Memo 2: 2018, June 24

First draft

Costs and Benefits of Currency Unions in Africa: The Case of the CFA Franc Zone

Jeffrey Frankel, Harpel Professor of Capital Formation and Growth, Harvard Kennedy SchoolSenior Consultant, UN Economic Commission for Africa

The author would like to thank for guidance Vera Songwe, Executive Secretary of the Economic Commission for Africa, and to thank for assiduous research assistance Youssouf Camara, Haiyang Zhang and Na Zhang.

Abstract

A currency union can be viewed as an institution that can give countries the two important benefits of promoting trade to help economic growth and anchoring monetary policy to achieve price stability, but that also carries the cost of losing the ability to respond to shocks. The record of the CFA zone covers more member countries and years than any other currency area in Africa, indeed the world. Estimates of effects on bilateral trade in the gravity model updated through 2017 confirm that the CFA boosts trade among its members by approximately 52 %, controlling for other factors. That is even more than the trade effect of typical currency unions and almost as much as the effect of a Regional Trading Arrangement. Further, we can also see that the CFA countries of West Africa have indeed achieved price stability, whereas many of their neighbors have not. Whether these benefits are worth the cost depends in part on the extent to which members’ economic shocks have been correlated with those of the group. Judging by the criteria of correlation statistics and labor mobility, CFA membership in West Africa may be most natural for Cote d’Ivoire and least natural for Guinea-Bissau. CFA membership seems less successful in Central Africa.

Contents

1. Introduction

2. Why Do Some Countries Achieve Better Economic Performance than Others?

3. The Currency Union Question in Light of the Determinants of Economic Performance

4. A Look at the Data for Sub-Saharan Africa: Growth, Inflation, and Trade

5. CFA Trade Creation, Estimated from the Gravity Model

6. The OCA Criterion of Labor Mobility in Africa

7. The OCA Criterion of Symmetric Shocks Among CFA Countries

8. ConclusionCosts and Benefits of Currency Unions in Africa: The Case of the CFA Franc Zone

1. Introduction

The first memo in this series surveyed in general the pros and cons of joining a currency union, versus the alternative of retaining some degree of exchange rate flexibility, as they appear in the standard open-economy macroeconomics literature.

The remaining memos will consider actual and proposed monetary unions of Africa. This second memo focuses on the case of the major existing currency zone in Africa which has a long track record, the CFA franc zone. CFA stands for Communauté Financière Africaine. To be precise, the CFA zone includes two currency unions. The larger one comprises eight countries in West Africa: Benin, Burkina Faso, Cote d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo. Its name in English is the West African Economic and Monetary Union, but it is more often called by its French name and its acronym UEMOA. Its central bank, based in Dakar, is the Central Bank of West African States in English, or BCEAO. The smaller one comprises six countries in Central Africa: Gabon, Cameroon, the Central African Republic (CAR), Chad, the Republic of the Congo and Equatorial Guinea. The name of the group in English is Central African Economic and Monetary Community, known by the acronym for its French name, CEMAC. Its central bank, based in Yaoundé, is the Bank of the Central African States in English, or BEAC.

Looking ahead, a third memo will address the question of the policy that an African currency union adopts regarding its exchange rate vis-à-vis the rest of the world, with a particular application to the experience of the CFA members’ trading pattern when the French franc, to which the CFA franc had been pegged, was replaced in 1999 by the euro. A fourth memo will consider the proposed currency union among the ECOWAS countries, that is, the other countries of West Africa.

The analysis will include relevant literature review, statistical and econometric evidence for African countries, and the logic of the complicated variety of sequential paths that are possible options. As a jumping-off point we begin with a brief consideration of the broad question: why do some countries do better than others, in terms of GDP growth and other indicators of economic performance? This will lead to the topic of the relatively low level of trade in Africa. One reason to expand currency areas in Africa, as for free trade areas, is to promote trade and thereby promote economic growth.

2. Why Do Some Countries Achieve Better Economic Performance than Others?

There have been many global econometric cross-country studies of economic growth and they have produced a variety of important conclusions, notwithstanding their limitations and ambiguities. A theme of recent years has been that it is not enough to identify the importance of good policies, such as promoting national saving, investment, education, and macroeconomic stability. We want to know what are the deeper fundamental determinants behind which countries adopt good policies and which do not.

Two of the most consequential determinants of economic performance that have claimed support are, first, trade and, second, the quality of institutions . Trade openness is often measured by the ratio of trade to GDP. The quality of institutions is measured by various proxies for the rule of law, property rights, an independent judiciary, ease of doing business, freedom from corruption and quality of the bureaucracy. Among the many authors emphasizing the role of trade are Sachs and Warner (1997), Collier and Gunnin (1999), and Frankel and Romer (1999). Among the many authors arguing the primacy of institutions are Rodrik (1999), Acemoglu, Johnson, Robinson (2001, 2003), Rodrik, Subramanian, and Trebbi (2004), and Acemoglu and Robinson (2013).

As always, correlation does not necessarily imply causality. The correlation between trade and GDP probably arises in part because of reverse causality; for example, as countries become richer they tend to lower tariffs (often because they develop alternative sources to tax revenue), and the lower tariffs in turn encourage trade. Similarly, the correlation between good institutions and income probably arises in part because of reverse causality. Econometric research has responded to the causality problem by looking to the next deeper layer structurally for exogenous determinants, i.e., has looked for instrumental variables. A popular instrumental variable for trade is geographic predisposition to trade as specified by the gravity model (Frankel and Romer, 1999). A popular instrumental variable for the quality of institutions is the settler mortality rate in colonial times (Acemoglu, Johnson, Robinson, 2001).

The famous Natural Resource Curse is also relevant: one interpretation is that a country endowed with oil or minerals tends often to develop poor institutions.[footnoteRef:1] [1: Frankel (2012) surveys the Natural Resource Curse, including its possible causal channels. Some of the many research contributions include Auty (1993), Sachs and Warner (1995, 2001), Wantchekon (2002), Sala-i-Martin and Subramanian (2003); Collier and Goderis (2007), Beny and Cook (2009), and Arezki and Van der Ploeg (2011).]

A third view, in addition to those emphasizing trade and institutions, is that tropical conditions are bad for economic performance. Probably the best interpretation of why tropical location is observed to be negatively correlated with growth is the presence of malaria and other tropical diseases. Sachs (2003) argues that specific geographic determinants of malaria are correlated with slow growth across countries.

Some of the more robust empirical findings include that remoteness, landlockedness, tropical location, and small population size[footnoteRef:2], are bad for economic performance, other things equal. These variables help explain why incomes are lower in Africa than in other parts of the world. Education and national saving tend to be good for economic performance. [2: E.g., Frankel and Romer (1999), Table 3.]

Clearly a major reason that remoteness and landlockedness hurt economies is that they impede international trade.

Many global studies find a negative dummy variable for Africa. It often can be attributed to some of the other variables on the list, however, especially tropical location, as becomes evident when the econometrician controls for them and the apparent Africa effect disappears.

While some of these variables may help explain low GDP in Africa as a whole, they do not necessarily help explain variation within Africa. Indeed, when using regression analysis to learn about differences in growth performance among African countries, many of the variables that are most significant on global data sets do much less within the continent.[footnoteRef:3] The more robust determinants, however, include trade and an important geographical determinant of trade: access to the sea. [3: Frankel (2016). Part of the problem is that variables like tropical location are shared by most Sub-Saharan African countries and thus cannot explain much variation within the region.]

It would be a mistake to interpret the academic literature as implying that countries in Africa or elsewhere do not have the power to promote good policies and institutions and thereby achieve good economic performance because it is all predetermined by their geography (such as landlockedness), their history (such as the settler mortality rate), their climate (pre-disposition to tropical diseases) or their geology (such as oil deposits). Economists are interested in these deep structural determinants because they offer a way to address the endogeneity problem. But the hope is that national leaders and their publics can make the deliberate decision -- regardless their history or geography -- to promote trade, for example by joining a free trade area; or to improve institutions, for example by legal reform; or to fight malaria, by eliminating mosquito breeding areas. The decisions whether to give independence to a central bank or whether to join a currency union are among the institutional choices that countries can and do make in real time.

3. The Currency Union Question in Light of the Determinants of Economic Performance

At first glance, there seems to be a chasm separating the Optimum Currency Area literature (see Memo 1) from the research on trade and institutions as determinants of economic development (Section 2 above). But strong connections are there.

The OCA literature (Optimum Currency Areas) sees a trade-off between the ability of national authorities to respond counter-cyclically to adverse shocks, on the one hand, versus advantages of giving up one’s currency on the other hand. Counter-cyclical monetary policy is important to think about because countries give it up when joining a currency union (though they might retain some scope for countercyclical fiscal policy). Advantages of giving up one’s currency include a possibly more credible nominal anchor for monetary policy and also facilitation of trade. Consider first the trade-off between countercyclical macroeconomic policy and the need for a nominal anchor, and secondly, the importance of facilitating trade.

How useful is the ability of macroeconomic policy to respond counter-cyclically to adverse shocks such as a decline in a country’s export market (particularly via monetary expansion, lower interest rates, and currency depreciation)? In theory it can be very useful, particularly if the country suffers from asymmetric shocks and lacks alternative mechanisms to adjust to them, such as labor mobility. But some countries in practice are chronically unable to apply the tools of macroeconomic policy counter-cyclically even when they have full control over them. Indeed, among developing countries, and particularly those that export commodities, macroeconomic policy is more often pro-cyclical: governments apply stimulus during booms and are forced to contract during downturns. Pro-cyclical macroeconomic policy of this sort exacerbates the swings in the business cycle. Recent research suggests that countries that systematically achieve counter-cyclical macroeconomic policy tend to be those that have good institutions.[footnoteRef:4] This brings us back to the fundamental determinants of economic performance. [4: Acemoglu, Johnson, Robinson, and Thaicharoen (2003), Kaminsky, Reinhart and Végh (2004), Lledó, Yackovlev, and Gadenne (2009), McGettigan, et al (2013), Frankel, Végh and Vuletin (2013), Végh and Vuletin (2013) and Calderon, Duncan and Schmidt_Hebbel (2016)..]

Another theme in the currency literature is the need for a nominal anchor for credible non-inflationary monetary policy. Monetary theory suggests that any country has a potential bias toward excessive inflation because of a temptation toward monetary expansion in order to finance budget deficits or toward attempts -- chronic and ultimately unsuccessful -- to stimulate the real economy. Examples of “good institutions” that can help overcome inflationary bias include: government spending that does not chronically exceed what can be financed by taxation or borrowing; a legally independent central bank, insulated from political pressures; and a credible nominal anchor such as an inflation target. In a country that lacks these institutions, it may be easier to import monetary stability from abroad by adopting the currency of a larger neighbor or currency area, provided that currency does not itself suffer from inflationary bias.

There is an obvious trade-off between the ability to conduct countercyclical monetary policy, on the one hand, and credible commitment to price stability as via a rule, on the other hand. It is nevertheless important to consider that strong institutions can help with both efforts. That is, good institutions may be able to improve the terms of the trade-off between rules and discretion. Guillaume and Stasavage (2000) study political institutions in Africa, for example measuring the frequency of “political shocks” by counting coups and cabinet changes. They argue that some countries lack political institutions strong enough for credible commitment to macroeconomic and financial stability, and that participation in the right monetary union might make up for it.

There are other implications of the growth discussion in Section 2 for the Currency Union question. In the cross-country growth regressions, small country size was seen to be a negative determinant of economic performance and trade to be a positive determinant. The two facts are closely related. Small countries are likely to lack a diversified set of endowments or lack an internal market big enough to achieve economies of scale, or lack both. Trade is a way that small countries can make up for these limitations. Singapore is a spectacular example.

Holding other factors equal (like geographic location and trade policy), small countries tend to have a much higher ratio of trade to GDP than large countries. This is one reason why the McKinnon (1963) condition telling a country to join a currency area if it is “small and open” so often is phrased as one criterion rather than two. But the two effects are logically distinct. A country that is already open in the sense that it has a high ratio of tradable goods will benefit a lot from giving up its own currency, because making things easy for importers and exporters matters more if they dominate the economy. But what about a small country that does not already have a high trade ratio? Such a country needs trade-promoting policies all the more, to make up for its small internal market and lack of diversity in resources.

Trade-promoting policies are especially important in countries that are less well-situated for trade, such as those that are landlocked or located in a remote part of the world. Trade-promoting policies include most obviously the elimination of tariffs and non-tariff trade barriers, either unilaterally or as part of an international agreement such as joining the WTO and/or a regional Free Trade Area. Another trade-promoting policy is to joining a regional Currency Union. We saw in Memo 1 the surprising robust econometric evidence that the trade-creating effects of a currency union are approximately as strong as the trade-creating effects of a Free Trade Area.[footnoteRef:5] [5: A third kind of trade-promoting policy is investment in infrastructure to promote trade, including roads, railroads, ports, airports, and information/telecommunications links such as fiber-optic cable. Limao and Venables (2001) find that the relatively low level of African trade in general is largely due to poor infrastructure.]

4. A Look at the Data for Sub-Saharan Africa: Growth, Inflation, and Trade

Table 1 reports some basic statistics for Sub-Saharan Africa alongside other major regions. On the one hand, income per capita is the lowest of any region. On the other hand, growth during the recent five-year period 2013-17, at 4.0% (simple average across countries) or 3.8% (size-weighted average), was the second-highest, lagging only the Asia/Pacific region.

The African inflation rate averaged from 4.1 % to 8.8 % during this recent 5-year period, depending on whether we look at the GDP deflator or the CPI, whether we look at the median across countries or the mean, and whether the mean is the simple arithmetic average or GDP-weighted. In general it is better to look at the median inflation rate within a group, because the mean can be distorted by a single country with high inflation, such as the hyperinflations experienced by Zimbabwe (2006-09) and Venezuela (2017- ).

Regardless of the method of calculation, African inflation tends to be substantially higher than the 1-2% numbers in North America, Europe or Asia/Pacific. Even though the median African inflation rate is lower than most developing countries experienced in the high-inflation 1970s and 1980s, and even today is lower than among the CIS states reported in the last column of Table 1, it is still higher than optimal. It suggests that much of Africa has not yet achieved an institutional framework for monetary policy that delivers full price stability.

Table 1: Statistics on trade, GDP, and inflation, for continental groupings

5-Year Averages1 (2013-17)

Sub-Saharan Africa

Latin America

North America

Europe

Asia & Pacific

Arab States

CIS

Intraregional Trade (trillion$)

0.07

0.14

1.20

4.51

3.25

0.12

0.10

Member Trade with the World2 (trillion$)

0.65

1.10

4.15

8.36

7.90

1.61

0.91

World Trade (trillion $)

35.85

Population (millions)

976

496

482

625

4,063

314

286

GDP (current trillion$)

1.70

4.58

21.09

20.50

24.96

2.59

2.54

World GDP (current trillion$)

77.96

Average Annual Growth

3.97%

2.60%

0.96%

1.80%

4.17%

2.92%

3.48%

GDP-Weighted Annual Growth

3.78%

0.61%

2.12%

1.84%

5.01%

3.22%

0.48%

 

 

 

 

 

 

 

 

Average Annual Inflation (%), GDP deflator

4.93

4.71

2.17

1.54

3.20

0.18

7.86

GDP-Weighted Annual Inflation (%), deflator

6.72

10.33

1.59

1.45

1.87

-1.21

7.56

Average Median Annual Inflation3 (%), deflator

4.10

3.06

1.94

1.33

2.67

0.05

5.68

Average Annual Inflation (%), CPI

8.18

6.76

2.06

1.18

4.38

3.42

7.89

GDP-Weighted Annual Inflation (%), CPI

8.82

7.33

1.43

1.27

2.81

3.74

8.39

Average Median Annual Inflation3 (%), CPI

4.86

2.96

1.60

0.97

3.30

2.88

5.65

 

 

 

 

 

 

 

Trade per Capita

$667

$2,212

$8,615

$13,375

$1,945

$5,135

$3,179

GDP per Capita (current $)

$1,742

$9,237

$43,759

$32,776

$6,142

$8,267

$8,891

Intraregional Trade / Member Trade with World

0.11

0.13

0.29

0.54

0.41

0.08

0.11

Member Trade with the World / World Trade

0.02

0.03

0.12

0.23

0.22

0.04

0.03

Trade Concentration Ratio(Ratio of Preceding 2 Rows)

6.00

4.13

2.51

2.31

1.86

1.68

4.44

1 Trade data from IMF DOTS. Other data from WB WDI Exports and imports only include merchandise.

2 The member countries' total trade, including trade with each other.

3 Starting from the median of annual inflation for countries in the region, we calculate the average of the medians over five years

Table 1 also illustrates that the countries of Sub-Saharan Africa have a low level of intra-regional trade as a share of their total trade: 11 %. That is well below the intra-regional trade shares calculated here for North America (29 %), Asia/Pacific (41 %), or Europe (54 %). The low share of intra-African trade is widely noted.[footnoteRef:6] It has been variously attributed to the similarity of export products, to a lack of good transportation infrastructure, or to the legacy of colonialism.[footnoteRef:7] [6: E.g., Longo and Sekkat (2001), Subramanian and Tamirisa (2001), Tavlas (2008), and Lopes (2016), among many others.] [7: Considering reasons for low intra-African trade, Longo and Sekkat (2001) list “besides traditional gravity variables, poor infrastructure, economic policy mismanagement, and internal political tensions.”  Yeats (1999) emphasizes similarity of export products. That especially refers to mineral, energy, and agricultural commodities, though it is worth noting that Latin America and the Middle East are also commodity exporters. Tavlas (2008) lists low per capita income, similar products, distance (understated by miles-as-the-crow-flies, due to limited transportation, and informal trade (missing from the official statistics).]

It is important to realize, however, that countries tend to trade with each other in proportion to their economic size. (This is the principle underlying the basic gravity model of trade.) It is natural that Zambia trades less with Gambia and more with the United States simply because the US economy is so much larger than Gambia’s. African countries constitute only 2% of world trade.[footnoteRef:8] As a simple indication whether there is any bias in African countries trade away from the others with whom they share the continent, or toward them, we can normalize the countries’ intra-regional trade shares by their importance in world trade. The last row of Table 1 reports intra-regional concentration, defined as the intraregional trade share divided by the region’s share of world trade. Notice that the concentration ratio is greater than one for every region, meaning that countries have a greater propensity to trade more with their neighbors than with those far away, reflecting some combination of transport costs and preferential trading arrangements. In this calculation, Africa, far from exhibiting bias away from intra-continental trade, appears to have the highest bias toward intra-regional trade of all regions! This goes beyond earlier findings.[footnoteRef:9] [8: It is a necessary property of the intra-regional share measure that the smaller the set of economies around which one throws the lasso, the lower will be the apparent bias toward trade within. (In the limit, if one throws the lasso around all countries of planet Earth, one would find an intra-Earth ratio of 100 percent.) Frankel (1997), Chapter 2.] [9: For example Frankel et al (1997, p.26) found that for most of the period 1962-1994 Africa had among the lowest concentration ratio of standard regional groupings. But African trade was seen to swing strongly in the intra-regional direction in the 1990s, especially (ib. p.28) according to the intensity coefficient, a slightly more refined measure of Anderson and Norheim (1993).]

A more complete calculation would use the full gravity model[footnoteRef:10], and would then estimate dummy variables for intra-regional trade in Africa and various other groupings. These points were first developed by Foroutan and Pritchett (1993), who reported that the actual share of SSA imports plus exports was an average of 8.1 per cent, while the gravity model predicted a slightly lower, not higher mean of 7.5 per cent. Others finding that Africa’s low intra-regional trade can be explained by gravity include Masson and Pattillo (2004), Debrun, Masson and Pattillo (2005), and Masson (2008). [10: As we will see shortly, the gravity model holds constant not only for country size, but also for bilateral distance, common borders, landlockedness, common language, and other variables. Head and Mayer (2014) and Frankel, Stein and Wei (1997).]

The point isn’t that African countries trade enough with each other already. The point is, rather, that they don’t trade enough with anybody. African trade is only $667 per capita, much the lowest of any region. Among the landlocked African countries, of which there are quite a few, trade is even lower than this overall average.

As with Africa’s low level of intra-regional trade, its low level of overall trade can be explained by the geographic and other natural determinants of the gravity model. It is not surprising that trade per capita is low, given that income per capita is low. Coe and Hoffmaister (1999), for example, conclude “that the unusually low level of African trade is explained by economic size, geographic distance and population. This result holds after controlling for a country’s access to the sea, composition of exports, linguistic ties with industrial countries and trade policies.” Tariffs and other government-influenced barriers to trade have played an important role as well,[footnoteRef:11] especially in the past. But this, too, is typical of less developed countries. [11: Rodrik (1999).]

Subramanian and Tamrisa (2003) find different trends with respect to trade integration policy in two different sets of African countries. They find that “countries in Francophone Africa…are currently underexploiting their trading opportunities and have witnessed disintegration over time, a trend that is most pronounced in their trade with technologically advanced countries.” But they find that the Anglophone countries have reversed that trend.

The important point for policy purposes is that, like most countries, especially small ones, African countries would benefit from more international trade. To the extent that trade is inhibited by inconvenient geography and inadequate transportation infrastructure, removal of any government-inflicted barriers is all the more important. Joining a regional free trade area is one way to promote trade. Joining a regional currency union may be another way to do it.

Why pursue regional arrangements, rather than free trade areas or currency unions with countries in other parts of the world? As a general rule, the wider the set of countries that reduce their trade frictions with each other, the better.[footnoteRef:12] But there exist both a political argument and an economic argument for pursuing agreements with countries that are geographic neighbors. The political argument is simply that there is often more political support for regional arrangements than for analogous arrangements with countries that are remote geographically, historically and culturally. The economic argument is that because transport costs are lower with neighbors, regional trading blocs are “natural” in a very specific sense: they are likely to promote trade-creation more than trade-diversion and thus to raise total trade and to raise economic welfare.[footnoteRef:13] [12: With respect to trade policy, this argues for multilateral reduction of trade barriers via the WTO (Yeats, 1999). With respect to currency policy, linking to all the major currencies is not logically an option, because they float against each other. But this leads to the question whether the CFA franc should peg to the euro or the dollar, or follow some other strategy (to be considered in Memo 3). [Savvides (1996) found that the CFA lowered the members’ exchange variability, not just vis-à-vis each other and the French franc, but multilaterally as well.]] [13: This argument in favor of regional trading arrangements is developed by Krugman (1991) and Frankel, et al (1997). Frankel and Rose (2002) offer evidence that an analogous argument extends to monetary unions, i.e., that there is little trade-diversion and that the resulting increase in trade is good for economic growth.]

Table 2 reports similar statistics as Table 1, but for regional sub-sets of countries in Sub-Saharan Africa. We are especially interested in the comparison between the currency unions and other country groupings, in particular between UEMOA (the CFA countries of West Africa) and the others in that region, i.e., the non-CFA ECOWAS countries.

Southern Africa has the highest levels of income per capita and trade per capita within Sub-Saharan Africa. But economic performance in any given country is dominated by many factors, beyond the monetary regime.

Table 2: Statistics for groupings within Sub-Saharan Africa

5-Year Averages1 (2013-17)

Geographic Region

Economic Region

Southern Africa

East Africa

Central Africa

West Africa

CEMAC

ECOWAS

Non-CFA ECOWAS

UEMOA

CFA

Intraregional Trade (trillion$)

0.0332

0.0055

0.0005

0.0129

0.0004

0.0119

0.0020

0.0034

0.0045

Member Trade with the World2 (trillion$)

0.2833

0.1048

0.0471

0.2297

0.0343

0.2313

0.1674

0.0640

0.0982

World Trade (trillion $)

35.85

 

 

 

 

 

 

 

Population (millions)

165

345

122

343

48

343

233

110

158

GDP (current trillion$)

0.57

0.33

0.13

0.67

0.09

0.66

0.57

0.10

0.19

World GDP (current trillion$)

77.96

Average Annual Growth

4.07%

3.96%

2.49%

4.68%

1.43%

4.89%

4.25%

5.37%

3.68%

GDP-Weighted Annual Growth

2.60%

5.11%

4.11%

4.08%

3.06%

4.09%

3.65%

6.43%

4.84%

 

 

 

 

 

 

 

 

 

 

Average Annual Inflation (%), deflator

7.54

7.32

-0.21

3.89

-2.41

4.14

6.61

1.98

0.09

GDP-Weighted Annual Inflation (%), deflator

6.08

12.17

-0.55

6.34

-2.51

6.36

7.13

2.02

-0.14

Average Median Annual Inflation1 (%), deflator

6.39

5.49

6.39

0.25

-2.62

3.43

5.30

1.57

0.62

GDP-Weighted Annual Inflation (%), consumer price

7.54

13.10

2.62

9.30

2.84

9.33

10.80

1.02

1.93

Average Annual Inflation (%), consumer price

7.98

14.56

5.29

4.24

4.81

4.49

8.49

0.99

2.59

Average Median Annual Inflation1 (%), CPI

5.83

5.75

6.54

0.51

2.83

2.27

7.82

0.89

1.37

 

 

 

 

 

 

 

Trade per Capita

$1,719

$303

$385

$669

$710

$675

$719

$581

$620

GDP per Capita (current $)

$3,478

$957

$1,063

$1,939

$1,963

$1,936

$2,437

$879

$1,209

Intraregional Trade / Member Trade with World

0.117

0.053

0.010

0.056

0.012

0.052

0.012

0.054

0.046

Member Trade with the World / World Trade

0.008

0.003

0.001

0.006

0.001

0.006

0.005

0.002

0.003

Trade Concentration Ratio(ratio of preceding 2 rows)

14.83

18.00

7.75

8.79

12.62

7.99

2.61

30.00

16.82

1 Starting from the median of annual inflation rates for countries in the region, we calculate the average of the medians over five years.

Macroeconomic performance was very good among the CFA countries of West Africa during the 5-year period 2013-17, compared in particular to their non-CFA neighbors. The annual growth rate was an impressive 6.4 % in UEMOA (size-weighted average) or 5.4% (simple arithmetic average). Either way, that is above the growth recorded by the non-CFA ECOWAS states.[footnoteRef:14] It is tempting to attribute some of the superior economic performance to the CFA. If the strong growth were due, for example, to a demand boom, then it should also have shown up in inflation. But inflation was admirably low in UEMOA at about 1 % for the CPI and 2 % for the GDP deflator, well below the 8 to 11 % CPI inflation rates in the non-CFA countries of West Africa, or even the 5 to 7 % rates of increase in the GDP deflator. The implication is that the CFA franc has delivered price stability for its West African adherents. [14: IMF (2107) confirms that 2013-17 was a period of good economic performance among the UEMOA countries.]

The GDP-weighted average of the non-CFA countries of West Africa is dominated in size by Nigeria which rode the commodity price boom in 2013-14, but then crashed in 2016, with a delayed and unwanted devaluation, following the 2014-16 crash in commodity prices. Perhaps the constraints of the CFA currency union prevented the UEMOA countries from getting overextended when commodity prices were high and thus from crashing when prices went down. Recall from Memo 1, Section 6.3, however, that we usually think that it is an advantage for a currency to appreciate and depreciate freely in order to accommodate big ups and downs of a dominant export commodity.

Table 3 Inflation rate by year (GDP deflator)

Median Inflation (annual %)

Geographic Region

Economic Region

Southern Africa

East Africa

Central Africa

West Africa

CEMAC

ECOWAS

Non-CFA ECOWAS

UEMOA

CFA

2013

6.42

9.38

10.55

0.55

1.84

4.81

7.85

4.71

3.39

2014

5.31

4.90

10.21

1.43

-2.37

2.75

5.87

-0.12

-1.00

2015

5.80

3.40

4.61

-0.15

-0.10

1.80

2.75

-0.13

-0.13

2016

4.69

4.86

1.58

0.28

-8.88

2.82

2.86

1.85

0.25

2017

9.70

4.89

5.00

-0.87

-3.61

4.97

7.14

1.55

0.57

5-Year Average

6.39

5.49

6.39

0.25

-2.62

3.43

5.30

1.57

0.62

Table 4: GDP growth rate

GDP-Weighted Growth (annual %)

Geographic Region

Economic Region

Southern Africa

East Africa

Central Africa

West Africa

CEMAC

ECOWAS

Non-CFA ECOWAS

UEMOA

CFA

2013

3.56

2.28

6.23

5.04

5.98

5.05

4.81

6.51

6.23

2014

4.07

6.22

3.95

5.70

2.49

5.71

5.68

5.95

4.15

2015

2.92

5.47

5.89

6.16

4.65

6.17

6.09

6.73

5.67

2016

1.91

5.90

3.67

3.09

2.11

3.10

2.55

6.31

4.36

2017

0.52

5.67

0.83

0.43

0.08

0.42

-0.89

6.64

3.78

5-Year Average

2.60

5.11

4.11

4.08

3.06

4.09

3.65

6.43

4.84

Graph 1

Graph 2

Graph 3

(Graphs are thanks to Haiyang Zhang.)

Turning to trade patterns, trade per capita in the UEMOA countries is slightly lower than among their non-CFA neighbors. But that likely reflects that income per capita is lower (which may in turn reflects Nigeria’s oil wealth). The trade/GDP ratio is higher in UEMOA than in any of the other regions. The CFA effect we most expect to see is an intra-regional concentration of trade and, indeed, it is there: The last row of Table 2 shows that the concentration ratio, at 30.0 for UEMOA (and 12.6 for CEMAC, working out to 16.8 for the CFA zone as a whole), is far higher than for ECOWAS (2.6). On the one hand, this finding is consistent with other empirical findings globally: (i) currency unions promote trade creation and (ii) trade-diversion, if any, is so small that total trade/GDP comes out higher. On the other hand the high trade intensity among the UEMOA countries could easily be due to other factors -- such as the contiguity, French language, and colonial heritage that most of them share. (The non-CFA countries are generally not contiguous, although a majority of them share an English colonial history.) We need to control for such factors before we can draw conclusions. For this, we need the gravity model. In the following section we see what is to be learned from the full estimation of the gravity equation.

A simple way to test the effect of membership in the CFA zone would be to look at before-and-after comparisons for the few cases of countries that have joined it or left it. Guinea exited the CFA in 1960 and Mali did so in 1961. Equatorial Guinea joined in 1985. Unfortunately we lack bilateral trade data preceding these three dates for these countries. That leaves four cases: the exits of Madagascar and Mauritania in 1973, the re-entry of Mali in 1984, and the entry of Guinea-Bissau in 1997. Madagascar is a particularly interesting case because it is far removed from the others geographically, while Guinea-Bissau is an interesting case because it is not Francophone but rather was a Portuguese colony. (Equatorial Guinea would also have been an interesting case, as a former Spanish colony.)

Figure 1a: CFA trade when Madagascar exited Figure 1b: CFA trade when Mali re-entered

(Graphs are thanks to Na Zhang.)

The four cases show varied outcomes. As hypothesized the share of Madagascar’s trade with CFA countries as a share of its total trade fell abruptly when it left the currency area in 1973. (See Figure 1a.) The case of Mauritania seems tainted because the data are missing in the first six years after its exit in 1973.[footnoteRef:15] Mali shows a surprising fall in its CFA trade share after re-joining in 1984. In many years the trade seems negligible (Figure 1b). This could well be due to missing data for some country pairs. Guinea-Bissau shows no clear pattern comparing the few years before and after its entry in 1997.[footnoteRef:16] In all these cases, the results may be heavily distorted by missing bilateral data, in addition to the usual sort of measurement error. [15: If one relies on the Mauritanian data from 1985 onward they show a much higher share of CFA trade than before 1973. ] [16: Guinea-Bissau reports very high trade with other CFA countries in two years, 1992 and 1993, which pulls the pre-entry average share up slightly above the post-entry average. ]

The weak results for this experiment could perhaps be attributed not only to limitations in the data but also to unknown idiosyncrasies of particular country experiences. We need to bring a lot more data to bear on this question. The gravity model does that.

5. CFA Trade Creation, Estimated from the Gravity Model

The gravity equation has been highly successful as the appropriate framework for modeling bilateral trade data, as judged either by empirical fit or by theoretical foundations.[footnoteRef:17] Just as the gravity theory of Newtonian physics says that attraction between two bodies is proportionate to the product of their masses and inversely related to the distance between them, so the gravity theory of international economics says that trade between two countries (or other geographical units) is proportionate to the product of their sizes and inversely related to the distance between them. [17: Surveys of the gravity model are offered by Anderson (2011), Head and Mayer (2014) and Frankel, et al (1997).]

“Size” is usually measured by GDP, but population is also used and occasionally land area. “Distance” between the two countries is usually measured by straight-line distance. But occasionally more sophisticated measures of transport costs are used. Distance in itself works less well for many African countries than elsewhere in the world. (The air distance from Mali to Spain is only 2,500 kilometers or 1,600 miles, but the transport costs are high.) Distance is supplemented in the equation by other geographical determinants of bilateral transport costs and frictions, particularly dummy variables for contiguous country pairs (common border) and landlocked countries, and sometimes a measure of the length of the country’s coastline. Other variables that are commonly used are dummy variables representing country pairs that share a common language, colonial past, current political links, or trade preferences.

A great virtue of the gravity model is the number of observations that bilateral trade data afford. If there are 200 countries in the data set, then there are 39,800 pairs (200x199) for every year. (Data are sometimes missing for small countries, especially relevant for Africa.) The large number of data points makes it possible to allow for the effects of all those variables (size, distance, common language, etc.), and to find that they all have significant effects on bilateral trade. Most importantly for our purposes, there is enough explanatory power left over that one can add such dummy variables as one to represent membership in the CFA currency zone and test for its effects on bilateral trade conditional on the other variables. Even though there is heavy overlap between the three criteria of CFA membership, former French colonial status, and sharing of the French language, it may be possible to distinguish the independent trade effects of each of these three determinants so long as the overlap is not complete.

One might ask the purpose of studying currency unions and trade among all countries, if the goal is only to draw conclusions about Africa. We have already noted the benefits of bringing a lot of data to bear. Even if we want to allow the trade effect of the CFA zone to differ from the analogous effects of currency unions elsewhere in the world, the more observations we have, the better will be our estimates of the gravity parameters (the effects of distance, etc.), the better we can control for these other factors, and the more accurate will be our estimates of the remaining influence of the African currency union.[footnoteRef:18] [18: For example, it has been shown that estimates of the effects of the euro on intra-zone trade may be inaccurate if the data net is not cast widely enough. Frankel (2010) and Rose (2017).]

A number of authors have applied the gravity model to investigate the effects of regional trading and currency arrangements. Elbadawi (1997) and Elbadawi and Mwega (1998) found in the gravity equation a fall-off in the estimated effect of African regional arrangements in the second half of the 1980s. The two CFA currency unions, in particular, seemed to have suffered trade-diversion in 1986-90, without trade-creation. Later authors have had more encouraging findings, perhaps due to longer or better data sets.

Carrère (2004) attempts to disentangle the effect of membership in the CFA or other currency zones on intra-regional trade from the effect of Preferential Trading Arrangements (PTA), despite heavily overlapping membership. This is particularly difficult to do if one seeks to also distinguish the effects of Currency Union membership (which does away with different currencies altogether) from reducing bilateral exchange rate variability to zero (as with an ordinary fixed exchange rate). Her takeaway conclusion is that, of an estimated doubling in intra-regional trade, it seems that about half is attributable to the PTA and half to the common currency. She also makes the interesting observation that African customs unions are more susceptible to trade diversion than those in richer countries, because governments feel the need to make up lost tariff revenue by raising tariffs on non-members.

Masson and Pattillo (2004) and Tsangarides, Ewennczyk, and Hulej (2006), and Tsangarides, et al (2009) find estimated trade effects of African currency unions, controlling for other factors, that are roughly similar to the standard estimates by Rose (2000) and those who came after. Tsangarides, Ewenczyk, Hulej, and Qureshi (2009) find that the trade benefits of membership in the African currency unions increase with the passage of time.

Table 5: Estimation of Effects on Log Bilateral Exports in the Gravity Model, Panel 1948-2017

Fixed Effects:

Exporteryear, Importeryear

Exporteryear, Importeryear, Dyadic

All CUs

Disaggregate CFA

Disagg. CFA and EMU

All CUs

Disaggregate CFA

Disagg. CFA and EMU

All CUs

0.472

(0.017)

0.338

(0.018)

CUs without CFA

0.407

(0.019)

0.333

(0.017)

CUs without CFA & EMU

0.726

(0.022)

0.282

(0.023)

CFA franc zone

0.631

(0.034)

0.643 (0.034)

0.421

(0.083)

0.398

(0.083)

EMU

-0.697 (0.034)

0.444

(0.021)

RTA (regional)

0.598

(0.009)

0.598 (0.009)

0.620 (0.009)

0.396

(0.009)

0.396

(0.009)

0.393

(0.009)

Currently in colonial relationship (curcol)

0.936

(0.034)

0.960

(0.034)

0.834

(0.033)

0.277

(0.032)

0.279

(0.032)

0.293

(0.032)

Log of distance (ldist)

-1.397

(0.003)

-1.398 (0.003)

-1.396

(0.003)

Common language (comlang)

0.387

(0.007)

0.385

(0.007)

0.382 (0.007)

Common land border (border)

0.333

(0.014)

0.327

(0.014)

0.336

(0.014)

Common colonizer post- 1945 (comcol)

0.836

(0.010)

0.829 (0.010)

0.817 (0.010)

Ever in colonial relationship (colony)

1.363

(0.014)

1.368

(0.014)

1.356

(0.014)

Observations

967,710

967,710

967,710

965,616

965,616

965,616

0.726

0.726

0.726

0.852

0.852

0.852

All coefficients are significant at the 1 percent level. (Standard errors in parentheses.)

Gravity estimates are thanks to Na Zhang.

Table 5 reports our base case estimates of the gravity model. The data set and estimation are similar to those in Glick and Rose (2016) and Rose (2017), but we have added another four years of data, expanding the time period from 1948-2013 to bring it up to 2017.[footnoteRef:19] The dependent variable is the log of bilateral exports, and so the coefficients can be read as percentage effects on trade. The data set contains over 960,000 observations on bilateral trade, allowing us plenty of room to control for effects of geographic determinants. It has become routine to allow generously for fixed effects in order to assure robust results. All results reported in Table 5 allow fixed effects for the country and year. Allowing fixed effects for the country implies that unilateral variables such as country size, income per capita, landlockedness and remoteness drop out of the equation, along with the so-called “multilateral trade resistance term” or other variables that might otherwise be missing. The three columns at the right of the table also allow for dyadic fixed effects, that is, pairwise. [19: We have also dropped the Glick-Rose dummy variable for when two geographical units are parts of the same country, focusing instead on results for independent countries.]

The conventional gravity variables are highly significant statistically. A one percent increase in the distance between two countries reduces their bilateral trade by an estimated 1.4 percent. Contiguity, that is, sharing a common land border, increases the trade between them by 39 % [=exp(.33)-1]. Sharing a common language increases the pair’s trade by 47%. And so forth.

As in earlier gravity studies, we have enough explanatory power left over even after allowing for all these fixed effects and geographical variables that the effect of membership in a currency union shows up clearly. Before Rose (2000) it was assumed that the trade effects of a common currency would be much smaller than the effects of a customs union or other Regional Trading Arrangement (RTA). But he found that the effects were approximately as large, and the finding has been replicated many times. It holds in Table 5 as well: in the first column, currency unions in general boost trade among their members by 60 % [=exp(.47)-1], which is almost as large as the 82 % effect of RTAs [=exp(.60)-1].[footnoteRef:20] [20: When the equation is estimated as in Glick and Rose (2016), for the time period 1948-2013 without the additional four years of data, the intra-CFA effect is a full doubling [=exp(.7)-1] .]

Many subsequent critiques of Rose (2000) regarding problems of endogeneity or missing variables and corresponding attempts to address them have resulted in a reduction of the estimated coefficients. The corrections, however, tend (i) to reduce the effects of RTAs commensurately with CUs and (ii) to leave both effects substantial. That is true here as well. When dyadic fixed effects are applied [in the 4th column], the estimated coefficient of currency unions still implies an effect that is substantial at 40 % (=exp(.34)-1] and almost as large as the effect of RTAs at 49 % [=exp(.40)-1].

We are particularly interested in the effect of the CFA. The other columns allow the possibility that CFA membership could have different trade effects than membership in other currency unions. It does. The CFA is estimated to have a modestly greater effect than other such arrangements: 52% [=exp(.42)-1] instead of the more standard 39% [=exp(.33)-1]. Interestingly, it is possible to distinguish the effect of CFA membership from the variables that indicate when two countries share a common language and a common historical colonizer, even though there is heavy overlap of the categories in the case of Africa. The gravity model has been able to give a more powerful answer to the question that we attempted in Section 4 by the simple before-and-after tests of the CFA entries by Mali in 1984 and Guinea-Bissau in 1997 or the exits of Madagascar and Mauritania in 1973 [as in Figures 1a and 1b].

To summarize the conclusion of the gravity results, the CFA appears to have expanded intra-regional trade by an estimated 52%, more than the 39% boost from other currency unions.

6. The OCA Criterion of Labor Mobility in Africa

A key principle of OCA theory concerns the so-called problem of asymmetric shocks, explained in Memo 1, Section 6.6. The point is that if a group of countries tends to experience low correlation of their business cycles with each other, stemming from idiosyncratic shocks, they are not good candidates to join in a common currency area. The reason is that it may be too difficult for such countries to give up the ability to follow different monetary and exchange rate settings attuned to their individual needs. The eurozone has suffered from this problem (as many predicted it would). The point has not been lost on those contemplating currency unions in Africa.[footnoteRef:21] [21: Asongu, Nwachukwu, and Tchamyou (2017).]

Countries may be able to adjust to idiosyncratic shocks in spite of the loss of their own currency if labor mobility is high across national borders, as explained in Memo 1, Section 6.8. Data on the bilateral movement of workers have recently become more extensively available, from the United Nations Population Division. The migration that has received the most attention is from South to North. The number of migrants from sub-Saharan Africa rose 25 percent over the first decade of the 21st century and then accelerated to 31 percent from 2010 to 2017, according to the Pew Research Center.[footnoteRef:22] The leading ten sources of emigrants worldwide in 2010-2017, judged by percentage rate of growth, following Syria in first place, were African countries: South Sudan, Central African Republic, Sao Thome and Principe, Eritrea, Namibia, Rwanda, Botswana, Sudan and Burundi.[footnoteRef:23] Clearly much of this out-migration is motivated more by politics and security concerns than by pure economics only. [22: Connor (2018).] [23: For the small-population countries like Sao Thome and Principe and Botswana, out -migration was of course low in absolute numbers even though high in percentage terms.]

The concept of labor mobility that Mundell (1961), introduced in his original conception of the Optimum Currency Area, mainly concerned the freedom of people within countries or provinces that are candidates to adopt a common currency. The idea is that a country is more able to give up the ability to respond monetarily to shocks that differ from those of its neighbors, if workers can move from low-employment countries to high-employment countries. Among most of the nine countries mentioned above, many of the migrants seek to leave Africa altogether. (An important exception is the case of Namibia and Botswana and the other countries in Southern Africa, where South Africa is the dominant host country. Such labor mobility facilitates currency links among some of those countries, particularly in the form of the Common Monetary Area.)Intra-continental migration in Africa in absolute terms is well above the levels of Latin America, North America, and Oceana, but well below the levels of Asia and Europe, as the table shows. To draw meaningful conclusions, however, more detailed analysis is required.[footnoteRef:24] [24: We should normalize countries’ migration flows by the size of their populations or even estimate a full-fledged migration version of the gravity model.]

Table A: Within-continent flow of migration in 2017, in millions[footnoteRef:25] [25: Data Source: UN Department of Economic and Social Affairs, Population Division, via Porter and Russell (2018).]

Africa 19.4Asia 63.3

Europe 41.0

Latin America 6.1

North America 1.2

Oceana 1.1

We are most interested in labor mobility within West Africa. There is an observable tendency for migration to flow more freely among countries that share borders or that share languages. Consider Cote d’Ivoire. Citizens move both from and to the other CFA countries. Cote d’Ivoire’s greatest migration partner by far is Burkina Faso, judged by stocks of migrants. Similarly, Burkina Faso’s biggest inward and outmigration are with Cote d’Ivoire.[footnoteRef:26] This must facilitate their common membership in the CFA franc zone. Guinea-Bissau is something of an outlier: Only one CFA member, Senegal, is among its top ten sources or hosts of migrants. [26: Source for international migrant data: UN Department of Economic and Social Affairs, Population Division, via Connor (2018).]

Labor mobility is another one of those “parameters”, like trade concentration and cyclical correlation, that can respond endogenously to the decision to join a common currency. For example, at the same time that many EU members were adopting the common euro they also experienced an increase in the freedom of movement among their members, particularly those who joined the Schengen Agreement. But the social and political willingness to accept immigrants has often run into limits, as we have observed recently not only in the European Union but in Southern Africa and elsewhere.

In any case, the ability of workers to move across national boundaries and their ability to send remittances back are useful safety valves, but are only partial substitutes for the ability to use monetary policy to respond to asymmetric shocks.

7. The OCA Criterion of Symmetric Shocks Among CFA Countries

A variety of authors have studied the symmetry of shocks in Africa by looking at cyclical correlations. Bénassy‐Quéré and Coupet (2005) find that the existing CFA franc zone cannot be viewed as an optimum currency area: CEMAC and UEMOA countries do not belong to the same clusters. But Houssa (2005) finds that the francophone countries of West Africa do share common demand shocks.[footnoteRef:27]Tables 6 and 7 present some updated statistics on the correlation of economic conditions in CFA member countries with economic conditions in the larger group. Table 6 reports correlations for GDP growth rates and for inflation. It is important to realize that we are not here estimating structural parameters, such as the correlation of world prices across the countries’ baskets of export commodities. Two countries might have fundamentally dissimilar economies and yet might develop a high cyclical correlation as the result of joining a common currency. For one thing, simple correlations might be high precisely because the countries share a common monetary policy. For another thing, even if we tried to estimate the correlation of exogenous shocks in a structural model, the “exogenous” structure could really be endogenous: it could change as the result of the currency union’s trade-promotion effects. [27: He finds that they do not share common supply shocks. But monetary policy cannot do much to counteract supply shocks anyway, an under-appreciated point in the context of optimum currency areas.]

Indeed, as explained in Memo 1 (Section 6.12), the “new” theory of Optimum Currency Area criteria pointed out that the correlation of shocks across members is probably endogenous with respect to the decision to join a common currency, because a common currency promotes intra-regional trade which in turn increases positive transmission of shocks.[footnoteRef:28] In theory, trade integration could lead, via increased specialization of production, to a reduction in the correlation of shocks. That was the a priori reasoning of Kenen (1969), Eichengreen (1992) and Krugman (1993), for example. But the empirical finding seems to be that a strengthening of trade – including trade-creation from a currency union -- leads to an increase in the cyclical correlation, not a decrease.[footnoteRef:29] Tsangarides, et al (2009), for example, find that African currency unions increase the correlation of price changes among members. An implication is that any group of countries that considers forming a common currency area is more likely to satisfy the Optimum Currency Area ex post than ex ante.[footnoteRef:30] If we find in Tables 6 and 7 that CFA members are well-suited to a common currency, but the reason for the finding is a structural response to their common currency, that is still worth knowing. [28: Frankel and Rose (1997, 1998), de Grauwe (2007), and Tavlas (2008). ] [29: Frankel and Rose (1998), Imbs (2004), Calderón and Chong (2007), di Giovanni,  Levchenko and Mejean (2018).] [30: Frankel and Rose (1997).]

This memo covers only the correlations of the CFA members. We will consider other countries in a later memo.

Table 6 reports correlations of growth and inflation of the individual countries with the simple currency union average. A basic message is that the francophone countries of West Africa look relatively well-suited to be members of the CFA zone, and more specifically of UEMOA, judging by the extent to which their annual growth rates and inflation rates move together. In rough order, the correlations for growth plus inflation are high for Cote d’Ivoire, Niger, Burkina Faso, Senegal, Mali, and Togo. For Benin, the correlation of GDP is low, but the correlation of inflation is very high (Perhaps its Aggregate Supply relationship is steep, i.e., its output is inelastic.) The outlier in UEMOA is Guinea-Bissau: the correlations are close to zero. This might be because it has historically lacked connections with the francophone countries, or more specifically because it did not join the CFA franc until 1997.

Another basic message is that the CFA countries of central Africa, the CEMAC members, do not appear as well-suited to the CFA zone as their West African brethren. Only in Equatorial Guinea does growth appear to have a reasonably high correlation with growth among other members of the currency union, and the same for inflation.

Table 6: Correlations of CFA members with the larger group: Growth and Inflation

1983-2017

(1) GDP Growth*

 

(2) Inflation** (CPI)

CFA

CEMAC

UEMOA

CFA

CEMAC

UEMOA

CEMAC

Cameroon

0.39

0.34

0.27

0.79

0.85

0.62

Central African Republic

0.31

0.34

0.09

0.52

0.69

0.29

Chad

0.43

0.45

0.15

0.81

0.84

0.67

Congo, Republic of

0.02

0.12

-0.17

0.87

0.91

0.71

Equatorial Guinea

0.80

0.88

0.20

0.78

0.81

0.63

Gabon

0.20

0.16

0.15

0.79

0.86

0.60

UEMOA

Benin

0.26

0.13

0.36

0.97

0.94

0.93

Burkina Faso

0.50

0.28

0.66

0.91

0.90

0.79

Guinea-Bissau

0.14

0.01

0.30

0.23

-0.15

0.54

Côte d'Ivoire

0.27

0.00

0.65

0.82

0.73

0.79

Mali

0.40

0.20

0.58

0.88

0.89

0.76

Niger

0.30

0.05

0.62

0.87

0.86

0.73

Senegal

0.22

0.05

0.41

0.84

0.87

0.69

Togo

0.55

0.35

0.65

0.90

0.84

0.81

1968-2017

(1) GDP Growth*

 

(2) Inflation** (CPI)

CFA

CEMAC

UEMOA

CFA

CEMAC

UEMOA

CEMAC

Cameroon

0.29

0.21

0.26

0.80

0.86

0.66

Central African Republic

0.31

0.34

0.11

0.50

0.68

0.26

Chad

0.45

0.48

0.17

0.81

0.84

0.67

Congo, Republic of

0.00

0.11

-0.19

0.87

0.91

0.71

Equatorial Guinea

0.79

0.87

0.19

0.78

0.81

0.63

Gabon

0.28

0.33

0.09

0.81

0.89

0.65

UEMOA

Benin

0.12

0.04

0.19

0.97

0.94

0.93

Burkina Faso

0.50

0.30

0.60

0.84

0.70

0.80

Guinea-Bissau

0.29

0.11

0.45

0.23

-0.15

0.54

Côte d'Ivoire

0.28

0.03

0.58

0.81

0.71

0.79

Mali

0.38

0.16

0.56

0.88

0.89

0.76

Niger

0.28

0.01

0.59

0.80

0.76

0.72

Senegal

0.34

0.15

0.48

0.82

0.83

0.71

Togo

0.32

0.22

0.32

0.89

0.80

0.84

* Correlation with average growth for CFA, CEMAC, UEMOA

** Correlation with median inflation for CFA, CEMAC, UEMOA

Table 7: Correlations of CFA members with the larger group: Growth, Inflation and Exports

1968-2017

(1) GDP Growth1

(2) Inflation2 (CPI)

(3) Export Growth3

CFA

CEMAC

UEMOA

CFA

CEMAC

UEMOA

CFA

CEMAC

UEMOA

CEMAC

Cameroon

0.56

0.58

0.29

0.92

0.96

0.83

0.35

0.39

0.15

Central African Republic

0.23

0.23

0.10

0.48

0.51

0.42

-0.06

-0.05

-0.08

Chad

0.34

0.40

0.09

0.76

0.75

0.73

0.54

0.63

0.05

Congo, Republic

0.10

0.34

-0.21

0.93

0.93

0.89

0.65

0.67

0.41

Equatorial Guinea

0.29

0.30

0.12

0.66

0.61

0.67

0.46

0.42

0.30

Gabon

0.39

0.50

0.13

0.87

0.90

0.80

0.69

0.67

0.46

UEMOA

Benin

-0.02

-0.02

0.00

0.99

0.97

0.99

0.04

0.02

0.15

Burkina Faso

0.47

0.26

0.51

0.81

0.67

0.87

0.22

0.16

0.37

Guinea-Bissau

0.25

0.19

0.21

-0.06

-0.15

0.04

0.10

0.10

0.01

Côte d'Ivoire

0.59

0.20

0.82

0.88

0.76

0.93

0.61

0.43

0.86

Mali

0.37

0.10

0.53

0.90

0.87

0.91

0.08

-0.04

0.26

Niger

0.38

0.05

0.61

0.81

0.75

0.82

0.32

0.22

0.28

Senegal

0.49

0.27

0.54

0.86

0.85

0.82

0.50

0.37

0.62

Togo

0.14

0.08

0.14

0.88

0.82

0.89

0.49

0.37

0.62

1 correlation with average GDP growth for CFA (or CEMAC or UEMOA), weighted by GDP

2 correlation with median inflation for CFA, CEMAC, UEMOA, weighted by GDP

3 correlations with average export growth for CFA, CEMAC, UEMOA, weighted by exports

(Tables 6 and 7 are thanks to Haiyang Zhang.)

Table 7 refines the correlations a bit. It adds the correlation of export growth as a third test of symmetry of shocks. Exports are not just another indicator of economic activity like GDP; a particular advantage of exchange rate flexibility is the ability to accommodate export shocks and thereby equilibrate the balance of payments. By the criterion of export correlations reported in panel 3 of the Table, Cote d’Ivoire again is best suited to the currency union, followed by Senegal and Togo. Again Guinea-Bissau is the outlier, with a correlation that is barely above zero.

Gauging the CEMAC members by export correlations, Gabon, Republic of Congo, and Chad are now the most suited for a common currency, undoubtedly reflecting the importance of oil in those countries. The Central African Republic is the outlier, showing a correlation below zero.

9. Conclusions

A currency union can give countries two benefits that are of particular importance: promoting trade to help economic growth and anchoring monetary policy to achieve price stability. But membership in a currency union also carries the cost of losing the ability to respond to shocks.

Estimates of effects on bilateral trade in the gravity model updated through 2017 confirm that the CFA boosts trade among its members by approximately 52 %, controlling for other factors. That is even more than the trade effect of typical currency unions and almost as much as the effect of a Regional Trading Arrangement. The boost to trade may be especially important for small landlocked economies like Mali, Burkina Faso, Niger, Chad, and the Central African Republic.

Further, we can see that the CFA countries of West Africa have indeed achieved price stability, whereas many of their neighbors have not. Whether these benefits are worth the cost depends in part on the extent to which members’ economic shocks are correlated with those of the group. Judging by correlation statistics for domestic economic activity, CFA membership in West Africa may be most natural for Cote d’Ivoire, Senegal and Burkina Faso. Judging by correlation statistics for exports, CFA membership in West Africa may be most natural for Cote d’Ivoire, Niger, Burkina Faso, and Senegal. CFA membership may be least natural for Guinea-Bissau, judged either by labor mobility of symmetry of shocks. The CFA seems less successful in Central Africa than in West Africa.

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5-Year Moving Average of Median Inflation

(GDP Deflator)

CEMAC1967-19721968-19731969-19741970-19751971-19761972-19771973-19781974-19791975-19801976-19811977-19821978-19831979-19841980-19851981-19861982-19871983-19881984-19891985-19901986-19911987-19921988-19931989-19941990-19951991-19961992-19971993-19981994-19991995-20001996-20011997-20021998-20031999-20042000-20052001-20062002-20072003-20082004-20092005-20102006-20112007-20122008-20132009-20142010-20152011-20162012-20174.47391050249539734.61416855620225615.22015185060852636.06320262421288626.95875240057365397.81110263071410148.90812483932882428.661654320150523410.11500340673969612.3033916722426212.74387223036578314.00778379428020414.06671132122406813.68616705905982310.3449363186453354.18382189185202251.00175268946251480.25888957190617817-1.6721483910798185-0.56939061481298272.73921148018403662.22670115160193791.43896921978943618.29829339039893028.231994399076915810.60164851075138911.1048095603524937.81045000844608244.29815205548199238.37985395922759226.05443118257494195.54854576102612469.12162699513240767.23021111342829945.87823317119801387.90885054176719398.83295936751591712.7851662155653937.22340775784542726.27038390635449886.40755567553183356.01947404610706641.31981755028517974.7644804152435283-0.47991259073413345-3.6024950172940864UEMOA1967-19721968-19731969-19741970-19751971-19761972-19771973-19781974-19791975-19801976-19811977-19821978-19831979-19841980-19851981-19861982-19871983-19881984-19891985-19901986-19911987-19921988-19931989-19941990-19951991-19961992-19971993-19981994-19991995-20001996-20011997-20021998-20031999-20042000-20052001-20062002-20072003-20082004-20092005-20102006-20112007-20122008-20132009-20142010-20152011-20162012-20172.19586157122264772.96678263803537464.20530731825176045.8311370478720856.7708874536318068.626248432629983810.10961990536750610.3923844496073069.328894945825485510.0975305642557549.9049382575858589.55017882806090239.848799995088089810.0230712486448867.82658793995758825.46704057584291723.72024152457742611.78899875603701860.458900767862760960.747489624922921810.957288739102070020.851566752415145341.17557810286362637.69347185610155749.507610643199788210.48526902137304110.9779047306850911.6401454722470975.39191457961139793.59725831969215463.49191396025616863.52198261248297362.65173738347757392.13789040425676812.72918183712939572.07188441043489882.09696718481893773.57944404924730993.92452380302158723.46804651384333344.36630527633334964.57606627944126213.05362422381062392.55240191369009882.37012215325214591.5727575090183108Non-CFA ECOWAS1967-19721968-19731969-19741970-19751971-19761972-19771973-19781974-19791975-19801976-19811977-19821978-19831979-19841980-19851981-19861982-19871983-19881984-19891985-19901986-19911987-19921988-19931989-19941990-19951991-19961992-19971993-19981994-19991995-20001996-20011997-20021998-20031999-20042000-20052001-20062002-20072003-20082004-20092005-20102006-20112007-20122008-20132009-20142010-20152011-20162012-20171.94791270751112643.18977053032127025.35182393314956968.41002281276369611.89532409449060413.24192446437847615.37561560048873215.0065743689596713.20244703108903611.01811176642383910.6167136646569389.996334268287483810.06115095224112510.176707011154411.66334375982703415.28168127517494718.27634167187104120.5047425730306922.54985365437407221.8534262544992921.80248132411102818.58049983884913616.63234897790550414.38775456175400313.296985572657619.80696538252429667.86377417886176926.56356149168690015.44402594876898425.22262259434790813.90999620315159295.21406246179622636.7104863807855518.176502425253408610.08851174226788313.19498051654483512.96332047520913912.38298471634695910.85380169531321411.214444300158149.844577942635400110.0556160047211829.2318226916931828.74684322572976215.96618999604486975.296596274422666Rest of Africa1967-19721968-19731969-19741970-19751971-19761972-19771973-19781974-19791975-19801976-19811977-19821978-19831979-19841980-19851981-19861982-19871983-19881984-19891985-19901986-19911987-19921988-19931989-19941990-19951991-19961992-19971993-19981994-19991995-20001996-20011997-20021998-20031999-20042000-20052001-20062002-20072003-20082004-20092005-20102006-20112007-20122008-20132009-20142010-20152011-20162012-20174.0238784091171085.50532589242659757.14844821521716249.169881302394859510.42059250799684911.79097100245818712.02649377549687211.94018572267404111.71775047678314912.56840652790933412.57909691631109812.73663188962367712.96038400259277512.64710826234827912.22966156514586812.83281586004966712.86146181245176114.41757105567492114.82511170415446614.23680094925599714.07657686003210514.4509612637578713.20884159555877212.33562422784146212.66497772245013612.30762809937042211.17369187184708310.0658889645140489.39385042923090558.74552039970167888.0821700140501267.94338592252411957.49736920470381387.792390338697527.31749779989443027.66534258523599917.67746737108564948.79073564717377928.58223021981299898.69288244502220138.4700896792097628.41522949350900087.08048612722351056.62712015761046125.9672257962991285.5406728307192905

%

5-Year Moving Average of Median Inflation

(Consumer Price)

CEMAC1967-19721968-19731969-19741970-19751971-19761972-19771973-19781974-19791975-19801976-19811977-19821978-19831979-19841980-19851981-19861982-19871983-19881984-19891985-19901986-19911987-19921988-19931989-19941990-19951991-19961992-19971993-19981994-19991995-20001996-20011997-20021998-20031999-20042000-20052001-20062002-20072003-20082004-20092005-20102006-20112007-20122008-20132009-20142010-20152011-20162012-20172.99288213159999963.29475215079999994.06905957259999965.888592561199999410.81664810759999914.07830882020000216.15140430960000417.06280989599999716.23878876559999913.0162720895999999.849276079200000910.076951819210.45257280419999910.0334616679999999.03637497120000088.61808859200000124.42493048059999962.10256007200000021.068942662-0.23046818299999999-1.2319712468000001-0.82409631360000013-0.923431398800000036.16200657979999997.91973847779999849.380356582199999210.86644575059999911.33045540393.82436582372.53573979869999992.39456637609999982.57132277150000022.44763443420000032.81211593580000012.91310241919999993.45369908939999973.10922979444.12900824459999964.98558031859999944.53622245739999923.48252231120000034.10568978139999972.93920972880000032.86299632560000023.30466760699999983.4034613451999993UEMOA1967-19721968-19731969-19741970-19751971-19761972-19771973-19781974-19791975-19801976-19811977-19821978-19831979-19841980-19851981-19861982-19871983-19881984-19891985-19901986-19911987-