedokun (2000) used quarterlydata on official exchange rates and the CPI totest whether long-run PPP holds in 35 Africancountries over 1980–91. Odedokun�s study in-cluded both CFA franc zone and non-CFAcountries. He found support for long-run PPPin 17 (mostly non-CFA countries) out of 35countries included in the total sample. Half ofthe remaining 18 countries where there was nosupport for PPP were CFA franc zone coun-tries. Holmes (2000) found support for PPPwhen he applied panel unit root tests to quar-terly data for selected African countries cover-ing 1974–97. But, he found no support for PPPwhen unit root tests were applied to individualcountry data during the same period. Kargbo(2003) used Johansen�s cointegration techniqueto explore the relationship between bilateralofficial exchange rates and food price indices in25 African countries over 1958–97. He foundoverwhelming support for long-run PPP.Nagayasu�s (1998) paper is the first to ex-

amine on a large scale whether the behavior ofparallel market exchange rates in Africa isconformable to the long-run PPP relation.Nagayasu used panel cointegration tests onannual data for black market exchange ratesand the CPI in 16 countries covering 1981–94,and found support for long-run PPP. 5 But,when Nagayasu applied cointegration tests toindividual country annual data there was noempirical support for the presence of a long-runPPP relation. As Nagayasu (1998, p. 13) noted,one needs to interpret the results cautiouslybecause of the small number of observationsused in the individual country tests. Based onthe literature reviewed so far, it seems that themixed empirical results on the PPP doctrinereflected the data and econometric techniquesused, and the time period under investigation.

In our paper, we also use black market ex-change rates and the CPI to examine whetherthere is empirical support for long-run PPP inAfrica. But, unlike Nagayasu�s (1998) paper, weused annual data for 30 African countriescovering 1960–97, and utilized the multivariatecointegration technique developed by Johansen(1988, 2000) and Johansen and Juselius (1992).The Johansen procedure which tests for thepresence of long-run equilibrium relationshipsis estimated by maximum likelihood and takesinto account the error structure of the dataprocesses while allowing for interactions in thedetermination of relevant economic relation-ships in the system. Dornbusch (1988) arguedthat the PPP theory should be supplementedwith an adjustment mechanism, because in thereal world goods from various countries are notstrictly identical. Spatial arbitrage is adequatein the case of identical goods. But, when goodsare not strictly identical, then more work isneeded. The mechanism through which ex-change rate-adjusted prices are kept in equi-librium internationally is through extensivesubstitution in world trade. Moreover, weknow from the Dornbusch sticky-price mone-tary model of exchange rate determination thatthere are differential speeds of adjustment be-tween assets and goods prices. Thus, use ofcointegration analysis is appropriate in thiscase. In contributing to the current policy de-bate, our paper covered a longer estimationperiod than previous studies of PPP in Africa,and covered all but two countries (i.e., Mauri-tius and Mozambique) included in Nagayasu�spaper. The number of countries included in ourstudy was determined by the uniform avail-ability of data.The rest of the paper is divided as follows:

Section 2 presents a brief discussion of blackmarket exchange rates within the context ofAfrican development. Section 3 deals with theformulation of PPP. Section 4 presents the unitroot and cointegration tests, and their results.Section 5 presents the summary and conclu-sions reached by this study.

2. BLACK MARKET EXCHANGE RATESIN AFRICA

Parallel market activities for foreign ex-change in Africa started with the outbreak ofthe WWII and have increased since then. Ac-cording to various issues of the World CurrencyYearbook (Cowitt, various issues) black market


activities for foreign exchange vary widelyacross African countries and include variousschemes such as, over- and underinvoicing offoreign trade, the smuggling of paintings, goldcoins, jewelry, and other items of high value.Furthermore, African countries have experi-enced frequent changes in their exchange ratearrangements during the past four decades,often resulting in long periods of adjustable orfixed official exchange rates. The overvaluationof exchange rates created a heavy tax on ex-ports, rent seeking activities on imports and abooming parallel market for foreign exchangeand other goods. In particular, the impositionof trade restrictions (e.g., tariff and non-tariffbarriers), exchange rate and capital controlsenhances the development of parallel marketfor foreign exchange to finance capital flightand portfolio decisions to hold foreign curren-cies as a hedge against very high domestic in-flation, political instability, and otherunforeseen circumstances. Parallel market ac-tivities in the CFA franc zone resemble theFrench style march�ee parall�eele with activeFrench participation. According to Cowitt(1989, p. 64), the CFA franc zone is a majorloophole for capital flight from France andsome African countries.The rationing of foreign exchange creates a

wedge between the official and black markets.

Table 1. Basic statistics on black market premium (

Country Mean Maximum Minim

Algeria 157.58 402.11 2.4

Botswana 19.56 69.23 )5.0Burundi 30.55 61.81 7.2

CFA zone 3.36 41.26 )2.5Egypt 73.47 214.29 0.0

Ethiopia 74.24 235.00 3.7

Gambia 5.17 33.33 )21.7Ghana 446.31 3353.57 )20.0Kenya 16.61 58.10 )67.6Malawi 23.98 122.22 )44.2Morocco 7.75 24.51 0.0

Nigeria 87.26 322.22 )34.2Sierra Leone 330.40 1860.78 0.0

South Africa 7.41 39.15 )24.5Sudan 116.73 1403.29 )89.1Tanzania 123.67 442.25 2.1

Tunisia 30.67 300.00 0.0

Uganda 26,724.84 108,042.90 11.3

Zambia 86.01 670.00 )44.0Zimbabwe 50.24 240.67 )44.2

The domestic currencies float freely in the blackmarket where the marginal cost for foreignexchange is determined. Table 1 presents basicstatistics on the parallel market premium invarious African countries over 1960–97. Thereis extensive fluctuation in the parallel marketpremium across African countries, thus, re-flecting the extent of developments in parallelmarket exchange rates in the region. The av-erage premium ranged from 3.4% in the CFAzone to 26,725% in Uganda during 1960–97.The coefficients of variation ranged from 51%in Burundi to 266% in The Gambia over thesame period. The Democratic Republic ofCongo (not shown in the table) is really anoutlier, basically a dysfunctional state with se-vere political instability, corruption and policyfailure dating back to the days of late PresidentMobutu. Within a two-year period in the early1990s, the black market premium in the Dem-ocratic Republic of Congo rose to over 3.6million% and dropped sharply thereafter. Thetable also shows the parallel market premiumto be negative in some countries, which impliesmarket participants were paying more than theofficial rate in selling foreign currency (e.g.,US$, £ sterling, etc.) for the local African cur-rency (e.g., cedi, rand, niara, etc.). Dornbusch,Dantas, Pechman, De Rezende Rocha, andSimoes (1983, p. 26) argued that the negative

percentage) for exchange rates in Africa, 1960–97

um Standard

deviation

Coefficient of

variation (%)

Number of

observations

0 131.91 83.71 37

0 17.32 88.54 21

2 15.51 50.76 22

7 8.16 242.86 29

0 57.59 78.39 37

1 68.19 91.85 38

3 13.73 265.57 22

0 777.75 174.26 37

3 20.82 125.34 32

8 41.89 174.68 33

0 6.19 79.87 38

9 107.11 122.75 31

0 478.67 144.88 22

5 10.76 145.75 37

3 265.12 227.12 37

8 116.32 94.06 32

0 56.20 183.24 38

5 31,674.33 118.52 32

0 132.07 153.55 33

9 65.32 130.02 33


premium, which they referred to as a ‘‘laun-dering charge’’ reveals the high risk associatedwith participation in the black market.The exchange rate policy reforms imple-

mented as part of the structural adjustmentand macroeconomic adjustment programs haveargued for the unification of the official andparallel market exchange rates, because multi-ple exchange rates misallocate resources. Inparticular, the reforms aim at curtailing theblack markets and reducing the premium onforeign exchange. However, Pinto (1989, p.321) argued that since multiple exchange ratesare a means of taxation, unification of the rateswithout the implementation of proper com-plementary monetary and macroeconomicpolicies could lead to significant increases ininflation and government fiscal deficits, under-mine the credibility of the reforms and result inpolicy reversal. It is well known that there aresignificant political costs associated with epi-sodes of high inflation in a country. Recentevents in Zimbabwe demonstrated this clearly,wherein food riots were triggered by a 35%increase in the prices of food items such as,sugar, bread and soft drinks during the monthof October 2000 (see CNN.com, 2000, October23).

3. THE PPP FORMULATION

As Cassel (1916, 1921) puts it, PPP is only abench mark toward which prices and nominalexchange rates would tend to converge in thelong-run following real and monetary distur-bances. As a simple theory of exchange ratedetermination, PPP states that the change inexchange rates between two currencies denotedherein as eb is determined by the relative pricelevels of those two countries. Thus,

ebt ¼ Pdt=Pft; ð1Þ

where ebt is the black market nominal exchangerate expressed in units of local currency per unitof foreign currency, Pdt is the domestic pricelevel at time period t, and Pft is the foreign pricelevel at period t. The absolute version of PPPdepends on the ‘‘law of one price,’’ which saysthat for any commodity, in the absence of tar-iffs, quotas, and other trade barriers, arbitrageand trade in commodity markets ensures priceequalization across countries. Thus, Eqn. (1)can be rewritten as:

Pdt ¼ ebtxPft: ð2Þ

The law of one price depends on the interna-tional integration of commodity markets. Inreality, however, transport and informationcosts, trade impediments (e.g., tariffs, quotas,etc.), technological changes, factor supplies,and differences in weighting schemes for priceindices and consumption patterns in countriescan contribute to market segmentation, therebycreating wedges between prices across countriesover time (see Dornbusch, 1988; Officer, 1976).A weaker version of PPP casts the theory interms of changes in relative prices and the ex-change rate. Now let us write the PPP rela-tionship in logarithmic form as:

log ebt ¼ bþ a1 log Pdt þ a2 log Pft þ ut; ð3Þ

where ut is the error term that captures devia-tions from PPP. The absolute version of PPPrequires that b ¼ 0, and the symmetry condi-tion ensures that Pdt and Pft have coefficientsequal to )1 and +1, respectively. Crownover,Pippenger, and Steigerwald (1996) argued thattesting for absolute PPP can be done only bytesting for the equality between the nominalexchange rate and ratio of price levels. In thispaper, we are interested in applying a liberalversion of PPP, thus we relax all restrictions onthe coefficients so that the parameters b, a1 anda2 can assume different values. Our tests con-centrate on the existence of any linear combi-nation(s) of the variables that is stationary. AsCheung and Lai (1993) have argued, the im-position of symmetry and proportionalityconditions in the analysis can cause the re-stricted model to ignore possible interactions inthe determination of exchange rates and pricesthat are allowed in the unrestricted model.Even though exchange rates and capital con-trols were prevalent in Africa during much of1960–97, it is appropriate to test for PPP withEqn. (3) because domestic currencies floatfreely in the black market (see Edwards, 1989;Nagayasu, 1998).

4. DATA, UNIT ROOT ANDCOINTEGRATION TESTS

The annual data for black market nominalexchange rates were obtained from various is-sues of the World Currency Yearbook, pub-lished by the International Currency Analysis,Inc.; and the World Bank Africa Database 2000diskettes. The annual data on CPI were fromvarious issues of the International Financial


Statistics Yearbook, published by the Interna-tional Monetary Fund.We need to establish the properties of the

individual series before conducting the cointe-gration tests. Working with stationary timeseries is crucial for economic modeling andforecasting, and avoiding the presentation ofspurious regression results. We utilize the aug-mented Dickey–Fuller (ADF) unit root test todetermine stationarity of the series. The ADFtest controls for higher-order correlation byadding lagged difference terms of the dependentvariable to the right-hand side of the regression.For further details about the tests, see for ex-ample, Davidson and MacKinnon (1993). Alltests and regressions were performed with theEVIEWS 3.1 statistical package. Due to spaceconsiderations, the results of the unit root testsare not reported here, but we found that blackmarket exchange rates and prices are Ið1Þ, im-plying that stationarity was achieved after dif-ferencing the series once. The black marketnominal exchange rate follows a pure randomwalk, which is consistent with earlier studies(see for example, Bahmani-Oskooee, 1993a,1993b). Results of the unit root tests areavailable from us upon request.The cointegration technique is increasingly

used in the economics profession to examinethe linear relationship among various eco-nomic variables (see for example, Davidson &MacKinnon, 1993; Engle & Granger, 1987;Johansen, 2000; Krichene, 1998; Liu, 1992). Ifa linear combination of exchange rates andprices as shown in Eqn. (3) gives a stationaryerror term, ut; the variables are said to becointegrated, then PPP holds in the long-run.This means there is a common stochastic trendamong these variables that makes it unlikelyfor them to deviate from each other withoutbound. The cointegration tests were performedwith the Johansen (1988, 2000) and Johansenand Juselius (1992) method designed to testthe restrictions imposed by cointegration onthe unrestricted vector autoregression (VAR)model. The VAR model is estimated with themethod of maximum likelihood, and the pro-cedure has the advantage of permitting thejoint determination (endogeneity) of prices andblack market exchange rates, it takes into ac-count the short-run dynamics of the variables,while permitting the system of variables toreturn to long-run equilibrium according tothe PPP doctrine. The variables to be testedcan be written in vector error correction formas:

DGt ¼ bþ g1DGt�1 þ g2DGt�2

þ � � � þ gk�1DGt�kþ1 þPGt�1 þ vt; ð4Þwhere Gt is an m-dimensional vector of vari-ables, i.e., Gt ¼ ðebt; Pdt; PftÞ; 6 k is the numberof lags, and b is a constant. vt is the errorvector, it is multivariate normal and indepen-dent across observations, and t ¼ 1; 2; . . . ; T isthe number of observations. According to theGranger representation theorem, if the coeffi-cient matrix, P has reduced rank r < m, thenthere exists m� r matrices a and U each withrank such that P ¼ aU0 and U0G is stationary(Engle & Granger, 1987). The elements of a arecalled the adjustment parameters in the vectorerror correction model, and r is the cointe-grating rank. Each column of U is the cointe-grating vector. The trace or likelihood ratio(LR) test statistic for testing the hypothesis ofat most r cointegrating vectors is used in de-tecting the presence or lack of a cointegratingrelationship between variables in the system.An alternative test statistic also available is themaximum eigenvalue statistic (see for example,Davidson & MacKinnon, 1993; Johansen,2000). We reject the null hypothesis if the cal-culated value of the statistic exceeds the criticalvalue.Table 2 presents the results of cointegration

tests with regards to the rank of coefficientmatrix, P. We have not listed the critical valuesfor both the trace and maximum eigenvaluestatistics to avoid cluttering the table. Thesoftware used in our analysis displays the crit-ical values for the trace/LR test statistic di-rectly. Thus, we used them in testing the nullhypothesis. The critical values for both teststatistics are also available in Osterwald-Lenum(1992). See also the footnotes to this table. Thehypothesis of at least one cointegrating relationbetween prices and black market nominal ex-change rates cannot be rejected in any of thecountries included in this study. For example,the calculated LR value of 37.41 in Algeriaexceeds the critical LR value of 35.65 for r ¼ 0.We therefore accept the hypothesis that r6 1,implying the trace test indicates one cointe-grating equation for Algeria. The statistics aresignificant at the 5% level or better. There isstrong support for a long-run PPP relation inall of these countries. In effect, black marketexchange rates and prices tend to revert to theirlong-run equilibrium paths. Thus, the necessarycondition for PPP holds as shown by the em-pirical results. About 33% of the countries inTable 2 show that r ¼ 2, thereby suggesting two

Table 2. Johansen cointegration tests for relative version of PPP in Africa, 1960–97a

Country Number of lags

and DTb

Hypothesized number of CE(s) and trace test

statistic

Rank (r)

r ¼ 0 r6 1 r6 2

Algeria 1; C 37.414� 12.713 2.710 1

Botswana 1; CT 59.127� 25.365�� 7.418 2

Burkina Faso 1; CT 55.668�� 18.520 5.799 1

Burundi 1; CT 47.216�� 20.990 4.264 1

Cameroon 2; CT 45.173�� 23.825 10.624 1

C.A. Rep. 2; CT 44.836�� 22.498 9.528 1

Chad 1; C 29.977� 12.430 2.745 1

Congo, D.R. 1; CT 45.329�� 11.981 3.526 1

Congo, Rep. 1; C 32.197�� 14.888 5.016 1

Coote d�Ivoire 2; CT 54.172� 24.393 10.366 1

Egypt 2; CT 56.471� 31.221� 8.714 2

Ethiopia 1; CT 48.288�� 18.805 3.194 1

Gabon 2; C 42.306� 21.525�� 5.763 2

The Gambia 1; C 54.733� 25.390�� 10.129 2

Ghana 1; C 92.220� 10.766 0.266 1

Kenya 1; CT 39.677�� 16.181 4.670 1

Malawi 1; CT 45.151� 20.183�� 0.729 2

Morocco 1; CT 64.227� 21.836 8.152 1

Niger 1; CT 42.845�� 20.637 7.184 1

Nigeria 1; CT 61.269� 28.901�� 4.757 2

Senegal 2; CT 59.747� 26.305�� 9.643 2

Sierra Leone 1; CT 45.531� 20.465 25.723 1

South Africa 1; CT 46.828�� 21.894 4.690 1

Sudan 1; CT 58.377� 25.482�� 7.969 2

Tanzania 1; CT 39.392�� 14.438 1.939 1

Togo 1; CT 47.426�� 19.682 6.113 1

Tunisia 1; CT 53.863� 20.433�� 2.085 2

Uganda 1; CT 34.909� 13.586 3.008 1

Zambia 3; CT 68.672� 31.766� 8.980 2

Zimbabwe 2; CT 59.761� 22.457 5.299 1

aCritical values for LR statistic when there is linear deterministic trend in the data (but without trend in the CE) forr ¼ 0, r6 1, and r6 2 are as follows: at 1.0% level: 35.65, 20.04 and 6.65; at 5.0% level: 29.68, 15.41 and 3.76; at10.0% level: 26.79, 13.33, and 2.69, respectively. For other critical values, see Osterwald-Lenum (1992, pp. 467–470).The EVIEWS 3.1 software does not provide critical values for the maximum eigenvalue statistic. Interested readersshould see Osterwald–Lenum for those critical values.bDT refers to the type of deterministic trends that are present in the data. For example, C¼ constant was included inthe cointegrating equations (CE), and CT¼ constant and trend included in the CE. The number of lags was de-termined by minimizing the Akaike information criterion. r tests for the number of cointegrating relations against thealternative hypothesis of full rank, i.e., all series in the model are stationary.�Denotes rejection of hypothesis at 1.0% level.��Denotes rejection of hypothesis at 5.0% level.


cointegrating relationships among variables inthose countries.Overall, our results suggest that commodity

trade and arbitrage work efficiently in Africancountries. The results also reflect the increasinglevels of intraregional and trade with non-African countries, and the quick transmissionof information about prices and profit oppor-tunities. Similar results have been reported by

Krichene (1998) for five East African countries,and Kargbo (2003) for 25 African countries.Furthermore, gains or losses in external com-petitiveness by a particular country tend to becorrected over time. Moreover, the effects ofmonetary and other shocks seem to have beenneutralized over the long-run as real exchangerates in the various countries return to theirlong-run paths according to PPP.


Since we have established that black marketexchange rates and prices are cointegrated, weproceed with finding an error correction modelfor each country included in our study. ThePPP hypothesis views relative prices as thefundamental determinants that can be utilizedin calculating the long-run exchange rates, andin assessing the appropriate levels of exchangerates when a long-run relationship holds (Cas-sel, 1916, 1921). Our empirical findings supportthis view because of the statistical significanceof the coefficients in majority of the countriespresented in Table 3. The cointegrating vectorsare normalized to unity by the black marketnominal exchange rates; and by both prices andexchange rates in cases where there is morethan one cointegrating equation. The table de-scribes the long-run behavior of black marketexchange rates in Africa. There are long-runparameters for each cointegrating relationshipin the system. Even though no specific tests forthe strong version of PPP were conducted, acursory examination of the evidence in Table 3soundly rejects the symmetry and proportion-ality conditions implied by PPP. In addition,the signs of the coefficients are mixed. This isreally not surprising since as already discussedearlier, a host of factors, including differencesin compilation of price indices, trade restric-tions, and changes in technology and factorsupplies of each country could cause the strictversion of PPP not to hold.Furthermore, an error–correction model was

estimated to gauge the speed of adjustmenttowards equilibrium in each of the countrieswith a single cointegrating relationship (i.e.,r ¼ 1) as reported in Table 2. Granger (1986)argued that when there is more than a singlecointegrating vector, there are several equili-brium relationships linking variables, thereby,forming an equilibrium subspace. The transi-tion to error–correction representation is noteasily defined in such situations. Thus, noerror–correction estimates were estimated forthose countries with two cointegrating vectors.The error–correction term captures the changesin black market exchange rates required toeliminate past departures of actual values of thedifferent variables from the equilibrium levels. 7

The estimated speeds of adjustment in variouscountries are presented in the last column ofTable 3. The speeds of adjustment (the coeffi-cients of ECT) vary widely across the region.For example, only 13% of the deviation fromPPP is corrected each year in Morocco com-pared to very short periods within which ex-

change rates tend to revert to their PPP levels inKenya, Uganda and Burkina Faso.

5. SUMMARY AND CONCLUDINGCOMMENTS

African policy makers are implementing ex-change rate and other macroeconomic policyreforms based on the implicit assumption thatlong-run PPP holds in African economies.There has however, been a great deal of con-troversy in the economics literature concerningthe usefulness of the PPP theory in the deter-mination of exchange rates. It seems that themixed empirical results on the PPP doctrineobtained by various economists over the yearsreflected the data and econometric techniquesused, and the time period under investigation.This paper conducted a detailed investigation

to determine whether there is empirical supportfor the PPP relation in African countries. Weused the Johansen cointegration technique andannual data on black market exchange ratesand the CPI of 30 countries covering 1960–97.Our research shows overwhelming support forthe PPP doctrine as a useful guide for exchangerate determination and policy formulation invarious African countries. Similar evidencesupporting PPP has been provided by Aggar-wal and Simmons (2002) for Caribbean coun-tries, Liu (1992) for Latin American countries,along with Bahmani-Oskooee (1993a, 1993b)and Salehizadeh and Taylor (1999) for otherdeveloping countries outside of Africa. More-over, our estimated econometric relationshipsshowed that prices were important determi-nants of the black market exchange rates.Research has shown that the black marketpremium is an implicit tax on economic activ-ities, and there continues to be a significant gapbetween official and black market exchangerates in African countries. The widespreadtaxation of exports in Africa creates disincen-tives to produce exports, lowers a particularcountry�s export competitiveness, therebyhampering capacity utilization and employ-ment creation activities in the countries con-cerned.It is generally believed that PPP tends to be

weak in countries with high and volatile exter-nal financial flows. 8 In view of the severalconstraints faced by African countries, ourempirical support for PPP points to the appli-cability of this hypothesis in developing coun-tries. As African countries become more

Table 3. Normalized cointegrating vectors for long-run relative version of PPP in Africa, 1962–97a

Country Coefficients

eb Pd Pf Constant Trend ECT

Algeria (1971–97)b 1.000 )3.080 (7.219)c 2.716 (4.206) )0.146 –d )0.036()1) (0.417)

Botswana (1979–97) 1.000 0.000 )3.445 (2.790) 14.150 0.028 (0.851) –

0.000 1.000 )4.478 (4.357) 13.481 0.061 (2.201) –

Burkina Faso (1971–97) 1.000 )4.019 (5.828) 5.365 (5.469) )5.442 )0.168 (3.588) 2.378()2) (1.759)��

Burundi (1978–97) 1.000 )1.440 (2.668) 4.632 (3.416) )14.542 )0.154 (7.736) )0.321()1) (1.582)

Cameroon (1972–97) 1.000 )1.701 (2.681) 2.143 (2.006) )6.249 )0.027 (0.528) )0.602()1) (1.760)��

C.A. Rep. (1972–97) 1.000 )1.526 (7.240) 3.361 (6.803) )9.718 )0.136 (6.330) )0.887()1) (2.181)��

Chad (1970–95) 1.000 2.951 (0.996) )2.349 (1.272) )9.595 – 0.592()1) (2.643)��

Congo, D.R. (1972–96) 1.000 0.156 (2.297) 4.765 (2.519) )6.200 )0.750 (5.250) )0.544()1) (2.106)��

Congo, Rep. of (1971–96) 1.000 )1.438 (4.310) 1.330 (3.757) )4.612 – )0.380()1) (2.784)�

Coote d�Ivoire (1972–97) 1.000 3.601 (5.269) )5.375 (5.479) )2.121 0.102 (3.790) )0.585()1) (1.825)��

Egypt (1964–97) 1.000 0.916 (2.389) 0.000 )1.873 )0.111 (4.069) –

0.000 )0.263 (2.766) 1.000 )2.256 )0.029 (4.383) –

Ethiopia (1967–97) 1.000 )1.922 (5.010) 3.899 (7.277) )6.125 )0.134 (8.338) )0.351()1) (4.212)�

Gabon (1972–97) 1.000 0.305 (1.561) 0.000 )7.341 (7.888) – –

0.000 )0.923 (9.244) 1.000 )0.358 (0.756) – –

Gambia, The (1978–97) 1.000 0.000 23.260 (0.439) )86.490 )0.680 (0.506) –

0.000 1.000 20.914 (0.405) )82.993 )0.553 (0.422) –

Ghana (1964–97) 1.000 )0.964 (5.836) 2.058 (2.043) )6.548 )0.121 )0.222()1) (2.738)�

Kenya (1968–97) 1.000 )1.303 (4.043) 3.609 (6.010) )8.412 )0.147 )1.649()1) (3.158)�

Malawi (1970–97) 1.000 1.649 (1.414) 0.000 )2.139 )0.259 –

0.000 )1.134 (2.020) 1.000 )0.254 0.057 –

Morocco (1962–97) 1.000 )1.018 (5.323) 1.107 (3.120) )1.381 )0.025 (2.291) )0.129()1) (2.832)�

Niger (1971–97) 1.000 )8.907 (0.860) 9.104 (0.853) )1.727 )0.080 (0.445) 0.684()1) (2.799)�

COIN

TEGRATIO

NTESTSOF

PPP

1681

Table 3––continued

Country Coefficients

eb Pd Pf Constant Trend ECT

Nigeria (1969–97) 1.000 2.427 (1.990) 0.000 0.527 )0.540 (2.788) –

0.000 )0.895 (2.527) 1.000 )2.022 0.086 (1.539) –

Senegal (1972–97) 1.000 0.156 (0.711) 0.000 )5.161 )0.049 (2.382) –

0.000 )0.643 (14.200) 1.000 )0.441 )0.029 (6.957) –

Sierra Leone (1978–97) 1.000 )0.245 (0.581) 16.152 (3.583) )45.846 )0.982 (7.182) )0.244()1) (2.460)��

South Africa (1963–97) 1.000 )1.230 (4.932) 1.359 (3.904) )0.911 )0.018 (1.113) )0.527()1) (3.636)�

Sudan (1963–97) 1.000 0.555 (2.744) 0.000 1.925 )0.235 (9.485) –

0.000 )0.196 (10.770) 1.000 )2.629 )0.028 (12.599) –

Tanzania (1968–97) 1.000 0.117 (0.914) 1.780 (4.438) )5.484 )0.269 )0.232()1) (3.335)�

Togo (1971–97) 1.000 17.453 (0.974) )24.997 (0.977) 0.052 0.726 (0.957) )0.338()1) (3.069)�

Tunisia (1962–97) 1.000 0.000 )0.382 (0.965) 1.626 0.012 –

0.000 1.000 )1.626 (5.781) 1.347 0.037 –

Uganda (1977–97) 1.000 )0.565 (9.325) 1.704 (1.666) )6.390 )0.161 )1.198()1) (2.792)�

Zambia (1969–97) 1.000 0.000 9.056 (4.741) )24.654 )0.637 (6.929) –

0.000 1.000 0.527 (0.227) )0.192 0.303 (2.715) –

Zimbabwe (1968–97) 1.000 )0.440 (4.508) 1.143 (6.116) )0.148 )0.131 (10.922) )0.286()1) (2.241)��

a The number of equations per country reflect the cointegrating rank, r, reported in Table 2. The coefficients normalized to 1 and others that are not identified do nothave standard errors. For further details, see QMS (1998, p. 512).bRefers to the estimation period.c The number in parentheses refers to the absolute t-statistic. The numbers in parentheses adjacent to the estimated coefficients refer to the number of lags which weredetermined with use of the Akaike Information criterion.dNot available.�Significant at the 1.0% level.��Significant at the 5.0% level.��Significant at the 10.0% level.

WORLD

DEVELOPMENT

1682


integrated into the global financial system, weexpect more empirical evidence in support ofPPP because of various reasons, including thefact that flexible exchange rates ushered in bypolicy reforms are expected to move faster to-ward equilibrium than fixed exchange rates.Market forces are influencing exchange ratebehavior in several African countries. Recentstudies by Holmes (2000), Krichene (1998) andNagayasu (1998) tend to support this view be-cause their studies cover largely the period ofextensive policy reform in Africa.A major policy implication of our findings is

that for African policy makers to stabilize do-mestic prices, reduce the high inflation rates andincrease export competitiveness, concerted ef-forts must be made to implement the appro-priate exchange rate policies because PPPprovides a bench mark for monitoring exchangerate movements. Complementary macroeco-nomic policies should focus on the extent towhich prevailing exchange rates and policies areconsistent with the simultaneous internal andexternal equilibrium of the economy over time.

Thus, policies must further stem the flow offoreign exchange into the black market bynarrowing and eventually eliminating the gapbetween official and parallel market exchangerates. This involves lowering and stabilizing thevalue of the local currency in the black marketthrough gradual government intervention tolevels that are consistent with domestic eco-nomic policies. There must be an effective co-ordination of microeconomic (e.g., agriculturalprice, food and other household/farm support)policies with macroeconomic policies dealingwith, for example, exchange rates, interest rates,money supply, trade, income and wage rates ineach country. The mechanics of how this pro-cess can be achieved effectively in each countrydeserves further research. Our empirical resultsreveal remarkable differences amongst variouscountries. For instance, there is significantvariation within the CFA zone and betweenCFA and non-CFA zone countries. Therefore,the set of policies designed for each countrymust reflect the prevailing economic, social andpolitical conditions in that country.

NOTES

1. Hakkio (1986) concluded that the reason for the

contradictory evidence on whether the exchange rate

follows a random walk was due to the lack of power of

the statistical tests employed in the analysis. He consid-

ered four tests in his analysis, and found the Box–Pierce

Q-statistic to have performed the best. Hakkio (1986, p.

227) cautioned, however, that even though the hypo-

thesis that the exchange rate follows a random walk

cannot be rejected, one should not put much weight on

such a conclusion.

2. Bahmani-Oskooee (1993a) included five African

countries in his sample of 25 countries. The African

countries are: Cameroon, Egypt, Ethiopia, South Africa

and Tunisia. Salehizadeh and Taylor (1999) included

nine African countries out of 27 countries in their total

study sample. The African countries are: Algeria,

Egypt, The Gambia, Ghana, Kenya, Mauritania, Mau-

ritius, Morocco and Zimbabwe. Their paper used the

consumer price index (CPI) as national prices. Aggar-

wal and Simmons (2002) provide evidence for Carib-

bean countries. Bahmani-Oskooee (1993b) used both

black and official nominal exchange rates in testing for

PPP in Iran. He found support for PPP when the black

market exchange rates were used as opposed to no

support for PPP in the case of official nominal exchange

rates.

3. Nine of the countries included in our study had

annual inflation rates of 3–9%, and 18 countries had

inflation rates of 10–370% in 1995. For a systematic

presentation of inflation rates in African countries

during the past 30 years see, for example, World Bank

(1996, 2000).

4. See IMF (various years) for exchange rate arrange-

ments and list of domestic currencies for individual

countries. The CFA franc zone consists of 14 African

countries––12 of them were former French colonies. The

CFA zone is made up two monetary unions, viz: the

West African Monetary Union (WAMU) includes:

Benin, Burkina Faso, Coote d�Ivoire, Guinea Bissau,

Mali, Niger, Senegal and Togo. The Central African

Monetary Area (CAMA) consists of Cameroon, Central

African Republic, Chad, the Republic of Congo, Equa-

torial Guinea and Gabon. Guinea Bissau and Equatorial

Guinea are CFA members that had no colonial ties with

France. There is extensive coordination of monetary

policy within CFA countries and France. After more

than 40 years of stability, the CFA franc was devalued

by 50% in January 1994. The only other monetary area

in Africa is the RandMonetary Area (RMA) made up of

South Africa, Lesotho and Swaziland. The latter coun-

tries peg their currencies to the South African rand.

There is no coordination of monetary policy in the


RMA. For more details on the CFA franc zone and

RMA see Hadjimichael and Galy (1997); Kargbo (2003)

and Odedokun (2000). Our data sources provided a

single black market exchange rate series for all CFA

member countries.

5. The 16 countries included in Nagayasu�s paper are:Botswana, Burundi, Cameroon, The Gambia, Ghana,

Kenya, Malawi, Mauritius, Mozambique, Nigeria, Si-

erra Leone, South Africa, Tanzania, Uganda, Zambia,

and Zimbabwe.

6. The CPI for France represents Pf in the CFA

countries, whilst the CPI of the United States was used

as Pf in all other countries (see Hadjimichael & Galy,

1997; Kargbo, 2003 for details).

7. The error–correction model is specified as:

D log ebt ¼ a0 þ b1D log ebt�k þ b2D log Pdt�k

þ b3D log Pft�k þ kECTt�1 þ �t;

where ECTt�1 is the lagged residual from the cointegra-

tion regression, �t is the stochastic disturbance term, a0is a constant, whilst b1, b2, and b3 are parameters repre-

senting elasticities, k is the length of lag, and k is the speedof adjustment. D refers to the first difference operator.

8. Thanks to an anonymous referee for bringing this

point to our attention.

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