Monetary union in West Africa and asymmetric shocks: A dynamic structural factor model approach

Journal of Development Economics 85 (2008) 319 – 347 www.elsevier.com/locate/econbase

Monetary union in West Africa and asymmetric shocks: A dynamic structural factor model approach Romain Houssa ⁎ Department of Economics, CES-KULeuven, Belgium Received 19 November 2004; received in revised form 6 February 2006; accepted 2 May 2006

Abstract This paper analyzes the economic costs of a monetary union in West Africa by looking at the fluctuations of aggregate demand and aggregate supply shocks across countries. Previous studies have estimated shocks with Vector Auto-Regressive (VAR) models. This paper discusses the limitations of VAR models and applies a new technique based on dynamic factor models. The results show negative and low positive correlations among supply shocks of West African countries, indicating that these countries will find it difficult to adjust to supply shocks if they form a monetary union. However, demand shocks are more similar among the French-speaking countries of the region. © 2006 Elsevier B.V. All rights reserved. JEL classification: C33; F42; E52; E58 Keywords: Asymmetric shocks; Monetary union; Factor model

1. Introduction Following the introduction of the euro as the single currency of the European Monetary Union, there has been a renewed and a growing interest in monetary integration around the world.1 One special case is the monetary union project in the Economic Community of West African States ⁎ Tel.: +32 16 32 68 42; fax: +32 16 32 67 96. E-mail address: [email protected]. 1 Apart from the monetary union project in West Africa, which is the focus of this paper, there are other regional monetary integration initiatives in Africa: the Southern African Development Community is working to establish a currency union by 2016; the East African Community envisages to form a monetary union; even the African Union envisages the establishment of a monetary union for the whole Africa in 2018. In Asia, China, Japan, and South Korea are planing to form a monetary union. In North America, Canada, Mexico, and the United States of America have a monetary union project, which may absorb Latin American countries. In Europe, many countries are envisaging to join the EMU. 0304-3878/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jdeveco.2006.05.003

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(ECOWAS). ECOWAS is a regional group of 15 countries, which already includes a monetary union.2 This monetary union only involves the former French colony members of ECOWAS. It is known as the West African Economic and Monetary Union3 (WAEMU). In April 2000, ECOWAS adopted a strategy of a two-track approach to the implementation of a monetary union in the whole area. As a first step, the non-WAEMU members of ECOWAS agreed to form a second monetary union, the West African Monetary Zone4 (WAMZ). Later on, WAEMU and WAMZ will merge to form a wider monetary union in ECOWAS. When countries decide to participate in a monetary union, they abandon their national currency and fix their nominal exchange rates with respect to each other. From then on, the member countries of the union can no longer change the price of their currency, nor can they determine the quantity of their money anymore. Moreover, they can no longer change their short-term interest rate. Factor (capital and labor) mobility and wage flexibility remain the main adjustment mechanisms as alternatives to the exchange rate.5 If wages are rigid and factor mobility is limited, countries will find it harder to adjust to asymmetric shocks because the single monetary policy at the disposal of the common central bank will not be appropriate to respond to idiosyncratic shocks. In the presence of asymmetric shocks, one group of countries in a monetary union may need an expansionary monetary policy to respond to cyclical downturns while the other might require a contractionary monetary policy to respond to cyclical booms.6 For these reasons, the presence of asymmetric shocks to member countries of a monetary union is referred to as the costs of a monetary union in the Optimal Currency Area (OCA) literature (see De Grauwe, 2005; Mundell, 1961; Mckinnon, 1963; Kenen, 1969). In this paper, I analyze the economic7 costs of a monetary union in West Africa by looking at the fluctuations of aggregate demand and aggregate supply shocks across countries during the period 1966–2000. Unlike in previous studies, I estimate shocks with a dynamic factor model. Moreover, I use a recent technique of sign restrictions to identify the shocks. In particular, I assume that a positive supply shock does not have a negative impact on output and a positive effect on prices, while a positive demand shock does not lead to decreases in output and prices. The signs of the impulse response functions are important for the analysis of cross-country correlations. This aspect was ignored in previous studies on Sub-Saharan African countries.8

2 ECOWAS members are Benin, Burkina Faso, Cape Verde, Cote d'Ivoire, The Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo. 3 WAEMU includes Benin, Burkina Faso, Cote d'Ivoire, Guinea Bissau, Mali, Niger, Senegal, and Togo. WAEMU is also part of the CFA zone which has the CFA franc as the common currency. Fig. A1 in Appendix A summarizes the overlapping membership of WEAMU. Apart from Guinea Bissau, which is a Portuguese colony, WAEMU countries are French-speaking. For details on institutional features on the CFA zone see Fielding (2002). 4 WAMZ members are The Gambia, Ghana, Guinea, Nigeria and Sierra Leone. Liberia and Cape Verde are the two remaining ECOWAS countries that are not involved in the monetary project. Liberia has declined to participate in WAMZ project. Cape Verde has its currency, the Cape Verde Escudo, pegged to the euro with the support of Portugal. WAMZ countries are English-speaking except Guinea, which is a French-speaking country. See Addison et al. (2005) for a recent review on institutional arrangements and Obasseki (2005) for WAMZ achievements. 5 For empirical evidences see Atkeson and Bayoumi (1993), Bentivogli and Pagano (1999), Blanchard and Katz (1992), Decressin and Fatas (1995), EC Commission (1990), and Eichengreen (1990). 6 Fiscal policy can help to offset negative shocks. However, the excessive use of this instrument may pose the problem of sustainability of government budget deficits and the problem of debts spiraling. These facts may destabilize the monetary union. 7 There are also political costs related to the formation of a monetary union. These, however, are outside the scope of this paper. 8 I thank the referee for drawing my attention to this point.

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Previous studies have mainly used Vector Auto-Regressive (VAR) models to estimate shocks for West Africa.9 Fielding and Shields (2001) adopt this methodology to identify output and price shocks for CFA franc countries. They use a 4-variable VAR model and find a high degree of correlation between inflation shocks across countries. However, only eight CFA franc countries display similar output shocks. Fielding and Shields (2003) extend their study to WAMZ by estimating a 3-variable VAR model. The results suggest that real exchange rates between WAMZ countries are more variable than those among CFA members. In a related analysis, Hoffmaister et al. (1998) use a 5-variable VAR model to analyze macroeconomic fluctuations in Sub-Saharan Africa. They find that terms of trade shocks have greater influences on macroeconomic fluctuations in CFA countries than in other Sub-Saharan African countries. More recently, Addison et al. (2005) apply a 4-variable VAR model to WAMZ countries and find very low crosscountry correlations of terms of trade shocks and real exchange rate shocks. In recent literature (see for example Hansen and Sargent, 1991; Lippi and Reichlin, 1993, 1994; Faust, 1998; Leeper et al., 1996; Christiano et al., 1999) three main criticisms have been addressed to VAR models. First, VAR models assume that the shocks and the impulse response functions are fundamental10, i.e. they are innovations to the variables included in the system. However, economic agents (individuals, firms, policy-makers, etc.) might observe a superior information set to the one used in the small VAR models of previous studies. In this case, the few variables included in these VAR models will not be sufficient to recover the “true” shocks faced by economic agents. Second, the number of parameters estimated with VAR models grows with the square of the number of variables included in the system. For example, a 4-variable VAR model with one lag has 16 auto-regressive parameters; a 5-variable VAR model with one lag has 25 auto-regressive parameters, etc. This fact may lower the number of degrees of freedom of the estimation. Third, the number of shocks estimated with VAR models is always equal to the number of variables included in the system such that a large number of restrictions is needed to achieve identification of structural shocks. Given these criticisms to VAR models, this paper applies a new method based on dynamic factor models to estimate aggregate demand and aggregate supply shocks for West African countries. In my specification, each time series of a country is the sum of two unobserved components: a common component and an idiosyncratic component. The common component is driven by two common shocks (aggregate demand and aggregate supply shocks), which are identical across all the cross-sectional units of the country, while the idiosyncratic component is driven by variable specific shocks. Factor models have received many applications in recent literature. They have been applied for predictions (Bernanke and Boivin, 2003; Forni et al., 2005; Stock and Watson, 2002a,b); for structural analysis (Forni and Reichlin, 1998; Forni et al., 2003; Giannone et al., 2002; Stock and Watson, 2005) and for constructing economic indicators (Forni et al., 2001; Kose et al., 2003; Otrok and Whiteman, 1998). There are three reasons to believe that factor models provide a better representation of the macroeconomy than VAR models. First, factor models distinguish measurement errors and other 9

There are other papers that apply different methods to analyze the feasibility of a monetary union in West African countries. See for example Bayoumi and Ostry (1997), Bénassy-Quéré and Coupet (2005), Debrun et al. (2005), Masson and Pattillo (2001), Ogunkola (2005), and Yehoue (2005). 10 The Vector Auto-Regressive Moving Average (VARMA) obtained from a stationary VAR model is fundamental. This implies that the shocks can be recovered as a linear combination of the present and past of the variables included in the system. However, there exists non-fundamental VARMA representations that are observationally equivalent to the fundamental one. In non-fundamental representations the shocks belong to the linear space spanned by the present, past and future of the variables in the system. For additional details on this topic, see Lippi and Reichlin (1994).

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idiosyncratic disturbances from structural shocks. Macroeconomic data are computed with errors. These errors can be especially large in developing countries where data are subject to substantial revisions and where data are released with long delays. Therefore, the consideration of measurement errors can improve the efficiency of the results. Second, factor models allow a large information set to be analyzed and they exploit the dynamic structure of the data set to extract a limited information set. This reduces the number of restrictions needed to identify shocks. I use a panel of 54 time series to estimate only two structural shocks. This would not be possible with VAR models and any other techniques applied in previous studies. Third, Forni et al. (2003) show that the fundamentalness assumption is less restrictive within factor models. The remainder of the paper is organized as follows. In Section 2, I analyze the correlation of output growth and inflation across countries. Section 3 sets out the methodology used to estimate aggregate demand and aggregate supply shocks. Section 4 presents empirical results. Section 5 discusses the implications of the results. The last section provides a conclusion. 2. Correlation of output growth and inflation Different preferences about inflation and output growth of countries may make the introduction of a common currency costly. A fast-growing country will have to follow deflationary policies, which constrain growth, if it forms a monetary union with a slow-growing country.11 On the other hand, a high inflation country will increasingly lose its competitiveness if it forms a monetary union with a lower inflation country.12 These points motivate the comparative analysis of inflation and output growth across West African countries. To begin with, I plot the historical performance of output growth and inflation of WAEMU and WAMZ in Fig. 1. The data are annual and span the period from 1966 to 2000. Lack of data forces me to exclude some countries. My aggregate WAEMU involves Benin, Burkina Faso, Cote d'Ivoire, Niger, Senegal, and Togo. The WAMZ countries that are considered in this paper are The Gambia, Ghana, and Nigeria. In the paper, I will sometimes refer to WAEMU countries as CFA countries or French-speaking countries and to the WAMZ ones as English-speaking countries. I compute aggregate inflation and output growth of WAEMU and WAMZ as the weighted sum of inflation and output growth of countries in each group. The weights are one period lag shares of real GDP as suggested by Beyer et al. (2001). One fact that emerges from Fig. 1 is that inflation is much lower in CFA countries. Moreover, English-speaking countries have on average the highest volatility of both output growth and inflation. The relative low level of inflation in WAEMU has been explained by the peg of the CFA franc to the French franc before 1999 and now to the euro (see Bleaney and Fielding, 2002). Regarding output growth, there is not a clear difference between the two groups. There are some periods when English-speaking countries did better than French-speaking ones and vice-versa. Next, I analyze the correlation of output growth and inflation across countries. Figs. 2 and 3 display the correlations of output growth and inflation with WAEMU and WAMZ, respectively. Tables 1 and 2 report the correlation matrices of output growth and inflation. The results indicate that inflation has been highly correlated across WAEMU countries. The values of their correlation coefficients are over 0.7. However, only one WAMZ member, Ghana, 11

A fast-growing country will need fast-growing imports, and allowing exports to grow at the same rate as imports, this country has to make its exports cheaper; otherwise there will be a large current account deficit. If this country were not in a monetary union, it could just depreciate its currency to increase exports. 12 This follows from the standard PPP theorem.

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Fig. 1. Historical output growth and inflation.

has a significantly positive correlation of inflation with WAEMU. For output, the WAEMU members also display significantly positive correlations, although two countries (Benin and Togo) have relatively idiosyncratic behavior. Moreover, the values of the correlation coefficients of output growth are a bit smaller than those of inflation correlations (see Tables 1 and 2). Furthermore, WAMZ countries have low or negative output correlations with WAEMU countries. According to correlations with respect to WAMZ, Nigeria has correlation coefficient values of one for inflation and one for output. These results reflect the high weight of Nigeria in the computation of aggregate output and inflation for WAMZ. Due to these reasons I focus on results relative to Nigeria. I find that only Ghana has a significant positive output correlation with Nigeria. However, the value of the correlation coefficient is only 0.37. In addition, Nigeria has no significant positive inflation correlations with other WAMZ members. The main conclusion of this section is that inflation and output fluctuations have been significantly more similar among CFA countries than among WAMZ members. Moreover, the correlations for any pair of countries, each from WAEMU and WAMZ, respectively, are negative or very low. These results already suggest the presence of costs for a monetary union in West Africa. In particular, the finding that WAMZ countries have a relative high level of inflation implies a loss of competitiveness for these countries if they form a monetary union with CFA members. WAMZ countries can reduce these costs by lowering their inflation. This will, however, create another problem. A lower inflation implies less seigniorage forcing WAMZ countries to increase taxation or let the budget deficit increase. Sections 3 and 4 further analyze the cost of a monetary union in West Africa by examining the coherence of aggregate demand and aggregate supply shocks across countries.

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Fig. 2. Correlation of output growth and inflation with WAEMU.

Fig. 3. Correlation of output growth and inflation with WAMZ.

Benin Burkina Cote d'Ivoire Gambia Ghana Niger Nigeria Senegal Togo WAEMU WAMZ

Benin

Burkina

Cote d'Ivoire

1 0.66⁎⁎⁎ (0.00) 0.68⁎⁎⁎ (0.00) − 0.23 (0.19) 0.20 (0.25) 0.58⁎⁎⁎ (0.00) 0.19 (0.29) 0.66⁎⁎⁎ (0.00) 0.47⁎⁎⁎ (0.00) 0.74⁎⁎⁎ (0.00) 0.19 (0.28)

Gambia

Ghana

Niger

Nigeria

Senegal

Togo

WAEMU

1 0.64⁎⁎⁎ (0.00) − 0.05 (0.80) 0.49⁎⁎⁎ (0.00) 0.58⁎⁎⁎ (0.00) 0.05 (0.80) 0.62⁎⁎⁎ (0.00) 0.40⁎⁎ (0.02) 0.72⁎⁎⁎ (0.00) 0.06 (0.74)

1.00 − 0.08 (0.66) 1.00 0.25 (0.15) 0.05 (0.78) 1 0.74⁎⁎⁎ (0.00) − 0.30⁎ (0.09) 0.29⁎ (0.10) 1 0.02 (0.92) − 0.01 (0.97) 0.07 (0.68) 0.00 (0.98) 1 0.76⁎⁎⁎ (0.00) 0.21 (0.24) 0.31⁎ (0.08) 0.58 (0.00) − 0.07 (0.68) 1 0.71⁎⁎⁎ (0.00) 0.14 (0.44) 0.16 (0.37) 0.43⁎⁎⁎ (0.01) 0.05 (0.80) 0.63⁎⁎⁎ (0.00) 1.00 0.98⁎⁎⁎ (0.00) − 0.06 (0.72) 0.31⁎ (0.07) 0.81⁎⁎⁎ (0.00) 0.02 (0.91) 0.84⁎⁎⁎ (0.00) 0.71⁎⁎⁎ (0.00) 1.00 0.02 (0.90) 0.00 (0.98) 0.09 (0.61) 0.01 (0.97) 1.00⁎⁎⁎ (0.00) − 0.07 (0.71) 0.05 (0.79) 0.03 (0.88)

The numbers in brackets are p-values. ⁎⁎⁎Significant at 1%; ⁎⁎significant at 5%; ⁎significant at 10%. WAEMU members are Benin, Burkina Faso, Cote d'Ivoire, Niger, Senegal, and Togo. WAMZ members are The Gambia, Ghana, and Nigeria.

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Table 1 Correlation matrix of inflation

325

326

Benin

Burkina

Cote d'Ivoire

Benin 1 Burkina 0.14 (0.43) 1 Cote d'Ivoire −0.18 (0.31) 0.21 (0.23) 1 Gambia −0.41⁎ (0.02) − 0.21 (0.24) 0.08 (0.66) Ghana 0.25 (0.15) − 0.07 (0.70) 0.04 (0.84) Niger −0.03 (0.89) 0.48⁎⁎⁎ (0.00) 0.28 (0.11) Nigeria 0.00 (0.99) − 0.11 (0.52) 0.22 (0.22) Senegal −0.09 (0.62) 0.35⁎⁎ (0.04) 0.13 (0.46) Togo 0.15 (0.39) − 0.12 (0.50) 0.14 (0.42) WAEMU −0.07 (0.68) 0.49⁎⁎ (0.00) 0.85⁎⁎⁎ (0.00) WAMZ 0.00 (0.98) − 0.11 (0.55) 0.21 (0.22)

Gambia

Ghana

Niger

Nigeria

Senegal

Togo

WAEMU

1 − 0.22 (0.22) 1 − 0.25 (0.15) 0.15 (0.41) 1 − 0.08 (0.65) 0.37⁎⁎ (0.03) 0.12 (0.49) 1 − 0.06 (0.73) −0.40⁎⁎ (0.02) 0.32⁎ (0.07) 0.07 (0.68) 1 − 0.03 (0.86) 0.25 (0.16) 0.02 (0.91) 0.10 (0.56) − 0.23 (0.20) 1 − 0.07 (0.69) − 0.02 (0.90) 0.63⁎⁎⁎ (0.00) 0.18 (0.30) 0.51⁎⁎⁎ (0.00) 0.07 (0.70) 1 − 0.09 (0.63) 0.38⁎⁎ (0.03) 0.13 (0.46) 1.00⁎⁎⁎ (0.00) 0.07 (0.68) 0.11 (0.55) 0.18 (0.30)

The numbers in brackets are p-values. ⁎⁎⁎Significant at 1%; ⁎⁎significant at 5%; ⁎significant at 10%. WAEMU members are Benin, Burkina Faso, Cote d'Ivoire, Niger, Senegal, and Togo. WAMZ members are The Gambia, Ghana, and Nigeria.

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Table 2 Correlation matrix of output growth

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3. Methodology This section gives a brief description of my dynamic structural factor model and the estimation procedure. More details on the model can be found in Forni et al. (2000, 2003), and Giannone et al. (2002). 3.1. The model A zero mean stationary process xit is the sum of two unobservable components, the common component χit, and the idiosyncratic component ξit, xit ¼ vit þ nit ;

ð1Þ

where xit is the observed data for the ith cross-section unit at time t (i = 1, … , n; t = 1,… , T). The common component is driven by r common factors Ft = (F1t, F2t, …, Frt)′, which are identical across the cross-section units. On the contrary, the idiosyncratic component is driven by variable specific factors. Rewriting Eq. (1) in matrix notation gives xt ¼ KFt þ xt ;

ð2Þ

where xt = (x1t,… , xnt) is a vector of n time series on a country, ξt = (ξ1t,… , ξnt), and Λ is the n × r matrix of factor loadings. The r-dimensional vector of common factors Ft has a VAR representation driven by two common shocks, Ft ¼ DFt1 þ et ¼ DFt1 þ But ;

ð3Þ

where D is an r × r matrix of auto-regressive coefficients, B is an r × 2 matrix of rank 2, ϵt is a vector of error terms, and ut represents a 2-dimensional vector of structural shocks. The vector ut is orthonormal white noise and orthogonal to ξit, i = 1, …, n. Additional assumptions which are needed to identify the dynamic factor model can be found in Forni et al. (2003, Assumptions FM2 to FM4). The variance–covariance matrix of ϵt can be written in two ways. First, given that E(utu′) t =I and ϵt = But, the variance–covariance matrix of ϵt is equal to Σϵ = BB′. Moreover, for any 2 × 2 orthogonal matrix R, Σϵ = BRR′B′. I choose the matrix R such that the two structural shocks can be interpreted as aggregate supply and aggregate demand shocks (see the next subsection). Second, the eigenvalue–eigenvector decomposition of Σϵ gives Σϵ = PMP′, where P and M are eigenvector and eigenvalue matrices, respectively. Combining the two expressions of Σϵ implies et ¼ PM 1=2 Rut ;

ð4Þ

where M is a 2 × 2 diagonal matrix having on the diagonal the 2 largest eigenvalues of Σϵ and P is the r × 2 vector of the corresponding eigenvectors. Inverting the VAR and substituting (3) and (4) into (2) gives xt ¼ Bn ðLÞut þ xt ;

ð5Þ

where Bn(L) = Λ(I + DL + D2L2 + …)PM1/2R is the impulse response function and L represents the lag operator.

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3.2. Estimation Following Stock and Watson (2002a,b), I estimate the common factors, Ft, by the first r principal components of xt: F t̂ ¼V ̂ Vxt ;

ð6Þ

where Vˆ is an n × r matrix whose columns are the eigenhvectors corresponding to the r largest eigenvalues of the sample variance–covariance matrix of xt. Using information criteria suggested by Bai and Ng (2002), I find the values for r ranging between 3 and 6. I choose 5 common factors to allow comparison of the results across countries.13 Based on (2) I estimate Λ by regressing xt on the estimated factors: !1 T T X X F t̂ F t̂ V xt F t̂ V ¼ V ̂: ð7Þ K̂ ¼ t¼1

t¼1

Having obtained Fˆ t, I estimate the parameters of the VAR in (3): D̂ ¼

T X t¼2

̂V F t̂ F t1

T X

!1 ̂ ̂ F t1 F t1 V :

ð8Þ

t¼2

! T T X X 1 1 ̂ F t1 ̂ D̂ V: Rê ¼ F t̂ F t̂ V D̂ F t1 T  1 t¼2 T  1 t¼2

ð9Þ

After that, I estimate ut by ût = R′M− 1/2P′ϵˆ t, where ϵˆt = V′xt − Dˆ Vˆ ′xt−1 and M and P are respectively the eigenvalue and eigenvector matrices of Σˆ ϵ, in the ways defined in the previous subsection. Finally, the rotation matrix is chosen as   cosðaÞ sinðaÞ R¼ ; ð10Þ sinðaÞ cosðaÞ where a, a ∈ [0, π), is determined using the short run predictions of the aggregate demand– aggregate supply model. More formally, I assume that a positive supply shock does not have a negative impact on output and a positive effect on prices, while a positive demand shock does not lead to decreases in output and prices. This approach of shocks identification is based on the recent technique of sign restrictions in SVAR models (see Canova and De Nicoló, 2002; Faust, 1998; Peersman, 2005; Uhlig, 2005). The technique is implemented as follows. I draw values for a from the uniform distribution on [0, π). For each draw of a I check the required signs for contemporaneous responses of output and prices to shocks.14 If the restrictions are satisfied I keep the draw, otherwise the draw is rejected. I continue this process until 1000 draws satisfy the restrictions. Finally, I use the 1000 successful draws to plot the median together with the 84th and 16th percentiles. 13 14

The results do not change fundamentally when we use different numbers of static factors. The sign restrictions are imposed on the impact period and 1 year after the shock.

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4. Empirical results The framework presented in the previous section is separately estimated for each ECOWAS country. For each country, I use 54 annual time series. The data cover a wide range of economic variables including real, nominal, and financial variables (see Appendix B for data description). In most cases, the series are I(1) such that I estimate the model with first difference variables. The series are also taken in deviation from the mean value and divided by their standard deviation. First, I examine the comovement among time series of each country. To this end, I compute the percentage of the variance explained by five dynamic principal components. Technical details are given in Appendix C. Table 3 reports the results. I observe that two dynamic principal components capture on average 60% of the variance of the panel in each country (see column 2 of the top panel in Table 3). These values are significant for standardized data. They imply a strong comovement between the series and two shocks. Table 3 Percentage of variance explained by the DPCs Number of DPCs

1

2

3

4

5

Average Benin Burkina Faso Cote d'Ivoire The Gambia Ghana Niger Nigeria Senegal Togo

34.94 37.07 42.73 37.00 39.86 41.68 39.22 42.31 38.57

57.49 58.20 61.83 59.35 61.30 62.68 60.76 62.45 60.12

72.04 71.80 75.83 73.69 74.65 75.50 73.57 76.00 74.76

82.09 81.78 84.27 82.83 83.70 83.86 82.28 84.03 83.77

88.64 88.37 89.82 88.78 89.34 89.49 88.34 89.56 89.55

Output growth Benin Burkina Faso Cote d'Ivoire The Gambia Ghana Niger Nigeria Senegal Togo

64.96 85.07 85.30 69.35 76.66 82.87 72.73 85.53 42.72

78.93 90.00 96.91 82.51 93.74 94.49 88.59 93.32 81.36

96.20 95.51 96.77 94.00 95.15 97.84 96.13 98.18 95.81

98.51 98.66 98.57 95.88 98.50 99.36 97.27 99.41 97.85

99.06 99.23 99.17 98.05 99.28 99.66 99.19 99.57 98.78

Inflation Benin Burkina Faso Cote d'Ivoire The Gambia Ghana Niger Nigeria Senegal Togo

36.68 48.55 50.02 58.82 71.81 31.01 40.70 44.23 63.46

62.19 78.14 88.48 91.31 88.79 60.08 65.54 78.11 82.02

93.26 94.04 96.94 93.60 92.10 88.77 81.24 91.89 94.52

96.48 97.60 98.81 95.38 96.07 97.01 93.49 98.11 96.59

97.76 98.74 99.41 98.23 98.20 98.58 98.39 98.88 97.40

DPC = dynamic principal component.

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Moreover, the two dynamic principal components capture more than 80% of the variance of output growth and inflation (see column 2 of the middle and the bottom panels in Table 3). This result suggests that my two macroeconomic variables of interest strongly comove with other variables in each economy. 4.1. Historical fluctuations of macroeconomic shocks Let me now examine what the shocks capture. Fig. 4 displays the underlying aggregate demand and aggregate supply shocks for each country. The series of the shocks are averaged over the 1000 estimated individual shocks from the successful draws. The fluctuations in the shocks reflect the major economic events of the last 30 years in these countries. Supply shocks are related to three kinds of events. First, supply shocks illustrate the booms in oil prices in the 1970s and 1980s. An increase in oil prices is an adverse supply shock because it leads to the increase of firms' production costs. Second, supply shocks stem from the increase in the prices of imported capital goods, and intermediate inputs in most West African countries in the 1970s and 1980s. An increase in import prices is also a negative supply shock for West African countries. Imports account for more than 40% of GDP in SubSaharan African countries. Moreover, imports of intermediate inputs and capital goods account for about 50% and 28%, respectively, of total import in Sub-Saharan Africa (see Kose and Reizman, 2001). Therefore, any fluctuations in the prices of these products affect economic activities in these countries.15 Finally, supply shocks correspond to the rise in the world real interest rate in the 1980s. Most of the African countries were severely indebted in the 1980s such that the increase in the interest rate led to the expansion of debt services. Fosu (1996) presents evidences of adverse debt effects on economic growth in Sub-Saharan African countries. These three negative effects explain the large negative supply shocks in Benin in 1975, in Burkina Faso in 1970–1973, in Cote d'Ivoire in 1980 and 1983–1984, in The Gambia in 1971 and 1986, in Ghana in 1975 and 1981, in Nigeria in 1974, and in Togo in 1971 (see the top panel of Fig. 4). For Nigeria, an oil shock has two opposing effects. The rise in oil prices is a positive term of trade shock because Nigeria is an oil producer country. This positive term of trade shock leads to output expansion as a larger volume of import can be purchased with a given volume of export. On the other hand, a boom in oil prices is a negative supply shock along the lines described above. Moreover, the appreciation of the domestic currency that follows a positive term of trade shock weakens the competitive conditions of domestic firms. These two negative effects are presumably the driving forces of the decrease in manufacture production in Nigeria in 1974. Supply shocks also capture fluctuations in rainfall. For example, supply shocks illustrate the negative rainfall shocks in Niger and Senegal in the 1970s and the positive rainfall shock in Burkina Faso in 1986. Demand shocks are associated with monetary and fiscal policy shocks. In the case of WAEMU members, the data display a large positive demand shock in 1994. This corresponds to an unprecedented devaluation of the CFA franc by 50%. Fiscal policy is closely related to terms of trade shocks in developing countries. In many African countries, positive terms of trade shocks led to government expenditure increases, while negative terms

15

Bruno and Sachs (1985) present the argument of adverse effects of import prices on supply.

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Fig. 4. Aggregate demand and supply shocks.

of trade shocks led to fiscal crises because of the difficulties in cutting the long term investments undertaken during boom periods. To solve this problem many African countries have borrowed from international markets. This fact is at the origin of the debt crisis of the

332

Benin Burkina Faso Cote d'Ivoire The Gambia Ghana Niger Nigeria Senegal Togo

Benin

Burkina

Cote d'Ivoire

The Gambia

Ghana

Niger

Nigeria

Senegal

Togo

1 0.14 (0.43) 0.23 (0.20) − 0.06 (0.73) 0.34⁎⁎ (0.05) − 0.10 (0.58) 0.08 (0.67) − 0.07 (0.70) 0.34⁎⁎ (0.05)

1 0.09 (0.62) − 0.13 (0.46) − 0.09 (0.61) 0.45⁎⁎⁎ (0.01) − 0.11 (0.54) 0.03 (0.89) 0.00 (0.98)

1 −0.01 (0.94) 0.01 (0.97) −0.08 (0.68) 0.23 (0.19) 0.06 (0.72) −0.08 (0.66)

1 −0.27 (0.13) −0.14 (0.43) − 0.11 (0.55) 0.53⁎⁎⁎ (0.00) 0.19 (0.28)

1 −0.24 (0.17) 0.45⁎⁎⁎ (0.01) −0.15 (0.40) 0.23 (0.20)

1 − 0.17 (0.34) − 0.02 (0.90) − 0.14 (0.44)

1 0.11 (0.54) − 0.04 (0.83)

1 0.27 (0.12)

1

The numbers in brackets are p-values. ⁎⁎⁎Significant at 1%; ⁎⁎significant at 5%. WAEMU members are Benin, Burkina Faso, Cote d'Ivoire, Niger, Senegal, and Togo. WAMZ members are The Gambia, Ghana, and Nigeria.

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Table 4 Correlation matrix of supply shocks

Benin Burkina Faso Cote d'Ivoire The Gambia Ghana Niger Nigeria Senegal Togo

Benin

Burkina

Cote d'Ivoire

The Gambia

Ghana

Niger

Nigeria

Senegal

Togo

1 0.11 (0.53) 0.09 (0.62) − 0.38⁎⁎ (0.03) −0.12 (0.49) 0.01 (0.94) 0.33⁎ (0.06) 0.03 (0.88) 0.22 (0.22)

1 0.51⁎⁎⁎ (0.00) 0.24 (0.17) 0.01 (0.94) 0.28 (0.12) 0.01 (0.95) 0.74⁎⁎⁎ (0.00) 0.48⁎⁎⁎ (0.00)

1.00 0.20 (0.27) 0.06 (0.74) 0.38⁎⁎ (0.03) 0.14 (0.44) 0.58⁎⁎⁎ (0.00) 0.64⁎⁎⁎ (0.00)

1.00 0.21 (0.24) 0.17 (0.36) −0.31⁎ (0.08) 0.29⁎ (0.10) 0.20 (0.27)

1.00 − 0.09 (0.61) − 0.16 (0.36) − 0.02 (0.93) 0.13 (0.46)

1.00 − 0.14 (0.45) 0.47⁎⁎⁎ (0.01) 0.26 (0.15)

1.00 − 0.02 (0.91) 0.24 (0.19)

1.00 0.65⁎⁎⁎ (0.00)

1.00

The numbers in brackets are p-values. ⁎⁎⁎Significant at 1%; ⁎⁎significant at 5%; ⁎significant at 10%. WAEMU members are Benin, Burkina Faso, Cote d'Ivoire, Niger, Senegal, and Togo. WAMZ members are The Gambia, Ghana, and Nigeria.

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Table 5 Correlation matrix of demand shocks

333

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1980s in these countries. In addition to the effect of terms of trade shocks on aggregate demand through fiscal policy, terms of trade shocks also affect the private demand component of aggregate demand via the wealth channel. The data reported in the bottom panel of Fig. 4 show large positive demand shocks in Nigeria in 1974 corresponding to the boom in oil prices, and large demand shocks in Cote d'Ivoire in the 1970s and 1980s, corresponding to the fluctuations of cocoa and coffee prices. The large negative demand shocks in Burkina Faso, The Gambia, and Senegal in 1979 are presumably associated with the decline in groundnut prices. Finally, demand shocks in West African countries stem from the global recession of the 1980s. In Togo, the effects of the recession have been amplified by the political crisis of 1993. 4.2. Cross-country correlations of shocks Tables 4 and 5 report the correlation matrices of aggregate demand and supply shocks across countries. I focus on results relative to Cote d'Ivoire and Nigeria, the two largest economies of ECOWAS. Figs. 5 and 6 display the correlations of demand and supply shocks with respect to Cote d'Ivoire and Nigeria, respectively. For comparison, I plot in the same graphs the results for European countries found by Fidrmuc and Korhonen (2003). The correlations for European countries are evaluated with respect to Germany.

Fig. 5. Aggregate demand and supply shocks correlation with Cote d'Ivoire.

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Fig. 6. Aggregate demand and supply shocks correlation with Nigeria.

Cote d'Ivoire's supply shocks are positively correlated with those experienced by five ECOWAS countries: Benin, Burkina Faso, Ghana, Senegal, and Nigeria. However, the values of the correlation coefficients are very low. They range from 0.01 to 0.23 and none of them is significant (see Table 4). The other three ECOWAS countries (The Gambia, Niger, and Togo) have negative correlations of supply shocks with Cote d'Ivoire, on the order of −0.08 to − 0.01. In the case of Nigeria, the data display similar patterns but with higher correlations in absolute values than those with respect to Cote d'Ivoire. Four ECOWAS countries (Benin, Cote d'Ivoire, Ghana, and Senegal) have positive correlations of 0.08–0.45, although the result is only significant for Ghana. The other four countries (Burkina Faso, The Gambia, Niger, and Togo) have negative correlations, on the order of − 0.17 to −0.04. The results for demand shocks are different. All countries have positive correlations with respect to Cote d'Ivoire. The values of the correlation coefficients range from 0.06 to 0.64; however, the results are not significant for any WAMZ country or for Benin. In the case of Nigeria, demand shocks correlations are significantly lower than those with respect to Cote d'Ivoire. The correlation coefficients are in the range of − 0.31 to + 0.33 and the result is only significant for Benin. Moreover, Nigeria has negative demand shock correlations with other WAMZ members. In summary, supply shocks are poorly correlated across ECOWAS countries. The comparison to results among EU members indicates that supply shocks are significantly more idiosyncratic among West African countries than among EU members. This result implies that West Africa countries need different policy responses to adjust to supply shocks. At a given time, a group of countries in West Africa may need an expansionary monetary policy to respond

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to cyclical downturns while another might require a contractionary monetary policy to respond to cyclical booms. Accordingly, West African countries will find it difficult to operate their monetary union if wages are rigid and/or factor mobility is limited. In the case of demand

Fig. 7. Impulse response functions to supply shocks. Median impulse responses in bold lines and 68% errors bounds in dashed lines.

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Fig. 8. Impulse response functions to demand shocks. Median impulse responses in bold lines and 68% errors bounds in dashed lines.

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Table 6 Variance decomposition Horizon

Supply shocks

Demand shocks

Output

Prices

Output

Lower

Median

Upper

Lower

Median

Upper

Lower

35.59 38.58 40.35 41.42 42.15

67.72 70.60 72.29 73.32 74.01

92.97 94.43 95.27 95.78 96.12

35.33 30.66 28.40 27.08 26.19

68.60 63.83 61.53 60.18 59.28

92.57 89.80 88.46 87.67 87.15

Burkina Faso 1 98.98 2 98.51 3 98.22 4 97.99 5 97.80

99.43 99.06 98.84 98.65 98.50

99.72 99.45 99.29 99.15 99.03

7.16 5.24 4.06 3.33 2.81

8.56 6.42 5.09 4.25 3.65

Cote d'Ivoire 1 32.19 2 20.43 3 14.32 4 10.77 5 8.50

58.06 44.41 36.35 31.22 27.68

79.15 67.53 59.93 54.78 51.07

14.54 13.51 12.61 11.88 11.28

Gambia 1 2 3 4 5

72.87 78.75 82.36 84.54 86.04

82.61 87.35 90.09 91.76 92.88

89.94 93.24 94.94 95.98 96.65

Ghana 1 2 3 4 5

38.07 37.73 36.09 35.27 34.49

73.99 73.68 72.12 71.34 70.59

97.37 97.25 96.60 96.28 95.95

Benin 1 2 3 4 5

Prices Median

Upper

Lower

Median

Upper

7.03 5.57 4.73 4.22 3.88

32.28 29.40 27.71 26.68 25.99

64.41 61.42 59.65 58.58 57.85

7.43 10.20 11.54 12.33 12.85

31.40 36.17 38.47 39.82 40.72

64.67 69.34 71.60 72.92 73.81

10.14 7.79 6.30 5.35 4.67

0.28 0.55 0.71 0.85 0.97

0.57 0.94 1.16 1.35 1.50

1.02 1.49 1.78 2.01 2.20

89.86 92.21 93.70 94.65 95.33

91.44 93.58 94.91 95.75 96.35

92.84 94.76 95.94 96.67 97.19

36.37 34.93 33.65 32.58 31.69

64.77 63.31 61.99 60.87 59.93

20.85 32.47 40.07 45.22 48.93

41.94 55.59 63.65 68.78 72.32

67.81 79.57 85.68 89.23 91.50

35.23 36.69 38.01 39.13 40.07

63.63 65.07 66.35 67.42 68.31

85.46 86.49 87.39 88.12 88.72

73.48 70.40 66.93 65.38 64.07

83.68 81.31 78.35 77.04 75.90

91.68 90.20 87.97 86.99 86.11

10.06 6.76 5.06 4.02 3.35

17.39 12.65 9.91 8.24 7.12

27.13 21.25 17.64 15.46 13.96

8.32 9.80 12.03 13.01 13.89

16.32 18.69 21.65 22.96 24.10

26.52 29.60 33.07 34.62 35.93

27.13 33.74 36.84 38.52 39.52

61.63 68.75 71.97 73.69 74.72

89.88 93.78 95.42 96.28 96.80

2.63 2.75 3.40 3.72 4.05

26.01 26.32 27.88 28.66 29.41

61.93 62.27 63.91 64.73 65.51

10.12 6.22 4.58 3.72 3.20

38.37 31.25 28.03 26.31 25.28

72.87 66.26 63.16 61.48 60.48

The lower and upper bounds correspond to the 16th and 84th percentiles, respectively.

shocks, WAMZ members also display asymmetric behavior. However, demand shocks are significantly more correlated across CFA countries and the results are comparable to the ones across European countries. This finding for demand shocks among WEAMU countries presumably reflects the existence of a common currency among these countries. However, the correlation coefficients reported here for WAEMU countries are much smaller than output and inflation shock correlations found by Fielding and Shields (2001). The difference between their results and mine may be due to measurement errors and the use of a small information set in Fielding and Shields (2001).16 Finally, the findings suggest similar shocks between neighboring 16

The difference between the results may also be due to different identification restrictions. Fielding and Shields (2001) impose long run zero restrictions.

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Table 7 Variance decomposition (end) Horizon

Supply shocks

Demand shocks

Output

Prices

Output

Prices

Lower

Median

Upper

Lower

Median

Upper

Lower

Median

Upper

Lower

Median

Upper

Niger 1 2 3 4 5

97.10 96.11 95.45 94.71 94.09

98.86 98.15 97.68 97.13 96.66

99.82 99.42 99.15 98.79 98.48

80.18 71.31 64.66 60.09 56.76

84.83 76.74 70.50 66.15 62.96

88.82 81.64 75.91 71.85 68.84

0.18 0.58 0.85 1.21 1.52

1.14 1.85 2.32 2.87 3.34

2.90 3.89 4.55 5.29 5.91

11.18 18.36 24.09 28.15 31.16

15.17 23.26 29.50 33.85 37.04

19.82 28.69 35.34 39.91 43.24

Nigeria 1 2 3 4 5

33.18 40.70 45.41 48.37 50.39

68.91 75.36 79.12 81.45 83.02

94.05 96.27 97.27 97.65 97.70

10.50 14.17 20.61 26.89 32.27

39.83 45.01 52.41 58.85 63.98

75.44 79.53 84.13 87.44 89.69

5.95 3.73 2.73 2.35 2.30

31.09 24.64 20.88 18.55 16.98

66.82 59.30 54.59 51.63 49.61

24.56 20.47 15.87 12.56 10.31

60.17 54.99 47.59 41.15 36.02

89.50 85.83 79.39 73.11 67.73

Senegal 1 2 3 4 5

47.47 37.52 31.55 27.93 25.02

55.25 45.40 39.50 36.04 33.16

62.04 52.87 47.40 44.29 41.61

5.30 4.02 3.27 2.75 2.39

11.84 9.61 8.35 7.46 6.81

21.49 18.36 16.62 15.37 14.46

37.96 47.13 52.60 55.71 58.39

44.75 54.60 60.50 63.96 66.84

52.53 62.48 68.45 72.07 74.98

78.51 81.64 83.38 84.63 85.54

88.16 90.39 91.65 92.54 93.19

94.70 95.98 96.73 97.25 97.61

3.98 2.67 2.06 1.70 1.46

9.33 7.11 6.08 5.46 5.05

16.58 13.60 12.23 11.40 10.86

4.30 5.18 5.94 6.33 6.66

8.80 10.39 11.60 12.27 12.81

15.91 18.15 19.78 20.71 21.44

83.42 86.40 87.77 88.60 89.14

90.67 92.89 93.92 94.54 94.95

96.02 97.33 97.94 98.30 98.54

84.09 81.85 80.22 79.29 78.56

91.20 89.61 88.40 87.73 87.19

95.70 94.82 94.06 93.67 93.34

Togo 1 2 3 4 5

The lower and upper bounds correspond to the 16th and 84th percentiles, respectively.

countries. For example, the pairs Benin–Togo; Burkina Faso–Niger; and Senegal–The Gambia have significantly positive supply shock correlations. The same conclusion applies to demand shocks for the pair Benin–Nigeria. 4.3. The dynamic effects of shocks Figs. 7 and 8 report the impulse response functions of output and prices to the structural shocks. In general, the sign of the dynamic responses of output and prices to supply and demand shocks are compatible with the identification restrictions. Following a positive supply shock output expands significantly and prices decrease, while output and prices have increased following a positive demand shock in every country.17 However, differences exist across 17 As indicated in the Introduction, the signs of the impulse response functions were not adequately identified in previous studies. For example Hoffmaister et al. (1998) impose zero restrictions within a VAR model and find that output and prices increase following a positive supply shock.

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countries. First, supply shocks have temporary effects in Senegal, and Togo. One explanation of this result is that supply shocks may be dominated by rainfall shocks in these countries. Bayoumi and Ostry (1997) make the point on the possibility of temporarily supply shocks, due to rainfall shocks, in Sub-Saharan African countries. Second, the responses of output to demand shocks are permanent in Cote d'Ivoire and Ghana. This result presumably reflects the fact that demand shocks are dominated by terms of trade shocks in these countries. This explanation is consistent with Cashin et al. (2005). Using data on 42 Sub-Saharan African countries these authors find that terms of trade shocks are long-lived in one-third of the countries, while half of the countries have short-lived terms of trade shocks. 4.4. Variance decompositions Finally, I examine the contribution of the shocks to the fluctuations of output and prices. Tables 6 and 7 summarize the results. The data show that supply shocks explain the largest part of output variations in seven out of the nine countries: Benin, Burkina Faso, The Gambia, Ghana, Niger, and Nigeria. The percentage variance of output due to supply shocks is more than 70% in these countries. In Cote d'Ivoire and Senegal supply and demand shocks explain about equal proportions of output variation in the short run. In the long run, however, demand shocks account for the largest part of output variations in these two countries. Togo is the only country where demand shocks account for the largest part of output variations. In the case of prices, two groups emerge. Supply shocks account for the main fluctuations of prices in Benin, The Gambia, Ghana, and Niger, while demand shocks are the driven forces of price movements in Burkina Faso, Cote d'Ivoire, Nigeria, Senegal, and Togo. If the percentage of output variance due to shocks is taken as a measure of the size of shocks, then I conclude that supply shocks dominate. This result combined with the early finding that supply shocks are more asymmetric among ECOWAS countries reinforce the conclusion that these countries will find it difficult to adjust to supply shocks if they form a monetary union. 5. Discussion The main conclusion of the last section was that ECOWAS countries display idiosyncratic shocks, implying that these countries will find it difficult to operate a monetary union. The weights that should be given to these results depend on whether wages are flexible and/or whether labor is mobile. As mentioned in the Introduction, wage flexibility and/or labor mobility can help to adjust to shocks. Let me examine each of these mechanisms.18

18

In addition to these market adjustment mechanisms (wages flexibility and labor mobility) are institutional adjustment mechanisms (see Bayoumi and Mason, 1995; De Grauwe, 2005). Institutional mechanisms take the forms of fiscal federalism or specific actions by public authorities. In ECOWAS, these institutions have been put in place. For example, WAMZ established the Stabilization and Cooperation Fund (SCF) to provide financial assistance to member states that may experience temporary disequilibria. One obstacle to the functioning of the SCF is that member states do not regularly pay their contributions. The SCF was established in 2003 with an initial capital of $50 million which may be increased to $100 million. Out of the initial capital, only $27.15 million have been collected as follows: Ghana has fully paid up its contribution; three countries have made part payment; and one country has not made any contribution.

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5.1. Wage flexibility A country that is subject to a recession can restore equilibrium if real wages decrease. The reduction of wages will shift the aggregate supply curve to the right making domestic goods more competitive and simulating demand. As can be seen, the reduction of wages acts as a devaluation to restore competitivity. In the same way, the increase in wages in a country that temporarily faces a boom can restore equilibrium by shifting the aggregate supply curve to the left. The empirical literature on wage flexibility in West Africa is mixed. Azam (2005) presents evidence on nominal wage rigidity in the public sector of many West African countries. In this case, real wage flexibility can be achieved with higher inflation (without a peg to a hard currency, e.g. euro). However, nominal wage flexibility would be needed to adjust to asymmetric shocks. Rama (2000) finds real wage rigidities in CFA countries in the 1980s and 1990s. However, Hoddinott (1996) finds that real wages have responded significantly to unemployment in urban Cote d'Ivoire. In particular, Hoddinott shows that a rise in unemployment by 10% lowers wages by 1% and that the results are comparable to those found for the United States, Canada, Britain, and other developed countries. Teal (2000) finds that real wages of unskilled workers have fallen to reduce unemployment in Ghana. However, real wages of skilled workers have been rigid over the same period. This difference between the responses of real wages of skilled and unskilled workers implies a partial adjustment of the labor market in Ghana. 5.2. Labor mobility The adjustment mechanism through labor mobility works as follows. If labor is mobile between countries, workers will move from depressed countries to the ones where there is excess demand for labor. Adepoju (2005) documents the movement of a significant amount of workers from depressed countries (Benin, Burkina Faso, Niger, Mali, Ghana, and Togo) to Nigeria during the boom of oil prices in the 1970s. The author also discusses the massive immigration of workers from Burkina Faso, Mali and Togo to Cote d'Ivoire and Ghana after the increase in cocoa and coffee prices in the 1970s. Labor data are difficult to obtain for African countries. Bocquier and Traoré (2000) report figures of 6.4 millions of intermigration movements among seven West African countries (Burkina Faso, Cote d'Ivoire, Guinea, Mali, Mauritania, Niger, and Senegal) during the period 1988–1992. The World Bank (2000) estimates the foreign residents at 26% in Cote d'Ivoire, 14% in The Gambia, and 8% in Guinea in 1998. These values are significant. ECOWAS has also introduced a common passport between member states in 2000, which is expected to increase labor mobility. However, one factor might constrain the adjustment mechanism through labor mobility in West Africa. A large-scale labor migration generally creates social tensions in the host country. In general, residents think that migrants aggravate their poor economic situation. Adepoju (2005) presents figures of a large number of migrants that were expelled from Cote d'Ivoire, Ghana, and Nigeria in the 1980s, leaving all their properties behind. Moreover, the high proportion of Burkinabe living in Cote d'Ivoire is documented as one of the cause of the actual political crisis in Cote d'Ivoire. 6. Conclusion In this paper, I have used a dynamic structural factor model to extract information on aggregate demand and aggregate supply shocks for West African countries. The correlations of the

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underlying shocks show asymmetric supply shocks across West African countries. This implies that West African countries will find it difficult to operate their monetary union because the presence of asymmetric shocks implies a need for different policy responses to adjust to supply shocks in the region. In the case of demand shocks, WAMZ members also display asymmetric behavior. However, demand shocks are more similar among CFA countries of the region. The results for demand shocks among WEAMU countries presumably reflect the existence of a common currency among these countries. The framework used in this study can be extended to identify four structural shocks for West African countries: supply shocks, terms of trade shocks, demand shocks, and nominal shocks. Such an analysis would be interesting from two perspectives. First, the disaggregation of shocks will permit the identification of the sources of shocks which are crucial to implement successful stabilization policies. Second, Hoffmaister et al. (1998) impose zero restrictions within a VAR model and find that terms of trade shocks explain a trivial percentage variation of output in Sub-Saharan African countries. This is an unsatisfactory result because Sub-Saharan African countries are dependant on revenues of exported commodity goods, and the dynamic stochastic general equilibrium model discussed in Kose and Reizman (2001) predicts that terms of trade account for about 50% of the variations of output in these countries. In this paper, I have concentrated on the analysis of costs. The West African monetary union may be an OCA if the benefits of forming a monetary union outweigh the costs. The potential benefits of a monetary union include the enlargement of the regional market and the enhancement of economic competitiveness. A monetary union is also expected to increase intra-regional trade via lower transaction costs and the reduction of the exchange rate uncertainty (see De Grauwe, 2005; Frankel and Rose, 1998, 2002). In the case of developing countries, a monetary union may also lead to fiscal and monetary discipline. For example, the autonomy of the common central bank can make low inflation a time consistent monetary policy goal. Therefore, it would be interesting to analyze the costs and benefits of a monetary union in West Africa in a unified framework. Acknowledgement Earlier versions of this paper were Working Paper and Discussion Paper at the University of Oxford and at KULeuven, respectively. I would like to thank the referee and a co-editor for their insightful comments and suggestions. I thank Christopher Adams, Paul De Grauwe, Geert Dhaene, Domenico Giannone, Marco Lyrio, Lucrezia Reichlin, Saverio Simonelli, Francis Teal, Fay Dunkerley, and Marijke Verpoorten for their helpful comments. I am especially grateful to my supervisor, Hans Dewachter, for enriching discussions. I also benefited from comments of participants at the CES (KULeuven); the CSAE (Oxford University); and the National Bank of Benin (BCEAO-Cotonou) seminars. All errors are mine. Financial support from KULeuven is gratefully acknowledged.

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Appendix A

Fig. A1. Membership of ECOWAS and the CFA zone.

Appendix B. Data description No.

Variables

Sources

Transformation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

GDP (constant LCU) GDP deflator (base year varies by country) GDP per capita (constant LCU) Consumer price index (1995 = 100) Agriculture value added per worker (constant 1995 US$) Agriculture, value added (constant LCU) Food production index (1989–1991 = 100) Livestock production index (1989–1991 = 100) Land use, area under cereal production (ha) Aid per capita (current US$) Cereal yield (kg/ha) Exports as a capacity to import (constant LCU) CO2 emissions (kt) Fertilizer consumption Final consumption expenditure (constant LCU) Final consumption expenditure, etc. (constant LCU) General gov. final cons. expenditure (constant LCU) Gross capital formation (constant LCU)

World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank

DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG (continued on next page)

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Appendix B (continued) No.

Variables

Sources

Transformation

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

Gross domestic income (constant LCU) Gross national expenditure (constant LCU) Gross national income (constant LCU) Gross value added at factor cost (constant LCU) Household final cons. exp. (constant LCU) Household final cons. exp. per capita (constant 1995 US$) Household final cons. exp., etc. (constant LCU) Imports of goods and services (constant LCU) Industry, value added (constant LCU) Manufacturing Value added (Const. Loc. Curr.) Labor force, total Foreign assets (LCU) Foreign liabilities (LCU) Reserve money (LCU) Central gov. dep. (LCU) Assets claimed on centr. gov. (LCU) Assets claimed on private sector (current LCU) Domestic credit (current LCU) Demand deposits (current LCU) Time and savings and foreign currency deposits (LCU) Money (current LCU) Money and quasi money (M2, current LCU) Liquid liabilities (M3, current LCU) Net taxes on products (constant LCU) Official dev. assistance and official aid (current US$) Official exchange rate (LCU per US$, period average) Services, etc., value added (constant LCU) Total reserves minus gold (current US$) Terms of trade index Rainfall (area weighted average) Rainfall (crop weighted average) Rainfall (rain weighted average) Gross nat. disposable income (Const. Loc. Curr.) Age dependency ratio Agricultural machinery Bank liquid reserves to bank assets ratio

World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank World Bank IMF IMF IMF IMF IMF IMF IMF IMF IMF IMF IMF IMF World Bank World Bank World Bank World Bank World Bank World Bank Jefferson and O'Connell Jefferson and O'Connell Jefferson and O'Connell World Bank World Bank World Bank World Bank

DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG DLOG D D D

DLOG = first difference of log variables. D = first difference of variables in level. For some countries one or two variables are missing.

Appendix C. Technical details C.1. Dynamic principal components In this part, I explain the estimation of the variance of the dynamic principal components. Let me denote by xt = (x1t,…, xnt) a vector of n time series on a country where each xit, i = 1,…, n, has T observations. First of all, I estimate a smooth spectral density matrix of xt by: Rxn ðhÞ ¼

m 1 X wk Cnk eihk ; 2p k¼m

ðC1Þ

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pffiffiffiffi PT 1 where m ¼ roundð T Þ. Cnk ¼ T k t¼kþ1 xt xtk is the k-lag covariance matrix of xt, k = 0, …, m. jkj wk ¼ 1  ðmþ1Þ are the weights corresponding to the Bartlett lag window. I compute the spectra at 2pj 101 equally spaced frequencies, hj ¼ 100 , where j = −50,… , 50 and θj ∈ [− π, π]. Second, I compute the n eigenvalues and eigenvectors of Σnx(θ) at each frequency θj. The eigenvectors represent the dynamic principal components and the eigenvalues correspond to the variances of these dynamic principal components at each frequency. After that I arranged the eigenvalues in decreasing order of magnitudes. Thereafter, I compute the percentage of the variance explained by each dynamic factor at each frequency. Finally, I take the average over the 101 frequencies of these percentages. C.2. Percentage variance explained by the DPCs for selected series To compute the percentage variance explained by the DPCs for selected series I follow Forni et al. (2000). The technique proceeds in four steps as follows: (i) compute the spectral density matrix Σnx(θ) (ii) compute q eigenvalues and eigenvectors of Σnx(θ) at each frequency. Denote by Λ(θ) the diagonal matrix containing these eigenvalues and by W(θ) the matrix containing the corresponding eigenvectors; (iii) find the filter corresponding to W(θ) by using the inverse discrete Fourier transform of W(θ) to have W(L); (iv) compute Zt = W(L)xt as the principal component; (v) project xt on Zt to have the common component; (vi) compute the percentage variance explained by the DPCs for a selected series as the R2 of the projection of the series on its component. References Addison, E.K.Y., Opoku-Afari, M., Kinful, E., 2005. Terms of Trade and Real Exchange Rate Shocks and Implications for the West African Monetary Zone, Bank of Ghana Working Paper No. 12. Adepoju, A., 2005. Migration in West Africa, Global Commission on International Migration Paper No. RS8. Atkeson, A., Bayoumi, T., 1993. Do private capital markets insure regional risk? Evidence from the United States and Europe. Open Economies Review 4, 303–324. Azam, J.P., 2005. Poverty and growth in the WAEMU after the devaluation. Journal of African Economies 4, 536–562. Bai, J., Ng, S., 2002. Determining the number of factors in approximate factor models. Econometrica 70, 191–221. Bayoumi, T., Mason, P., 1995. Fiscal flows in the United States and Canada: lessons for monetary union in Europe. European Economic Review 39, 253–274. Bayoumi, T., Ostry, J., 1997. Macroeconomic shocks and trade flows within Sub-Saharan Africa: implications for optimum currency areas. Journal of African Economics 6, 69–182. Bénassy-Quéré, A., Coupet, M., 2005. On the adequacy of monetary arrangements in Sub-Saharan Africa. World Economy 29, 349–373. Bentivogli, C., Pagano, P., 1999. Regional disparities and labor mobility: the Euro-11 versus the USA. Labour 13, 737–760. Bernanke, B.S., Boivin, J., 2003. Monetary policy in a data-rich environment. Journal of Monetary Economics 50, 525–546. Beyer, A., Doornik, J., Hendry, D.F.A., 2001. Constructing historical Euro-Zone data. Economic Journal 111 (469), 102–121. Blanchard, O.J., Katz, L.F., 1992. Regional evolutions. Brookings Papers on Economic Activity 1–61. Bleaney, M., Fielding, D., 2002. Exchange rate regimes, inflation and output volatility in developing countries. Journal of Development Economics 68, 233–245.

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