Chapter 4 Structural Factors Affecting Manufacturing Competitiveness: Comparative Results from Cameroon, Côte d’lvoire, Nigeria and Senegal
- International Monetary Fund
- Published Date:
- August 2001
The economic situation of sub-Saharan Africa has improved markedly in recent years. Increased stability—macroeconomic and political—and market liberalisation in many countries enhance the opportunities for economic development led by the private sector. Previous examples from, for instance, Mauritius and Tunisia as well as Southeast Asia, point to the potential of manufacturing exports for making sustained growth possible. The advantages of manufacturing exports include spillover effects, such as competitive pressure, economies of scale and technology transfer. Several studies provide empirical and theoretical indications that manufacturing exports have a beneficial impact on total factor productivity; a few of them include Edwards (1997), de Melo and Robinson (1990), Biggs, Shah and Srivastava (1995), Tybout (1992), Bigsten et al. (1997) and Lucas (1993).
The income elasticity of demand for manufactured goods is higher than for primary goods. If foreign income increases, countries specialised in manufacturing can expect higher growth than those dependent predominantly on exports of primary goods. Moreover, the price elasticity of both demand and supply is higher for manufactured goods than for primary goods, which has a stabilising effect on volatility in the terms of trade and has particular importance given Africa’s heavy dependence on exports of primary products.
African entrepreneurs can integrate into the global economy only if they can compete on international terms. Nehru and Dhareshwar (1994) concluded that sub-Saharan Africa is the only region with productivity growth, hence competitiveness, significantly lower than its initial levels of human capital, GDP per capita and political stability would indicate. The World Economic Forum (1998) recently published its first African Competitiveness Report, which points to a number of structural policies necessary to promote competitiveness. Besides political and economic stability, they include openness to trade, human development and investment in infrastructure. This study focuses on the determinants of productivity growth in Cameroon, Côte d’lvoire, Nigeria and Senegal. It draws on findings from previous work carried out at the OECD Development Centre, including both sectoral and firm-level studies and microeconomic surveys done by the Development Centre in Cameroon, Côte d’lvoire and Senegal. The studies include Adenikinju and Soludo (1997), Berthélemy et al. (1996), Berthélemy and Bourgignon (1996), Latreille and Söderling (1997), Latreille and Varoudakis (1996) Sekkat and Varoudakis (1998) and Söderling (1999). The four countries studied show several similarities. Except for Senegal, all have suffered from severe terms-of-trade shocks and had a clear pattern of responses to them. Initially, substantial improvement in the terms of trade induced an excessive surge in investment. Its poor quality translated into declining productivity. The revenues generated during the boom years thus helped little when luck turned and commodity prices plummeted. To make things worse, all four countries had highly protected and inward-looking manufacturing industries. Labour markets were rigid and regulated. Attempts at reform were largely unsuccessful. The devaluation of the CPA franc may have been a turning point for Cameroon, Côte d’lvoire and Senegal. Nigeria’s policy reversal in the early 1990s is more worrisome.
Structure of the Manufacturing Sector
Table 4.1 on the following page begins the analysis with a look at detailed information available for manufacturing sub-sectors in the four countries. Food processing generally dominates. It accounts for about half of manufacturing value added in Côte d’lvoire and Senegal and somewhat more in Cameroon. For the most part, this industry transforms or packages locally produced agricultural products (and fish, in Senegal), with relatively low value added. It is still important but significantly less prominent in Nigeria, at about 30 per cent of total manufacturing. This reflects the more advanced development of manufacturing in Nigeria, the most industrialised country in West Africa.
The food industries in Côte d’lvoire and Cameroon primarily transform cocoa and coffee for export. Cameroon also has a relatively important beverage industry serving the domestic market. Manufacturing in Cameroon suffered severely from the economic crisis induced by falling oil prices in the mid-1980s. Falling cocoa and coffee prices at about the same time compounded it and caused a particularly severe food-industry decline.
In Senegal, canning fish and agricultural products and processing groundnuts are the chief food-manufacturing activities. The groundnut industry is declining and is no longer an engine of growth. The fish-canning industry, one of the fastest growing until the late 1980s, contracted in the early 1990s. The products of the Senegalese food-processing industry are less sensitive to international commodity prices than those of Cameroon and Côte d’lvoire, and the industry has felt fewer effects from variations in the terms of trade.
The chemical industry is important in Senegal, Nigeria and to some extent Côte d’lvoire. In Senegal, phosphate fertiliser plants predominate. This industry saw a boom in the mid to late 1980s, when international phosphate prices were high. Employment in the chemical industry grew by almost 20 per cent per year in 1974-84, but its growth slowed significantly during the past decade. The chemical industry in Nigeria produces mainly soap and detergents, as well as rubber. Despite the country’s large oil reserves, refineries are negligible. In Côte d’lvoire, however, petroleum refining is an important part of the chemical industry. The country imports crude-oil feedstock and exports refinery products. Since 1995, it has also produced its own crude oil, but the impact on refining remains doubtful because the characteristics of domestic oil appear to make it inappropriate for refineries built to handle imported crude. Côte d’lvoire also has a relatively advanced rubber industry. The chemical industry in Cameroon has only limited importance. It produces primarily pharmaceuticals and cosmetics, including perfume and soap.
|Oil-seeds and fats||10.3|
|Other food products||31.7|
|Textile and Leather Products||1.4||16.9||15.4||8.4|
|Wood and Paper Products||16.2||9.5||8.2||3.5|
|Mechanical (mostly metalworking)||4.4||5.7||7.6||7.0|
The textile industry, often mentioned as a potential driving force for manufacturing in Africa, plays an important role in Côte d’lvoire, Nigeria and Senegal. Textiles began expanding in Côte d’lvoire and Senegal during the 1970s but declined in the 1980s. The development of a synthetic fabric industry in Nigeria has to some extent offset the more disappointing record of cotton textiles in the past 15 years.
Except in Senegal, wood processing is another important industry although its future is less than bright. It contributes only a relatively moderate share of Cameroon’s industrial output, despite the country’s rich endowment of tropical rainforests. Only about 10 per cent of the forest area licensed for exploitation is actually used, and only a minor share of the wood is transformed locally. Environmental concerns threaten the industry. In Côte d’lvoire, wood processing is in crisis due to forest depletion.
Determinants of Total Factor Productivity
International competitiveness arises from both price factors (e.g. the exchange rate, wage costs or the costs of inputs) and more structural elements, linked largely to productivity gains, on which this section focuses. To look at variations in TFP, separate production functions have been estimated for each of the countries under study (Table 4.2)1. The poor TFP performance of the four countries is striking. All experienced negative productivity growth on average2, with the most obvious declines in the textile, leather-working and food industries. Senegal had positive productivity growth only in construction materials, chemicals and “other food”. Cameroon and Côte d’lvoire showed the most disappointing record, with average annual productivity declines of 3.1 and 4 per cent, respectively. In Cameroon, only the mechanical industry saw gains. They have been significant since the devaluation of the CFA franc, and the industry has managed to increase production, particularly for exports. The economic crisis in Cameroon hit its food industry the hardest, but an exceptionally strong performance during the boom years at the beginning of the 1980s offset the losses to some extent. In Nigeria, only two sub-sectors, rubber and transport equipment, showed positive TFP growth. The consumer-goods industries (food processing, textiles, leather working, wood processing and papermaking) performed significantly worse than the capital-goods industries (transport equipment and electrical machinery). The capital-goods branch enjoys substantially less protection than the import-substituting consumer-goods industries, which points to the importance of trade liberalisation.
|Oil-seeds and fats||5.9|
|Other food products||0.2|
|Textile and Leather Products||0.1||-6.4||-2.2||-10.0|
|Wood and Paper Products||-5.2||-1.6||-2.4||-2.2|
|Mechanical (mostly metalworking)||5.6||-2.4||-3.3||-1.4|
Weak productivity performance has put a severe strain on competitiveness in all four countries. This merits attention to the determinants of TFP. They can be grouped (Table 4.3) as follows:
— human capital development or skilled labour availability;
— external trade and openness of the economy;
|Variables relating to human capital|
|Skilled labour availability||+||+||+|
|Investment in health and education||+|
|Firms’ capacity to innovate||+||+|
|Firms’ propensity to train workers||+||+|
|Variables relating to openness|
|Variables relating to infrastructure|
|Availability of general infrastructure||+|
|Availability of telephone lines||+|
The Role of Human Capital
Several studies indicate the importance of human capital for productivity. As one of their main contributions, Nehru and Dhareshwar (1994) elevated the role of human capital accumulation as a source of TFP growth. Edwards (1997) points out that the availability of skilled labour can facilitate technology transfer, because trained personnel can adapt new technology more easily. Imitation of new technology is likely to be important for productivity gains in African countries. Lucas (1993) suggests that human capital accumulation is the most important element in TFP growth. He emphasises the effect of learning by doing. According to his model, certain more sophisticated products induce greater technology spillover effects than other, simpler ones. The best TFP growth occurs when firms produce goods that demand technology close to their maximum technical capacity and when they constantly introduce new, higher-quality goods. The studies of all four countries provide evidence that human capital or skilled labour is important for productivity growth. The estimated production function for Senegal examines the impact of the quality of labour on productivity by including a proxy for the availability of skilled labour (Table 4.4), formulated as the ratio between actual salary levels and minimum salaries by sector. The regression confirms that skilled labour has a beneficial impact on TFP growth, as indicated by the positive and significant coefficient for this variable. The coefficient is rather high, underlining the importance of human capital for productivity gains.
|Dependent variable: log (value added)||Dependent variable: dLog (value added)|
|Log (capital stock)||0.35||5.47||dx||0.44||6.26|
|Productivity trends:||dX * (Kp/K)||0.54||3.25|
|Construction||0.06||5.60||No. of observations||197|
|Mechanical||-0.015||1.50||Estimation method||Ordinary least squares|
|Canning||-0.043||3.90||Definitions of indep. variables (in first differences) in the expression dx – α*dlog Lt + (1 – α)*dlog Kt:|
|Other foods||-0.01||0.80||Lt = labour for the sector.|
|Adjusted R2||0.921||Kt = capital stock for the sector.|
|No. of observations||207||α = the estimated capital coefficient from the regression at left (= 0.35).|
|Hausman test||γ2(6) = 10.08|
|Estimation method||Random effects||dX = the equivalent of dx for all manufacturing.|
|dX*(Kp/K) = interactive variable, the size of the total manufacturing sector multiplied by the ratio of private to public capital.|
|dlog(H) = proxy for skilled labour availability.|
|dlog(E) = production of electricity.|
|T = import taxation.|
Both skilled labour and investment in the educational system emerged as important, with positive and significant coefficients, in the estimated production function for Nigeria (Table 4.5). The finding for Cameroon (Box 4.1) that the ratio of highly skilled workers to total labour had a positive and significant effect on TFP further confirms these results.
|Dependent variable: log (value added)|
|Variable||Coefficient||t-statistic||Definitions of independent variables|
|Constant||0.59||0.22||FOROWN = the share of foreign ownership|
|Log (capital stock)||0.19||2.41||in each sector’s capital structure.|
|Log (labour)||0.82||15.18||HEDU = the ratio of public capital in health|
|Log (FOROWN)||0.15||1.98||and education to total capital stock.|
|Log (HEDU)||0.32||1.80||PHONE = the number of telephone lines.|
|Log (PHONE)||0.31||1.58||EFLAB = labour, defined in efficiency|
|Log (EFLAB)||0.68||5.90||units, as an indicator of human capital in|
|ATR||-0.004||-1.58||each sector, weighted by sectoral labour|
|No. of observations||231||ATR = the average tariff rate, by sector.|
|Estimation method||Ordinary least squares|
In the OECD Development Centre’s survey of manufacturing firms in Senegal and Côte d’lvoire, respondents were asked to what degree they considered themselves as having a disadvantage in innovation vis-à-vis their competitors. They also were asked to what extent they offer training to their employees. Analysis of the responses established a statistically significant relation between both of these variables and TFP growth, with the expected signs (Table 4.6). This highlights the importance of vocational training of employees, as well as the need to innovate continuously in manufacturing. It may be difficult to think about technological innovation in its proper sense in an African context, but imitation in the form of technology transfer can be assumed to depend on employee skill levels. This suggests that governments can help promote manufacturing competitiveness by financing and co-ordinating private initiatives for industry-specific training. Such training is particularly important for smaller enterprises, given their limited resources. Another, longer-term objective must be to focus on the educational system in a larger sense, to prepare the younger generation for continuous training. Enterprises are more willing to invest in training for employees who have already attained higher educational levels.
Box 4.1.The Dynamics between Exports and Productivity: Cameroon
This chapter argues that exports are important for productivity gains, but one would expect the reverse to hold true as well—i.e. higher levels of productivity allow producers to set more competitive prices, which enhance their export potential. An analysis for Cameroon demonstrates this. By definition, if such a mutually reinforcing relation exists, so docs endogeneity. Applying an instrumental variable method will avoid biased results.
To study the dynamics between productivity and export performance, one can estimate a production function and an export function. The production function is a value-added function:
where VA is value added, K is the capital stock, L is labour. X is exports (instrumented; see below), skill is skilled labour as percentage of total labour, Ti is a sector-specific time trend and Di is a sectoral dummy. Assume an export function of the following type:
where X, L, and VA are defined as above, and l is a function determining the ratio of exports to value added. Assume further that l depends on the level of TFP, firm size, the real effective exchange rate (REER), and the sector itself. Sector-specific time trends are also included to capture time-variable effects. To separate the indirect influence of TFP on exports, stemming from the effect on production volume, and to avoid simultaneity bias, rearrange the equation and instrument TFP (see below). Restrict log (VA/L) to equal log (TFP. instrumented), + a*log (K/L). where a is estimated at 0.3 from the production function. All this produces the following relation:
where ExpEmpl is defined as [log (X/L) - log (TFP, instrumented) - 0.3*log (K/L);size is a dummy for small, medium-sized, and large firms; REER is the real effective exchange rate; T1 is a sectoral trend, and Di. is a sectoral dummy. The estimates combine equations (1) and (3), and instrument the TFP level and exports with all exogenous variables from the two equations. The pooling technique is applied, which is justified by the inclusion of sectoral dummies assumed to capture the existing fixed effects common to the firms of each sector.
The following results emerge (sectoral dummies and trends not reported, t-values in parentheses):
The estimates confirm that productivity has a direct effect on exports aside from its indirect effect on production volume. This strengthens the argument that African governments should continue the current trend of trade liberalisation. The beneficial impact of export growth on productivity produces a feedback effect on exports, which further enhances productivity. Note that exports came out highly significant for productivity, whether measured by exports per employee, a dummy variable for exports for each year or a dummy variable for firms exporting throughout the period. This underlines the robustness of the relation. Equation (4) indicates that a 10 per cent increase in exports per employee would increase productivity by approximately 1.8 per cent. Note also that imports turn out positive and significant (not reported here), implying that firms can improve their productivity by importing better-quality intermediate goods. There is also a probable effect of technology transfer induced by imports.
The estimate shows a positive and significant impact of REER on exports. A 10 per cent REER depreciation would boost exports by about 19 per cent, keeping the number of employees constant. Moreover, a secondary effect arises through the impact of exports on productivity, which would further affect exports.
Some claim that firm size plays an important role in export performance. This argument is based on the high initial fixed costs of exporting—for setting up a distribution network, gathering market information, retooling equipment for production for exports and training personnel, for example. This study shows that medium-sized firms export more per employee than do small ones.
|Dependent variable: TFP growth rate|
|Variable||Coeff.||t-stat.||Definitions of Independent Variables|
|ADVINNOV||-0.48||-2.29||ADVINNOV: a qualitative variable indicating the degree to which survey respondent firms consider they have weaknesses in innovation vis à vis competitors.|
|VFINVA||0.90||8.48||VFINVA: the average annual growth of financial costs as a percentage of value added. Its unexpected positive and significant value may suggest that it is better viewed as a proxy for investment rather than an indication of financial distress. This ambiguity demands cautious interpretation of the estimate results.|
|EMPFORM||0.28||3.00||EMPFORM: a measure of the extent to which firms offer training to employees.|
|PLUS 15||0.36||2.37||PLUS 15: a dummy variable indicating firms that export more than 15% of output.|
|OBCOFIN||-0.11||-1.58||OBCOFIN: a qualitative variable indicating financial problems as an obstacle to competitiveness.|
|INFRASEN||-0.11||-1.64||INFRASEN and INFRACIV indicate the degree to which firms encounter infrastructure problems in Senegal and Côte d’lvoire respectively. Based on principal-component analysis, each is a global measure of responses to 18 survey questions relating to various aspects of electricity, water, transport and telephone problems.|
TFP92: the level of TFP in 1992. It captures TFP convergence.
|Estimation method||Ordinary least squares (on averages)|
Commercial Openness and Exports
The influence of external trade on TFP is also partly linked to the issue of human capital. Several studies give theoretical and empirical support to the idea that productivity gains come through factors induced by commercial openness. Tybout et al. (1997) concluded that exporters had better productivity growth than non-exporting firms in Cameroon. Edwards (1997) claimed that international trade facilitates technology transfer and hence the ability to imitate existing production techniques. Lucas (1993) developed the notion that an increasingly sophisticated product mix induces productivity gains through the effects on employees of learning by doing. A high-growth product mix, however, may not be compatible with the domestically consumed one, and domestic markets in developing countries are seldom, if ever, large enough to support full-fledged industrialisation. For both of these reasons, large-scale exports become crucial for continued productivity growth. Nishimizu and Robinson (1986) argued that openness promotes TFP growth, mainly for three additional reasons. First, trade liberalisation increases competitive pressures, which force companies to improve their productivity. Second, market expansion through exports may bring economies of scale. Third, import liberalisation facilitates imports of capital goods and non-substitutable intermediate inputs. De Melo and Robinson (1990) demonstrated models in which openness promotes productivity growth through all these types of externalities.
This chapter’s firm-level studies of Cameroon (see Box 4.1) and of Senegal and Côte d’lvoire provide evidence that exports affect productivity positively. The Nigeria and Senegal studies demonstrate the negative influence of commercial restrictions, measured by import tariffs. The Senegal/Côte d’lvoire study revealed a positive and significant coefficient for the dummy variable representing companies exporting at least 15 per cent of their production (Table 4.6). These exporting firms saw annual productivity improvement in 1992-95 more than 30 per cent higher, on average, than did non-exporting firms. The sectoral studies of Senegal (Table 4.4) and Nigeria (Table 4.5) showed results pointing in the same direction. In both cases, negative and significant coefficients for import tariffs, a proxy for trade restrictiveness, demonstrated the importance of openness for productivity—although the elasticity of productivity to trade protection was rather small in Nigeria (Table 4.5). According to these results, a complete liberalisation of Nigerian imports would imply less than a 1 per cent productivity gain. This probably understates the importance of trade liberalisation, given the connection between openness and the real effective exchange rate (REER). Sekkat and Varoudakis (1998) showed in a recent study that protectionism tends to lead to an appreciation of REER. In Cameroon (see Box 4.1), REER emerges as one of the most important factors determining export performance, which in turn affects productivity. Given Nigeria’s high level of protection during the period studied, one would expect higher potential gains from trade liberalisation.
Physical infrastructure—such as roads, ports, energy-production facilities and telephone lines—also potentially affects TFP growth. The existence or lack of it may influence investment decisions and future productivity growth. By affecting productivity, poor infrastructure may, thus, indirectly impair competitiveness and exports. A well-functioning infrastructure network likely will improve communication, enhance production efficiency and decrease costs, thus promoting competitiveness. Deficient infrastructure also has more direct repercussions on exports and commercial openness. It will increase shipment costs, impeding exports as well as imports. The study of Senegal and Côte d’lvoire, which asked firms to identify obstacles to exporting and rank them in importance, underlined this (Box 4.2). Figures 4.1 and 4.2 show the frequency of these obstacles in the two countries (in less detail than in Box 4.2), weighted by the importance attached to the obstacle and described by export destination.
Box 4.2.What Types of Infrastructure Should Be Prioritised to Improve Productivity? Some Indications from Senegal and Côte d’lvoire
What kinds of infrastructure deficiencies throw up the greatest impediments to efficiency? The studies for this chapter indicate that the answer may vary substantially from country to country. To direct infrastructure investment to areas likely to yield the highest returns requires detailed study. This box suggests priorities for such investment in Senegal and Côte d’lvoire.
The firm-level survey asked respondents to identify the types of infrastructure with which they encounter problems. Participants in Senegal cited electricity supply as by far the most frequently encountered problem. Nearly 80 per cent of the surveyed firms reported electricity cuts, and about 45 per cent reported electricity quality as a key difficulty, followed by road quality and—much further down on the scale of concern—telephone costs and the state of ports and airports. The privatisation and restructuring of the inefficient public electric utility, Senelec, could well improve power production, but the transition years will be difficult.
In Côte d’lvoire, however, almost all firms complained about poor telephone services, not electricity. The problems probably stem from either poor technological standards or mismanagement, because Côte d’lvoire compares well with other sub-Saharan countries in terms of main telephone lines per capita. Unlike Senegal, Côte d’lvoire privatised the production and distribution of electricity in 1989. The main weaknesses of this sector have disappeared, although many surveyed firms mentioned the price of electricity as another obstacle.
Econometric analysis of the survey data shows infrastructure weaknesses as having a negative and significant impact on TFP growth in Senegal but an insignificant one in Côte d’lvoire (Table 4.6). That these results differ comes as no surprise, given the great difference in the types of issues that respondents cited. The responses to the survey suggest that Senegal has more serious infrastructure weaknesses than Côte d’lvoire.
Figure 4.1.Senegal: Obstacles to Exports by Destination
Source: OECD Development Centre.
Figure 4.2.Côte d’lvoire: Obstacles to Exports by Destination
Source: OECD Development Centre.
Firms consistently report transportation-related issues (cost, availability and quality) as dominant in both Senegal and Côte d’lvoire. In Senegal, they perceive them as a greater obstacle to exports within Africa than outside it, a likely consequence of the poor quality of roads and other transportation networks, but in Côte d’lvoire perceived differences between export destinations are much less clear.
The sectoral study of Senegal suggests that, although the infrastructure indicator alone was not significant (Table 4.4), infrastructure plays an important role for externalities related to economy size. A large manufacturing sector may bring productivity gains through spillover effects derived from, for instance, reduced transaction costs due to a greater concentration of firms, enhanced access to suppliers of primary or intermediate inputs or improved labour quality resulting from the effects of learning by doing. Poor infrastructure could jeopardise such positive externalities. In fact, it is possible that a growing manufacturing sector could have negative external effects on productivity, due to congestion, if the quality of infrastructure lies below a certain level. To study the effects of externalities in conjunction with the quality of infrastructure, the regression reported on the right-hand side of Table 4.4 introduced a variable for the size of manufacturing and an interactive variable capturing the dynamic between it and the infrastructure network. This interactive variable uses a measure of the availability of infrastructure multiplied by the total size of manufacturing—namely the ratio of public capital to total private capital in the manufacturing sector. Public capital is taken in the widest sense, to include physical, educational and social infrastructure. The results show that lack of infrastructure has the effect of congesting economic activities, while externalities are in fact positive and increasing with the level of infrastructure.
Conclusions and Policy Implications
The results for all four countries have pointed to the importance of commercial openness for the development of a competitive manufacturing sector. They demonstrate that trade restrictions hamper TFP growth but exports improve productivity. Further, indications also suggest the reverse, that productivity improves exports. It therefore becomes important not only to liberalise trade but also to implement complementary policies that increase incentives to pursue it. Such policies include good management of the exchange rate, market deregulation to eliminate price distortions between tradables and nontradables, and avoidance of unrealistic increases in real wages. Nigeria and Senegal have certainly lost out by pursuing inward-looking, import-substituting policies for their manufacturing.
Investment in infrastructure and human capital seems crucial to improving competitiveness. Building trade capacity in the form of adequate infrastructure and a more highly skilled workforce helps the economy to respond better to reforms, such as trade liberalisation and improved exchange-rate management. While the analysis here found evidence for the importance of the availability of skilled labour for productivity growth in all four countries, the impact of infrastructure was significant only in Senegal and Nigeria.
The devaluation of the CFA franc in 1994 did induce some gains in both exports and productivity. It appears mostly to have benefited firms already exporting or sectors generally more prone to be involved in trade. This tells us that more needs to be done to convince economic players of the viability of trade. Nigeria, apart from managing its political instability, needs to put itself back on the liberalising, outward-looking track it followed before the policy reversals of the 1990s.
The level of TFP is defined as the exponential of log (Y/L) - a*log (K/L), where Y is value added, L is labour, K is the capital stock, and a is the estimated capital coefficient. The estimated production functions appear in Tables 4.4–4.6. The capital coefficient is not estimated for Côte d’lvoire. Instead, the value for Senegal (0.35) is applied.
This downward trend in TFP in the CFA franc zone countries could have been the origin of a fall in the equilibrium real exchange rate and hence partly responsible for the overvalued CFA franc before its devaluation.
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