c COPYRIGHT

by

Moon Oulatta

2018

ALL RIGHTS RESERVED ii

NOMINAL GDP TARGETING: AN OPTIMAL RULE FOR THE WEST AFRICAN ECONOMIC AND MONETARY UNION?

by Moon Oulatta

ABSTRACT

This dissertation investigates the prospect of adopting a flexible exchange rate regime with the nominal gross domestic product (GDP) as the intermediate target for the conduct of monetary policy for the members of the West African Economic and Monetary Union (WAEMU).

In the first chapter, we begin by assessing the choice of exchange rate regime based on three criteria: exchange rate volatility, exposure to real shocks, and institutional capacities. The current peg with the euro remains the de facto political choice for the WAEMU, but also a reliable option for ensuring monetary policy discipline and allaying risks of currency convertibility. However, we argue that exchange rate flexibility is increasingly becoming more viable due to the overall improvement in the central bank’s operational and statistical capacities, the high degree of exposure to terms of trade shocks, and the recent change in the WAEMU’s trade patterns away from Europe. Therefore, given the case for exchange rate flexibility, we examine the pros and cons of three types of flexible exchange rate regime: inflation targeting, monetary aggregate targeting, and nominal GDP targeting.

The main conclusion is that while all three types of flexible exchange rate regime would provide a firm commitment to a nominal anchor, monetary aggregate targeting would be the most practical option to pursue, because monetary aggregate data are highly observable, prone to almost no revisions, and price stability would be guaranteed by preventing excessive money growth. Alternatively, given the poor growth performance realized in the last decade and increasing vulnerability to supply shocks (for example, falling commodity prices and drastic droughts), we argue that under the right circumstances

(for example, when the economy is operating far below potential output), choosing a flexible exchange rate regime that allows monetary authorities to prioritize both employment and price stability such as nominal GDP targeting would be most desirable as compared to adopting a flexible exchange rate regime in which the intermediate target of monetary policy is to keep a monetary aggregate or the inflation rate iii on target. A commitment to stabilize nominal GDP would force the monetary authorities to prioritize employment in the short run without giving up on achieving price stability in the long run.

In the second chapter, we examine the stabilizing properties of using nominal GDP as the inter- mediate target as compared to using the inflation rate, a monetary aggregate, or the exchange rate. We provide a contribution to Bhandari and Frankel’s (2015) model by imposing a cost for data uncertainty into the central bank quadratic loss functions, especially under inflation targeting and nominal GDP targeting. First, we show that no precommitment to a nominal anchor leads to a suboptimal outcome.

Then, we derive the optimal conditions to determine whether a precommitment to a nominal GDP target would minimize the variability of real output, inflation, and the nominal exchange rate more efficiently than a precommitment to an inflation target, a monetary aggregate target, or an exchange rate target.

We draw three main conclusions from the result of the simulations. First, the exchange rate is the least optimal among the choices of nominal anchors considered for the WAEMU. Second, nominal GDP targeting minimizes the central bank’s quadratic loss function in an open economy setting given some parameter values for nominal GDP data uncertainty. Third, monetary aggregate targeting is slightly more robust than inflation targeting and exchange rate targeting. In conclusion, the main policy recom- mendation is that the central bank should prioritize nominal GDP among the choices of nominal anchors.

However, in the event of large nominal GDP data revisions or poor data quality, then the secondary option should be to commit to a monetary aggregate target.

In the third chapter, we devise a strategy for implementing nominal GDP targeting for the WAEMU by taking into consideration the existing practical concerns of data revisions and the unavailability of quarterly estimates of aggregate nominal GDP. Given the macroeconomic structure of the WAEMU and the magnitude of the estimated shocks affecting the economy, we demonstrate that a monetary aggregate instrument is superior to an interest rate instrument for the WAEMU. Hence, we propose a money-based rule similar to McCallum’s (1987) rule, where the central bank is to announce an annual target for nominal GDP growth and use the monetary base as the operational target.

Given the discovery of a robust empirical link between aggregate nominal GDP and aggregate nominal trade (value of exports plus imports) and the availability of trade data on a high frequency with minor revisions, we propose to rely on quarterly estimates of nominal trade as a means to solve the existing data issues coming from large revisions and low frequency data. The proposal to rely on nominal trade as a means to approximate trends in nominal GDP emanates from the fact that nominal trade shocks explain about close to 85 percent of variation in nominal GDP growth every year and trade data can be made available on a quarterly basis. Therefore, relying on quarterly estimates of nominal trade iv would allow the central bank to detect and respond to nominal GDP fluctuations more accurately and on a timely basis. v

ACKNOWLEDGEMENTS

I want to show my sincere gratitude to the United States Department of Education. Doctor Park has built a strong foundation for me. I learned a great deal of advanced mathematical concepts from him. I am deeply graceful to Doctor Park. Doctor Blecker introduced me to advanced macroeconomics.

I would be half of the student I am today without his teachings. One of the most important things I have learned from Doctor Blecker is the essence of perfection. I want to sincerely thank Doctor Blecker.

Doctor Mathy has influenced me in every possible way and assisted me in all stages of the dissertation process.

I want to thank my greatest inspiration, my father, Colonel Major Pierre Oulatta, a United States

Army Command and General Staff college graduate and President of the National Security Committee of Cote d’Ivoire. I want to thank my mother, Maimouna Ciss´e,the Executive director of Great Mind

Counseling and Wellness Center. I want to thank my God Jesus Christ for protecting me against all the odds and giving me the strength and the capacity to succeed in my program.

I want to thank Jean Claude Nascimento for providing useful guidance. I want to thank Senior

Economist Tidiane Kinda for his gracious help. I want to thank his excellence Mr. Sansan Hien, the director of policy forecasting and economic statistics at the Ministry of Economy and Finances of Cote d’Ivoire. I want to thank the director of research at American University (CTRL) Assen Assenov.

I want to thank the hard-working people at the Center for Environmental Data Analysis (CEDA) for providing quality rainfall data. I want to thank my closest friends who supported me throughout the program, most all Robert Vainshtein and Alpha Barry. I want to officially dedicate this dissertation to the people Cote d’Ivoire and all the members of the West African Economic and Monetary Union, whom

I love deeply. vi

TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGEMENTS ...... v

LIST OF TABLES ...... ix

LIST OF FIGURES ...... xi

CHAPTER

1. The Choice of Exchange Rate Regime for the WAEMU ...... 1

1.1 Introduction ...... 1

1.2 The West African Economic and Monetary Union ...... 6

1.2.1 The Institutional Background ...... 6

1.2.2 The Current Monetary Policy Framework ...... 7

1.2.3 Is the WAEMU an Optimal Currency Area? ...... 10

1.2.4 The Institutional Capacities of the BCEAO ...... 12

1.3 The Choice of Exchange Rate Regime ...... 14

1.3.1 Exchange Rate Volatility ...... 14

1.3.2 Real Shocks ...... 18

1.3.3 Institutional Capacities ...... 23

1.3.4 Which Exchange Rate Regime is Appropriate for the WAEMU? ...... 25

1.4 Alternative Types of Flexible Exchange Rate Regimes ...... 27

1.4.1 Monetary Aggregate Targeting ...... 27

1.4.2 Inflation Targeting ...... 30

1.4.3 Nominal GDP Targeting ...... 31

1.4.4 Which Flexible Exchange Rate Regime is Appropriate? ...... 33

2. The Stabilizing Properties of a Nominal GDP Rule ...... 37 vii

2.1 Introduction ...... 37

2.2 Literature Review ...... 38

2.3 Theoretical Framework ...... 39

2.3.1 The Closed Economy Framework ...... 40

2.3.2 The Open Economy Framework ...... 45

2.4 Simple AS-AD Model ...... 49

2.4.1 The Aggregate Supply Side ...... 50

2.4.2 The Aggregate Demand Side ...... 52

2.4.3 Comparative Statics ...... 54

2.4.4 Estimating Structural Parameters ...... 56

2.4.5 Data ...... 56

2.4.6 Empirical Results ...... 66

2.5 Case Studies ...... 74

2.5.1 Calibration Process ...... 74

2.5.2 Optimal Conditions ...... 76

2.5.3 Simulation Results ...... 77

2.6 Conclusion ...... 81

3. Implementing Nominal GDP Targeting for the WAEMU ...... 82

3.1 Introduction ...... 82

3.2 The Optimal Choice of Monetary Policy Instruments ...... 88

3.2.1 The Money Supply Instrument ...... 89

3.2.2 The Interest Rate Instrument ...... 90

3.2.3 Calibration Process ...... 91

3.2.4 Data ...... 92

3.2.5 Estimating the IS Equation ...... 92

3.2.6 Estimating the LM Equation ...... 93

3.2.7 Robustness check ...... 95

3.2.8 Which Instrument is Optimal? ...... 98 viii

3.3 The Importance of Relying on Nominal Trade Data for Implementing Nominal GDP Targeting ...... 100

3.3.1 Nominal Trade: Data Generating Process ...... 100

3.3.2 Nominal Trade: Data Composition ...... 101

3.3.3 Nominal Trade as a Proxy or Intermediate Target for Implementing Nominal GDP Targeting ...... 101

3.3.4 The Operational Target and Nominal Trade ...... 105

3.3.5 How Useful is Nominal Trade in Forecasting Nominal GDP? ...... 106

3.4 The Benchmark Rule ...... 110

3.4.1 Selected Case Studies ...... 117

3.4.2 Historical Counterfactual Simulation ...... 119

3.5 Nominal GDP Forecast Targeting Model ...... 123

3.6 Conclusion ...... 124

APPENDIX

A. APPENDIX A ...... 126

B. APPENDIX B ...... 128

C. APPENDIX C ...... 132

REFERENCES ...... 140 ix

LIST OF TABLES

Table Page

2.1. 2SLS Results, First-Stage Results for WAEMU-7 ...... 67

2.2. 2SLS Results, Second-Stage Results for the WAEMU-7 ...... 69

2.3. 2SLS Results, Second-Stage Results for Burkina Faso ...... 70

2.4. 2SLS Results, Second-Stage Results for Cote d’Ivoire ...... 71

2.5. 2SLS Results, Second-Stage Results for Niger ...... 72

2.6. 2SLS, Second-Stage Results for Senegal ...... 73

2.7. 2SLS Results, Second-Stage Results for Togo ...... 74

2.8. Critical Parameters of the Central Bank Quadratic Loss Functions ...... 75

2.9. Quadratic Loss Functions Estimates: Open Economy ...... 78

3.1. Optimal Monetary Policy Instruments, List of the Critical Parameters ...... 91

3.2. IS Equation: Short-Run Estimates (Annual Data) ...... 93

3.3. LM Equation, Long-Run Estimates (Annual Data) ...... 94

3.4. LM Equation, Short-Run Results (Annual Data) ...... 95

3.5. LM Equation, Short-Run Results (Quarterly Data) ...... 97

3.6. LM Equation, Long-Run Estimates (Quarterly Data) ...... 97

3.7. Optimal Choice of Monetary Policy Instruments: Parameters Calibration ...... 98

3.8. Quadratic Loss Function Estimates ...... 98

3.9. ARDL Regressions of Nominal GDP Growth and Nominal Trade Growth ...... 108

3.10. The Sum of the Squared Errors: Benchmark Rule Versus Historical Fixed Exchange Rate Rule...... 117

3.11. The Sum of the Squared Errors: Benchmark Rule Versus Historical Fixed Exchange Rate Rule (Cote d’Ivoire) ...... 118

3.12. The Sum of the Squared Errors: Benchmark Rule Versus Historical Fixed Exchange Rate Rule (Senegal) ...... 118 x

3.13. Sum of the Squared Errors: Benchmark Rule Versus Historical Fixed Exchange Rate Rule(1988-1993) ...... 122

A.1. Johansen Cointegration Test Results: Price Convergence Evidence ...... 126

B.1. Central Bank Quadratic Loss Functions: Parameters Calibration ...... 129

B.2. Augmented Dickey Fuller Tests: Inflation Proxies ...... 130

B.3. Augmented Dickey Fuller Tests: Other Variables ...... 131

C.1. Granger Causality Test Results: Export Growth and Import Growth ...... 132

C.2. Augmented Dickey Fuller Tests: IS and LM Equation (Annual Data) ...... 133

C.3. Augmented Dickey Fuller Tests: LM Equation (Quarterly Data) ...... 133

C.4. Johansen Cointegration Test: The Log of Nominal Trade (nt) and the Log of Nominal GDP (yt)...... 134

C.5. Augmented Dickey Fuller Tests: Nominal Trade Growth and Nominal GDP Growth (WAEMU)134

C.6. The Sum of the Squared Errors (SSE): Benchmark Rule (3.18), New Benchmark rule (C.2), and Historical Fixed Exchange Rate rule ...... 139 xi

LIST OF FIGURES

Figure Page

1.1. Annual Average Domestic Credit Provided by the Financial Sector % of GDP for the Period of (2004-2016)...... 8

1.2. Annual Average Inflation Rates (2004-2016) for the WAEMU, the Southern African De- velopment Community (SADC) and the East African Community (EAC)...... 9

1.3. Consumer Price Indices (WAEMU) ...... 11

1.4. Correlation: Annual Average Real GDP per Capita Growth (2004-2016) and Annual Av- erage Rate of Depreciation of the Nominal Exchange Rate (Local Currency/$)...... 15

1.5. West African CFA Franc Fluctuations: Annual Nominal Exchange Rate Fluctuations (West African CFA Franc vs Foreign Currencies) ...... 16

1.6. Exchange Rate Volatility (2004-2016): Annual Standard Deviation of the Nominal Ex- change Rate (Local Currency/$)...... 17

1.7. IS-LM Diagram: The Impact of a Fall in Net-Exports Caused by a Drastic Fall in the Export Price Under a Fixed Exchange Rate Regime...... 19

1.8. Time Series Plot of the Cyclical Components of Annual Days of Rainfall (Rainfall Surprise) and the Real GDP Gap ...... 22

1.9. Correlation of Annual Average Inflation (CPI) and Annual Average Real GDP per Capita Growth (2004-2016) ...... 24

1.10. Trade Patterns: Top Export and Import Destinations of the WAEMU ...... 26

1.11. Correlation: Quarterly Inflation (CPI) and Money Base Growth (2003Q1 - 2017Q4). . . . 28

2.1. Inflation, GDP Deflator (Base Year=2010) ...... 58

2.2. The Real Output Gap (Base Year=2010) ...... 59

2.3. Co-Movements of Nominal Economic Activities (Nominal GDP Gap) and World Demand (Key Trading Partners’Income Gap) ...... 60

2.4. Commodities Price Indicators for the WAEMU-7: Real Cocoa Price Surprise (cocoasDt), Real Tobacco Price Surprise (tabasDt), Real Cotton Price Surprise (cottonsDt), and the Log of the Commodity Price Index (compexDt)...... 61

2.5. Co-Movements of Real Economic Activities and Real Government Spending Surprise for the WAEMU-7 ...... 62 xii

2.6. Correlation of the Real Output Gap and Real Monetary Mass Surprise (mmsDt)..... 63

2.7. Correlation of Rainfall Surprise, Ex Post Inflation, and the Real Output Gap ...... 64

2.8. Co-Movements of Real Crude Oil Inflation (goilDt) and the Ex Post Inflation Rate (INFRWD) forTogo...... 65

2.9. Constrained Optimization Plots: Nominal GDP Targeting Versus Monetary Aggregate Targeting for the WAEMU-7 ...... 79

2.10. Constrained Optimization Plots: Nominal GDP Targeting Versus Inflation Targeting for the WAEMU-7 ...... 80

2.11. Constrained Optimization Plots: Nominal GDP Targeting Versus Exchange Rate Targeting for the WAEMU-7 ...... 81

3.1. Co-Movements of Annual Industrial Production Growth and Annual Real GDP Growth (Constant Local Prices) ...... 83

3.2. Co-Movements of Annual Aggregate Nominal GDP Growth (Current Local Prices), Annual Aggregate Nominal Trade Growth, and Annual Nominal GDP Growth Proxy by the Sum of Annual Industrial Production Growth and Annual CPI Inflation ...... 84

3.3. Co-Movements of the Nominal GDP Gap, the Imports Gap, and the Exports Gap . . . . 85

3.4. Time Series Plots of Industrial Production, CPI, Aggregate M2, and Money Market Rates 96

3.5. Constrained Optimization: Money Instrument Versus Interest Rate Instrument ...... 99

3.6. Co-Movements in Nominal Trade and Nominal GDP ...... 102

3.7. Co-Movements in Exports Growth and Rate of Change in the CF A/$ Exchange Rate. . . 104

3.8. Panel VAR, Impulse Responses for Money Based Growth and Nominal Trade Growth . . 106

3.9. Co-Movements in Nominal GDP Growth and Nominal Trade Growth ...... 107

3.10. Out-of-Sample Dynamic Forecast of Nominal GDP Growth (Dependent Variable) Based on Nominal Trade Growth (Independent Variable) ...... 109

3.11. Variance Decomposition of Nominal GDP Growth Due to Nominal Trade Shocks . . . . . 110

3.12. Target Path (TAR) of Nominal GDP Versus Actual Path of Nominal GDP ...... 112

3.13. Time Series Plot of the Historical Path Money Base Growth Versus Benchmark Rule . . . 115

3.14. Domestic Credit Gap, Fiduciary Circulation Gap, and Monetary Mass Gap (1986-1990s) . 119

3.15. The External Position Gap of the BCEAO (1985-1995) ...... 120

3.16. Simulated Path of Domestic Credit Growth Versus Historical Path of Domestic Credit Growth (1988-1993) ...... 122

A.1. The Operational Framework of the BCEAO’s Financial Payment System ...... 127

B.1. France’s Inflation (CPI), Unit Roots Test with Break Points Results ...... 128 xiii

C.1. Panel VAR: Eigenvalues of the Companion Matrix ...... 133

C.2. Forecast Errors: Nominal GDP Growth ...... 135

C.3. Benchmark Rule Against Actual Money Base Growth (Cote d’Ivoire)...... 135

C.4. Benchmark Rule Against Actual Money Base Growth (Senegal) ...... 136

C.5. Currency Fluctuations: (CFA Franc/$) Index and (French Franc/$) Index, (Base Year=1985)136

C.6. Actual Nominal GDP Growth Versus Nominal GDP Growth Target (10 Percent) . . . . . 137 1

CHAPTER 1

THE CHOICE OF EXCHANGE RATE

REGIME FOR THE WAEMU

1.1 Introduction

While the West African CFA franc remains a beacon of economic integration in West Africa,1 the question of whether it is still beneficial to keep its value fixed to the euro remains an open question.

This chapter will address this question by examining the choice of exchange rate regime based on three criteria: institutional capacities, exposure to real shocks, and exchange rate volatility.

The collapse of the Bretton Woods system in the 1970s led most industrialized nations to abandon their exchange rate pegs. However, most developing countries, including the members of the West African

Monetary Union (WAMU), retained some form of fixed exchange rate arrangements. For the WAMU, the customary argument for preserving a fixed exchange rate policy during the 1970s was based on the fact that its members lacked the institutional capacity, financial market depth, and monetary policy discipline to manage a flexible exchange rate regime. Hence, given the close economic links with France and the stability of the French franc vis `avis the US dollar, the WAMU decided to continue the peg with the

French franc, so as to anchor expectations about trade and remove the option of discretion from the hands of local policy makers.

According to Devarajan and de Melo (1987), the peg with the French franc provided guaranteed stability for the WAMU, especially during the early stages of establishing the peg. In the 1970s, as commodity prices began to rise sharply, the rigid peg provided a strong commitment to a nominal anchor, which in turn created the proper conditions for exploiting the benefits of rising export prices.

Hence, during the 1970s, the members of the WAMU experienced a defining period of robust growth

1The CFA stands for Communaut´eFinanciaire d’Afrique and note that we are referring to the West African CFA franc throughout all the chapters. 2 accompanied by manageable inflation. However, at the onset of the 1980s, a number of flaws associated with the peg began to emerge. The WAMU began to experience a series of shocks (for example, the Sahel droughts, overvaluation (Lane, 1989), and falling commodity prices (Nascimento, 1994) ) that proved to be difficult to accommodate given the lack of policy adjustment tools available at the disposition of the

Central Bank of West African States (BCEAO).

Therefore, the WAMU experienced poor economic performance throughout the late 1980s and early

1990s (Gulde and Tsangarides, 2008). At first, the World Bank advised the WAMU to liberalize the exchange rate as a means to improve competitiveness, but instead a few structural adjustment programs were introduced (for example, export subsidies and import tariffs), which turned out to be unsuccessful in preventing the severe balance of payments crisis that preceded the first postcolonial devaluation of the

CFA franc in 1994.

During the mid-1980s, as the French franc was rapidly appreciating against the dollar,2 the BCEAO could have negotiated a deal that would have allowed the CFA franc to crawl in the direction of a depreciation, but it did not do so. As a result, the members of the WAMU found themselves battling on two different fronts. First, to restore external competitiveness weakened by the overvaluation of CFA franc against the dollar. Second, to restore internal balance (for example, reducing the primary deficit) weakened by falling commodity prices.

The restrictive posture imposed by the peg during the crisis period of 1988-1993 triggered a long economic depression which eventually led to the devaluation of the CFA franc in 1994. Few months after the devaluation, the WAMU decided to put more emphasis on economic growth. Therefore, a series of reforms were introduced that led to creation of West African Economic and Monetary Union (WAEMU), which officially replaced the WAMU in 1994.

When France abandoned the French franc in favor of the euro in 1999, the WAEMU did not renounce its existing structure of monetary policy, but instead the peg was readjusted to the euro and the French treasury retained its legitimizing role of guaranteeing the convertibility of the CFA franc. In the pre-euro era, the main argument for liberalizing the nominal exchange rate was that maintaining a

fixed parity with the French franc did not provide enough of a buffer against the series of terms of trade shocks and periods of overvaluation experienced by the members of the WAMU during the mid-1980s and early 1990s (Lane, 1989).

2In February 1985 the French franc was valued at 10.09 per dollar, by the end of 1993 it had appreciated by almost 50 percent and was valued at 5.84 per dollar. 3

Nonetheless, the introduction of the peg with the euro led to an improvement in the macroeconomic environment, due to the relative stability of the euro vis `avis the US dollar and the fact that other

European trading partners adopted the euro. After the introduction of the euro in 1999, although the threat of overvaluation became less of an issue due to the stability of the euro versus other currencies, other sources of external and internal imbalances remained potent (for example, the persistent occurrence of adverse commodity price shocks) and worst of all, the degree of exposure to international financial assets increased significantly as the WAEMU’s financial system became increasingly globally integrated.

While it remains uncontested empirically that the WAEMU zone has enjoyed a higher degree of price stability in contrast to their non-CFA franc neighbors (for example, Ghana, Kenya, Nigeria, and

Tanzania), it is also well noted that this privilege has been achieved at the expense of a tight historical monetary policy, which according to Amin (2000) has resulted in limited growth relative to those non-

CFA African countries. Empirically, there is some merit to Amin’s findings, because we find that those

Sub-Saharan African countries that have allowed some degree of exchange rate flexibility since the 1990s

(for example, Ghana, Kenya, Nigeria, Uganda, Tanzania, Zambia, and Rwanda) did not experience calamitous growth or untamable inflation in the last decade. Furthermore, another intriguing fact is that the institutional capacities of these countries during the early 1990s were not that much different and currently remain very similar to those of the WAEMU.

Nonetheless, while the CFA franc remains a beacon for regional economic integration, the con- troversy over the decision to keep its value fixed to the euro perseveres, particularly because of two important arguments that we have identified in the academic literature: the lack of monetary policy

flexibility (Thiam, 2011) and the lack of robust growth associated with the peg (Simwaka 2010, Amin

2000). Although the political class of the WAEMU remains gratified with the idea of prolonging the peg, the tone of discussions in the media and the academic literature is increasingly narrowing towards identifying a less rigid and more flexible exchange rate regime to improve competitiveness and promote growth.

In the last decade, a couple of mixed exchange rate strategies have been proposed to improve competitiveness: Pegging to a basket of currencies to address the recent change in the WAEMU’s trade patterns (Ngouana, 2012) and adopting an adjustable peg (for example, a peg with a crawling-like ar- rangement) with the euro as a means to cope with periods of overvaluation (Coulibaly 2017, Thiam 2011).

These recommendations have been proposed as a means to bolster the role of the exchange rate in stabi- lizing the economy. Nonetheless, although much work has been done on identifying mixed exchange rate strategies, few studies have focused on the case of pure flexible exchange rate regimes. In this chapter, we 4 bridge this existing gap found in the economic literature by assessing the choice of exchange rate regime for the WAEMU by focusing on the case of fixed versus flexible exchange rate regimes.

It is true that pegging the nominal exchange rate to a basket of currencies would be a useful strategy to mitigate the threat of exchange rate volatility, especially if the selected basket of currencies is representative of the key trading partners. However, note that if the goal is to use the nominal exchange rate as a shock absorber, acting as a self-correcting mechanism to respond to economic fluctuations, then any types of fixed exchange rate regime would surely complicate this process.

Furthermore, even if a basket of currencies was to be selected as the intermediate target for the

WAEMU, the BCEAO would still have decide on whether to follow a crawling-like strategy or keep the exchange rate rigidly fixed to the selected basket of currencies. For these complex reasons, we simply do not emphasize multilateral exchange rate pegs or mixed exchange rate strategies in this chapter. Another reason is that under a flexible exchange rate regime, the exchange rate can always be managed as seen

fit by the central bank. In fact, according to Cavallo et al. (2005), most central banks that pretend to

float rarely do so completely. Therefore, when we consider all these factors, the cases for a crawling peg or a multilateral exchange rate peg become less interesting.

In any case, the current peg with the euro continues to guarantee maximum security for foreign investors, because it eliminates uncertainty about the future value of the CFA franc. Indeed, this is beneficial for those foreign companies and investors that have made significant investments in the WAEMU zone. Nonetheless, while a few trading partners enjoy the benefits of a fixed parity, the BCEAO remains limited in its capacity to cope with real shocks given the restrictive monetary policy stance imposed by maintaining a rigid peg.

As a result of the limited role of monetary policy, external and domestic imbalances (for example, a fiscal deficit or a current account deficit) are usually corrected by taking contractionary measures.

For instance, falling commodity prices are usually followed by large social spending cuts (Kakpo, 2017).

Since both fiscal and monetary policies remain pro-cyclical, the respective governments of the WAEMU and the central bank remain limited in their capacity to stabilize the WAEMU or engage in competitive depreciations against the US dollar whenever the economies require a counter-cyclical action. These are some of the many disadvantages of maintaining a rigid peg with the euro.

According to Amin (2000), the CFA franc members face severe difficulties in coping with external shocks as compared to those Sub-Saharan countries with more exchange rate flexibility (for example,

Ghana, Tanzania, Nigeria, and Kenya), particularly because of the strict monetary policy framework imposed by the fixed exchange rate policy. Amin’s findings have also been supported by multiple studies, 5 including the significant study undertaken by Broda (2013) on the effects of terms of trade shocks for developing countries with rigid types of exchange rate regime versus those with flexible types of exchange rate regime.

Broda (2013) finds that African countries with no exchange rate flexibility tend to suffer more from terms of trade shocks in contrast to those that allow some degree of exchange rate flexibility.

Furthermore, although correlation does not imply causality, a study conducted by the International

Monetary Fund (2016) reveals that Sub-Saharan African countries with flexible types of exchange rate regime have registered stronger growth performance in the last decade as compared to their counterparts with rigid types of exchange rate regime.

As much as there are benefits from maintaining the current rigid peg with the euro, we can also argue that there are also disadvantages for doing so. For the WAEMU, liberalizing the exchange rate is increasingly becoming more viable given the degree of exposure to global financial markets, extreme vulnerability to real shocks, the recent change in trade patterns away from the euro zone,3 and the improvement in the institutional capacities of the BCEAO (for example, making the monetary policy committee more independent, modernizing the WAEMU’s financial payment system, and the recent improvements in the statistical and operational capacities are all valid examples of recent changes in the

WAEMU’s institutional capacities). Nonetheless, if the WAEMU was to shift towards a flexible type of exchange rate regime, it would depend on whether it is optimal to do so in the first place and, if that is the case, it would also require choosing the appropriate type of flexible exchange rate regime.

While we tackle the issue of the optimal monetary policy in an empirical fashion in the second chapter, in this chapter we begin by assessing the choice of exchange rate regime for the WAEMU based on three key criteria: exchange rate volatility, institutional capacities, and vulnerability to real shocks. Then, we move on to assess and compare three alternative types of flexible exchange rate regime: inflation targeting, monetary aggregate targeting, and nominal GDP targeting. We structure the rest of this chapter as follows. In section 1.2, we present the institutional background of the WAEMU, discuss the set up of the current monetary policy framework, the institutional capacities of the BCEAO, and address the issue of an optimal currency area. In section 1.3, we evaluate the appropriate choice of exchange rate regime for the WAEMU based on exchange rate volatility, exposure to real shocks, and

3Please refer to Figure 1.10 for more information on trade patterns. In 2016, the EU was reported to be the leading export and import destination for only two out of the eight members of the WAEMU. India is now competing as one the leading export destination for the WAEMU and China is now winning the import destination battle, as it is now the leading import destination for the WAEMU. We can naturally conclude that as trade patterns continue to diverge away from the traditional European base, the argument for maintaining a bilateral peg with the euro as a means to minimize risks from exchange rate volatility is increasingly becoming more dubious. 6 institutional capacities. In section 1.4, we compare and contrast the three types of flexible exchange rate regime. In the last section, we discuss the appropriate choice of flexible exchange rate regime for the

WAEMU.

1.2 The West African Economic and Monetary Union

1.2.1 The Institutional Background

The WAEMU was created in 1994 by seven former French colonies, namely Benin, Burkina Faso,

Cote d’Ivoire, Mali, Niger, Senegal, and Togo.4 The initial establishment was called the WAMU, and it was established on May 12, 1962. There were seven original members, namely Benin, Burkina Faso,

Cote d’Ivoire, Mali, Mauritania, Niger, and Senegal. The state of Togo joined the Union in 1963, but

Mauritania left in 1973 in pursuit of its own monetary policy agenda. Prior to 1960, Benin, Burkina

Faso, Cote d’Ivoire, Mali, Mauritania, Niger, Senegal, and Togo were all French colonies. In 1945, France introduced the Colonies Francaise d’Afrique (CFA) franc, which represented the legal currency of its colonies.

In 1945, the CFA franc was fixed at 1 CFA franc for 1.70 French franc. In 1948, following the devaluation of the French franc against the dollar, the CFA franc went through a minor revaluation and the parity was readjusted at 1 CFA franc for 2 French franc. By 1960, the colonies gained their independence from France, but quite intriguingly the CFA franc remained the official currency for these countries. The only trait that changed was its name, which adapted to Communaut´eFinanciere Africaine.

Despite the fact that the French franc underwent a series of devaluations during the postcolonial era, the value of the CFA franc remained fixed until 1994, following the first historical devaluation of the postcolonial CFA franc. The CFA franc remains the official currency of the WAEMU.

After the devaluation of the CFA franc in 1994, there were numerous reasons for creating the

WAEMU. We can summarize these reasons as follows: enhancing competitiveness, establishing a common market, and ensuring economic and political convergence. There was an acute need to harmonize the legal environment and remove the existing barriers of trade facing the members in order to enhance the competitiveness of the WAEMU. Therefore, treaties were ratified in order to achieve these objectives (for example, article 1 of the UMOA was modified to meet these new demands).

The monetary authorities established convergence criteria, including the creation of a surveillance body responsible for enforcing these criteria. Finally, in order to fulfill the commitment to create a

4The state of Guinea-Bissau joined the monetary union in the summer of 1997. 7 common market, the monetary authorities put in place a plan to implement joint policies in regard to employment, energy, transportation, telecommunication, agriculture, mining, and taxes.5

1.2.2 The Current Monetary Policy Framework

According to the current Governor of the BCEAO Mr. Tiemoko, the central bank has four main obligations: ensuring the stability of the banking and financial system, ensuring the proper functioning of payment systems, implementing the fixed exchange rate policy specified by the monetary policy com- mittee, and managing international reserves efficiently. The current monetary policy framework is set up under three important cornerstones: a fixed parity with the euro, free capital flows, and the guaranteed convertibility of the CFA franc secured through the establishment of a pooled foreign exchange reserves account supervised by the French treasury. Although the intermediate target of the BCEAO is a rigid exchange rate peg with the euro, the ultimate objective of the BCEAO has always been to maintain price stability and the peg has been quite effective in attaining this objective.

After the devaluation of the CFA franc in 1994, with the help of the IMF, the BCEAO undertook comprehensive monetary policy reforms to reduce systematic risks and manage inflation more effectively.

Eliminating seigniorage and deepening reserves were some of the major steps taken to achieve these objectives. On a positive note, it is safe to argue that inflation has been less volatile under the peg.6

On the downside, maintaining a fixed parity has put enormous pressure on the BCEAO’s inter- national reserves and its ability to provide domestic credit and intervene in the economy. As shown in

Figure 1.1, there is empirical evidence of weak domestic credit provided by the financial sector relative to other non-CFA African countries (for example, Ghana, Nigeria, Kenya, Zambia, Namibia, Seychelles,

Mauritius, and Cabo Verde).

The peg has refrained the ability monetary authorities to propagate inflation from the monetary sector. Therefore, although inflation has been very stable and continues to remain so as Figure 1.2 shows, adjustment to terms of trade shocks has been the main issue with the peg. As a result, in the last decade we have witnessed no inflation persistence, but lower growth relative to those non-CFA African countries that have allowed some degree of exchange rate flexibility since the 1990s.7

5All information about the WAEMU’s treaties were collected from the UEMOA (2017).

6Please see Figure 1.2 for empirical evidence.

7Please refer to Figure 1.7 for empirical evidence. 8

Furthermore, the absence of needed capital controls and the inability to adjust the nominal exchange rate continue to leave the financial account vulnerable to capital flight and self-fulfilling crises. These are some of the main issues that currently motivate the idea of allowing some degree of exchange rate

flexibility. 100 80 60 40 20 Average domestic credit provided by financial sector % of GDP 0

Kenya Burundi Ghana Nigeria Zambia Mauritius Namibia Tanzania WAEMU Seychelles Cabo Verde Mozambique Non-CFA Block Data Source: WDI, author's calculations

Figure 1.1: Annual Average Domestic Credit Provided by the Financial Sector % of GDP for the Period of (2004-2016).

While almost all of the former African British colonies facing similar institutional capacities and types of shocks as the WAEMU have moved on to adopt some degree of exchange rate flexibility, the

WAEMU remains tied to the currency of its former colonial power. This leads us to the fundamental question of whether the decision to remain pegged to the euro has been more politically driven or eco- nomically driven. Some economists have attributed the lack of monetary policy autonomy experienced by the former French colonies to the style of French’s colonialism,

The French and English traditions in monetary theory and history have been

different ... The French tradition has stressed the passive nature of monetary policy and

the importance of exchange stability with convertibility; stability has been achieved

at the expense of institutional development and monetary experience. The British 9

countries by opting for monetary independence have sacrificed stability, but gained

monetary experience and better developed monetary institutions (Mundell, 1970). 10 8 6 4 Average inflation rate (percent) 2 0 EAC SADC WAEMU Data Source: WDI, author's calculations

Figure 1.2: Annual Average Inflation Rates (2004-2016) for the WAEMU, the Southern African Devel- opment Community (SADC) and the East African Community (EAC).

On that note, it is possible to suspect that the WAEMU’s decision to maintain a rigid peg with

France’s currency since 1948 may have been less economically driven and more politically driven. Nonethe- less, it is also important to recognize that the current monetary policy regime has had and continues to provide some benefits for the WAEMU. For example, the structure of the CFA franc itself has been a reference for economic integration and price stability, but whether these benefits outweigh the disadvan- tages of maintaining the peg remains an empirical matter that we are going to address in the second chapter, as we analyze the welfare properties of fixing versus floating.

Certainly, a move towards a flexible exchange rate regime would render the nominal exchange rate more volatile, but it would also provide room for the central bank to prioritize employment, especially since commodity price shocks tend to be most frequent. We acknowledge that a move towards a flexible exchange rate would require significant domestic political will and may probably face stiff opposition from current policy makers whom are closely in line with the idea of keeping close political and economical 10 links with France, which may not ultimately be counterproductive. In any case, whether the WAEMU decides to keep its peg to the euro or abandon it will be influenced by various external as well as internal political factors.

Internally, it will clearly be a matter of political will and less about institutional capacities, given a decade sound monetary policy conduct and recent improvements in the operational and statistical capacities of the BCEAO. Externally, it will depend on the strength of the macroeconomic performance of France. The ability of France to guarantee its financial arrangements with the WAEMU will ultimately depend on whether the French treasury has the economic capacity to do so beyond means of relying on the comptes d’op´eration. Recently, France’s economy has been facing high unemployment and sluggish growth (Weisbrot et al., 2017); the country has also been running both a primary and trade deficit since

2005.

In light of these circumstances, it is difficult to envision that the French treasury will be capable of guaranteeing the convertibility of the CFA franc indefinitely. If a situation arises such that the French treasury is incapable of correcting a large foreign exchange disequilibrium in the WAEMU zone, it could eventually trigger another devaluation or end the peg. The monetary authorities on both sides cannot digest these concerns lightly and in fact the WAEMU should always consider other alternatives to the euro (for example, a peg with the US dollar or adopting a flexible exchange rate regime would be some viable alternative options) so as to minimize risks from these types of situation occurring in the future.

1.2.3 Is the WAEMU an Optimal Currency Area?

While examining the choice of exchange rate regime for the WAEMU, we do not envisage the break- up of the WAEMU and in this section we briefly discuss the key factors that motivate this conclusion.

There are many factors that can influence the costs and benefits of membership in a monetary union.

In this section, we briefly consider three key factors: labor mobility, macroeconomic convergence, and correlation of shocks.

Labor mobility

Mundell (1961) argued that the presence of labor mobility could smooth out asymmetric responses to shocks affecting a monetary union. For instance, if a drought hits the WAEMU, countries like Niger,

Burkina Faso, and Senegal are likely to absorb more of the burden of the shock as compared to Guinea-

Bissau, Benin, Togo, or Cote d’Ivoire, because these are places where it rains more average. Therefore, if there is a high degree of labor mobility in the WAEMU zone, farmers would easily move from the most vulnerable regions towards the less affected areas in order to seek employment. 11

Hence, labor mobility should impact the effectiveness of a monetary union. For the WAEMU, there is an acute need to boost the lack of labor mobility, as it remains one of the major challenges facing policy makers. French is the common denominator across each country, but most importantly across each border lies a similar tribe with similar dialects and sets of customs, which makes integration and labor mobility more likely to occur if the proper legislation is put in place to catalyze this process.

Macroeconomic convergence

Macroeconomic convergence is a vital condition for ensuring the success of a currency area. There are many macroeconomic variables that could be utilized to determine whether the members of a mon- etary union are converging. For the WAEMU, given the fact that price stability remains the ultimate macroeconomic objective of the BCEAO, we decided to focus on the convergence of the consumer price index as the main criterion for indicating the degree of macroeconomic convergence. If prices converge, it would indicate monetary policy efficiency to some extent.

Consumer price index levels (WAEMU) 11.7 11.7 11.6 11.6 11.5 11.5 Logarithm Logarithm 11.4 11.4 11.3 11.3 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 2018m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 2018m1

Benin Burkina Faso Cote d'Ivoire Guinea Bissau 11.7 11.7 11.6 11.6 11.5 11.5 Logarithm Logarithm 11.4 11.4 11.3 11.3 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 2018m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 2018m1

Mali Niger Senegal Togo

Data Source: BCEAO, author's calculations

Figure 1.3: Consumer Price Indices (WAEMU)

We follow a similar approach take Mentz and Sebastian (2003) by using the Johansen cointegration approach as a means to examine the extent to which prices converge in the WAEMU. We use monthly 12

CPI data collected from the BCEAO. We use the X-13 ARIMA method to seasonally adjust the data.

Figure 1.3 plots the log of the consumer price index for each member of the WAEMU. The consumer price indices were found to be I (1). Table A.1 provides the estimates of the trace test. We found the existence of six cointegrating vectors, which indicates a high degree of price convergence across the WAEMU.

Correlation of shocks

A strong correlation of shocks between the members of a monetary union is a desirable outcome for monetary policy efficiency. The members of the WAEMU are mostly agriculture based economies, heavily reliant on favorable weather conditions for agricultural production and strong export prices for national revenues. The Sahel droughts in the 1980s have shown that rainfall shocks tend to be highly correlated across the WAEMU (Dembo, 2012). The period of 1986-1993, which was marked by a severe collapse in commodity prices has also shown that commodity price shocks tend to be strongly correlated across the WAEMU (Nascimento, 1994).

Conclusion on optimal currency area

In any case, whether the WAEMU represents an optimal currency area is a different matter of whether it represents an effective one. Often studies tend to conflate those two issues. An optimal currency area can always improve with the right policies and proper legislation, whereas a set of countries with no similar cultural traits, no common language, and facing asymmetric shocks are not likely to benefit by forming a currency area. For the WAEMU, given the strong desire for close economic integration expressed by the political class and common exposure to real external shocks (for example, trade shocks), we can conclude that the WAEMU is potentially an optimal currency area, but one that still requires much work in order to enhance the gains from monetary integration. However, this dissertation is more concerned with how to make the WAEMU more effective rather than whether it constitutes an optimal currency area.

1.2.4 The Institutional Capacities of the BCEAO

The BCEAO is not a currency board; it is a functioning central bank working with modern tools of monetary policy and established goals. The BCEAO performs three essential tasks of modern central banking: dictating the monetary monetary stance of the WAEMU, banking supervision, and open market operations in the primary market. The institutional capacities of the BCEAO can be regarded to as up to standards of those of modern central banks, because the BCEAO uses a modern operational framework to conduct monetary policy, has a strong and integrated financial payments system, and possesses a strong statistical and research department that contributes to the success of the implementation of monetary 13 policy for the WAEMU. The BCEAO uses an interest rate policy, open market operations, and reserve requirements to conduct monetary policy. The interest rate policy is implemented with three main policy rates.

The discount rates (often referred to as the REPO rate or the taux marginal des injections de liquidit´epar appels d’offres) are primarily used at the discount window to inject liquidity into the financial system. The commercial banking rates (for example, the lending rates and deposit rates) are used to protect banks from market risks and depositors from inflation. Although the WAEMU’s inter-bank market remains less liquid, the BCEAO still sets the minimum inter-bank rates at which commercial banks can lend to each other.

The leading money market rate in the WAEMU is the weekly inter-bank rate. A minimum reserve requirement ratio is set by the BCEAO in order to regulate bank reserves and domestic credit. The

BCEAO also supports government internal funding needs in the regional public debt securities market by issuing government bonds by auctions and by syndication. The BCEAO manages its fiduciary circulation of coins and banknotes by using various branches established in the countries of its members.

Industrial production and consumer price data are now being reported on a quarterly basis for the entire WAEMU zone. The BCEAO also produces forecast of key variables such nominal GDP measured in current local prices. The BCEAO uses an advanced payments system called STAR-UEMOA. According to the BCEAO, this financial payments system meets the international standards of Real-Time Gross

Settlement systems. Figure A.1 provides a graphical depiction of the modernized payments system utilized by the BCEAO.8 According to the BCEAO, The STAR-UEMOA system harmonizes four key financial systems: the national and regional checks clearing system, the external currency exchange system, the securities settlement system, and the regional stock market system.

As stated by the BCEAO, the modernized payments system was designed to enhance efficiency, reduce financial transaction costs, improve inter-banking activities, and facilitate financial transactions by bolstering the use of non-cash transactions. Clearly, the BCEAO is making significant institutional improvements, as far as improving the overall health of the financial system. Furthermore, the BCEAO is comprised of an independent monetary policy committee, a board of directors, an audit committee, a human resource department, and a research department. In the last decade, the bank has shown improved institutional capacities, especially in its ability to manage risks, harmonize data, and conduct monetary policy according to its strategic objectives.

8The picture was collected from the BCEAO’s archives at www.bceao.int. 14

1.3 The Choice of Exchange Rate Regime

A central bank can choose to conduct its monetary policy by fixing its exchange rate in various ways.

One way is to fix the exchange rate to a single currency or a basket of currencies at a predetermined parity and commit to defend this parity at all costs; we shall refer to these types of exchange rate arrangements as rigid pegs. Another way is to fix the exchange rate to a single currency or a basket of currencies at a predetermined value, but allow the monetary authorities to retain the power to adjust that parity within a specific band. This is known as an adjustable peg or a crawling peg, depending of whether the adjustment is made discretely or gradually towards a depreciation.

A central bank can also choose to conduct its monetary policy by allowing its exchange rate to be flexible. One way is to allow the exchange rate to be determined freely by market forces. Another way is to allow the exchange rate to float, but then allow monetary authorities to retain the power to influence the exchange rate as needed to achieve a specific macroeconomic objective; we shall refer to these types of exchange rate arrangements as flexible exchange rate regimes. For developing countries, the empirical literature on floating versus fixing is vast. For our purpose, we focus on three relevant factors that should affect the choice of exchange rate regime for the WAEMU: exchange rate volatility, institutional capacities, and exposure to real shocks.

1.3.1 Exchange Rate Volatility

The volatility of the nominal exchange rate has been at the heart of the academic debate on whether a country should fix or float (Calvo and Reinhart, 2002). Since the nominal exchange rate affects the value of trade in accounting terms, its volatility has contributed to the so-called phenomenon of “fear of floating”, especially for those countries that are price-takers. A flexible exchange rate regime should naturally contribute to some degree of exchange rate volatility, but what remains controversial is whether allowing exchange rate flexibility for small open economies would pave the way for poor growth, or worsen export competitiveness and lead to drastic changes in welfare.

There is a widely held concern that allowing exchange rate flexibility for developing countries would pave the way for more competitive depreciations, especially against the US dollar. In Figure 1.4, we find the empirical evidence that supports this fact: In the last decade, we find that those Sub-Saharan African countries that have allowed some degree of exchange rate flexibility since the 1990s did experience more currency depreciations against the dollar. The major concern expressed in the academic literature against this phenomenon is that persistent currency depreciations could impose severe balance sheet effects for 15 these economies (Cavallo et al., 2005), especially if a sizable proportion of domestic firms depend on foreign intermediaries for borrowing.

Persistent currency depreciations would naturally decimate the domestic value of wealth and firms’ balance sheets, causing increased risk premiums and worsening financial conditions, thereby hindering the ability of domestic firms to borrow internationally especially if the central bank isn’t committed to some form of nominal anchor other than the exchange rate. Sub-Saharan African countries such as Ghana,

Kenya, Nigeria, Zambia, Tanzania, Liberia, Rwanda, and Uganda have allowed some degree of exchange rate flexibility since the 1990s, but they have also all made some form of commitment to an inflation or a monetary aggregate target. In the last decade, although they have experienced higher inflation and a higher rate of depreciation against the world’s reserve currency, they have also managed to achieve more robust growth as compared to the WAEMU.

Rwanda 5

Angola

Nigeria

4 Mauritius Mozambique Ghana

Zambia TZA SYC Namibia Cabo Verde 3 Lesotho DRC Botswana Kenya

Sierra Leone

2 Liberia

Swaziland South Africa Average real GDP per capita growth (percent) WAEMU 1

0 5 10 15 Average rate of depreciation of the nominal official exchange rate (percent) Data Source: WDI, author's calculations

Figure 1.4: Correlation: Annual Average Real GDP per Capita Growth (2004-2016) and Annual Average Rate of Depreciation of the Nominal Exchange Rate (Local Currency/$).

In Figure 1.4, we examine the correlation between exchange rate fluctuations proxied by the annual average rate of depreciation of the nominal exchange rate against the US dollar and economic growth 16 measured by the annual average growth rate of real GDP per capita between the WAEMU and a block of those Sub-Saharan African countries that have embraced some degree of exchange rate flexibility since the 1990s. In the last decade, we can conclude that the WAEMU’s exchange rate had barley depreciated against the dollar, whereas the nominal rate of depreciation against the dollar was much stronger for the block of Sub-Saharan countries that have allowed some degree of exchange rate flexibility.

For the WAEMU, the lack of competitive depreciation against the dollar has been strongly associ- ated with poor growth, whereas greater depreciation against the dollar has been strongly associated with higher growth rates for the Sub-Saharan African countries that have allowed some degree of exchange rate flexibility. Furthermore, the argument for maintaining a rigid peg with the euro to provide a buffer against exchange rate volatility has clearly been misleading, because in Figure 1.5 we find that in the last decade, the CFA franc has been less competitive and more volatile against the key trading partners currencies (for example, the Indian rupee, Nigerian naira, US dollar, Chinese yuan, and South African rand).

Exchange rate fluctuations: key trading partners 7 16 90 6 80 14 5 70 12 4 60 10 50 3 CFA franc/Indian rupee CFA franc/Nigerian naira CFA franc/South African rand 8 40 2

2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 100 800 90 700 80 600 70 CFA franc/dollar 500 CFA franc/Chinese yuan 60 400 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 Data Source: IMF, author's calculations

Figure 1.5: West African CFA Franc Fluctuations: Annual Nominal Exchange Rate Fluctuations (West African CFA Franc vs Foreign Currencies) 17

Therefore, we can conclude that the peg with the euro has provided little protection against ex- change rate volatility with the key trading partners. Even worst, the WAEMU has been less competitive against certain key trading partners with a similar export structure such as Nigeria and South Africa. In addition, the cost of WAEMU’s imports from China, which is now the leading import source, has risen by

43 percent in the last decade, while imports have also risen by 19 percent. These comptemporary issues raise many question marks over the effectiveness of the current exchange rate regime and the necessity of prolonging it. Even more important, in the last decade, despite maintaining a hard peg with the euro, the CFA franc has been more volatile against the world’s reserve currency (the US dollar) as shown in

Figure 1.6. 40 30 20 10 0 Standard deviation of official exchange rate (Local currency/$) SYC Angola GhanaKenya Liberia Nigeria Zambia Lesotho Mauritius Namibia WAEMU Botswana Swaziland Cabo Verde Mozambique South Africa Data Source: WDI, IMF, author's calculations

Figure 1.6: Exchange Rate Volatility (2004-2016): Annual Standard Deviation of the Nominal Exchange Rate (Local Currency/$).

In contrast, most of the Sub-Saharan countries that have allowed some degree of exchange rate

flexibility since the 1990s have done better in terms of minimizing the volatility of their local currencies against the dollar, with the exception of a few countries such as Tanzania, Uganda, Malawi, and DRC.

Figures 1.4, 1.5, and 1.6 reiterate two important facts that policy makers should not downplay: bilateral 18 pegs do not completely isolate countries from exchange rate volatility and the correlation between eco- nomic performance and exchange rate fluctuations depends on the direction in which the exchange rate is fluctuating.

Concluding remarks on exchange rate volatility

For Sub-Saharan Africa, the fear of floating based on exchange rate volatility is questionable, because countries such as Kenya, Ghana, Nigeria, and Zambia have had outstanding growth performances and have also managed to achieve less exchange rate volatility against the world’s reserve currency as compared to the WAEMU in spite of having floating exchange rates. Even countries such as Tanzania and Uganda, which have experienced a higher degree of exchange rate volatility relative to the WAEMU, have also managed to outperform the WAEMU in terms of growth.

Therefore, we cannot conclude that allowing exchange rate flexibility would have disastrous conse- quences on growth for the WAEMU, but what we can agree on is that even if allowing some exchange rate

flexibility could render the exchange rate a little bit more volatile, the degree of exchange rate volatility exerted by floating and its impact on the economy would be conditional on whether the central bank is committed to a nominal anchor other than the exchange rate.

1.3.2 Real Shocks

Most developing countries that choose to fix the exchange rate also opt to sacrifice their monetary policy sovereignty (for example, the ability to conduct monetary policy to achieve strategic goals) so as to prevent capital volatility and speculative bubbles.9 When the economy is least vulnerable to real shocks

(for example, terms of trade shocks or pure supply shocks), the usage of the nominal exchange rate as the main nominal anchor becomes more convincing, especially if the monetary authorities decide to peg the exchange rate with the main trading partners or the world’s reserve currency (the US dollar).

Unfortunately, most developing countries that choose to fix are small open economies and price- takers that are heavily dependent on commodity prices and productivity shocks driven by rainfall or other factors. Hence, given the volatile nature of commodity markets and the highly unpredictable weather, these countries remain at the mercy of real shocks driven by large swings in commodity prices and unforeseeable weather shocks, which require a bigger role from monetary policy in order to mitigate their effects on the economy. When the economy is extremely vulnerable to real shocks that lead to painful recessions as they occur, a fixed exchange rate regime becomes difficult to manage both politically and

9See the impossible trinity argument which clearly states that when a central bank chooses to fix its exchange rate and at the same allow free capital mobility then it must be willing to sacrifice its monetary policy sovereignty. 19 economically, because it calls for a tighter stance of monetary policy while the economy is undergoing difficulties. For the WAEMU, the main drawback from maintaining a rigid peg with the euro emanates from the drastic consequences of falling commodity prices and the inability to use monetary policy to mitigate the burden of these shocks on growth.

We represent this argument in Figure 1.7 in an analysis that involves an investment-saving (IS) and liquidity preference and money supply (LM) equilibrium. In this model, not only do falling commodity prices affect negatively real income, but under a rigid peg their effects are also more depressive, simply because the central bank has to tighten in order to prevent interest rates misalignment. In Figure 1.7, we can think of a scenario where a fall in cocoa prices causes the IS curve to shift to IS0, causing the economy to contract from Y A to Y B. In order to prevent the domestic interest rate (i) from falling below the foreign interest rate (i∗) (for example i < i∗), the central bank has to tighten despite the fact that the economy is already in recession, causing the LM curve to shift to LM0 as the economy depresses further to Y C .

Figure 1.7: IS-LM Diagram: The Impact of a Fall in Net-Exports Caused by a Drastic Fall in the Export Price Under a Fixed Exchange Rate Regime.

Even if a central bank was operating under a mixed exchange rate strategy, namely a crawling peg or an adjustable peg, the ability to respond to temporary commodity prices shocks would be limited, particularly because the central bank would lose its credibility if it was inclined to consistently react to real output fluctuations. Often, when the exchange rate is fixed and the country is experiencing a severe 20 shock that puts tremendous pressure on its foreign exchange reserves (for example, a temporary fall in the main export price), the shock can force the central bank to seek a devaluation, especially if the shock exacerbates the current account deficit. Regardless of whether a devaluation is introduced as a surprise, when the exchange rate is pegged and the country is perceived to be undergoing a serious economic crisis, a self-fulfilling currency crisis can always occur, which can lead to massive capital outflow and force the monetary authorities to abandon the peg as was seen during the 1990s with the Mexican peso crisis and

Asian financial crisis.

The usage of a fixed exchange rate regime is one of the simplest ways to enhance the credibility of a central bank. However, one of the elusive flaws of sacrificing monetary policy sovereignty is the inability to use monetary policy to stabilize the economy whenever it is hit by a real severe shock (for example, a severe temporary fall in the real export price). Therefore, regardless of how a central bank decides to

fix its exchange rate, either through a crawling peg, or a rigid peg, no fixed exchange rate regime can offer any credible solutions to an economy that faces persistent real shocks. Whereas, real shocks would be easier to accommodate under a flexible exchange rate regime, either under a pure or a managed float, because the exchange rate could serve as a shock absorber in the event of a real shock, as it would remain unconstrained and could be altered either naturally or through a calculated sterilized intervention.

For the WAEMU, a flexible exchange rate regime would allow the BCEAO to accommodate com- modity price shocks without giving up on all the other benefits that a fixed exchange rate regime provides, if it is accompanied by making a firm commitment to a nominal anchor other than the exchange rate.

Under a flexible exchange rate regime, it would be possible for a central bank to commit to other nominal anchors (for example, a monetary aggregate target, an output target, a price target, or both of the latter two) and still be capable of conducting monetary policy or using the exchange rate as a shock absorber.

Under a flexible exchange rate regime, there exists a self-correcting mechanism that can help to stabilize the economy during the good or bad times. Under the right conditions (for example, when wages and prices are sticky in the short run) as the exchange rate depreciates in response to an adverse com- modity price shock, this will cause an expenditure-switching effect in favor of domestic goods, enhancing exports and thereby creating a real positive effect on the balance of trade, ceteris paribus (assuming no persistent J-Curve effects). This was certainly the case for the WAEMU following the devaluation of the

CFA franc in 1994. Certainly, the degree of exposure to real shocks makes the case for fixing the nominal exchange rate less desirable. 21

Nonetheless, vulnerability to real shocks and the choice of exchange rate regime remain empirical questions; in this regard, we observe that empirical findings corroborate with the theoretical claim that the effects of real shocks (for example, adverse terms of trade shocks) on the economy tend to be worst for developing countries that have less exchange rate flexibility in contrast to those that allow some degree of exchange rate flexibility (Edwards and Yeyati, 2005).

The members of the WAEMU are extremely vulnerable to real shocks driven by large swings in commodity prices and unforeseeable weather shocks, particularly because of their geographic location and the fact that they are extremely reliant on commodity prices and good weather for agriculture. Each member is either located in the warm or monsoon climate zone, which are regions in which monsoons and droughts tend be frequent naturally. According to the International Monetary Fund (2011), developing countries tend to be most vulnerable to pure supply shocks and terms of trade shocks. For the WAEMU, this is not surprising, given the geographic location of each member and the volatile nature of international commodity markets.

Badolo and Kinda (2014) confirmed that rainfall volatility was a significant and negative deter- minant of food production in Sub-Saharan Africa. Dembo (2012) also showed that the Sahel droughts had a disastrous impact on real output for the WAMU. Kinda (2011) found rainfall to be a significant determinant of inflation in Chad. In Figure 1.8, we examine the relationship between rainfall surprise and the real output gap during the Sahel droughts for the founding members of the WAEMU. Dembo’s

(2012) findings are quite consistent with our observation, as Figure 1.8 shows a strong covariance between the decline in the rainfall gap and the decline in the real output gap during the Sahel droughts in the early 1980s.

The WAEMU’s dependence on commodity prices stems from the fact that the primary source of income is exports of cash crops and minerals. For instance, in the case of Cote d’Ivoire, which represents the largest economy of the WAEMU, Jensen (2000) found that over 70 percent of Ivorian households derived their income from agriculture. Given the volatile nature of international commodity markets, it is practically impossible to rule out the occurrence of adverse terms of trade shocks. According to

Nascimento (1994), the crisis of 1988-1993 which led to the first postcolonial devaluation of the CFA franc was a direct result of falling commodity prices.

A recent example of the impact of falling commodity prices on the economy took place in Cote d’Ivoire. According to Kakpo (2017), President Alassane Ouattara made a decision to cut social spending in the second quarter of 2017 due to the prospect of falling cocoa prices. This decision was enacted as a means to minimize pressure on the fiscal stance given the likelihood of falling tax revenues from cocoa 22 exports. It was a rational decision taken by President Ouattara, given the limited role of monetary policy, but it was a decision that came at the expense of social spending cuts, which means that there were some adverse welfare consequences associated with this policy.

Real output gap

10 Rainfall surprise 5 0 -5 (in percent) -10 The Sahel droughts -15 1975 1980 1985 1990

Data Source: CEDA, WDI, author's calculation

Figure 1.8: Time Series Plot of the Cyclical Components of Annual Days of Rainfall (Rainfall Surprise) and the Real GDP Gap

Note: Rainfall surprise and the real output gap were calculated with the Hodrick-Prescott filter.

Concluding remarks on real shocks

History shows that despite maintaining a fixed exchange rate regime over the years, real shocks have had and continue to exert tremendous pressure on the economies of the WAEMU via large swings in commodity prices and unforeseeable weather shocks. Monetary policy cannot create growth or completely isolate the WAEMU from those types of shocks, but it should certainly play a role in stabilizing the economy. Under the current fixed exchange rate regime, the central bank is limited in addressing those types of shocks due to its inability to utilize the exchange rate as a shock absorber.

Certainly, vulnerability to real shocks cannot be overlooked or taken lightly and should definitely play a significant role in gauging whether the WAEMU should continue to remain rigidly fixed to the euro or allow some degree of exchange rate flexibility. Under a fixed exchange rate regime, the monetary authorities do not have enough discretion to respond to real shocks, whereas under a flexible exchange 23 rate regime a central bank could anchor inflation expectations by committing to a nominal anchor other than the exchange rate and still be capable of intervening in the economy during an economic crisis without losing credibility in doing so. This is precisely the main argument for why countries that are extremely vulnerable to real shocks can benefit from a flexible exchange rate regime.

1.3.3 Institutional Capacities

A flexible exchange rate regime may be the optimal regime for a country, but the ability to manage this regime effectively would depend on institutional capacities (for example, monetary policy capaci- ties and experience, the maturity of the financial system, and the statistical capacities of the central bank are all valid examples). Therefore, the institutional capacities argument has also become a key factor contributing to the fear of adopting a flexible exchange rate regime for many Sub-Saharan African countries.

For developing countries that are closely linked to global capital markets with mature domestic

financial markets, the argument for pegging has been less favorable (Simwaka, 2010), particularly because of the likelihood of exposure to risk and idiosyncratic shocks feeding from global financial markets, especially if the pegs are arranged under perfect capital mobility. On the other hand, for these developing countries with a poor track record of monetary policy, less exposure to global financial markets, and less developed capital markets, the argument for hard pegs remains more tenable.

During the 1980s, the institutional capacities argument against floating would have been more convincing for the WAEMU, because few Sub-Saharan African countries had experienced or shown the prospect of operating under a flexible exchange rate regime. Furthermore, at that time the BCEAO was still gaining valuable monetary policy experience. The statistical capacities were still developing and the capital market was less mature and less exposed to global financial markets. However, in the last decade, we have witnessed many improvements in the BCEAO’s institutional capacities. For example, the

BCEAO is now capable of producing and reporting key statistical indicators such as the CPI, monetary aggregates, and industrial production data for the entire union with minor revisions and short reporting lags.

The WAEMU’s financial sector has gained great exposure to external financial markets. Also, the BCEAO has made significant steps towards modernizing its financial payments system. Finally, the operational capacities have clearly improved and as a result of all these improvements, foreign exchange reserves as a reflection of capacity to import have reached unprecedented levels and inflation has been 24 kept closely near a two percent target.10 For the WAEMU, the institutional capacities argument against

floating becomes even less convincing when we examine the case of those Sub-Saharan African countries that have allowed some degree of exchange rate flexibility since the 1990s (for example, Kenya, Ghana,

Tanzania, Zambia, and Nigeria).

These countries were not as heavily monetized as the WAEMU zone and did not have superior institutional capacities either, but in the last decade, despite experiencing some degree of exchange rate volatility, they have managed to achieve moderate inflation and stronger growth as compared to the

WAEMU. In Figure 1.9, we examine the correlation between the annual average growth rate of the economy and annual average inflation for the WAEMU and a block of Sub-Saharan African countries that have allowed some degree of exchange rate flexibility since the 1990s.

Rwanda 5

Angola

Nigeria

4 Mozambique Mauritius Ghana

Zambia SYC TZA Namibia Cabo Verde 3 Lesotho DRC Botswana Kenya

Sierra Leone Malawi Liberia 2

Swaziland South Africa

Average real GDP per capita growth (percent) WAEMU 1 0 5 10 15 20 Average inflation rate (percent) Data Source: WDI, author's calculation

Figure 1.9: Correlation of Annual Average Inflation (CPI) and Annual Average Real GDP per Capita Growth (2004-2016)

10In the last decade, the WAEMU’s capital market, namely the Bourse R´egionaleDes Valeurs Mobili`eres (BRVM) has integrated quite well with other capital markets. The BRVM is now heavily linked to other global capital markets and currently depends heavily on foreign capital inflows. In 2014, the BVRM became integrated into the MSCI and S&P indices. 25

We observe that the majority of those Sub-Saharan countries that have allowed some degree of exchange rate flexibility since the 1990s have managed to outperform the WAEMU zone in terms of growth performance, despite having achieved higher relative average inflation as well. The most surprising part about these findings is that countries such as Liberia, Rwanda, Sierra Leone, and DRC, which have experienced harsh civil wars and more social unrest have managed to achieve moderate average inflation accompanied by robust real growth, whereas the members of the WAEMU which have been more stable with a stronger track record of central bank credibility, have achieved lower real growth.

Concluding remarks on institutional capacities

If the case for floating is strictly based on institutional capacities, then it certainly should not impede the WAEMU’s case, because those Sub-Saharan African countries that are currently floating did not and currently do not have superior institutional capacities as compared to the WAEMU. Therefore, we can conclude that the institutional capacities argument against floating remains questionable and the burden of doubt should not impede or lead to spurious conclusions that the BCEAO would not be capable of managing a flexible exchange rate regime.

Furthermore, despite the fact that the BCEAO operates under a rigid peg, it is not a currency board as mentioned before. The BCEAO’s balance sheet includes domestic assets that the central bank uses to conduct open market operations, whenever it finds it necessary to do so. As discussed earlier, the central bank has an established monetary transmission mechanism with an outstanding track record.

The BCEAO operates with moderns tools of monetary policy and in the last decade, it has shown tremendous maturity in its ability to manage its reserves, tame inflation, and inject liquidity in the economy. Inflation has been kept on target despite moderate credit expansion. Money market rates, deposit rates, and lending rates have all been closely aligned with the main policy rates of the central bank. Clearly, the central bank has shown significant improvements in its institutional capacities and its ability to manage the WAEMU.

1.3.4 Which Exchange Rate Regime is Appropriate for the WAEMU?

In the case of the WAEMU, the institutional capacities argument against floating has become more questionable, particularly because the financial system is maturing and increasingly becoming linked to global financial markets, the financial payments system has modernized and the overall statistical and operational capacities of the BCEAO have improved as well. Furthermore, the argument for maintaining a rigid peg with the euro as a means to achieve price stability and promote monetary policy discipline remains valid, but there are other ways to achieve similar objectives when the exchange rate is flexible 26

(for example, committing to a nominal GDP target, an inflation target, or a monetary aggregate target would be viable alternative options).

The rigid peg with the euro provides a misleading sense of security to policy makers, because maintaining the peg since 1999 not only has been correlated with calamitous growth, but also has limited the ability of the central bank to allow necessary competitive depreciations to accommodate terms of trade shocks. Furthermore, the peg with the euro has provided little buffer from exchange rate volatility.

Figures 1.5 and 1.6 show that the CFA franc remains quite volatile against the dollar and other currencies.

The argument for maintaining a rigid peg with euro as a means to bolster trade used to be stronger, but given the recent change in the WAEMU’s trade patterns away from Europe as shown in Figure 1.10, we can conclude that this argument is increasingly becoming less tenable. The EU remains the top export destination for only two out of the eight countries in the WAEMU zone. The members of the WAEMU are now increasingly exporting to each other and India is now one of the top export destinations. China is now the leading import source for the WAEMU zone and intra-WAEMU imports are also on the rise.

Top exports destination (2016) Top imports destinations (2016)

25.00% 25.00% 25.00%

37.50%

12.50% 12.50%

25.00% 12.50%

25.00%

EU India China South Africa EU Switzerland Nigeria WAEMU WAEMU

Data Source: OEC, author's calculations

Figure 1.10: Trade Patterns: Top Export and Import Destinations of the WAEMU

The inability to use monetary policy to accommodate frequent export price shocks, or temporary droughts, and real appreciations may have welfare implications that may potentially make the current 27 exchange rate peg least optimal. Whereas, a flexible exchange rate regime could catalyze some degree of exchange rate volatility, but would also provide several advantages. A flexible exchange rate regime would: allow the exchange rate to help absorb real shocks, anchor expectations through a firm commitment to other nominal anchors (for example, a nominal GDP target, an inflation target, or a monetary aggregate target); and provide an opportunity for the BCEAO to learn how to operate when the exchange rate is

flexible, because it has never done so since its creation.

Concluding remarks on which exchange rate regime is appropriate

In spite of the benefits that the current exchange rate peg with the euro provides (for example, guaranteed exchange rate convertibility and stable imported inflation), we argue that given the strong shocks affecting the members of the WAEMU via commodity prices, the recent change in the WAEMU’s trade patterns away from the EU, and increasing links to global financial markets, there is clearly a valid case for liberalizing the exchange rate. Furthermore, since the problem of institutional capacities is now becoming more of an historical issue, we argue that now may be the appropriate time for the WAEMU to consider the possibility of adopting a flexible exchange rate regime to prioritize employment without giving up on anchoring inflation expectations.

A simple peg with the euro cannot satisfy both conditions, whereas a flexible exchange rate regime may, particularly because it would create more room for monetary policy interventions and at the same time allow the central bank to commit to a nominal anchor other than the exchange rate. Hence, in section 1.4 we will compare and contrast three types of flexible exchange rate regime that could be useful alternatives to the current peg with the euro.

1.4 Alternative Types of Flexible Exchange Rate Regimes

1.4.1 Monetary Aggregate Targeting

Monetary aggregate targeting refers to a flexible exchange rate regime in which the monetary authorities seek to control the overall growth rate of the money supply. Therefore, in order to achieve this objective, the monetary authorities conduct monetary policy so as to achieve a specific money growth target. In some countries, such as Kenya, Tanzania, Sierra Leone, and Rwanda, monetary aggregate targeting is practiced by targeting the money supply with reserve money as the operational target. For

Sierra Leone, Kenya, Tanzania, and Rwanda, the intermediate target is broad money or M2. We begin by discussing the feasibility of implementing monetary aggregate targeting for the WAEMU and then we move on to discuss any possibly welfare consequences that could arise in doing so. 28

For the WAEMU, monetary aggregate targeting is very feasible for three key reasons. First, inflation is closely link to money growth, domestic credit is heavily correlated to M2 growth, and interest rates are negatively related to real money balances. Second, monetary aggregate data (for example, bank reserves, domestic credit, M1, and M2) are easily accessible explicitly on a monthly basis, but they are also implicitly observable on a weekly basis by the BCEAO’s monetary policy committee. Third, a target path for M2 would provide a guaranteed buffer against excessive money growth. 4 2 0 Inflation rate CPI (percent) -2 -4

0 20 40 60 Money base growth (percent) Data Source: IMF, BCEAO, author's calculations

Figure 1.11: Correlation: Quarterly Inflation (CPI) and Money Base Growth (2003Q1 - 2017Q4).

We examine the correlation between reserve money growth and the inflation rate (CPI) for the

WAEMU in Figure 1.11. The four quarterly lags of money base growth explain about 35 percent of variation in inflation for the WAEMU. Further empirical work is needed to examine the relationship between monetary aggregates, interest rates, inflation, and real variables. In the third chapter, we estimate a monetary equilibrium for the WAEMU so as to examine the sensitivity of real money balances to interest rates and the magnitude of money demand shocks.

Welfare Implications

The theoretical underpinnings and the strength of monetary aggregate targeting lie on the funda- mental theoretical claim that inflation is determined by money growth in the long run. Therefore, by 29 preventing excessive money growth, the central bank can achieve price stability in the long run. We can express a monetary equilibrium as follows

m + v = p + y (1.1) where v is the velocity of money and assumed to be constant for simplicity, m is the money supply, y is real output, and p is the price level. All variables are expressed in logs. Equation (1.1) can be linearized as follows

dm = dp + dy

In growth rate form we obtain

dp dm = p m note that dy drops out of the equation because we assume that money is neutral in the long-run. Sim- plifying further we obtain

. . p=m (1.2) where a change in money growth impacts the inflation rate as follows

. ∂ p . = 1 ∂ m

In the long run, there is a 1 for 1 relationship between changes in the money supply and change in prices; that is of course if we assume that v is constant and money is neutral. The welfare consequences of implementing monetary aggregate targeting would depend on the stability of the empirical relationship between inflation and money growth and on the magnitude of money demand shocks, precisely v. Money demand shocks may be present, but they should not be excessively large and frequent, otherwise monetary aggregate targeting may not necessarily be optimal. In the next chapter, we take all these concerns into account as we attempt to quantify the welfare consequences of implementing monetary aggregate targeting in contrast to a rigid peg, inflation targeting, and nominal GDP targeting. 30

1.4.2 Inflation Targeting

Inflation targeting (IT) refers to a flexible exchange rate regime in which the monetary authorities attempt to target the inflation rate directly. In order to achieve this objective, the central bank announces an official inflation target, which it guarantees to attain. Credibility is gained by transparency and accountability. In Sub-Saharan Africa, countries such as Zambia, Uganda, South Africa, and Ghana are now practicing inflation targeting. We begin by discussing briefly the feasibility of implementing inflation targeting for the WAEMU and then we move on to discuss any potential welfare consequences that could arise in doing so.

For the WAEMU, inflation targeting is very feasible for two key reasons. First, the central bank has already developed an effective statistical mechanism for collecting and reporting aggregate inflation data on a timely basis for each member individually and for the entire union as a whole (for example, consumer prices data are explicitly reported every quarter for the entire WAEMU). Second, the BCEAO has already established enough credibility for its ability to keep inflation on target by communicating effectively its objectives to the public and by being accountable for keeping monetary policy discipline.

The BCEAO can implement IT by using either a money aggregate or a key interest rate as the operational target (for example, in the third chapter we provide an extensive empirical analysis on the optimal choice of monetary policy instruments for the WAEMU). So far, we can conclude that the

BCEAO has been successful in keeping inflation on target by using an effective interest rate policy.

Inflation forecast targeting requires forecasting inflation and the BCEAO is already producing forecasts of key economic indicators.

Welfare Implications

The welfare consequences of implementing IT for the WAEMU would depend on the strength of the relationship between output, inflation, and the monetary policy instruments. In chapter 2, we estimate a simple structural model to analyze the relationship between these key variables. IT implies that the central bank has to respond aggressively to offset any factors that would cause the ex post inflation rate or the inflation forecast to deviate from the inflation target.11

For example, this implies that a central bank may have to tighten harder during a drought or in light of an adverse import price shock in order to prevent inflation from rising above target. As a result of this restrictive manner of responding to advsere supply shocks under IT, there has been a concern that implementing IT could also impede growth. For instance, according to Blanchard (2005), the United

11See Taylor (1993) and Svensson (1997). 31

States experienced a recession during the early 1980s as a result of tightening too aggressively in order to curb domestic inflation caused by the 1970s oil shocks.

Furthermore, empirical findings on the impact of IT implementation on output and inflation remains quite ambiguous, especially for developing countries. For instance, Brito and Bystedt (1997) found no practical gains in implementing IT for developing countries; this result was largely driven by robust empirical findings of lower growth performance realized during the period of IT’s adoption. Also, Risler

(2016) found mostly negative effects of IT implementation on growth for developing countries. Whereas, de Mendon¸caand de Guimar˜aese Souza (1997) and Eduardo and Salles (1997) found empirical evidence of lower inflation and output variability for developing countries during the period of IT implementation.

The threat of deflation has also been a major concern for countries seeking to implement IT, because choosing an inflation target too close to zero could eventually lead to a tight monetary policy and cause deflation. For these reasons, Sub-Saharan African countries that practice inflation targeting tend to choose moderate inflation targets (for example, in Ghana the inflation target is eight percent and in

Uganda it is five percent).

The magnitude of supply shocks and the shape of the short-run supply curve should also influence the decision to commit to an inflation target, especially when wages and prices are flexible in the short run and firms are producing at high levels of output, close to full employment, the case where the aggregate supply curve is close to vertical. In the next chapter, we estimate the slope of the short-run aggregate supply curve and the magnitude of supply shocks in order to estimate the welfare consequences of implementing IT for the WAEMU.

1.4.3 Nominal GDP Targeting

A central bank can set a target path for output and prices and then use its monetary policy instruments to minimize the volatility around this target. Economists define this manner of conducting monetary policy as nominal GDP targeting. The idea was introduced during the late 1970s as a result of the failures of monetary aggregate targeting. Meade (1978) introduced the idea with the objective of achieving price stability and full employment. The main objective of the central bank under a nominal

GDP rule is to stabilize demand without inducing an inflationary or deflationary bias.

This means that when the economy is hit by an adverse temporary shock that causes output and prices to fluctuate in the same direction (for example, an adverse AD shock caused by a fall in foreign demand or the main export price index), then the central bank must intervene to stabilize both output and prices, thereby stabilizing nominal GDP. When it is an adverse supply shock that causes output and 32 prices to fluctuate in the opposite direction (for example, a drought or an oil shock), the central bank is not required to tightened at all or as much and in fact should only intervene to stabilize demand if necessary and consistent with the nominal GDP target.

In recent years, this idea has reemerged, most particularly in context of developing countries ex- posed to large swings in commodity prices and unforeseeable weather shocks, based on the belief that a commitment to stabilize nominal GDP would allow the central bank to prioritize employment without los- ing the edge on achieving price stability in the long run. No country in the world has ever implemented nominal GDP targeting and it has never been considered in the context of the WAEMU. Therefore, we are contributing to the economic literature by providing the first study to discuss the prospect of implementing nominal GDP targeting for the WAEMU.

Nominal GDP targeting seems appropriate for the WAEMU for three key reasons. First, the

WAEMU remains extremely vulnerable to large swings in commodity prices and weather shocks, so under nominal GDP targeting, an adverse commodity price shock would be automatically accommodated and a drought would not require excessive tightening as compared to under a strict inflation rule. Second, the

WAEMU’s economies could be operating far below potential output, which means that the argument for prioritizing employment in the short run may be more appropriate under these circumstances. Third, the

BCEAO is already producing annual forecasts of aggregate nominal GDP measured in current local prices, so if the forecasts are reliable then the central bank could implement nominal GDP forecast targeting by using the forecast of nominal GDP as the intermediate target.

Welfare Implications

When the economy is hit by a drought or an import price shock, under scenario (1) if the economy is operating at excess capacity, in which case the short-run aggregate supply curve is relatively flat, then firms could continue to supply their markets without having to raise prices significantly in order to make up for the loss in nominal revenues caused by the drought due to low marginal costs. Under these circumstances, the central bank could attempt to stimulate demand, depending on whether nominal GDP is falling below the target or do nothing if it remains close to the target. The idea is that under nominal

GDP targeting, a drought would not require excessive tightening as compared to if the central bank was operating under a strict inflation rule.

Under scenario (2), if the economy is operating at low or practical capacity, in which case the short-run aggregate supply curve is relatively steep, then firms would have no choice but to raise prices significantly in order to make up for the loss in nominal revenues caused by the drought. Any attempt 33 to stimulate demand under these circumstances would only contribute to bolster inflation, given the steepness of the supply curve. Under scenario (2), nominal GDP targeting would be least efficient and the pragmatic solution would be to allow the burden of the shock to fall entirely on real output, which would require contracting the economy in order to curb inflation.

The welfare consequences of implementing nominal GDP targeting depend mainly on the magnitude of supply shocks and the shape of the short-run aggregate supply curve. In chapter 2, when we examine the welfare properties of nominal GDP targeting, we will also estimate a range of estimates for the slope of the short-run aggregate supply curve and the magnitude of supply shocks for the WAEMU.

Other criticisms of implementing nominal GDP targeting emanate from the practical concerns of encountering large nominal GDP data revisions and long lags in reporting nominal GDP estimates. These concerns may be more significant for developing countries such as those included in this study. For most developing countries, nominal GDP data are available with a year or two lag and the data tend to be susceptible to some revisions. If revisions are large and data are not available on a timely basis, it would be difficult to keep nominal GDP on target as compared to keeping the money supply or the inflation rate on target.

1.4.4 Which Flexible Exchange Rate Regime is Appropriate?

On top of anchoring expectations, a nominal anchor should also be robust to the type of shocks that buffet the economy (Frankel, 2014). Therefore, the appropriate choice of flexible exchange rate regime for the WAEMU must also provide a nominal anchor that is robust to the types of shocks that buffet the

WAEMU. We examine robustness to two key shocks under each types of flexible exchange rate regime considered for the WAEMU (for example, an adverse oil shock and an unfavorable export price shock).

Adverse oil price shock

The import price shocks that emanate from oil imports tend to be important for countries in which oil prices affect markups. For a net oil importer, a large transitory oil price shock should normally cause a temporary increase in domestic inflation and a decrease in real output in the short run (Blanchard,

2005). Furthermore, the propagation of the shock after its initial impact would ultimately depend on which nominal anchor the central bank is targeting.

Under inflation targeting, the intermediate target would be the inflation rate, so the primary goal of the central bank would be to curb the pass-through of the oil shock into domestic inflation. Hence, if inflation tolerance is critically low (for example, if the monetary authorities prefer a relatively low inflation target), then the central bank would be inclined to tighten harder so as to reduce aggregate demand in 34 order to force farmers and firms to cut prices. This response would inevitably involve contracting the money supply, reducing the demand for liquid assets, and increasing the cost of borrowing. As the central bank tightens, it could indirectly put pressure on the nominal exchange rate to appreciate.

Absorbing an oil shock through an appreciation of the exchange rate would inevitably reduce the cost of imports as purchasing power rises, but this could also worsen external competitiveness. The

financial account may improve given the prospect of rising capital inflows induced by a surge in interest rates, but the balance of trade could also suffer as well. Under monetary aggregate targeting, the intermediate target would be the money supply. The primary objective of the central bank would be to control money growth given some implicit inflationary objective. Hence, when the money growth target is set for a given period, the monetary authorities would have a constrained ability to utilize monetary policy to address an unanticipated oil shock or any other kind of shocks without having to use discretion and abandon the target path.

The German experience of monetary aggregate targeting during the 1970s precisely reveals this

flaw. As oil prices began to rise sharply, the exchange rate was also appreciating and the economy was contracting. However, despite officially practicing monetary aggregate targeting, the Bundesbank did intervene to stabilize both output and the exchange rate during the 1970s. As a result of these interventions, the money growth targets were frequently missed in order to attain the implicit goals of the Bundesbank. Announcing a money growth target and achieving this target is very feasible in practice, but sticking to the target is the main issue, because it would depend on the severity of the shocks and whether the central bank is independent enough and has enough credibility to withhold any political attack stemming from government authorities pushing to abandon the target in order to score a political victory. For example, monetary aggregate targeting has been very effective in Kenya, because the central bank has been very credible and transparent, and as a result inflation has been very stable and growth has been robust in the last decade.

Under nominal GDP targeting, the intermediate target would be the sum of potential output and a price target. The primary objective of the central bank would be to stabilize both prices and output. If a severe oil shock affects the economy, the central bank would want to split the burden of the shock between prices and output such that the losses in output cancels out the gain prices and thereby keeping nominal GDP on target. Under the right conditions (for example, when wages are sticky and

firms are producing at excess capacity), firms won’t be inclined to raise prices significantly in order to make up for increasing costs; thereby creating some expansionary room for the central bank to intervene if demand weakens or if nominal GDP is falling off target. 35

Unfavorable export price shock

Under inflation targeting, export price shocks would be weighted less importantly than import price shocks, unless the fall in the export price exacerbates the financial account to the point where the central bank is running out of foreign exchange reserves. Therefore, in light of an adverse export price shock, the central bank would be reluctant to intervene, particularly because of the unwillingness to unleash the inflationary pressure that would prevail in doing so. Hence, under these circumstances, the burden of the shock would be allowed to fall entirely on the current account.

Under monetary aggregate targeting, the central bank would keep the money supply growing on target regardless of what is happening in the goods market. In the event of a large decrease in the export price, of course monetary authorities would be tempted to overshoot the money growth target, but in doing so would mean dampening credibility, which could lead to an inflation bias. Under nominal

GDP targeting, an export price shock would be given the highest priority because both output and prices would fall, prompting a swift response from the central bank to stabilize nominal GDP.

Concluding remarks on which flexible exchange rate regime is appropriate

If concerns for implementing a flexible exchange rate regime are strictly based on providing a strong commitment to a nominal anchor and being able to have reliable data, then monetary aggregate targeting should be given considerable priority for the WAEMU, because it would guarantee monetary discipline by preventing excess money growth and would be the easiest regime to implement given the fact that monetary aggregates are highly observable and prone to almost no revisions. Furthermore, if velocity shocks are predictable, then they can be taken into account when the central bank is formulating its money growth target. Inflation targeting or nominal GDP targeting would be more difficult to implement, because consumer price and industrial production data are available with a three month lag and could be subject to minor revisions.

Furthermore, although monetary authorities in the WAEMU are now taking the appropriate steps to reduce the reporting lag of aggregate nominal GDP, currently it is being reported on an annual frequency with an additional annual forecast, which means that it would be difficult to implement nominal GDP targeting without relying on additional proxies. Therefore, we can conclude that inflation targeting and nominal GDP targeting could be desirable but not necessarily practical and we are not attempting to evade these existing data concerns in deciding which types of flexible exchange rate regime would be appropriate for the WAEMU.

With respect to output stabilization, monetary aggregate targeting and inflation targeting would put less emphasis on output stabilization (Mishkin and Posen, 1997). In contrast, under nominal GDP 36 targeting the central bank would prioritize output in midst of a drought or an adverse terms of trade shock without giving up on committing to a long-run price or inflation target (Bhandari and Frankel,

2015).

Given the poor growth performance realized under the rigid peg with the euro, the BCEAO should consider a change in the ultimate policy objective of the WAEMU, as price stability and guaranteed exchange rate convertibility have not been enough to create a conducive environment for promoting growth. Implementing nominal GDP targeting may be a possible solution to resolve the lack of growth problem without giving up on anchoring inflation expectations, because the intermediate target under nominal GDP targeting would include both an output target and a long-run price or inflation target, but only if the relevant practical concerns can be addressed.

Therefore, we can conclude that, if the goal is to prioritize output stabilization without inducing an inflation bias, then nominal GDP targeting should be given considerable thought, given the types of shocks that buffet the WAEMU and the acute need to prioritize employment without inducing an inflation bias.

Indeed, we find nominal GDP targeting to be an interesting case to examine, given the attractiveness of its theoretical underpinnings, especially its robustness to temporary supply shocks. Therefore, in the next chapter, we will assess the stabilizing properties of nominal GDP targeting in contrast to other monetary regimes (for example, a rigid peg, discretion, monetary aggregate targeting, and inflation targeting). 37

CHAPTER 2

THE STABILIZING PROPERTIES OF A

NOMINAL GDP RULE

2.1 Introduction

In this chapter, we examine the stabilizing properties of using nominal GDP as the intermediate target as compared to using other alternatives such as the inflation rate, a money aggregate, or the exchange rate. We focus on the case of the West African Economic and Monetary Union (WAEMU).

The main objective is to determine whether a precommitment to a nominal GDP target would lead to an improvement in the conduct of monetary policy for the WAEMU, as far as minimizing a central bank quadratic loss function when the objective is to minimize the variability of real output, inflation, and the exchange rate.

The main argument for committing to a nominal GDP target is that it would allow the central bank to prioritize employment in the short run while anchoring inflation expectations by committing to a long-run inflation or price target. The main argument against committing to a nominal GDP target emanates from a practical concern of encountering large nominal GDP data revisions, especially for developing countries. Other concerns emanate from the lags in data availability, lack of high frequency nominal GDP data, and the fact that no country has ever tried it.

We employ a theoretical framework introduced by Bhandari and Frankel (2015) to identify the optimal monetary policy. We add our contribution to this model by imposing a cost for nominal GDP data uncertainty proxied by the variance of output and inflation revisions as a means to penalize the central bank quadratic loss function under nominal GDP targeting and inflation targeting. The main objective is to show that the desirability of implementing nominal GDP targeting would decrease as revisions to nominal GDP data get extremely large; the same would apply for inflation targeting, if 38 revisions to inflation were found to be extremely large. The rest of the chapter will be structured as follows. Section 2.2 introduces a brief literature review on key findings about nominal GDP targeting.

Section 2.3 explains the main theoretical underpinnings of our model. Section 2.4 estimates the key structural parameters linked to the main theoretical framework (for example, we estimate the magnitude of supply shocks and the slope of the short-run aggregate supply curve). In section 2.5, we derive some optimal conditions and run simulations to determine whether a nominal GDP target would satisfy these conditions. The last section draws conclusions from the results of the simulations.

2.2 Literature Review

Some of the leading studies supporting the idea of nominal GDP targeting include Bean (1983),

McCallum (1988), Meade (1978), Frankel and Chinn (1995), Sumner (2014), and Tobin (1980). Hall and Mankiw (1993) found stable inflation to be the only principal benefit of implementing nominal

GDP targeting. Bean (1983) showed that nominal income targeting was superior to monetary aggregate targeting in stabilizing the economy for the United States. Garin et al. (2015) analyzed the stabilizing properties of nominal GDP targeting, output gap targeting, and inflation targeting. They concluded that when the economy was subject to strong nominal rigidities and large supply shocks, the two rules that gave priority to real output stabilization (for example, output gap targeting and nominal GDP targeting) were superior to inflation targeting. Azariadis et al. (2015) argued a that commitment to increase inflation when the economy is positioned at the zero lower bound would expand credit, stabilize nominal GDP, and improve the overall health of the economy. Frankel and Chinn (1995) argued for the superiority of nominal GDP targeting over monetary aggregate targeting and exchange rate targeting in an open economy setting.

Clark (1994) concluded that the stabilizing properties of nominal GDP targeting differed under different structural models and therefore suggested that it was important for policy makers to consider this issue when gauging the effectiveness of a simple nominal GDP rule. Rudebusch (2002) raised the practical issues of data uncertainty in the process of implementing nominal GDP targeting. He found that during the postwar, nominal GDP growth rules performed better than inflation targeting rules under real-time output gap uncertainty and model uncertainty.

Billi (2015) showed that persistent supply and demand shocks worsen the case for nominal GDP targeting at the zero lower bound. Ball (1997) showed that a precommitment to a nominal GDP target would lead to disastrous instabilities in inflation and output, but McCallum (1997) showed that Ball’s

(1997) findings were least interesting and simply framed on preemptively assuming adaptive inflation 39 expectations. In contrast, McCallum’s (1997) results showed the stability of both inflation and output under nominal GDP targeting by allowing agents to form rational inflation expectations, which allowed the central bank to affect inflation expectations and output contemporaneously.

There have been fewer studies conducted on analyzing the stabilizing properties of nominal GDP targeting for developing countries, but the small existing literature provides some mix results on the stabilizing properties of nominal GDP targeting. For example, in a study of India, Bhandari and Frankel

(2015) found that nominal GDP targeting was superior to inflation targeting in both an open and closed economy setting when the economy was operating at excess capacity and supply shocks were large.

However, in a study of South Africa, Creamer and Botha (2017) acknowledged the stabilizing properties of nominal GDP targeting only in a closed economy setting, due to the steepness of the estimated short- run aggregate supply curve.

2.3 Theoretical Framework

In this section, we examine the optimal monetary policy in both the open and closed economy settings. In a closed economy setting, the ultimate objective of the central bank is to minimize the volatility of output and inflation. Whereas, in an open economy setting the ultimate objective is to minimize the volatility of output, inflation, and the nominal exchange rate. There are three key sectors: an aggregate supply sector, an aggregate demand sector, and an external sector. The aggregate supply relationship can be expressed as follows

y =y ¯ + b(π − πe) + u (2.1)

b > 1 where y denotes the growth rate of real output,y ¯ denotes the natural growth rate of real output or potential output, πe denotes the expected inflation rate, π denotes the actual inflation rate, u is a stochastic transitory supply shock, and b is the slope of the short-run aggregate supply curve. In the short run, given some nominal rigidities, we should expect a positive relationship between output and inflation.

When wages are slow to adjust at the beginning of each period, as prices begin to rise, firms’ profits should also rise, leading to more production and less unemployment until inflation expectations adjust to force workers to seek higher wages. Hence, we should expect the short-run aggregate supply curve to be 40 positively sloped. We use the equation of exchange to derive the short-run aggregate demand relationship as follows

y = (m − π) + v (2.2) where m is the growth rate of reserve money or la masse monetaire and represents the channel by which money authorities can affect domestic demand. v is a velocity or demand shock. For the external sector, the exchange rate and its fundamentals can be expressed as follows

(s − s∗) = m − y + e (2.3) where s denotes the nominal exchange rate, s∗ denotes the equilibrium or exchange rate target in case the central bank follows a peg, and e denotes a stochastic transitory balance of payments shock. Inserting equation (2.2) into equation (2.3) yields a simplified solution for the exchange rate as follows

(s − s∗) = π − v + e (2.4)

2.3.1 The Closed Economy Framework

In a closed economy framework, the objectives of the central bank can be reflected into the following quadratic loss function as follows

∗ 2 2 L = α(πm − π ) + (ym − λy¯) (2.5)

α > 0 ; λ > 0 where α denotes the central bank’s preference parameter for stabilizing inflation fluctuations, π∗ is the inflation target, πm denotes the measured inflation rate, which is vulnerable to revisions, ym denotes the measured rate of output, which is also vulnerable to revisions, λ reflects the real output objective of the central bank. We follow a similar idea but slightly different approach taken by Rudebusch (2002) to express data uncertainty as follows

πm = π + Ωεπ (2.6) 41

Ω ∈ [0, 1]

ym = y + Θεy (2.7)

Θ ∈ [0, 1]

where επ are the inflation revisions and εy are the output revisions. The parameters Ω and Θ are weights ranging from zero to 100 percent, signaling the importance of inflation and output revisions in the implementation of monetary policy. Weights are set accordingly to which nominal anchor the central bank is committing to. Under a discretion rule, data revisions are not important, because the central bank does not commit to an inflation target or output target. Therefore, Ω and Θ are both set at zero.

Under inflation targeting, the intermediate target is the inflation rate, so inflation revisions are fully weighted (Ω = 1) and output revisions are weighted less importantly (Θ = 0). Under nominal GDP targeting, the intermediate target includes both inflation and output, so we give full weights to both inflation revisions (Ω = 1) and output revisions (Θ = 1).

Under monetary aggregate targeting, since the goal is to keep a money growth target, inflation and output revisions do not play any role, so we set both Ω and Θ at zero. When there is a fixed exchange rate regime, the exchange rate is the intermediate target and it is fixed. Therefore, inflation and output revisions are weighted least importantly, so we set both Ω and Θ at zero.

For theoretical simplicity, we consider the case of strict inflation targeting, so we allow the inflation target to be zero, such that π∗ = 0. Next, we follow Bhandari and Frankel (2015) and assume that the central bank has an implicit expansionary objective that is beyond the explicit one. Hence, we assume that the desired growth rate of real output, equivalent toy ˆ ≡ λy¯ is always going to be be greater than the natural rate of output, such thaty ˆ > y¯. Hence, the central quadratic loss function in a closed economy setting can be expressed as follows

2 2 L = α(πm) + (ym − yˆ) (2.8) 42

Discretionary Monetary Policy Rule

The central bank wishes to minimize the loss function in each period given private sector expecta- tions about inflation as follows

minL = α(π)2 + ((¯y − yˆ) + b(π − πe) + u)2 (2.9) {π}

∂L = b(ˆy − y¯) + b2πe − bµ − (α + b2)π = 0 (2.10) ∂π

We then solve for the inflation rate as follows

b(ˆy − y¯) + b2πe − bµ π = (2.11) (α + b2)

Under discretion, the inflation rate derived in equation (2.11) will not converge to what the public expected it to be, because the central bank chooses an inflation rate that is higher than the expected inflation rate (Gordon and Barro, 1983). Hence, the following condition must hold

πe = π 6= π∗

We then solve for expected inflation as follows

b πe = (ˆy − y¯) (2.12) α

Note that under discretion, expectations about inflation are persistent, this reflects the inflationary bias.

We then solve for equilibrium inflation as follows

b bµ πq = (ˆy − y¯) − (2.13) α (α + b2) equilibrium output is

αµ yq =y ¯ + (2.14) (α + b2)

We can derive the central bank quadratic social loss function 43

b bµ αµ L = α( (ˆy − y¯) − )2 + ((¯y − yˆ) + )2 α (α + b2) (α + b2) and take the expectation of the quadratic social loss function as follows

α + b2 ασ2 EL = (ˆy − y¯)2 + µ (2.15) 11 α (α + b2)

α+b2 In (2.15), ( α ) reflects the inflation bias (Gordon and Barro, 1983).

Monetary Aggregate Targeting Rule

The central bank does not wish to unleash undesired inflation in the long-run. Hence, the central bank is convinced that this objective can be attained by setting the instrument of monetary policy according to what it expects the natural rate of output to be:

m = E(y) ≡ y¯

Then we use the aggregate supply and aggregate demand relations to solve for equilibrium output and inflation

v − µ πq = (2.16) (1 + b)

µ + bv yq =y ¯ + (2.17) (1 + b)

The central bank quadratic social loss function can be expressed as

v − µ bv + µ L = α( )2 + ((¯y − yˆ) + )2 (1 + b) (1 + b) and we take the expectation of the quadratic social loss function as follows

(α + b2)σ2 + (1 + α)σ2 EL = (ˆy − y¯)2 + v µ (2.18) 12 (1 + b)2

In (2.18), there is no inflation bias, but the economy is vulnerable to velocity shocks and supply shocks. 44

Nominal GDP Targeting Rule

Under nominal GDP targeting, the goal is to keep growth and inflation constant, so from the aggregate demand side, the central bank must offset any velocity or demand shock by adjusting the monetary policy instrument accordingly. Therefore, following Frankel and Bhandari’s (2015) approach, v drops out of the aggregate demand equation. Then we use the aggregate supply and aggregate demand relations to solve for equilibrium output and inflation as follows

−µ πq = (2.19) (1 + b)

µ yq =y ¯ + (2.20) (1 + b)

The central bank quadratic social loss function can be expressed as

−µ µ L = α( + ε )2 + ((¯y − yˆ) + + ε )2 (1 + b) π (1 + b) y and we can take the expectation of the quadratic social loss function as follows

2 2 (1 + α)σµ 2 2 EL13 = (ˆy − y¯) + + ασ + σ (2.21) (1 + b)2 επ εy whereas Frankel and Bhandari’s (2015) original solution can be expressed as follows

(1 + α)σ2 EL = (ˆy − y¯)2 + µ 013 (1 + b)2

In equation (2.21), there is no inflation bias and velocity or demand shocks are always accommodated so they do not amplify the central bank expected losses. On the other hand, supply shocks matter and the preference parameter for stabilizing inflation fluctuations clearly amplifies the effect of supply shocks on the central bank quadratic loss function. Nominal GDP data revisions also amplify the central bank expected losses under nominal GDP targeting. The main difference between Frankel and Bhandari’s

(2015) solution and our solution lies in the fact that we impose a cost for nominal GDP data revisions into equation (2.21) as a means to account for the cost of data uncertainty in implementing nominal GDP targeting. 45

Inflation Targeting Rule

Under inflation targeting, because the central bank builds credibility and is held accountable, the prevailing equilibrium inflation rate must always equal the inflation target as follows

πp = 0 (2.22)

We can then solve for equilibrium output as follows

yq =y ¯ + µ (2.23)

The central bank quadratic social loss function becomes

2 2 L = α(επ) + ((¯y − yˆ) + µ) and we take the expectation of the quadratic social loss function

EL = (ˆy − y¯)2 + σ2 + ασ2 (2.24) 14 u π whereas Frankel and Bhandari’s (2015) original solution can be expressed as follows

2 2 EL04 = (ˆy − y¯) + σu

In (2.24), there is no inflation bias and velocity shocks do not amplify the central bank quadratic loss function. On the other hand, supply shocks matter and since the intermediate target is the inflation rate, large inflation revisions would amplify the central bank expected losses, especially when the preference parameter for stabilizing inflation fluctuations is relatively high. The main difference between Frankel and Bhandari’s (2015) solution and our solution lies in the fact that we impose a cost for inflation data revisions into equation (2.24) as a means to account for the cost of data uncertainty in implementing inflation targeting.

2.3.2 The Open Economy Framework

In an open economy setting, the objectives of the central bank can be expressed in the following quadratic loss function 46

2 2 ∗ 2 L = α(πm) + (ym − yˆ) + c(s − s ) where c is the preference parameter for stabilizing exchange rate fluctuations. By including an exchange rate objective in the loss function, we take into consideration the impact of exchange rate volatility in determining the optimal nominal anchor.

Discretionary Monetary Policy Rule

The central bank wishes to minimize the quadratic loss function

minL = (απ2 + ((¯y − yˆ) + b(π − πe) + µ)2 + c(π − v + e)2 {π}

∂L = −b(¯y +y ˆ) + b2πe − bµ + cv − ce − (α + b2 + c)π = 0 ∂π

This solves for inflation

−b(¯y − yˆ) + b2πe − bµ + cv − ce π = (α + b2 + c)

We can solve for expected inflation

−b(¯y − yˆ) πe = (α + c) and the difference between actual inflation and expected inflation (for example, the ex-post inflation rate) is given by

−b bµ + c(v − e) πP = (¯y − yˆ) + α + c (α + b2 + c)

We can solve for equilibrium output

(α + c)µ + bc(v − e) yq =y ¯ + (α + b2 + c) 47

In this case, the central bank quadratic social loss function can be expressed as

2 −b −bµ+c(v−e) 2 (α+c)µ+bc(v−e) 2 −b −bµ+(α+b )(e−v) 2 L = α( α+c (¯y − yˆ) + (α+b2+c) ) + ((¯y − yˆ) + (α+b2+c) ) + c( α+c (¯y − yˆ) + (α+b2+c) ) and the expectation of the quadratic social loss function is

(α + b2 + c) (α + c)σ2 + c(α + b2)(σ2 + σ2) EL = (ˆy − y¯)2 + µ v e (2.25) 21 (α + c) (α + b2 + c)

(α+b2+c) in which the first term ( (α+c) ) reflects the inflation bias.

Monetary Aggregate Rule

We insert equilibrium inflation into the nominal exchange rate as follows

−(µ + bv) (s − s∗) = + e (1 + b)

The central bank quadratic social loss function can be expressed as

(v − µ) µ + bv −(µ + bv) L = α( )2 + ((¯y − yˆ) + )2 + c( + e)2 (1 + b) (1 + b) (1 + b)

We can write the expectation of the quadratic loss function as

(α + b2 + cb2)σ2 + (1 + a + c)σ2 EL = (ˆy − y¯)2 + cσ2 + v µ (2.26) 22 e (1 + b)2

Under monetary aggregate targeting, supply shocks, velocity shocks, and balance of payments shocks affect the nominal exchange rate, the inflation rate, and output, so we should expect the magnitude of these shocks to penalize the central bank quadratic loss function, especially when there is a greater weight placed on the parameters for stabilizing exchange rate and inflation fluctuations.

Nominal GDP Targeting Rule

Under nominal GDP targeting, the central bank varies its policy instrument to keep aggregate demand stable, thus v must also remain constant in the nominal exchange rate in equation (2.4). We must insert the equilibrium inflation into the nominal exchange rate as follows

−µ (s − s∗) = + e (1 + b) 48

The central bank quadratic social loss function can now be expressed as

−µ µ −µ L = α( + ε )2 + ((¯y − yˆ) + + ε )2 + c( + e)2 (1 + b) π (1 + b) y (1 + b) and the expectation of the quadratic social loss function is

2 (1 + α + c) 2 2 2 EL23 = (ˆy − y¯) + σ + cσ + ασ + σε (2.27) (1 + b)2 µ e επ y whereas Frankel and Bhandari’s (2015) original solution can be expressed as follows

(1 + α + c) EL = cσ2 + (ˆy − y¯)2 + σ2 023 e (1 + b)2 µ

Under nominal GDP targeting, in an open economy setting, although velocity shocks are accommodated, we cannot rule out balance of payments shocks or supply shocks, because they affect the nominal exchange rate, output, and inflation. Therefore, the magnitude of these shocks should also penalize the central bank quadratic loss function, especially when the short-run aggregate supply curve is relatively steep.

The magnitude of nominal GDP data revisions should also worsen the case for nominal GDP targeting.

The main difference between Frankel and Bhandari’s (2015) solution and our solution lies in the fact that we impose a cost for nominal GDP data revisions into equation (2.27) as a means to account for the cost of data uncertainty in implementing nominal GDP targeting.

Inflation Targeting Rule

In this case, we insert equilibrium inflation into the nominal exchange rate to obtain

(s − s∗) = e − v

The central bank social loss function can be expressed as

2 2 2 L = α(επ) + ((y − yˆ) + µ) + c(e − v) and the expectation of the quadratic social loss function becomes

EL = (ˆy − y¯)2 + ασ2 + cσ2 + cσ2 + σ2 (2.28) 24 επ e v µ whereas Frankel and Bhandari’s (2015) original solution can be expressed as follows 49

2 2 2 2 EL024 = (ˆy − y¯) + cσe + cσv + σµ

Here, velocity shocks and balance of payments shocks affect the nominal exchange rate, so the magnitude of these shocks should also penalize the central bank quadratic loss function under inflation targeting, especially when the preference parameter for stabilizing exchange rate fluctuations is relatively high. The magnitude of inflation revisions should also worsen the case for inflation targeting. The main difference between Frankel and Bhandari’s (2015) solution and our solution lies in the fact that we impose a cost for inflation data revisions into equation (2.28) as a means to account for the cost of data uncertainty in implementing inflation targeting.

Exchange Rate Peg Rule

We consider the example in which the BCEAO pegs the nominal exchange rate to the euro, so that there is no variation in (s − s∗). This argument can be represented by assuming

(s − s∗) = 0 in which case we can solve for the equilibrium exchange rate as follows

πq = v − e

We then solve for equilibrium output

yq =y ¯ + b(v − e) + u and the central bank quadratic social loss function becomes

L = α(v − e)2 + ((¯y − yˆ) + bv − be + u)2 with the expectation of the quadratic social loss function being

2 2 2 2 2 2 EL25 = (ˆy − y¯) + (α + b )σv + (α + b )σe + σµ (2.29)

Thus, even when the central bank commits to an exchange rate target, there is no guarantee that this move would send the correct signals to the economy. That is because inflation and output would remain 50 vulnerable to balance of payments shocks, velocity shocks, and supply shocks. Furthermore, the flatness of the short-run aggregate supply curve would amplify the central bank expected losses.

2.4 Simple AS-AD Model

This section is designed to provide some elementary economic theory behind the econometric model we will be using to estimate two key parameters: the slope of the short-run aggregate supply curve and the size of transitory supply shocks. The main proposition in this section is that the aggregate price level is determined by an interaction between the exogenous forces that affect the aggregate demand for goods and services and those that affect the ability of firms to supply these goods and services. On that note, on the supply side, the aggregate supply relationship is determined by a price-setting and wage-setting equilibrium and a production function linked to a productivity factor driven by rainfall. On the demand side, the goods market equilibrium is determined by an IS-LM equilibrium.

2.4.1 The Aggregate Supply Side

Let’s assume that in the short run, each firm employs a certain number of people given a fixed amount of capital. The firm’s production function can be expressed as follows

Yi = K¯iREi where R denotes rainfall, which is an important factor that drives labor productivity for most agricultural based economies (Kinda, 2011). Ei denotes the number of people employed by each firm and K¯i denotes the physical stock of capital employed by each firm. K¯i is expected to be fixed in the short run. For theoretical simplicity, we assume a homogeneous cost function. Hence, each producer faces the following cost function

Ci = ((1 + Υ)W )Ei + cK¯i

where Ci denotes the cost function for each firm. This cost function is simply comprised of a fixed cost and a variable cost. For simplicity, the only variable cost in this study emanates from labor costs and a mark up (Υ) that each firm charges over the nominal wage (W ). This mark up can arise as a result of changes in world energy prices, or other production costs driven by the sporadic increases in the world price of key fertilizers used in the farming process. c is the rental cost of capital. We aggregate all production and cost functions as follows 51

n X P = (1/n) Pi i=1

n X C = (1/n) Ci i=1 The profit maximization problem for the representative firm can be derived by

max Π = P KRE¯ − (1 + Υ)WE − cK¯ E taking the first order condition with respect to E yields the optimal price-setting condition. Note that since the capital stock is fixed in the short run, it can be viewed as a constant, so we can normalize by assuming that K¯ = 1. The price setting behavior of the representative firm would then be governed by the following price-setting function

P = P (R, W, Υ) (2.30) where rainfall (R) is an exogenous productivity factor that bolsters the ability of a farm to produce more output. The environment is not entirely competitive, thus the firm charges a mark up (Υ) over its marginal cost of production. An increase in the mark up is expected to put upward pressure on prices.

The price-setting relation can be linearized as follows

dP = PW dW + PΥdΥ + PRdR

PW > 0,PΥ > 0,PR < 0

The wage-setting relation can defined as

W = W (P e, u) (2.31)

Note that because rainfall shocks are unpredictable and difficult to forecast, they may or may not nec- essarily impact the wage-setting behavior.1 In this study, we assume that rainfall does not affect wage

1Note that the wage-setting equation is quite generic in this study. However, there are other exogenous factors that may affect the wage setting relation. For instance, changes in unemployment benefits, or minimum wage requirements are some plausible examples of exogenous factors that may directly impact wages. 52 contracts. u denotes the unemployment rate, while P e is the expected price level. An increase in the unemployment rate should reduce workers’ bargaining power and put downward pressure on nominal wages (Blanchard, 2005). Expectations about future increases in the price level should force workers to seek higher wages. The wage setting relation can be linearized as follows

e dW = WP e dP + Wudu

Wpe > 0,Wu < 0

Okun’s law predicts a negative relationship between real output and the unemployment rate. This relationship can be expressed as the following implicit function

u = U(Y ) (2.32) where Y denotes aggregate real output. A positive change in real output should put downward pressure on the unemployment rate. We can linearize Okun’s law as the difference equation

du = UY dY

UY < 0

Combining all necessary terms, the inverted short-run aggregate supply relation can be expressed as follows

e dP = λ1dP + λ2dY + λ3dΥ + λ4dR (2.33)

λ1 = PW WP e > 0, λ2 = PW WuUY > 0, λ3 = PΥ > 0, λ4 = PR < 0

The slope of the inverted short-run aggregate supply relation can be expressed as

dP = λ > 0 dY 2

Therefore, there should be a positive relationship between changes in real output and changes in prices. 53

2.4.2 The Aggregate Demand Side

The goods market equation can be expressed as follows

Y = A(i − P ) + NX(Y ∗, Y, ε) + G (2.34) where (A) denotes autonomous expenditures and Y denotes real aggregate output. NX denotes net exports, which is a function of a weighted average index of foreign income (Y ∗), domestic output (Y ), and an exogenous favorable terms of trade variable (ε). G is a purely exogenous fiscal expenditure factor.

The real interest rate (i − P ) affects the most sensitive components of aggregate demand (A) for private demand. These components are private consumption or private investment. We can express the monetary equilibrium as follows

M = m(Y, i∗) (2.35) P where M denotes the nominal money supply. (P ) is the aggregate price level. The demand for money is mainly influenced by domestic output and the opportunity cost of holding domestic bonds, which in this case is the foreign interest rate (i∗). Furthermore, the economy is governed by the following covered interest rate parity condition

i = i∗ + ee where ee denotes the expected rate of depreciation of the nominal exchange rate. We are assuming that the central bank operates under a fixed exchange rate regime. Hence, there is no reason to expect any changes in the spot rate. Therefore, there is also no reason to formulate any expectation about any future depreciation of the exchange rate. This assumption can be illustrated as follows ee = 0. Hence, the interest rate parity condition can be rewritten as

i = i∗ (2.36)

We insert the interest parity condition into the goods market equation as follows

Y = A(i∗ − P ) + NX(Y ∗, Y, ε) + G

We linearize the goods market as follows 54

∗ ∗ dY = Ai∗ di − AP dP + NXY ∗ dY + NXY dY + NXεdε + dG (2.37)

Ai∗ < 0,AP > 0,NXY ∗ > 0,NXY < 0,NXε > 0 where an increase in the foreign interest rate and the aggregate price level reduce the aggregate demand for domestic output. An increase in domestic output leads to an increase in domestic income leading to more imports, thereby decreasing net exports. An increase in foreign income and the world export prices of the country’s main cash crops improves net export, ceteris paribus. Let’s linearize (2.35) as follows

1 M ∗ dM − dP = m ∗ di + m dY P P 2 i Y

mi∗ < 0, my > 0 where an increase in the foreign interest rate leads to a decrease in the domestic demand for money. An increase in domestic output leads to an increase in the demand for money. We can solve for di∗ as

      ∗ 1 M mY di = dM − 2 dP − dY P mi∗ P mi∗ mi∗ and then insert di∗ into dY to obtain

h    i 1 dM M mY ∗ dY = Ai∗ − ( 2 )dP − dY − AP dP + NXY ∗ dY + NXY dY + NXεdε + dG mi∗ P P mi∗ mi∗

Solving for the aggregate demand relation yields

∗ dY = λ5dM − λ6dP + λ7dY + λ8dε + λ9dG (2.38)

2 Ai∗ Ai∗ M+P mi∗ AP λ5 = Θ > 0, λ6 = Θ( 2 ) > 0, λ7 = ΘNXY ∗ > 0, λ8 = ΘNXε > 0, λ9 > 0 P mi∗ P mi∗

  m ∗ Θ = i > 0 (1 − NXY )mi∗ + Ai∗ mY where the slope of the short-run aggregate demand relation is

dY = −λ < 0 dP 6 55

2.4.3 Comparative Statics

We can express the aggregate demand and aggregate supply relations as a system of linear equations as

      e 1 −λ2 dP λ1dP + λ3dΥ + λ4dR     =   (2.39)      ∗  λ6 1 dY λ5dM + λ7dY + λ8dε + λ9dG

Note that the aggregate supply side is over identified, because the number of exogenous demand instru- ments excluded from the short-run aggregate supply curve exceeds the number of endogenous variables.

The effect of an exogenous shock in rainfall on the change in the price level is given by

dP λ = 4 < 0 dR (1 + λ2λ6)

Thus, a positive change in rainfall shifts the short-run aggregate supply curve downward, putting down- ward pressure on prices. Alternatively, suppose that the firm’s mark up rises as a result of an increase in energy costs or greater monopoly of firms

dP λ = 3 > 0 dΥ (1 + λ2λ6)

In this case, a positive change in the mark up shifts the short-run aggregate supply curve upward, putting upward pressure on prices.2 The effects of a positive change in government spending on prices and output are given by

dY λ = 9 > 0 dG (1 + λ2λ6)

dP λ λ = 2 9 > 0 dG (1 + λ2λ6)

An exogenous shock in government spending shifts the short-run aggregate demand curve upward, causing both an increase in the price level and domestic output in the short-run. Finally, a favorable terms of trade shock (for example, a positive change in the world price of cocoa) would affect output as follows

dY λ = 8 > 0 dε (1 + λ2λ6)

2Note that in this example, we are assuming that the country in question is a net oil importer. 56

A favorable terms of trade shock caused by an increase in the export price of cocoa should cause real output to rise. Given some nominal rigidities, as farmers’ profits begin to rise, they would be inclined to produce more.

2.4.4 Estimating Structural Parameters

In this section, the main objective is to estimate two parameters: the slope of the short-run aggre- gate supply curve and the size of transitory supply shocks. The slope of the short-run aggregate supply curve would allow us to determine whether the economy is operating at excess or full capacity. A flat slope would indicate that the economy is operating within the excess capacity zone and a steep slope would indicate that the economy is operating within the full capacity zone.

At excess capacity, the economy would be operating far below its potential (for example, the case where short-run aggregate supply curve is relatively flat). Under these circumstances, if the economy is extremely vulnerable to supply shocks, a strict inflation rule would be depressive and less efficient as compared to a rule that would prioritize output. Therefore, in order to evaluate the optimal monetary policy, we must have some estimates of the elasticity of supply and the size of supply shocks. In order to achieve this objective, we must estimate the short-run aggregate supply curve. The inverted short-run aggregate supply curve can be expressed as a reduced form equation as follows

λ λ λ λ (λ dM + λ dY ∗ + λ dε + λ dG) dp = 1 dP e + 3 dΥ + 4 dR + 2 5 7 8 9 (2.40) (1 + λ2λ6) (1 + λ2λ6) (1 + λ2λ6) (1 + λ2λ6)

Our goal is to estimate (2.40) directly, but we already face two econometric issues: output is endogenous and the inverted short-run aggregate supply curve is over identified. Hence, given the endogeneity of output and the over identification issue, we can still obtain the unbiased estimates of all the parameters expressed in (2.40) by using the Two-Stage least squares (2SLS) approach specified in equation (2.41)

ˆ 0 0 −1 0 0 −1 0 φ2sls = (X GG G) (X GG G) G Y (2.41)

2.4.5 Data

We use annual data from 1970 to 2012 to estimate the determinants of inflation for the WAEMU.

Rainfall data are collected from the Center for Environmental Data Analysis (CEDA) database. Nominal commodity price data are collected from the Global Economic Monitor (GEM) database. Macroeconomic data are collected from the World Development Indicators (WDI) database, the International Finance 57

Statistics (IFS) database, and the BCEAO’s database. We collect data for the founding members of the

WAEMU (Benin, Burkina Faso, Cote d’Ivoire, Mali, Niger, Senegal, and Togo). Guinea-Bissau is omitted from the analysis, because the country joined the monetary union in the summer of 1997, so we faced a lack of historical data.

The Endogenous Variables

The Ex Post Inflation Rate

I used the GDP deflator and the consumer price index data as my two proxies for approximating the aggregate price level for the WAEMU. We calculate the inflation rate as follows

πt = pt − pt−1

where pt is the log of the GDP deflator or consumer price index and πt is the actual inflation rate measured by the first difference of the log of the price indicator. Figure 2.1 plots the inflation rate (deflator) series for the WAEMU over time. Although the MA(1) and ARMA(1 1) models provided some good empirical results, modeling inflation expectations with the random walk model produced the strongest empirical estimates.3 I used the MA(1), AR(1), and ARMA(1 1) processes to model inflation expectations. I used the residuals from the MA(1), AR(1), and ARMA(1 1) models as my three proxies for the ex post inflation rate and named them as follows: (INF ARMAD), (INF MAD), and (INFRWD).

3Please refer to Table B.2 for unit root tests. 58

Benin Burkina Faso Cote d'Ivoire 40 30 15 30 20 10 20 5 10 10 Percent Percent Percent 0 0 0 -5 -10 -10 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

Mali Niger Sengeal 30 30 30 20 20 20 10 10 10 Percent Percent Percent 0 0 0 -10 -10 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

Togo 40 30 20 10 Percent 0 -10

1970 1980 1990 2000 2010

Figure 2.1: Inflation, GDP Deflator (Base Year=2010)

The Real Output Gap

I used an aggregated index of real GDP expressed in local currency as the proxy for real output for the WAEMU. I followed a similar approach taken by Hodrick and Prescott (1997) to compute the

4 cyclical component of the index and used it as my main proxy for the real output gap (ogapt). Figure 2.2 presents the real output gap series for the WAEMU. We find that the effects of the Sahel droughts during the early 1980s were strongly correlated across the countries. Cross correlation of real shocks within a monetary union is a desirable outcome for monetary policy efficiency.

4Please refer to Table B.3 for unit root tests. 59

Benin Burkina Faso Cote d'Ivoire 4 15 5 2 10 0 5 0 -2 0 Percent Percent Percent -4 -5 -5

Sahel droughts (1983-1985) -6 Sahel droughts (1983-1985) Sahel droughts (1983-1985) 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

Mali Niger Senegal 10 10 10 5 5 5 0 0 0 -5 -5 Percent Percent Percent Sahel droughts (1983-1985) Sahel droughts (1983-1985) -10 -5 -10 Sahel droughts (1983-1985) -15 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

Togo WAEMU-7 10 10 5 0 0 Percent Percent -5 -10

Sahel droughts (1983-1985) Sahel droughts (1983-1985) -10

-20 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 Data Source: WDI, author's calculations

Figure 2.2: The Real Output Gap (Base Year=2010)

Exogenous Demand Instruments Foreign Income Proxy

We used nominal GDP data expressed in dollars for the key historical trading partners (for example, we used the United States, Germany, the Netherlands, India, China, and France). We converted the series from dollars to CFA francs and then deflated them by the price level proxy. I used various weights to create a weighted index of foreign income. The United States was given the highest weight, because it remains the most essential trading partner for the WAEMU. I used the cyclical component of the index

5 as my main proxy for world demand (wogapDt). Figure 2.3 shows a strong covariance between nominal economic activities proxy by the nominal GDP gap and our proxy for world demand.

5Please refer to Table B.3 for unit root tests. 60

We see a strong correlation between the economic crisis of 1988-1993 and the collapse in world demand. Furthermore, we can see that world demand was weak prior the devaluation of the CFA franc in

1994, but not surprisingly we observe a significant surge in world demand after this episode. This surge in world demand following the devaluation of the CFA franc in 1994 is an expected outcome since the price of the domestic currency fell, but it could have also been caused by the recovery of the US economy during the early 1990s. In any case, the strength of the covariance between the nominal GDP gap and world demand can only bolster the robustness of our world demand indicator.

Nominal GDP gap World Demand 20

Pre crisis Post crisis 10

Pre devaluation (1993) 0 Percent -10 -20

1985 1990 1995 2000

Data Source: WDI, author's calculations

Figure 2.3: Co-Movements of Nominal Economic Activities (Nominal GDP Gap) and World Demand (Key Trading Partners’Income Gap)

Export Price Proxies

I used the world prices of three key commodities: cocoa, cotton, and tobacco. The prices were converted into CFA francs and deflated by the domestic price level. I used the cyclical component of real cocoa prices as my real cocoa price surprise indicator (cocoasDt). I used the cyclical component of real tobacco prices as my real tobacco price surprise indicator (tabasDt). I used the cyclical component of real cotton prices as my real cotton price surprise indicator (cottonsDt). 61

I created a real commodity price index by using a weighted average price index based on real cocoa prices, real cotton prices, and real tobacco prices. I used the log of this index as my main commodity price index (compexDt). The index follows a random walk and is stationary in levels, which shows that real commodity prices remain highly unpredictable for the WAEMU. Figure 2.4 depicts the movements of the four export price proxies over time.

Real cocoa price surprise Real tabacco surprise 60 20 40 0 20 0 Percent Percent -20 -20 -40 -40 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

Real cotton price surprise Log of commodity price index 9.2 40 9 20 0 8.8 Level Percent 8.6 -20 8.4 -40 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

Data Source: WDI, GEM commodities, author's calculations

Figure 2.4: Commodities Price Indicators for the WAEMU-7: Real Cocoa Price Surprise (cocoasDt), Real Tobacco Price Surprise (tabasDt), Real Cotton Price Surprise (cottonsDt), and the Log of the Commodity Price Index (compexDt)

Fiscal Indicator

I used final government consumption expenditures deflated by the price level as the fiscal indicator.

I used the cyclical component of the index as my real government spending surprise indicator (gsDt). Figure 2.5 depicts the relationship between the real output gap and real government spending surprise.6

Fiscal policy is most effective in a fixed exchange rate regime, so we expect our real government spending surprise indicator to be strongly correlated to the real output gap.

6Please refer to Table B.3 for unit root tests. 62

Real output gap 20 Real government spending surprise

Booming commodity prices (1976-1979) 10 0 Percent

Pre Sahel droughts Post Sahel droughts -10 -20 1970 1975 1980 1985 1990 1995

Data Source: WDI, author's calculations

Figure 2.5: Co-Movements of Real Economic Activities and Real Government Spending Surprise for the WAEMU-7

In Figure 2.5, we see a strong covariance between real economic activities and real government spending surprise, but the interesting part is that government spending tends to be pro-cyclical. We observe strong government spending during booming commodities prices and weak government spending during adverse economic conditions. This is a stylized fact that has also been observed for other developing countries by (Frankel et al., 2012).

Monetary Policy Indicators

In a fixed exchange rate regime, the money supply it is endogenous to factors that are exogenously determined outside of the central bank’s control and this makes the overall money supply exogenously determined. Therefore, we treat the money supply as an exogenous demand factor. We use the monetary mass and M1 deflated by the price level as our two exogenous monetary policy indicators. I used the cyclical component of these two indicators as my two exogenous monetary policy indicators (mmsDt)

7 and (msDt). We see a strong positive correlation between real economic activities and the money supply in Figure 2.6.

7Please refer to Table B.3 for unit root tests. 63

Fitted values 10 5 0 Real output gap -5 -10 -20 -10 0 10 20 Real Monetary Mass Surprise

Data Source: WDI, author's calculations

Figure 2.6: Correlation of the Real Output Gap and Real Monetary Mass Surprise (mmsDt)

Backward and Forward Lags

We follow the New Keynesian approach and use a forward lag (ogapt+1) and a backward lag

(ogapt−1) of the real output gap as additional exogenous demand instruments.

Exogenous Supply Instruments Rainfall

I used the average number of days of rainfall per year in the WAEMU zone as my main proxy for rainfall. The variable is stationary in levels, which tells us that daily rainfall patterns follow a random walk. However, I also use the cyclical component of the variable to capture the effect of rainfall surprise

(rainst). We expect a robust correlation between rainfall surprise and the real output gap. A positive supply shock of rainfall should shift the short-run aggregate supply curve downward, thereby putting downward pressure on inflation.

We examine the correlation between rainfall surprise, the ex post inflation rate, and the real output gap (ogapt) in Figure 2.7. We can see a positive relationship between the real output gap and rainfall surprise, but as expected we also see a negative relationship between the ex post inflation rate and rainfall surprise. 64

Fitted values Fitted values 10 30 5 20 0 10 Real output gap Ex-post inflation rate AR(1) 0 -5 -10 -10 -20 -10 0 10 -20 -10 0 10 Rainfall surpise Rainfall surprise

Data Source: CEDA, WDI, author's calculations

Figure 2.7: Correlation of Rainfall Surprise, Ex Post Inflation, and the Real Output Gap

Devaluation

I used the devaluation of the CFA franc in 1994 as a negative terms of trade shock (devaluation1994) because the cost of imports rose rapidly in that specific year. We expect to find a significant pass-through of the devaluation into domestic inflation.

Mark-Up Proxy

I used the crude oil average spot prices as my proxy for the mark-up. I converted the oil prices from US dollars to CFA francs and then deflated the prices by the price level. Real crude oil inflation

(goilDt) was calculated by using the first difference of the real crude oil price index. Real crude oil price surprise (oilsDt) was calculated by using the cyclical component of the real oil price index. The effect of the 1970s oil shocks were mildly felt across the WAEMU, but the strongest impact was felt in the

Togolese case. We examine this case in Figure 2.8.8

8Please refer to Table B.3 for unit roots. 65

Real crude oil inflation Ex-post-inflation-deflator-ar(1) 100

(1973-1974) First oil shock

(1979) Second oil shock 50 Percent 0 -50 1970 1972 1974 1976 1978 1980

Data Source: WDI, GEM commodities, author's calculations

Figure 2.8: Co-Movements of Real Crude Oil Inflation (goilDt) and the Ex Post Inflation Rate (INFRWD) for Togo

Foreign Inflation Index

We use France’s consumer price inflation (finft) to capture the effect of an exogenous change in foreign inflation on domestic inflation. Since the CFA franc has been pegged to France’s exchange rate since 1948, it is likely that inflation across both countries would be strongly correlated. (finft) failed to satisfy the unit root test, but that was prior to the identification of a structural break identified in 1984.

The structural break could have been caused by the French authorities’ efforts to curb inflation through forceful legislation at the onset of the 1980s series of currency depreciations. In Figure B.1, we plot the results of the unit root test with breakpoints. The estimated ADF test statistic was found to be

-4.45 with a corresponding p-value of less than 0.02, allowing us to reject the previous findings of a unit root in the series. 66

2.4.6 Empirical Results WAEMU-7

Table 2.1 reports the first-stage results of the 2SLS model specified in equation (2.41) for the

WAEMU-7 (Benin, Burkina Faso, Cote d’Ivoire, Mali, Niger, Senegal, and Togo). The Breusch-Pagan/Cook-

Weisberg test for heteroskedasticity rejects the presence of heteroskedasticity in all the estimated regres- sions (1-8). In the first-stage results, the instrument relevance condition is satisfied in all the estimated regressions (1-8). The tests of weak instruments also suggest that the instruments are not weak.

Given the robustness of the diagnostic tests found in the first-stage results, we found no surprises in the second-stage results. Furthermore, all directions of causality found between the instruments and the output gap (ogapt) were as predicted by the theoretical framework. In models (1-8), rainfall surprise

(rainst) is highly significant in explaining the real output gap, which is consistent with our theoretical claim that rainfall remains a significant determinant of aggregate output for agriculture based economies.

The 1994 devaluation (devaluation1994) may have stabilized nominal output, but our results show that it has a negative impact on the real output gap.

As expected, our monetary policy indicators do play a significant role in explaining domestic de- mand. (mmsDt) and (msDt) are significant in explaining the real output gap. The fiscal indicator (gsDt) and the world income gap (wogapDt) are also statistically significant in explaining the domestic demand. Strong commodity prices remain the anchor of growth for the WAEMU zone. All the commodity price indicators are statistically significantly in explaining domestic demand.

Real cocoa price surprise (cocoasDt), real tobacco price surprise (tabasDt), and our commodity price index (compexDt) are all statistically significant in explaining domestic demand. It is safe to conclude that the first-stage results are consistent across all estimated models (1-8). Furthermore, the regression estimates presented in Table 2.1 are the respective first-stage regression estimates of each of the second-stage regressions (1-8) reported in Table 2.2. 67 ∗ ∗∗ t ∗∗ ∗∗∗ ∗∗∗ ogap 0.137 0.00156 (0.0563) (0.0352) -0.0363 0.456 0.217 0.0855 ∗ ∗ ∗ t ∗∗∗ ∗∗∗ ∗∗∗ ogap 0.0334 (0.0481) -0.0381 0.164 0.572 0.135 ∗∗ t ∗∗ ∗∗∗ ∗∗∗ ogap 0.118 (0.0490) 0.509 0.227 ∗∗ t ∗∗∗ ∗∗∗ ogap -0.0356 -0.0423 (0.0237) 0.221 0.642 0.0587 ∗ ∗ t ∗∗∗ ∗∗∗ ogap -0.0110 0.00260 0.00221 0.00161 -0.292 0.0269 (0.0903) (0.0145) -0.0388 0.184 0.619 ∗ ∗∗ t ∗∗ ∗∗∗ ∗∗∗ ogap (0.0197) (0.0183) 0.215 0.626 0.0423 t ∗∗ ∗∗∗ ∗∗∗ ogap 0.141 (0.0488) 0.552 0.150 t ∗∗ ∗∗∗ ∗∗∗ (1) (2) (3) (4) (5) (6) (7) (8) 0.63911.59 0.637 11.59 0.594 11.59 0.583 11.59 0.609 11.59 0.714 11.59 0.669 12.83 0.691 12.83 ogap (0.121) (0.105) (0.110) (0.113) (0.109) (0.0945) (0.103) (0.115) -0.0215 -0.0322 -0.0378 0.127 0.00111 0.0013522.0983 -0.369 21.9227 17.6653 16.1239 19.0191 32.5277 16.6731 18.7109 (0.0580) (0.0567) (0.0582) (0.0592) (0.0574) (0.0507) (0.0564) (0.0545) (0.0205) (0.0204) (0.0218) (0.0228) (0.0213) (0.0183) (0.0200) (0.0202) (0.0578) -0.00585 -0.00172 -0.0116 -0.0107 -0.0123 -0.00411 -0.00214 -0.00650 0.411 0.180 (0.00309) (0.00309) (0.173) (0.00520) (0.00320) (0.00274) (0.161) (0.00290) (0.00972) (0.0101) (0.0101) (0.0108) (0.00987) (0.00864) (0.00981) (0.00912) (1970-2012) (1970-2012) (1970-2012) (1970-2012) (1970-2012) (1970-2012) (1970-2012) Table 2.1: 2SLS Results, First-Stage Results for WAEMU-7 01 p < . chi2 0.5840 0.4507 0.2926 0.2582 0.2391 0.2832 0.4798 0.4692 ∗∗∗ > 05, . Prob 0 p < P agan ∗∗ 1994 Donald Minimum Eigenvalue Statistics − t t 10, − . t 1 t 0 t t t − Instruments are weak rejected rejected rejected rejected rejected rejected rejected rejected t t t t : p < P ercent Nominal W ald T est 2 cons R Breusch 5 Ho rains Y ears Cragg Standard errors∗ in parentheses goilD devaluation ogap gsD mmsD compexD finf cocoasD tabasD msD wogap 68

Table 2.2 reports reports the second-stage results of the 2SLS model specified in equation (2.41) for the WAEMU-7. The diagnostics tests support that the estimated regessions (1-8) are structured appropriately. This conclusion is based on the fact that we cannot reject the null hypothesis that the overidentifying restrictions are valid. Our estimates for the slope of the inverse short-run aggregate supply curve are statistically significant and the range of the point estimates is from .37 to .54 for the elasticty.

These results are consistent with the findings of Bhandari and Frankel (2015) for India. The slope is positive, suggesting that the short-run aggregate supply curve is upward sloping with a range of 1.85 to

2.70 for the elasticity.

Given this range of elasticity, it is safe to conclude that the short-run aggregate supply curve is relatively flat for the WAEMU. As expected, rainfall surprise puts downward pressure on inflation. There is a significant pass-through of imported oil inflation on domestic inflation. Crude oil inflation (goilDt) is statistically significant in explaining domestic inflation. The 1994 devaluation has a significant and positive impact on domestic inflation. Imported inflation from France (finft) is statistically significant in explaining domestic inflation for the WAEMU-7. The second-stage results are fairly consistent across the estimated regressions (1-8). 69 ∗ ∗ ∗∗∗ ∗∗∗ ∗∗∗ 0.374 0.286 0.00930 -0.271 0.0569 ∗ ∗ ∗∗∗ ∗∗∗ ∗∗∗ 0.410 0.288 0.00923 -0.278 0.0571 ∗ ∗ ∗∗∗ ∗∗∗ ∗∗∗ 0.417 0.288 0.00922 -0.279 0.0572 ∗ ∗ ∗∗∗ ∗∗∗ ∗∗∗ 0.474 0.291 -0.290 0.0576 ∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ 0.248 (0.128) 0.538 -0.00191 0.00910 0.304 -0.290 0.0492 ∗ ∗ ∗∗∗ ∗∗∗ ∗∗∗ 0.431 0.289 0.00919 -0.282 0.0573 ∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ 0.503 0.293 0.00904 -0.296 0.0578 ∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ 01 (1) (2) (3) (4) (5) (6) (7) (8) 0.684 0.681 0.689 0.703 0.685 0.690 0.690 0.693 Table 2.2: 2SLS Results, Second-Stage Results for the WAEMU-7 (0.241) (0.242) (0.252) (0.251) (0.249) (0.221) (0.231) (0.224) 0.476 (0.0977) (0.0983) (0.0983) (0.0968) (0.0985) (0.0953) (0.0961) (0.0953) (0.0152) (0.0153) (0.0151) (0.0155) (0.0152) (0.0150) (0.0150) (0.0150) (0.0345) (0.0347) (0.0346) (0.0340) (0.0347) (0.0338) (0.0340) (0.0338) 0.291 0.00910 (0.00494) (0.00497) (0.00491) (0.00737) (0.00494) (0.00489) (0.00489) (0.00487) -0.291 0.0576 p < . INFRWD INFRWD INFRWD INFRWD INFRWD INFRWD INFRWD INFRWD (1970-2012) (1970-2012) (1970-2012) (1970-2012) (1970-2012) (1970-2012) (1970-2012) (1970-2012) ∗∗∗ chi2 .852729 .497588 0.0972 0.9633 0.2770 0.2556 0.2453 0.2697 05, . 0 > p < Prob ∗∗ 1994 10, . 0 t t t t p < 2 cons Sargan T est ogap Y ears R Standard errors∗ in parentheses rains goilD devaluation finf 70

Individual Countries

Here we report the second-stage estimates for the individual countries for which we found meaningful econometric results. Table 2.3 reports the second-stage results of the 2SLS model specified in equation

(2.41) for Burkina Faso. The diagnostic test (for example, the Sargent test and test of weak instruments) give us mix results on the strength of the empirical models reported in Table 2.3. First, we cannot reject the null hypothesis of weak instruments, but the overidentifying restrictions condition is satisfied, suggesting that the model is appropriately structured to some extent. Hence, we can conclude that the problem lies in finding additional suitable instruments for Burkina Faso.

(1) (2) (3) INFMAINFRWINFRW ∗ ∗ ∗∗ ogapt 1.229 1.239 1.578 (0.662) (0.678) (0.757)

rainst -0.0974 -0.182 (0.190) (0.201)

oilst 0.0454 (0.0429)

∗∗∗ ∗∗∗ ∗∗∗ finft 0.862 0.848 0.868 (0.250) (0.246) (0.264)

∗∗∗ ∗∗∗ ∗∗∗ devaluation1994 0.277 0.299 0.305 (0.0684) (0.0725) (0.0722)

cons 0.000225 -0.00710 -0.00795 (0.0130) (0.0133) (0.0142) Y ears (1970-2012) (1970-2012) (1970-2012) R2 0.412 0.367 0.272 Sargan p-value 0.6293 0.5422 0.8190 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 2.3: 2SLS Results, Second-Stage Results for Burkina Faso

Our estimates for the slope of the inverse short-run aggregate supply curve remain statistically significant and the range of the point estimates is from 1.22 to 1.57 for the elasticity. The slope is positive, suggesting that short-run aggregate supply curve is upward sloping with a range of .63 to .81 for the elasticity. Rainfall surprise is statistically insignificant, despite having the correct expected sign.

Crude oil price surprise (oilst) is statistically insignificant, despite having the correct sign. The 1994 devaluation has a significant and positive impact on domestic inflation. Imported inflation from France

(finft) is statistically significant in explaining domestic inflation for Burkina Faso. 71

Table 2.4 reports the second-stage results of the 2SLS model specified in equation (2.41) for Cote d’Ivoire. The diagnostic tests do not impede the strength of the empirical estimates reported in Table

2.4. The Ivorian case is probably the most robust individual case included in this study. Our estimates for the slope of the inverse short-run aggregate supply curve are statistically significant and the range of the point estimates is from .29 to .49 for the elasticity. The slope is positive, suggesting that the short-run aggregate supply curve is upward sloping with a range of 2.04 to 3.44 for the elasticity. Given this range of elasticity, it is safe to conclude that the short-run aggregate supply curve is relatively flat for Cote d’Ivoire.

(1) (2) (3) INFRW INFMA INFMAD ∗ ∗∗ ∗ ogapt 0.318 0.295 0.490 (0.174) (0.148) (0.285)

∗ rainst -0.192 -0.0747 -0.0570 (0.111) (0.0977) (0.186)

oilst 0.0110 (0.0285)

∗∗∗ ∗∗∗ ∗∗∗ devaluation1994 0.224 0.232 0.343 (0.0412) (0.0364) (0.0691)

∗∗∗ finft 0.527 (0.142)

∗∗ oilsDt 0.109 (0.0458)

cons 0.00576 0.00873 0.0292∗∗∗ (0.00654) (0.00858) (0.0109) Y ears (1970-2012) (1970-2012) (1970-2012)

R2 0.438 0.581 0.409 Sargan p-value 0.8294 0.2192 0.6021 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 2.4: 2SLS Results, Second-Stage Results for Cote d’Ivoire

As expected, rainfall surprise puts downward pressure on inflation, but the effect of rainfall on domestic inflation is not statistically significant across all regressions estimates presented in Table 2.4.

Nonetheless, we find a significant pass-through of imported oil inflation on domestic inflation. Crude oil price surprise (oilsDt) is statistically significant in explaining domestic inflation, but again this result lacks empirical consistency in Table 2.4. The 1994 devaluation has a significant and positive impact 72

on domestic inflation. Imported inflation from France (finft) is statistically significant in explaining domestic inflation for Cote d’Ivoire.

Table 2.5 reports the second-stage results of the 2SLS model specified in equation (2.41) for Niger.

The diagnostic tests do not impede the strength of the empirical estimates reported in Table 2.5. The empirical results are quite robust for Niger. Our estimates for the slope of the inverse short-run aggregate supply curve are statistically significant and the range of the point estimates is from .50 to .60 for the elasticity. The slope is positive, suggesting that the short-run aggregate supply curve is upward sloping with a range of 1.73 to 2 for the elasticity.

(1) (2) (3) INF MAD INF RW D INF ARMAD ∗∗∗ ∗∗∗ ∗∗ ogapt 0.577 0.594 0.500 (0.214) (0.229) (0.222)

∗∗∗ ∗∗ ∗∗ rainst -0.256 -0.249 -0.207 (0.0991) (0.106) (0.103)

∗∗ finft 0.448 0.321 0.304 (0.211) (0.226) (0.219)

∗∗∗ ∗∗∗ ∗∗∗ devaluation1994 0.318 0.318 0.320 (0.0539) (0.0577) (0.0559)

cons 0.00583 0.00418 -0.0239∗ (0.0128) (0.0137) (0.0133) Y ears (1970-2012) (1970-2012) (1970-2012) R2 0.443 0.387 0.429 Sargan p-value 0.6027 0.6467 0.4049 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 2.5: 2SLS Results, Second-Stage Results for Niger

Given this range of elasticity, it is safe to conclude that the short-run aggregate supply curve is relatively flat for Niger. As expected, rainfall surprise puts downward pressure on inflation. The 1994 devaluation has a significant and positive impact on domestic inflation. Imported inflation from France

(finft) is statistically significant in explaining domestic inflation. We can conclude that the second-stage results are fairly consistent in Table 2.5.

Table 2.6 reports the second-stage results of the 2SLS model specified in equation (2.41) for Senegal.

The diagnostic tests (for example, the Sargent test and the test of weak instruments) give us mix results on the strength of the empirical estimates presented in Table 2.6. First, we cannot reject the null hypothesis of weak instruments and the overidentifying restrictions condition is only satisfied in regressions (1) and 73

(2) in Table 2.6. In any case, our estimates for the slope of the inverse short-run aggregate supply curve remain statistically significant and the range of the point estimates is from .69 to 1.07 for the elasticity.

(1) (2) (3) INFMAINFRWINFMA ∗ ∗∗ ∗ ogapt 0.956 1.070 0.698 (0.503) (0.455) (0.413)

∗ ∗∗ ∗∗ rainst -0.111 -0.125 -0.133 (0.0646) (0.0583) (0.0528)

∗∗∗ ∗∗∗ oilst 0.0923 0.0793 (0.0260) (0.0235)

∗∗∗ ∗∗∗ ∗∗∗ devaluation1994 0.302 0.323 0.326 (0.0427) (0.0386) (0.0359)

∗∗∗ finft 0.768 (0.143)

cons 0.0231∗∗∗ 0.0101∗ -0.00720 (0.00601) (0.00543) (0.00756) Y ears (1970-2012) (1970-2012) (1970-2012) R2 0.656 0.714 0.758 Sargan p-value 0.4449 0.3644 0.0239 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 2.6: 2SLS, Second-Stage Results for Senegal

The slope is positive, suggesting that the short-run aggregate supply curve is upward sloping with an elasticity in the range of .93 to 1.44 for Senegal. Rainfall surprise is statistically significant in explaining domestic inflation. Crude oil price surprise (oilst) is statistically statistically significant in explaining domestic inflation. The 1994 devaluation has a significant and positive impact on domestic inflation.

Imported inflation from France (finft) is strongly significant in explaining domestic inflation. Table 2.7 reports the second-stage results of the 2SLS model specified in equation (2.41) for Togo.

The diagnostic tests do not impede the strength of the empirical estimates presented in Table 2.7. Our estimates for the slope of the inverse short-run aggregate supply curve are statistically significant and the range of the point estimates is from .46 to .72 for the elasticity. The slope is positive, suggesting that the short-run aggregate supply curve is upward sloping with an elasticity in the range of 1.38 to 2.17 for

Togo.

Given this range of elasticity, it is safe to conclude that the short-run aggregate supply curve is relatively flat for Togo, but not as flat as in some of the other countries. As expected, rainfall surprise 74

puts downward pressure on inflation. Crude oil price surprise (oilsDt) is statistically significant in explaining domestic inflation. The 1994 devaluation has a significant and positive impact on domestic inflation. Imported inflation from France (finft) is also significant in explaining domestic inflation. We can conclude that the second-stage results are fairly consistent across the regression estimates presented in Table 2.7.

(1) (2) (3) INF RW D INF MAD INF MA ∗ ∗ ∗∗ ogapt 0.724 0.720 0.466 (0.419) (0.417) (0.223)

∗∗ oilsDt 0.102 (0.0506)

∗ ∗ ∗ rainst -0.302 -0.308 -0.172 (0.166) (0.165) (0.0933)

∗∗∗ ∗∗∗ ∗∗∗ devaluation1994 0.321 0.355 0.370 (0.0908) (0.0898) (0.0515)

∗∗ ∗∗∗ finft 0.702 0.680 (0.330) (0.191)

cons 0.0422∗∗∗ 0.0103 -0.00174 (0.0128) (0.0200) (0.0114) Y ears (1970-2012) (1970-2012) (1970-2012) R2 0.215 0.223 0.568 Sargan p-value 0.5459 0.2933 0.8117 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 2.7: 2SLS Results, Second-Stage Results for Togo

2.5 Case Studies

2.5.1 Calibration Process

In this section, our goal is to calibrate the key parameters needed to estimate the central bank quadratic loss functions for the open economy. We have already obtained a range of estimates for the slope of the short-run aggregate supply curve and the variance of supply shocks. In addition, we derive the other parameters as follows

n X (e − e¯) σ2 = (2.42) e n − 1 t=1 75

n X (v − v¯) σ2 = (2.43) v n − 1 t=1

n X (εy − ε¯y) σ2 = (2.44) εy n − 1 t=1

n X (επ − ε¯π) σ2 = (2.45) επ n − 1 t=1

2 CF A francs 2 where σe is the variance of the change in the nominal exchange rate ( US dollars ), σv is the variance of the change in the velocity of money ( nominal GDP ), σ2 is the variance of the historical revisions of masse monetaire εy real GDP growth, and σ2 is the variance of the historical revisions of inflation. Table 2.8 provides a επ summary of all the parameters derived from the theoretical framework.

Parameters Description α Preference parameter for stabilizing inflation fluctuations b The slope of the short-run aggregate supply curve c Preference parameter for stabilizing exchange rate fluctuations σ2 The historical variance of the real GDP growth revisions εy σ2 The historical variance of inflation revisions επ 2 σµ The variance of the estimated supply shocks 2 σe The variance of the change in the nominal exchange rate (CF A/$) 2 σv The variance of the change in the velocity of money

Table 2.8: Critical Parameters of the Central Bank Quadratic Loss Functions

There are four parameters that we could not calibrate directly: the variance of data revisions (σ2 εy and σ2 ) and the preference parameters for stabilizing inflation and exchange rate fluctuations (α and c). επ If the central bank is strongly committed in stabilizing the inflation rate, then the weight exerted on the preference parameter for stabilizing inflation fluctuations (α) should be closer to 1. Also, if the central bank is strongly committed in stabilizing the exchange rate, then the weight exerted on the preference parameter for stabilizing exchange rate fluctuations (c) should also be closer to 1.

On the other hand, if the central bank is least interested in stabilizing the exchange rate or the inflation rate, then the preference parameters for stabilizing the exchange rate and the inflation rate should have the least impact on the central bank quadratic loss functions. This would be the case when both α and c are less than 1 or closer to 0, which is the least interesting case in this chapter.

The nominal exchange rate (CF A/$) is extremely important for trade, so we would expect the

BCEAO to be eager to want stabilize it, especially under a flexible exchange rate regime such as nominal 76

GDP targeting, monetary aggregate targeting, or inflation targeting. Therefore, we calibrate our prefer- ence parameter for stabilizing exchange rate fluctuations (c) at 1. Also, price stability remains another primary objective for the WAEMU, so we calibrate our preference parameter for stabilizing inflation

fluctuations (α) at 1.

We do not estimate equations (2.44) and (2.45) directly, because of no data availability for real

GDP growth revisions and inflation revisions. Nonetheless, since we do not have the precise estimates of the variance of inflation and real GDP revisions for the WAEMU, we allow the reader to choose what he or she expects the variance of data revisions to be. In any case, the main argument in this chapter is that the magnitude of inflation and output revisions should reduce the desirability of implementing either inflation targeting or nominal GDP targeting as compared to implementing a rigid peg or monetary aggregate targeting. Whereas, when data revisions are small, their impact should only impose a small cost on the central bank quadratic loss function under inflation targeting and nominal GDP targeting.

2.5.2 Optimal Conditions

The main objective in this study is to determine whether nominal GDP targeting minimizes the central bank quadratic loss function in an open economy setting for the WAEMU. Therefore, we compare nominal GDP targeting to monetary aggregate targeting, inflation targeting, and exchange rate targeting. we derive the optimal conditions as follows

Nominal GDP targeting versus monetary aggregate targeting

2 2 2
2 2 σv α + b c + b EL23 − EL22 < 0 ≡ ασ + σ − < 0 (2.46) επ εy (b + 1)2 if the inequality constraint specified in (2.46) is satisfied, then nominal GDP targeting would dominate monetary aggregate targeting.

Nominal GDP targeting versus inflation targeting

2 σµ(α + c + 1) 2 2 2 EL23 − EL24 < 0 ≡ − cσ + σ − σ < 0 (2.47) (b + 1)2 v εy µ if the inequality constraint specified in (2.47) is satisfied, then nominal GDP targeting would dominate inflation targeting. 77

Nominal GDP targeting versus exchange rate targeting

2 2 2
2 2 2 2
2 2 σµ(c + α + 1) EL23 − EL25 < 0 ≡ cσ − b + α σ − σ − σ b + α + ασ + σ + < 0 (2.48) e e µ v επ εy (b + 1)2 if the inequality constraint specified in (2.48) is satisfied, then nominal GDP targeting would dominate a fixed exchange rate policy.

2.5.3 Simulation Results

We go beyond the case of rules versus discretion, because we have shown theoretically that no precommitment to an intermediate target leads to an inflation bias. Therefore, our debate focuses entirely on optimal monetary policy rules. We first begin by estimating the quadratic loss functions associated with each of the of four monetary policy rules (monetary aggregate targeting, inflation targeting, nominal

GDP targeting, and exchange rate targeting), then we plot the optimal conditions derived in equations

(2.46), (2.47), and (2.48). Table 2.9 reports the quadratic loss functions estimates for the open economy setting. The results are fairly consistent across each case study.

In Table 2.9, the results of the simulations indicate that when we do not consider the practical issue of data revisions, nominal GDP targeting minimizes the central bank quadratic loss function in an open economy setting for Burkina Faso, Cote d’Ivoire, Niger, Senegal, Togo, and for the entire WAEMU-

7 overall. Nonetheless, even when we do consider the case of data revisions, we find that there exist some parameters values for (σ2 and σ2 ) at which nominal GDP targeting minimizes the central bank επ εy quadratic loss function in an open economy setting.

In Table 2.9, the exchange rate target is the least optimal among the choices of nominal anchors that we have considered for Burkina Faso, Cote d’Ivoire, Niger, Senegal, Togo, and the WAEMU-7 overall. This result holds even in the presence of data revisions. Monetary aggregate targeting is largely superior to inflation targeting and exchange rate targeting, except for Cote d’Ivoire. Overall, given the results presented in Table 2.9, we can conclude that there are some benefits of using nominal GDP as the intermediate target as long as the presence of nominal GDP data revisions are small.

For the WAEMU-7, we evaluate the optimal conditions specified in equations (2.46), (2.47), and

(2.48) by plotting each inequality constraint with two unknown parameters (for example, the variance of output revisions and inflation revisions) so as to give the readers an approximation of how large data revisions would have to be in order for the optimal conditions to hold or fail. 78

Burkina Faso Expected losses α = c Monetary aggregate targeting {0.0471175} 1 Nominal GDP targeting {(σ2 + σ2 ) + 0.038701 } 1 επ εy Inflation targeting {σ2 +0.0505278} 1 επ Exchange rate targeting {0.0686174} 1 Cote d’Ivoire Expected losses α = c Monetary aggregate targeting {0.0458059} 1 Nominal GDP targeting {(σ2 + σ2 ) + 0.0333064} 1 επ εy Inflation targeting {σ2 + 0.0443836} 1 επ Exchange rate targeting {0.554418} 1 Niger Expected losses α = c Monetary aggregate targeting {0.0559514} 1 Nominal GDP targeting {(σ2 + σ2 ) + 0.0348747} 1 επ εy Inflation targeting {σ2 + 0.0594787 } 1 επ Exchange rate targeting {0.27623} 1 Senegal Expected losses α = c Monetary aggregate targeting {0.0420039} 1 Nominal GDP targeting {(σ2 + σ2 ) + 0.0358521} 1 επ εy Inflation targeting {σ2 + 0.0449088} 1 επ Exchange rate targeting {0.0808069} 1 Togo Expected losses α = c Monetary aggregate targeting {0.0490567} 1 Nominal GDP targeting {(σ2 + σ2 ) + 0.0337518 } 1 επ εy Inflation targeting {σ2 + 0.0500201} 1 επ Exchange rate targeting {0.275454} 1 WAEMU-7 Expected losses α = c Monetary aggregate targeting {0.0377133} 1 Nominal GDP targeting {(σ2 + σ2 ) + 0.0334165 } 1 επ εy Inflation targeting {σ2 + 0.0382942 } 1 επ Exchange rate targeting {0.202858} 1

Table 2.9: Quadratic Loss Functions Estimates: Open Economy

Notes: We report the calibration values for the parameters used to estimate the central quadratic loss functions in an open economy setting in Table B.1. 79

Any points inside the colored region or bounded area would satisfy the inequality constraints specified in equations (2.46), (2.47), and (2.48), which state that nominal GDP targeting minimizes the central bank quadratic loss function in an open economy setting. Any points outside the colored region or bounded area would state otherwise.

Nominal GDP targeting versus monetary aggregate targeting

Figure 2.9 plots the inequality constraint specified in (2.46). Given the size of the bounded area, we can clearly see that there exist some parameters values for (σ2 and σ2 ) at which nominal GDP επ εy targeting dominates monetary aggregate targeting in an open economy setting under data uncertainty.

Nominal GDP data revisions would have to be extremely large in order to convince us otherwise.

0.0040

0.0035

0.0030 π 2 ϵ 0.0025 σ

0.0020

0.0015

0.0010 0.001 0.002 0.003 0.004 σ2 ϵy Figure 2.9: Constrained Optimization Plots: Nominal GDP Targeting Versus Monetary Aggregate Tar- geting for the WAEMU-7

Nominal GDP targeting versus inflation targeting

Under both nominal GDP targeting and inflation targeting, the economy remains vulnerable to balance of payment shocks and supply shocks. In addition, under inflation targeting, velocity shocks are not accommodated unlike under nominal GDP targeting. Therefore, because velocity shocks affect the exchange rate in an open economy setting, the loss function under inflation targeting should suffer slightly more, because the central bank does not accommodate velocity shocks under inflation targeting. In any case, the difference in terms of optimality between the two rules will be determined by the difference between the magnitude of supply shocks and velocity shocks and this difference will be largely influenced by the slope of the short-run aggregate supply curve. 80

For the WAEMU-7, the short-run aggregate supply curve is relatively flat and velocity shocks are a bit large, so we should except nominal GDP targeting to dominate inflation targeting even in the presence of data uncertainty. We evaluate the optimal conditions specified in (2.47) by plotting the unknown parameter (σ2 ) and a range of estimates for the slope of the short-run aggregate supply curve εy (b). In Figure 2.10, we show that nominal GDP targeting dominates inflation targeting when the slope of the short-run aggregate supply curve is relatively flat (for example, as b approaches 3) and when output revisions are relatively low.

2.5

2.0

1.5 b

1.0

0.5

0.0 0.000 0.002 0.004 0.006 0.008 σ2 ϵy Figure 2.10: Constrained Optimization Plots: Nominal GDP Targeting Versus Inflation Targeting for the WAEMU-7

Furthermore, there exist some parameters values for (b and σ2 ) at which the inequality constraint εy specified in (2.47) is satisfied. In order for inflation targeting to dominate nominal GDP targeting, supply shocks would have to be nonexistent, the slope of the short-run aggregate supply curve would have to be relatively steep and output revisions would have to be significantly high.

Nominal GDP targeting versus exchange rate targeting

Figure 2.11 plots the inequality constraint specified in (2.48). Given the magnitude of the bounded area, we can clearly see that there is exist some parameters values for (σ2 and σ2 ) at which nominal επ εy GDP targeting dominates a rigid peg. The magnitude of the bounded area also entails that an exchange rate target is the least optimal among the choices of nominal anchors considered for the WAEMU-7. 81

In order for exchange rate targeting to dominate nominal GDP targeting, the variance of nominal

GDP data revisions would have to outweigh the combined variances of all the key shocks affecting the economy, which is categorically difficult to imagine in a practical sense.

0.10

0.08

0.06 π 2 ϵ σ 0.04

0.02

0.00 0.00 0.02 0.04 0.06 0.08 0.10 σ2 ϵy Figure 2.11: Constrained Optimization Plots: Nominal GDP Targeting Versus Exchange Rate Targeting for the WAEMU-7

2.6 Conclusion

In this chapter, the main objective was to examine the stabilizing properties of nominal GDP targeting in an open economy setting for the WAEMU. We have added a contribution to the model of

Bhandari and Frankel (2015) by imposing a cost for data uncertainty into the central bank quadratic loss functions, especially under nominal GDP targeting and inflation targeting. We have showed theoretically that no precommitment to a nominal anchor leads to an inflation bias. Therefore, we diverted our analysis to determining the optimal choice of nominal anchors for the WAEMU.

We draw three main conclusions from the results of the simulations. First, a flexible exchange rate regime is largely superior to a fix exchange rate regime for the WAEMU. Second, nominal GDP should be given primary consideration among the choices of nominal anchors for the WAEMU. Third, if nominal GDP revisions happened to be extremely large, then the BCEAO should automatically consider monetary aggregate targeting as a secondary option. 82

CHAPTER 3

IMPLEMENTING NOMINAL GDP

TARGETING FOR THE WAEMU

3.1 Introduction

In the previous chapter, the core objective was to assess the welfare properties of nominal GDP targeting in an open economy setting for the WAEMU. We came to the conclusion that under certain conditions, nominal GDP targeting was superior to a rigid peg, inflation targeting, and monetary aggre- gate targeting. However, we did not discuss a proposal for implementing nominal GDP targeting for the

WAEMU. Therefore, in this chapter, we test and propose a benchmark rule that the BCEAO can follow as a guideline for implementing nominal GDP targeting. In this process, we strongly emphasize the neces- sity of relying on sound data and choosing an operational target that would facilitate the implementation of monetary policy for the WAEMU.

The data problems

The presence of nominal GDP data revisions remains an important issue influencing the desirability of implementing nominal GDP targeting (Rudebusch, 2002). Our concerns stem in part from the same source, but in addition, we are also concerned with the lack of timely available nominal GDP data for the WAEMU. The main issue for the WAEMU is that the BCEAO does not produce or report aggregate nominal GDP data on a quarterly basis. Instead, the BCEAO releases an annual forecast of aggregate nominal GDP measured in current local prices, which is also usually prone to some revisions in the following year.

Although industrial production and consumer price data are now being reported on a monthly basis with a maximum of two to three month lags, we remain skeptical about the idea of relying solely on that data as a means to implement nominal GDP targeting, particularly because we found a baffling degree 83 of discrepancy between the growth rate of nominal GDP measured in current local prices and the growth rate of nominal GDP proxied by the combination of industrial production growth and the CPI inflation rate. We believe that this observable discrepancy may be due to the fact that industrial production data does not capture a sizable proportion real aggregate output, which is to be expected, given the stylized fact that the WAEMU remains by far a largely agricultural zone. In Figure 3.1, we observe a weak covariance between industrial production growth real GDP growth. 20 10 0 Percent -10 -20 2000 2005 2010 2015

Industrial production growth Real gdp growth (constant local prices)

Data Source: BCEAO, author's calculations

Figure 3.1: Co-Movements of Annual Industrial Production Growth and Annual Real GDP Growth (Constant Local Prices)

Furthermore, aggregate nominal GDP data are only accessible and reported on an annual frequency, which is exactly where the main data issue lie for the WAEMU. Given the unavailability of quarterly estimates of aggregate nominal GDP, it would be impossible for the BCEAO to respond to nominal GDP shocks on a timely basis. Therefore, if the goal is to implement nominal GDP targeting, the BCEAO would have to invest significantly in its statistical infrastructure in order to reach a level at which the bank is fully capable of reporting aggregate nominal GDP data on a quarterly basis. 84

Nonetheless, we consider this recommendation to be a long term initiative.1 Meanwhile, a pragmatic and intermediate solution is to rely on a proxy that is strongly correlated with aggregate nominal GDP and also observable on a quarterly basis with small data revisions. We do not recommend that the BCEAO rely solely on industrial production and consumer price data as a means to approximate trends in nominal

GDP. Instead, we strongly advise the BCEAO to consider the possibility of relying on aggregate nominal trade data measured by the combined value of exports and imports as a proxy.

In Figure 3.2, we examine the merit of this argument by looking at the covariance between the growth rate of aggregate nominal GDP measured in current local prices and the growth rate of nominal trade measured in current local prices. We find that nominal trade growth is strongly correlated with nominal GDP growth, whereas nominal GDP growth proxied by the CPI inflation rate and industrial production growth is weakly correlated with nominal GDP growth measured in current local prices. 20 15 10 10 0 Percent 5 -10 0 -20 2000 2005 2010 2015 2000 2005 2010 2015

Nominal GDP growth (local current prices) Nominal GDP growth (local current prices) Nominal GDP growth proxy (Industrial Production + CPI) Nominal Trade growth (exports value + imports value)

Data Source: BCEAO, WDI, author's calculations

Figure 3.2: Co-Movements of Annual Aggregate Nominal GDP Growth (Current Local Prices), Annual Aggregate Nominal Trade Growth, and Annual Nominal GDP Growth Proxy by the Sum of Annual Industrial Production Growth and Annual CPI Inflation

Therefore, if the goal is to implement nominal GDP targeting, we strongly advise the BCEAO to consider the possibility of relying on nominal trade data as a means to approximate trends in nominal

1Note that current initiatives are being taken by the members of the WAEMU to shorten the lags in reporting aggregate nominal GDP data. That is a desirable outcome for the prospect of implementing nominal GDP targeting. 85

GDP. Our recommendation stems from two facts: the combined value of imports and exports accounts for the largest proportion of total economic activities for the WAEMU and trade data can be made available on a quarterly basis with barely any revisions.2

Nominal trade can tell a lot about how the economy is performing, especially when the economy is heavily reliant on international trade. For instance, if a sizable proportion of national revenues emanates from exports, then a drastic fall in export prices would surely induce a fall in nominal income and thereby reduce imports as well. We examine the merits of this argument empirically in model (C.1) and Table

C.1 by examining the causal relationship between exports and imports for the WAEMU. In table C.1, we find a unidirectional causal link running from export growth to import growth, meaning that exports are statistically useful in forecasting and explaining imports for the WAEMU.

In Figure 3.3, we see a collapse in the nominal GDP gap, imports gap, and exports gap in the late

1980s, a period coinciding with the oil glut and adverse commodity price shocks. 40 Post- crisis 20 0 Percent -20 Pre- crisis -40

1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014

Nominal GDP gap export gap Import gap

Data Source: WDI, author's calculations

Figure 3.3: Co-Movements of the Nominal GDP Gap, the Imports Gap, and the Exports Gap

Note: the imports gap, the exports gap, and the nominal GDP gap are the cyclical components and were calculated with the Hodrick-Prescott filter

2The Direction G´enerale Des Douanes (DGD) is responsible for reporting and collecting trading activities on a daily basis from the local maritime ports, borders, and commercial airports in each of the eight countries forming the WAEMU. The BCEAO could mandate the DGDs to report monthly estimates of trade activities. The data would be noisy, but at the same time useful in detecting large swings in nominal GDP. 86

This scenario is exactly what prompted a monetary policy intervention by the BCEAO in 1994. The essential point is that if the goal is to stabilize nominal GDP, the BCEAO cannot ignore the nominal trade gap, because exports Granger causes imports and both variables seem to be strongly and positively correlated with aggregate nominal GDP.

The operational target dilemma

Besides the importance of using reliable data, we also stress the importance of choosing an oper- ational target that would facilitate and improve the conduct of monetary policy. Most modern central banks strive to achieve their goals by establishing intermediate targets that they can influence through their operational instruments or targets. However, our focus in this section is to determine the optimal monetary policy instrument for the BCEAO.

Should the operational target be a monetary aggregate or an interest rate? Essentially, the answer to this question boils down to whether the BCEAO would be better off by keeping its key policy rates (for example, the taux marginal des injections de liquidit´epar appels d’offres and the taux moyen mensuel du march´emon´etaire) on a constant target path, while allowing the money supply to adjust freely or do the opposite and keep its key monetary aggregates (for example, the masse monetaire) on a constant target path, while allowing its key policy rates to adjust freely.

Poole (1970) showed that the welfare implications of choosing either an interest rate or a monetary aggregate as the main instrument of monetary policy depended on certain structural parameters (for example, the slopes of the IS and LM curves) and the magnitude of two kind of shocks (for example, money demand and expenditure shocks). It is possible to conduct monetary policy with either an interest rate or a money aggregate as the operational target, however the major concern here is to determine which operational target would be most efficient for the WAEMU. To answer this question, we propose a framework in which we examine the optimal monetary policy instrument given the macroeconomic structure of the WAEMU and its exposure to three kind of shocks: expenditure shocks, exchange rate shocks, and money demand shocks.

In our optimal monetary policy instrument model, the optimal choice of instrument problem lies in

figuring out which of two instruments (for example, a key policy rate or a key monetary aggregate) must be kept at a target level or growth rate while the other is allowed to adjust freely without any constraints.

This analysis includes the usual LM and IS shocks, but we also add an open economy component (for example, the effect of real exchange rate fluctuations on aggregate demand and the presence of stochastic nominal exchange rate shocks) to determine the optimal choice of monetary policy instrument in an open 87 economy setting.3 The goal is to determine which operational target or policy instrument minimizes the central bank quadratic loss function whenever the objective is to minimize the volatility of output in an open economy setting.

Our results are fairly consistent with Poole’s (1970) original findings: we find that the presence of large LM shocks worsens the case for a money instrument, but the presence of a strong real exchange rate effect on aggregate demand and large IS shocks also worsen the case for an interest rate instrument.4 For the WAEMU, we find empirical evidence of a significant pass-through of real exchange rate fluctuations to aggregate demand and an unusually high pass-through of interest rates to real money balances, as well as the presence of large IS shocks and exchange rate shocks.5

Overall, our results show that that a money instrument minimizes the central bank quadratic loss function in the open economy setting for the WAEMU. This result strengthens the mainstream view that the use of a monetary aggregate as the operational target is most suitable for developing countries with less developed financial institutions. In addition to these findings, we propose a money rule similar to

McCallum (1987), where the BCEAO is to announce an annual target for nominal GDP growth and use the monetary base as the operational target.

Given the availability of nominal trade data on a high frequency and the strong association between aggregate nominal GDP and aggregate nominal trade, we propose relying on quarterly estimates of nominal trade as a means to approximate more accuratley trends in nominal GDP so as to allow the

BCEAO to respond to nominal GDP shocks on a timely basis. We also discuss a forward looking approach similar to Svensson (1997) based on using the forecast of nominal GDP as the intermediate target for implementing nominal GDP targeting.

The rest of this chapter is organized as follows. Section 3.2 discusses the optimal choice of monetary policy instruments in an open economy setting. Section 3.3 discusses the importance of relying on nominal trade data in the process of implementing nominal GDP targeting for the WAEMU. Section 3.4 introduces and examines the macroeconomic performance of the benchmark rule. Section 3.5 provides a summary of the key findings.

3We shall refer to LM shocks as the disturbances that affect the overall demand for real money balances and IS shocks as the disturbances that affect the expenditure sector.

4Note that we have added a contribution to Poole’s (1970) original idea by adding an open economy component within the IS function as a means to examine the optimal choice of instruments without downplaying the role of exchange rate fluctuations in doing so.

5For the WAEMU, we find that the demand for real money balances is extremely sensitive to changes in interest rates, which implies that the LM curve is relatively be flat; bolstering the case for a money instrument. 88

3.2 The Optimal Choice of Monetary Policy Instruments

If the demand for money is unstable due to the presence of large and unpredictable LM shocks, then it would be useless to use the money supply as the operational target. A money rule works better when the demand for real money balances is stable, predictable, and highly sensitive to changes in interest rates (Poole, 1970).

When the operational target is an interest rate, the central bank does not allow interest rates to adjust frequently; they are to be kept on target, while the money supply is permitted to adjust frequently.6

If stabilizing employment is a core objective of monetary policy, then it can be counterproductive to use a key policy rate as the operational target, especially if IS shocks are large and aggregate demand is extremely sensitive to real exchange rate fluctuations. An interest rate instrument is most preferable when aggregate demand is less vulnerable to real exchange rate fluctuations and IS shocks are not large enough to cause large fluctuations in real output.

This study uses Poole’s (1970) framework as a cornerstone for an optimal monetary policy in- struments model. The two fundamental equations that govern the open economy can be expressed as follows:

y = −γ(i − p) − α(e + p∗ − p) + η (3.1)

m − p = θy − βi + v (3.2) where (3.1) is the IS function and (3.2) is the LM function. The IS function is a decreasing function of the real interest rate and the real exchange rate. The LM function is a decreasing function of the nominal interest rate and an increasing function of real output. In these equations, y denotes real output, p is the average price level, e is the nominal exchange rate, p∗ is the foreign price level, i is a nominal interest rate that the central bank either controls or can influence indirectly, and m is a money aggregate that the central bank either controls or can influence indirectly.

All variables are expressed in logs, where v is a random LM shock and η is a random IS shock,

α denotes the real exchange rate effect, γ is the slope of the IS function, θ is the income effect and β is the slope of the LM function. v, η, γ, β, θ and α are the critical parameters that we must calibrate.

Assuming that prices remain fixed in the short run, the fundamental equations can be re-expressed as

6Note that the BCEAO is currently pursuing an interest rate policy given the peg with the euro. 89 follows

y = −γi − αe + η (3.3)

m = θy − βi + v (3.4)

3.2.1 The Money Supply Instrument

Combining (3.3) and (3.4) yields equilibrium output as follows

γ αβ β γ y∗ = m − e + η − v (3.5) (β + θγ) (β + θγ) (β + θγ) (β + θγ)

The central bank wishes to set the optimal money supply that would minimize output fluctuations from the desired target. This loss minimization process can be expressed as follows

 γ αβ β γ  2 minE m − e + η − v − yˆ m (β + θγ) (β + θγ) (β + θγ) (β + θγ) wherey ˆ denotes the central bank’s output target. Taking the first order condition yields the following results

 γ  αβ β γ γ  E 2 − e + η + m − v − yˆ = 0 (β + θγ) (β + θγ) (β + θγ) (β + θγ) (β + θγ)

 αβ  αβ  β   γ  γ where, E (β+θγ) e = (β+θγ) e¯; E (β+θγ) η = 0; E (β+θγ) m = (β+θγ) m¯ ;  γ  γ  γ  E (β+θγ) m = (β+θγ) m¯ ; E (β+θγ) v = 0; E(ˆy) =y ¯ The optimal money supply can be expressed as follows

(β + θγ)¯y + αβe¯ m¯ = (3.6) γ

Inserting (3.6) into (3.5) yields the optimal equilibrium output

αβ β γ y∗ =y ¯ − (e − e¯) + η − v (3.7) (β + θγ) (β + θγ) (β + θγ)

The central bank’s social expected losses under a money supply instrument can be derived from

E[(y∗ − y¯)2] 90 and the social expected loss function is

2 2 2 2 2 2 2 β σ + γ σ − 2βγσvη + α β σ EL = η v e (3.8) m (β + θγ)2 whereas Poole’s (1970) original solution can be expressed as follows

2 2 2 2 β σ + γ σ − 2βγσvη EL = η v (3.9) mP oole (β + θγ)2

The difference between equations (3.8) and (3.9) comes from the open economy component included in equation (3.8). The welfare losses associated with a money instrument depend on three key shocks: exchange rate shocks, IS shocks, and LM shocks, including the covariance of LM and IS shocks. The expected losses of the central bank are also influenced by the size of four structural parameters: the slope of the IS curve, the real exchange rate effect, the slope of the LM curve, and the income effect on money demand.

3.2.2 The Interest Rate Instrument

The central bank wishes to set the optimal interest rate that would minimize output fluctuations from the desired target. This loss minimization process can be expressed as follows

minE [((−γi − αe + η) − yˆ)]2 i wherey ˆ denotes the central bank’s output target. Taking the first order condition yields

E [−2γ (−γi − αe + η − yˆ)] = 0 where E (αe) = αe¯; E (η) = 0 ; E (γi) = γ¯i; E(ˆy) =y ¯. The optimal policy rate can be expressed as

−αe¯ − y¯ ¯i = (3.10) γ

Inserting (3.10) into (3.3) yields the optimal equilibrium output

y∗ =y ¯ − α(e − e¯) + η (3.11)

The social expected losses of the central bank under an interest rate instrument can be derived as 91

E[(y∗ − y¯)2]

The social expected loss function is

2 2 2 ELi = α σe + ση (3.12) whereas Poole’s (1970) original solution can be expressed as follows

2 ELiP oole = ση (3.13)

The difference between equations (3.12) and (3.13) comes from the open economy component in- cluded in equation (3.12). The welfare losses associated with an interest rate instrument depend on the size of IS shocks and exchange rate shocks. Furthermore, the impact of exchange rate shocks on the loss function amplifies as the real exchange rate effect strengthens.

3.2.3 Calibration Process

Our goal is to identify the optimal monetary policy instrument between two choices: an interest rate or a money aggregate. In order to achieve this objective, we derived a quadratic loss function associated with a money instrument in equation (3.8) and another one associated with the interest rate instrument in equation (3.12). In order to estimate these quadratic loss functions we must calibrate four structural

2 parameters (for example, γ, β, θ and α) and have precise estimates of the variance of 4 key shocks (σv, 2 2 2 ση, σe , σvη). We provide a summary of all the parameters derived from the theoretical framework in Table 3.1.

Parameters Description α The real exchange rate effect β The slope of the LM curve γ The slope of the IS curve θ The income effect 2 ση The volatility of the estimated IS disturbances 2 σv The volatility of the estimated LM disturbances 2 σe The volatility of the rate of change of the exchange rate 2 σvη The covariance of IS and LM shocks

Table 3.1: Optimal Monetary Policy Instruments, List of the Critical Parameters 92

3.2.4 Data

Annual data were collected from the BCEAO. We also employed quarterly data as a means to provide some robustness check for estimating certain critical parameters (for example, the slope of the

LM curve needed more robustness checks). Quarterly estimates of industrial production and consumer price data were obtained from the BCEAO, whereas quarterly M2 and money market rates data were taken from the International Financial Statistics (IFS) database.7 Real output, real effective exchange rate, and real interest data were obtained from the Indice des conditions mon´etaires dans L’UEMOA report.

3.2.5 Estimating the IS Equation

We must obtain precise estimates of the slope of the IS curve and the magnitude of IS shocks, including the real exchange rate effect. We can calibrate all these paramaters by following a similar approach taken by Dembo (2012). We estimate the IS function by using an autoregressive distributed lag (ARDL) model of the form

(y − y¯)t = δ0 + δ1(y − y¯)t−1 + δ2(y − y¯)t−2 + δ3(y − y¯)t−3 + δ4(r − r¯)t−1 + δ4(r − r¯)t−2

+δ3(r − r¯)t−3 + δ5(reer − reer¯ )t−2 + δ6(reer − reer¯ )t−3 + δ5(reer − reer¯ )t−3 + ηt

(3.14) where y is real GDP at constant prices, (y − y¯) is the real output gap measured with the HP filter, (r − r¯) is the real interest rate gap measured with the HP filter, (reer − reer¯ ) is the real effective exchange rate

8 gap measured with the HP filter, and ηt are the IS disturbances. We report the empirical estimates of equation (3.14) in Table 3.2. We use robust standard errors to deal with normality issues. According to the Breusch-Godfrey test results, we did not find any evidence of serial correlation in column (3) from Table 3.2. We find a significant and negative impact of real interest rates on aggregate demand. The real effective exchange rate is also significant in explaining aggregate demand and as expected the direction of causality is negative after three lags.

7The BCEAO’s data are provided in the Direction G´en´erale des Etudes´ Economique´ et de la Monnaie.

8Please refer to Table C.2 for stationary tests.r ¯ is the long-run trend of real interest rates andreer ¯ is the long-run trend of the real effective exchange rate. 93

(1) (2) (3) (y − y¯)t (y − y¯)t (y − y¯)t ∗∗∗ ∗∗∗ ∗∗∗ (y − y¯)t−1 0.597 0.613 0.562 (0.192) (0.203) (0.196)

(y − y¯)t−2 -0.0698 -0.0588 (0.146) (0.147)

(y − y¯)t−3 -0.0253 -0.0531 (0.149) (0.144)

∗ ∗ ∗∗∗ (r − r¯)t−1 -0.263 -0.222 -0.201 (0.134) (0.128) (0.0675)

∗∗∗ (r − r¯)t−2 -0.217 -0.181 -0.197 (0.142) (0.145) (0.0678)

(r − r¯)t−3 0.0188 0.000944 (0.118) (0.121)

(reer − reer¯ )t−1 0.0513 0.0340 (0.0515) (0.0494)

(reer − reer¯ )t−2 0.00354 -0.00251 (0.0657) (0.0661)

∗ ∗∗ (reer − reer¯ )t−3 -0.117 -0.0927 -0.0856 (0.0595) (0.0610) (0.0314)

∗∗∗ ∗∗∗ drought1983 -0.0311 -0.0330 (0.00856) (0.00602)

δ0 0.000359 0.00129 0.000674 (0.00318) (0.00324) (0.00310) Y ears (1971-2011) (1971-2011) (1971-2011) R2 0.667 0.691 0.669 D − W atson stat 2.32 2.36 2.05 Breusch − Godfrey stat Prob > chi2 .03 .02 .58 Robust standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 3.2: IS Equation: Short-Run Estimates (Annual Data)

3.2.6 Estimating the LM Equation

Here we calibrate the slope of the LM curve, the magnitude of LM shocks, and the income effect on money demand. We can calibrate these parameters by using the following error correction model 94

4(m − p)t = φ0 + φ14(m − p)t−1 + ... + φp4(m − p)t−p

+Φ14yt−1 + ... + Φp4yt−p + +Ψ14it−1 + ... + Ψp4it−p + τ[(m − p) − βγ − θy] + vt (3.15)

where 4 is the first difference operator, vt are the LM disturbances, mt is the log of the monetary mass, pt is the log of the GDP deflator or consumer price index, yt is the log of real GDP at constant prices or industrial production, and i is a money market interest rate proxy by inter-bank rates. All the variables were found to be I (0) in first differences.9

We report the long-run estimates of equation (3.15) in Table 3.3. We find the income effect to be the only significant parameter. The residuals of the long-run regression model (τ) are non-stationary at the 5 percent or 10 percent level, suggesting no evidence of a long-run relationship between the level of real output, interest rates, and real money balances.

(1) (m − p)t ∗∗∗ yt 1.672 (0.119)

it -0.247 (0.598)

cons -17.41∗∗∗ (2.037) Y ears (1982-2015) R2 0.909 Robust standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 3.3: LM Equation, Long-Run Estimates (Annual Data)

We report the short-run estimates of equation (3.15) in Table 3.4. The error correction term is not significant across all the regression estimates reported in Table 3.4. The income effect is highly significant in Table 3.4. Although the slope of the LM curve is significant and negative in column (5), this result lacks empirical consistency across all regression estimates reported in Table 3.4.

9Please refer to Table C.2 for stationarity tests. 95

(1) (2) (3) (4) (5) (6) (7) 4(m − p)t 4(m − p)t 4(m − p)t 4(m − p)t 4(m − p)t 4(m − p)t 4(m − p)t ∗∗∗ ∗∗ 4(m − p)t−1 0.258 0.382 0.307 0.0877 0.305 0.293 0.145 (0.165) (0.273) (0.180) (0.165) (0.106) (0.133) (0.196)

4(m − p)t−2 0.368 0.252 0.204 0.0877 0.360 (0.273) (0.210) (0.251) (0.237) (0.253)

4(m − p)t−3 -0.0500 -0.0909 (0.356) (0.246)

4yt−1 -0.509 -1.013 -1.122 (0.875) (1.273) (0.830)

∗∗∗ ∗∗ ∗∗∗ ∗∗ 4yt−2 1.605 1.396 1.154 1.278 1.644 (1.097) (0.435) (0.441) (0.443) (0.609)

4yt−3 -0.162 (0.755)

4it−1 0.0599 0.0264 0.0590 -0.155 -0.0457 (0.152) (0.303) (0.217) (0.191) (0.230)

∗ 4it−2 0.438 0.368 0.253 (0.269) (0.215) (0.150)

∗∗ 4it−3 0.0792 -0.147 (0.163) (0.0580)

τ -0.0930 -0.0991 (0.0815) (0.0909)

∗ φ0 0.0591 0.0105 -0.00569 0.0136 0.00580 0.0271 0.0303 (0.0309) (0.0368) (0.0177) (0.0195) (0.0195) (0.0214) (0.0187) N 32 30 31 32 30 32 31 R2 0.076 0.419 0.339 0.201 0.237 0.300 0.156 D − W atson stat 1.82 1.96 1.97 1.78 1.98 1.96 1.89 Breusch − Godfrey Prob > chi2 .37 .81 .88 .37 .91 .99 .62 Robust standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 3.4: LM Equation, Short-Run Results (Annual Data)

3.2.7 Robustness check

Since we did not find robust empirical point estimates for the slope of the LM curve, we decided

to run further tests as a robustness check. In this section we re-estimate equation (3.15) using quarterly

data. We use industrial production as a proxy for real output and the CPI as the proxy for the price

level. We use aggregate M2 deflated by the CPI for real money balances and short-term money market

rates as a proxy for interest rates.

M2, CPI, and industrial production data are seasonally adjusted using X-13 ARIMA. We present

the data in Figure 3.4. All variables are non-stationary in levels, but stationary in first differences.10 We report the long-run estimates in Table 3.6. Both the slope of the LM curve (the interest rate effect on

10Please refer to Table C.3 for stationarity tests. 96 money demand) and the income effect are highly significant in explaining the demand for real money balances in the long-run. τ is significant at the 5 percent level, pointing to the existence of a long-run monetary equilibrium. 12.2 11.65 12 11.6 11.55 11.8 logarithm logarithm 11.5 11.6 11.45 11.4 2007q1 2009q3 2012q1 2014q3 2017q1 2007q1 2009q3 2012q1 2014q3 2017q1

Industrial production Consumer price index Industrial production (seaonsally adjusted) Consumer price index (seasonally adjustsed) 17 .06 .055 16.5 .05 logarithm 16 .045 15.5 .04 2007q1 2009q3 2012q1 2014q3 2017q1

Aggregate M2 .035 2007q1 2009q3 2012q1 2014q3 2017q1 Aggregate M2 (seasonally adjusted) Money market rates Data Source: IMF, BCEAO, author's calculations

Figure 3.4: Time Series Plots of Industrial Production, CPI, Aggregate M2, and Money Market Rates

We report the short-run estimates of equation (3.15) using quarterly data in Table 3.5. The error correction term is significant in column (2) located in Table 3.5. Industrial production growth is weakly significant in explaining real money balances, suggesting that real GDP at constant prices may be a better proxy for capturing the income effect. The slope of the LM curve is robustly significant across all estimated regressions reported in Table 3.5.

Given the empirical point estimates of the slope of the LM curve reported in Table 3.5, we can conclude that the demand for real money balances is extremely sensitive to changes in interest rates, suggesting that the LM curve is relatively flat. This implies that a money instrument can be optimal for the WAEMU, especially if the real exchange rate effect is strong and IS shocks are large. 97

(1) (2) (3) (4) 4(m − p)t 4(m − p)t 4(m − p)t 4(m − p)t 4(m − p)t−1 -0.263 -0.289 -0.263 (0.190) (0.198) (0.174)

4(m − p)t−2 -0.118 (0.116)

4yt−1 0.0115 (0.159)

∗ 4yt−2 0.237 0.257 (0.149) (0.140)

4it−1 0.0717 1.542 (1.472) (1.238)

∗ ∗∗ ∗∗ ∗∗ 4it−2 -2.027 -2.221 -2.369 -2.258 (1.047) (1.038) (0.880) (0.980)

4it−3 -1.782 (1.276)

4it−4 0.494 (1.014)

τ -0.0405 -0.0962∗∗ (0.0444) (0.0435)

cons 0.0340∗∗∗ 0.0382∗∗∗ 0.0302∗∗∗ 0.0258∗∗∗ (0.00785) (0.00707) (0.00635) (0.00456) years (2007Q1-2016Q4) (2007Q1-2016Q4) (2007Q1-2016Q4) (2007Q1-2016Q4) R2 0.261 0.384 0.229 0.103 D − W atson stat 2.07 2.30 2.17 2.59 Breusch − Godfrey Prob > chi2 .50 .06 .23 .06 Robust standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 3.5: LM Equation, Short-Run Results (Quarterly Data)

(1) (m − p)t ∗∗∗ yt 1.573 (0.0955)

∗∗∗ it -12.17 (2.639)

cons -13.17∗∗∗ (1.136) Y ears (2007q1-2016q4) R2 0.823 Robust standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 3.6: LM Equation, Long-Run Estimates (Quarterly Data) 98

3.2.8 Which Instrument is Optimal?

We report all the calibration of the critical parameters specified in Table 3.1 in Table 3.7.

Parameters Estimates β -2.221 to -2.369 γ -0.197 to -0.201 α -0.0856 θ 1.154-1.644 2 σe .0331111 2 ση .0003075 2 σv .0043212 σvη .000153

Table 3.7: Optimal Choice of Monetary Policy Instruments: Parameters Calibration

Notes: All points estimates reported in Table 3.7 are statistically significant at either the 1 percent or 5 percent level.

In Table 3.8, we examine the difference between equations (3.8) and (3.12). Given the values of the parameters specified in Table 3.7, we find that a money instrument minimizes the central bank quadratic loss function in the open economy setting. These results are likely due to the (i) the high pass-through of interest rates on real money balances, (ii) the presence of large IS shocks, and (iii) a moderate real exchange rate effect.

Table 3.8: Quadratic Loss Function Estimates

γ α β θ ELm − ELi -0.197 -0.0856 -2.221 1.154 -0.000238899 < 0 -.201 -0.0856 -2.369 1.644 -0.000364343 < 0

These findings for the WAEMU are consistent with findings for other West Africans countries such as

Nigeria. Udom and Yaaba (2015) found a money instrument to be optimal for Nigeria in a closed economy setting. The corroboration of these results is good news for economic integration in West Africa, as Nigeria and the WAEMU are presently considering the idea of forming a currency area by 2020.

Futhermore, Poole (1970) argued that an interest rate instrument would be optimal if the slope of the LM curve was steep and the difference between the LM and IS disturbances was significantly large and positive. Nonetheless, we also argue that in an open economy setting, regardless of whether money demand shocks dominate the economy completely, an interest rate instrument becomes less optimal when aggregate demand is extremely vulnerable to real exchange rate fluctuations. In Figure 3.5, we explore the validity of our argument, including Poole’s (1970) argument by using constrained optimization. 99

In Figure 3.5, we allow money demand shocks to dominate the economy completely, but we allow exchange rate shocks and IS shocks to be relatively lower within the two objective functions (ELi and

ELm). Then we plot the following inequality constraint

ELi − ELm < 0 with three structural parameters in Figure 3.5. The colored region represents the area in which all possible combinations of α, γ, and β would satisfy the inequality constraint, which states that an interest rate instrument is optimal in an open economy setting. Any combination of α, γ, and β outside of that bounded area would indicate that a money instrument is optimal in the open economy setting.

Figure 3.5: Constrained Optimization: Money Instrument Versus Interest Rate Instrument

In Figure 3.5, we simulate the economy such that money demand shocks completely dominate all the other shocks. Given these circumstances, we find that an interest rate instrument minimizes the central bank quadratic loss function given the calibrated values of the key structural parameters specified in Table 3.8. This result is closely in line with Poole’s (1970) argument that when money demands shocks dominate completely, an interest rate instrument is vastly superior to a money instrument. However, note that when we consider the open economy side of the model, as we have done so far, we can see that even 100 in the presence of slight exchange rate volatility, a money instrument becomes increasingly superior to an interest rate instrument as the real exchange rate effect amplifies.11

My main argument is that when the real exchange rate effect is extremely strong, even if LM shocks dominate, if the nominal exchange rate is extremely volatile, it could lead to excessive fluctuations in real output when the central bank is committed to an interest rate instrument. For instance, a large exogenous shock in the nominal effective exchange rate (for example, a large nominal effective exchange rate appreciation) will cause the IS curve to shift downward. The real effect of the shock on real output will depend on the size of α. Nonetheless, assuming that α is sufficiently large, if the operational target is an interest rate, then downward gradual adjustment of interest rates will be prevented as the economy contracts, leading to excessive fluctuations in real output. Hence, in an open economy setting, we do not recommend an interest rate instrument when IS shocks are excessively large or when aggregate demand is extremely vulnerable to real exchange rate fluctuations.

For instance, Tanzania is well known for being susceptible to exchange rate volatility. Therefore, we support the choice of the Benki Kuu ya Tanzania for using a money aggregate as the operational target.

The Sub-Saharan African countries that have given up on fixing the exchange rate since the 1990s (for example, Rwanda, Sierra Leone, Kenya, Tanzania, Uganda, Nigeria, and Ghana) have all started to float by adopting a money aggregate as the operational target.

This leads us to conclude that if exchange rate volatility is the main concern for adopting a flexible exchange rate regime, then developing countries may be better off by using a monetary aggregate as the operational target, so as to mitigate the threat of exchange rate volatility on the economy. We also recommend the same treatment for the WAEMU in case the BCEAO abandons its fixed exchange rate policy in favor of a flexible type of exchange rate regime such as nominal GDP targeting.

3.3 The Importance of Relying on Nominal Trade Data for Implementing Nominal GDP Targeting

3.3.1 Nominal Trade: Data Generating Process

The Direction G´en´erale des Douanes (DGD) in Benin, Burkina Faso, Cote d’Ivoire, Mali, Senegal, and Togo represents the main body responsible for collecting and reporting trading activities on a daily basis. The vast majority of all trading activities emanates from the key entry and exit ports, but other trading activities are also collected and recorded from commercial airports and the WAEMU’s border posts. The countries that have direct access to the Atlantic ocean have a broader pool of trading data

11The case where the real exchange effect worsens, as α approaches -20 in Figure 3.5. 101

(for example, Benin, Cote d’Ivoire, Guinea-Bissau, Senegal, and Togo), because their key ports engage in transshipment for other members such Mali, Burkina Faso, and Niger.

With respect to the data reporting and recording process, for example in Cote d’Ivoire, the DGD keeps its own daily statistics, but it is mandated to report all trading activities on a monthly basis to other governmental agencies. Since all DGDs in the WAEMU record nominal trade statistics on a daily basis, the BCEAO can mandate them to report monthly estimates of trade activities in order to help gauge the overall mass of nominal economic activities in the WAEMU zone on a quarterly basis.

Exports and imports invoices are billed in foreign currencies (for example, US dollars and euros), but are recorded in CFA francs for statistical purposes by the DGDs. With respect to the physical process of collecting trade data, the DGDs have local agents operating at the borders, airports, and key maritime ports who are responsible for recording and collecting trade data. Furthermore, the DGDs do not make any distinction between non-tariff or tariff products; all merchandise is recorded as it arrives.

3.3.2 Nominal Trade: Data Composition

Nominal export data are composed of two principal categories: primary and processed goods. The primary goods category includes agriculture, livestock, fishing, and minerals products. The processed goods category includes manufactured products, canned foods, and processed raw materials. In the

WAEMU zone, the largest proportion of nominal export emanates from primary goods, particularly because of heavy external demand for raw materials and cash crops.

Nominal import data are composed of three principal categories: consumer, intermediate, and capital goods. The consumer goods category includes food products and other goods (for example, pharmaceutical, clothes, and tobacco). The intermediate goods category includes fuel, fertilizers, and other products (for example iron and steel). The capital goods category mostly includes machinery and transportation vehicles. In the WAEMU zone, the largest proportion of nominal import emanates from the consumer goods category.12

3.3.3 Nominal Trade as a Proxy or Intermediate Target for Implementing Nominal GDP Targeting

The availability of nominal trade data can ease the process of implementing nominal GDP targeting for the WAEMU for two principal reasons. First, aggregate nominal trade (exports + imports) and aggregate nominal GDP are strongly associated in terms of value and covariance. Second, nominal trade data are highly accurate and observable, because trade data are recorded on a daily basis by Customs

12Note that this is a standard finding across Sub-Saharan Africa. 102 with barely any revisions.13 We plot the historical levels of nominal trade and nominal GDP over time in order to picture the nature of the relationship between the two variables. In Figure 3.6, we find that nominal trade is extremely close to nominal GDP in terms of levels, but we also see a strong covariance between the two variables.

Benin Burkina Faso Cote d'Ivoire 30 29 30 28 29 28 27 28 26 26 logarithm logarithm logarithm 27 25 24 24 26

1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

Nominal Trade Nominal GDP Nominal Trade Nominal GDP Nomianl Trade Nominal GDP

Guinea-Bissau Mali Niger 30 30 29 28 25 28 27 20 26 logarithm 26 logarithm logarithm 25 15 24

1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

Nominal Trade Nominal GDP Nominal Trade Nominal GDP Nominal Trade Nominal GDP

Senegal Togo WAEMU-8 29 30 31 29 28 30 28 27 29 27 logarithm 28 logarithm 26 logarithm 26 27 25 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 Nominal Trade Nominal GDP Nominal Trade Nominal GDP Nominal Trade Nominal GDP

Data Source: WDI, author 's calculations

Figure 3.6: Co-Movements in Nominal Trade and Nominal GDP

We can certainly argue that this close relationship found between nominal trade and nominal GDP can be an indispensable advantage for the central bank when it comes to implementing nominal GDP targeting, especially since trade data are reported on a daily basis with barely any revisions. All the central bank has to do is to request that data to be made available directly from the DGDs. The availability of nominal trade data on a timely basis will allow the BCEAO to determine which of its members is performing poorly and how the members are performing jointly. Given the fact that nominal trade appears to be the leading source of nominal economic activities for the WAEMU, we can conclude that trade shocks are likely have a significant impact on nominal GDP.

13Note that when we are referring to nominal trade, we are referring to the combined value of export and imports. 103

The BCEAO is already implementing a domestic credit ceilings policy as a means to control the money supply in coordination with each member’s level of foreign exchange reserve; with nominal GDP targeting, they can also adjust the operational target in coordination with what is happening to nominal trade so as to stabilize nominal GDP. In any case, if the goal is to implement nominal GDP targeting, we recommend that the BCEAO would announce an annual target for nominal GDP and rely on quarterly estimates of nominal trade as a means to approximate trends in nominal GDP,14 or else use nominal trade directly as the intermediate target. However, the question remains how the BCEAO’s actions would feed back to influence nominal trade.

We consider two channels of monetary policy through which the central bank can influence nominal trade: the domestic credit and exchange rate channels. From the equation of exchange, the link between the operational target (for example, the monetary base) and nominal GDP is quite obvious, but what remains problematic is the link between nominal trade and the operational target. With respect to imports, imports are a function of income, which can also be influenced by changes in the money supply.

Therefore, we can expect easing or tightening monetary conditions to also affect imports.

With respect to exports, monetary policy has quite limited effects, because export prices and quantities exported are largely determined by exogenous factors. Nonetheless, the credit channel remains a viable route for transmitting the effects of monetary policy to export supply, as the central bank has already realized. The BCEAO has an opened channel available for providing domestic credit specifically tailored for exports.

The BCEAO could ease conditions for accessing credits for exports by expanding the money supply and reducing the taux d’int´erˆetmoyen des cr´editsa l’exportation whenever it observes a large decline in the nominal trade index caused by a decreased in the value of exports. This mechanism to provide domestic credits for boosting exports has already been put in place by the BCEAO. Under nominal GDP targeting, we can discuss proposals to improve this channel.

The exchange rate channel can also play a significant role in transmitting the effects of monetary policy to either exports or imports. Currently, the CFA franc is pegged to the euro, so the fluctuations of the CFA franc against the dollar are largely determined by fluctuations in the euro/$ exchange rate.

However, under nominal GDP targeting, we expect the CFA franc to be freed from that peg with the euro, giving the central bank the capacity to directly influence the CF A/$ exchange rate.

14It is practical to rely on nominal trade data to approximate trends in of nominal GDP, because the total value of trade accounts for a sizable proportion of nominal GDP for the WAEMU. Therefore, it is likely that nominal GDP fluctuations are likely to be highly correlated to nominal trade shocks. 104

The CF A/$ exchange rate is extremely important for determining export revenues, because com- modities are priced in US dollars so the domestic value of exports should also be strongly correlated with

fluctuations in the CF A/$ exchange rate. This assumption is clearly visible empirically for the WAEMU.

In Figure 3.7, we examine the relationship between the growth rate of exports and the rate of change of the CF A/$ exchange rate during the 1990s, a period coinciding with the first postcolonial devaluation of the CFA franc.

Benin Burkina Faso Cote d'Ivoire 1 .5 0 -.5

Mali Niger Senegal 1 .5 0 -.5 1990 1992 1994 1996 1998 1990 1992 1994 1996 1998

Togo 1 .5

0 Exports Growth (Domestic Value)

-.5 Change in (CFA/$) 1990 1992 1994 1996 1998

Data Source: WDI, author's calculations

Figure 3.7: Co-Movements in Exports Growth and Rate of Change in the CF A/$ Exchange Rate.

We observe a spike in the growth rate of exports in 1994 when the CF A/$ exchange rate underwent a massive depreciation against the dollar. This depreciation contributed to a strong boost in the domestic value of exports. In Figure 3.7, we also see an impressive degree of exposure to common external shocks; that is extremely important and beneficial for the prospect of implementing nominal GDP targeting for the entire monetary union. Furthermore, from 1975 to 2012, we find that the rate of change in the CF A/$ exchange rate explains about 75 percent of variation in the growth rate of nominal export. Therefore, we can conclude that the growth rate of nominal export is strongly correlated with changes in the CF A/$ exchange rate. 105

3.3.4 The Operational Target and Nominal Trade

Under the current monetary policy, the BCEAO must offset nominal trade shocks by allowing the money supply to adjust in order to prevent interest rates from misaligning with the European Union’s main policy rates. When a central bank uses interest rates as the operational targets, that is exactly how it behaves. For instance, a large decrease in nominal trade occasioned by a major fall in cocoa prices would cause the monetary base to fall, while a large increase in nominal trade caused by rising cocoa prices would call for the opposite reaction. We assess the empirical link between money base growth and nominal trade growth by using an empirical approach similar to that of Love and Zicchino (2006). We express the panel VAR

Xit = Λ1Xit−1 + Λ2Xit−1 + ... + ΛpXit−p + feit + eit (3.16)

where µit is a vector of idiosyncratic shocks, feit is a vector of fixed effects, and Xit is the vector containing money base growth and nominal trade growth. We use money base growth data from 1970 to 2015, collected for the founding members of the WAEMU (for example, Benin, Burkina Faso, Cote d’Ivoire, Mali, Niger, Senegal, and Togo) from the IFS database. We use nominal trade data from 1970 to 2015, collected for the same countries from the WDI database. We report the stability condition of the model in Figure C.1. Since all eigenvalues lie within the unit circle, we can conclude that our model is stable. We report the impulse responses in Figure 3.8.

As expected, money base reacts positively to offset nominal trade shocks. As discussed earlier, the

BCEAO has to allow the money supply to rise in response to a positive aggregate demand shock so as to prevent interest rates from rising above the target, therefore we are not really surprised about these results. The key point is that under nominal GDP targeting, the BCEAO would be able to adopt a counter-cyclical approach when faced with nominal trade shocks. For instance, a major fall in nominal trade caused by a large decrease in commodity prices would require easing in order to to stabilize nominal

GDP, whereas a positive shocks in nominal trade, leading to an increase in nominal GDP above target, would call for tightening. 106

Panel VAR (WAEMU)

Response of money base growth to money base shocks Response of money base growth to nominal trade shocks .3 .03

.2 .02

.1 .01

0 0

Response of nominal trade growth to money base shocks Response of nominal trade growth to nominal trade shocks .06 .2

.15 .04 .1 .02 .05

0 0

0 2 4 6 8 10 0 2 4 6 8 10

95% CI Orthogonalized IRF

Data Source: IMF, WDI, author's calculations

Figure 3.8: Panel VAR, Impulse Responses for Money Based Growth and Nominal Trade Growth

3.3.5 How Useful is Nominal Trade in Forecasting Nominal GDP?

In order to use nominal trade as a proxy for nominal GDP, we have to determine whether a strong empirical relationship exists between the two variables. Therefore, the main objective in this section is to examine the empirical relationship between these two variables. We first begin by showing how the

BCEAO could rely on nominal trade data as a means to stabilize the path of nominal GDP by picturing the co-movements of nominal trade growth and nominal GDP growth over time.

In Figure 3.9, we observe a strong and tight co-variance between the growth rate of nominal trade and the growth rate of nominal GDP for practically all the members of the WAEMU. We found no evidence of cointegration between the levels of nominal trade and nominal GDP.15 Therefore, we used two short-run models to examine the relationship between nominal trade and nominal GDP: an autoregressive distributed lag model (ARDL) and a vector autoregression model. We can express the ARDL model as follows

n n n 4yt = λ0 + λ14yt−1 + ... + λp4yt−p + ω14nt−1 + ... + ωpnt−p + Γ4nt + µt (3.17)

15Please refer to Table C.4 for the Johansen cointegration results. 107

n where 4 denotes the first difference operator, yt is the log of an aggregate index of nominal GDP calculated for the WAEMU and expressed in CFA francs, nt is the log of an aggregate index of nominal trade calculated for the WAEMU and expressed in CFA francs, and ut is an error term. We use annual data from 1970 to 2015, collected from WDI.16

We report the estimates of equation (3.17) in Table 3.9. As expected, we find both the lags and the contemporaneous effects of nominal trade growth to be statistically significant in explaining the growth rate of nominal GDP. We use robust standard errors and there is no evidence of serial correlation based on the Breusch-Godfrey test.

Benin Burkina Faso Cote d'Ivoire 1.5 1 .5 0 -.5

Guinea-Bissau Mali Niger 1.5 1 .5 0 -.5

1960 1980 2000 2020

Senegal Togo

1.5 Nominal GDP growth

1 Nominal trade growth .5 0 -.5

1960 1980 2000 2020 1960 1980 2000 2020

Data Source: WDI, author's calculation

Figure 3.9: Co-Movements in Nominal GDP Growth and Nominal Trade Growth

Furthermore, we select the optimal regression model based on Akaike information criterion (AIC) in column (5) in Table 3.9 to compute an out-of-sample dynamic forecast of nominal GDP growth based on nominal trade growth to examine the usefulness of using nominal trade data in forecasting the dynamics of nominal GDP.

16All variables were found to be stationary in first differences. Please refer to Table C.5 for unit root tests. 108

(1) (2) (3) (4) (5) n n n n n 4yt 4yt 4yt 4yt 4yt ∗ ∗∗ 4yt−1 0.478 0.254 0.136 0.170 (0.539) (0.150) (0.0601) (0.248)

∗∗ 4yt−2 -0.886 0.124 (0.792) (0.0511)

4yt−3 -0.0860 (0.373)

4yt−4 -0.638 (0.424)

4yt−5 -0.0492 (0.326)

∗∗ 4nt−1 -0.0798 -0.0409 0.0897 0.0112 (0.323) (0.0885) (0.0366) (0.114)

4nt−2 0.445 0.0792 (0.309) (0.0519)

4nt−3 0.197 (0.194)

∗ 4nt−4 0.432 (0.223)

4nt−5 0.100 (0.186)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ 4nt 0.495 0.506 0.507 0.500 (0.0562) (0.0569) (0.0632) (0.0711)

∗ ∗∗ ∗∗∗ ∗∗ λ0 0.0776 0.0210 0.0292 0.0160 0.0207 (0.0424) (0.00840) (0.00639) (0.00777) (0.0130) Y ears (1970-2015) (1970-2015) (1970-1994) (1970-2015) (1970-2015) R2 0.340 0.869 0.858 0.882 0.911 Durbin − W atson stat 2 2.24 1.73 2.03 1.90 Breusch − Godfrey stat Prob > chi2 .75 .06 .40 .82 .61 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < .01

Table 3.9: ARDL Regressions of Nominal GDP Growth and Nominal Trade Growth 109

We present the out-of-sample dynamic forecast of nominal GDP growth based on nominal trade growth in Figure 3.10. Judging from Figure 3.10, we can conclude that nominal trade data are very useful in predicting or forecasting nominal GDP for the WAEMU.17 Therefore, if the goal of the BCEAO is to implement nominal GDP targeting, the accuracy of the forecast can only strengthen the argument of relying on nominal trade data as a proxy for nominal GDP. .4 .3 .2 .1 0 -.1 1970 1980 1990 2000 2010 2020

Nominal GDP growth Nominal GDP growth (out of sample forecast, 1995-2015)

Data Source: WDI, author's calculations

Figure 3.10: Out-of-Sample Dynamic Forecast of Nominal GDP Growth (Dependent Variable) Based on Nominal Trade Growth (Independent Variable)

We conducted a further robustness check by estimating a VAR to examine the degree of variation of nominal GDP growth explained by nominal trade shocks. We provide a plot of the variance decomposition of nominal GDP growth due to nominal trade shocks in Figure 3.11. We find that nominal trade shocks explain about 85 percent of variation in nominal GDP growth within the first year; the empirical strength of this result is quite consistent with the previous results displayed in Table 3.9.

17We plot the forecast errors in Figure C.2. Furthermore, the root mean squared error (RMSE) associated with the forecast is .02. 110

Hence, we can conclude that relying on nominal trade data for implementing nominal GDP targeting could be beneficial for the WAEMU, not only because nominal trade fluctuations are highly correlated with nominal GDP fluctuations, but also because nominal trade data are highly accessible and prone to almost no revisions.

Percent of nominal GDP growth variance due to nominal trade shocks 100

80

60

40

20

0 1 2 3 4 5 6 7 8 9 10

Figure 3.11: Variance Decomposition of Nominal GDP Growth Due to Nominal Trade Shocks

3.4 The Benchmark Rule

The reaction function

In this section, the main objective is to introduce a benchmark rule that the BCEAO can use as a guideline for keeping nominal GDP on a target path. McCallum (1987) proposed a monetary policy rule in which the central bank would adjust the monetary base at the beginning of every quarter if the data obtained from the previous quarter suggested that nominal GDP was growing disproportionately from the central bank’s target. This idea is closely in line with our objectives, as McCallum’s rule uses a monetary aggregate as the operational target and nominal GDP as the intermediate target. McCallum

(1987) proposed the following central bank reaction function

n∗ n∗ n 4mbt = 4y − 4vt + λ(4y − 4yt−1) (3.18) λ>0 111

where 4 is the first difference operator, mbt denotes the log of the monetary base, vt is the log of the velocity of money, averaged over the past four years or 16 quarters, 4yn∗ is the nominal GDP growth

n target, 4yt−1 is the previous year or quarter’s growth rate of nominal GDP, and λ is the monetary response factor, which is expected to be positive.18

McCallum (1987) also accounts for an inverse relationship between money base and the velocity of money. This inverse relationship is beneficial and much needed to offset high inflation in the event of a large monetary policy expansion. Due to declining money velocity, a large increase in the money supply may not proportionally lead to an increase in inflation as the Quantity Theory of Money would have predicted. This is indeed one of the most important traits of McCallum’s rule.

The nominal GDP growth target

Nominal GDP targeting may be ineffective if the central bank cannot specify an appropriate target path for nominal GDP. Nominal GDP is the product of output and prices. Therefore, the nominal GDP growth target must be a combination of both an output target and an inflation target. Ideally, the central bank should target the rate of output that would prevail when the economy is in equilibrium, at which there is zero undesired inflation. Mainstream macroeconomics refers to this as potential output or the natural rate of output.

We can rely on the combination of labor productivity growth and population growth to approximate the long-run growth rate of the economy. However, given the lack of available labor market data for the

WAEMU, we do not have precise estimates of working labor hours. Therefore, we rely on the trend of real GDP growth as an alternative technique to approximate the long-run growth rate of the economy.

We are aware that this approach may not necessarily be the best method, but it remains a practical one. In any case, no one can truly know with any exact certainty what the long-run growth rate of the economy is, so the best we can do is try to approximate it. We approximate the long-run growth rate of the economy to be around four percent by relying on the trend of real GDP growth. Additionally, we use a band of different targets as a robustness check.

If the BCEAO was to choose a potential output target that is lower than the prevailing long-run growth rate of the economy, it would impose a restrictive monetary policy stance and deflation would eventually follow. On the other hand, if the potential output target is slightly higher than the prevailing long-run growth rate of the economy, then the WAEMU would have no choice but to accept a higher long-run inflation target, which may not necessarily be counterproductive, given that most developing

18McCallum (1987) proposed 25 basis point as the quarterly monetary policy response factor for the FED. For our purpose, the annual monetary response factor would be a 100 basis points for the BCEAO. 112 countries do not have zero long-run inflation targets, as long as the implied long-run inflation target is not too high. In fact, a moderate long-run inflation target would give more room to the central bank to respond to large transitory supply shocks without having to worry about reaching a deflationary situation.

The BCEAO should also be concerned about convergence, because those Sub-Saharan African countries that have allowed some degree of exchange rate flexibility since the 1990s did experience higher growth rates in the last decade.

We have approximated the long-run growth rate of the economy to be around four percent. However, given the threat of deflation and the likelihood of convergence, we are proposing a potential output target of six percent and a long-run inflation target of four percent, yielding an annual nominal GDP growth target of 10 percent. 32 31

WAEMU's golden era

30 euro peg era Logarithm

End of (1988-1993) crisis 29

Pre- crisis (1988-1993) 28

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Target Path(TAR) Log of NGDP(actual)

Data Source: WDI, author's calculation

Figure 3.12: Target Path (TAR) of Nominal GDP Versus Actual Path of Nominal GDP 113

From 1970 to 2015, the annual average growth rate of nominal GDP for the WAEMU has been around 8 percent; note that the 10 percent proposal is not too far away from this average. In Figure 3.12, we examine the feasibility of our proposal to commit to a 10 percent nominal GDP growth target. Is a

10 percent nominal GDP growth target feasible? based on Figure 3.12, we can conclude that this target is feasible and can be achievable.

For instance, during the WAEMU’s golden era, the WAEMU Zone was growing excessively above the 10 percent target path; preventing this sort of scenarios happening to the economy is one of many reasons why we are proposing a commitment to a 10 percent nominal GDP growth target. Furthermore, during the crisis period of (1988-1993), nominal GDP growth fell excessively below the target path; again, preventing this sort of scenarios happening to the economy is one of many reasons why we are proposing a commitment to a 10 percent nominal GDP growth target.19

After the crisis of (1988-1993), the devaluation did stabilize nominal GDP growth for a short period of time, but there is clearly no evidence that it had a stronger effect on real growth, because nominal

GDP growth barely returned on target or above target. If anything, nominal GDP growth continued to diverge below the 10 percent target path even in the aftermath of the adoption of the peg with the euro in 1999. Figure 3.12 does not reveal anything new about the economic performance of the WAEMU during the period of (2004-2015), it just reinforces previous findings showing that the WAEMU registered a lower growth performance relative to other Sub-Saharan African countries, especially those that have allowed some degree of exchange rate flexibility since the 1990s.

This poor growth performance is a key factor strengthening the argument for a 10 percent nominal

GDP growth target. However, based on Figure 3.12, it is also clear that monetary policy has limits, be- cause the large monetary expansion of 1994 only had a minor positive impact on the economy. Therefore, we acknowledge the merits of this limitation and argue that the WAEMU’s authorities will be obligated to pursue additional structural programs in order to increase the feasibility of sustaining a 10 percent annual nominal GDP growth target (for example, programs that will add value to agricultural exports by transforming raw materials locally would be a viable solution) .

In any case, we are in favor of choosing a 10 percent annual nominal GDP growth target among any other possible choices (for example, 6 %, 8%, 10%, or 12 %), in part because it is an acceptable danger for a central bank that wishes to prioritize employment without having to worry about deflation.

Certainly, this choice will not be a conservative one, but who is to say that a central bank would not be

19Please also refer to figure C.6 for more empirical evidence. 114 content with a four percent long-run inflation target, as long as this minor penalty is accompanied with robust growth? Hence, if the goal is to implement nominal GDP targeting, we recommend the BCEAO to choose a 10 percent annual nominal GDP growth target so as to minimize the pressure of deflation and to achieve convergence.

The main proposal follows: The BCEAO should announce an annual target for nominal GDP growth and adjust the monetary base accordingly in order to achieve this objective. We are well aware that nominal GDP shocks are likely to occur within the 1 year control lag, but we also know that trends in nominal GDP growth are likely to emanate from trade shocks. Therefore, given the fact that nominal trade shocks explains about 85 percent of the variation in nominal GDP growth, the BCEAO could mandate the DGDs in the WAEMU to report monthly estimates of nominal trade so as to allow the central bank to respond to nominal GDP trends on a quarterly basis (for example, a large decline in cotton prices or a large increase in petroleum prices would be likely to show up in nominal trade data, prompting a swift response from monetary authorities in order to stabilize nominal GDP).

Evaluating the benchmark rule

We begin by examining the actual path of money base and the desired path conditioned by equation

(3.18). We calculate the average velocity of money over the past four years and combine it with a 10 percent annual nominal GDP growth target to estimate the desired path of money base as guided by equation (3.18). We plot the historical path of money base growth and its desired path in Figure 3.13.

Obviously, in terms of stability, the benchmark rule has the upper hand over the historical policy stance of the BCEAO and it is in fact slightly more expansionary on average.

Furthermore, we see excessive money base growth during the late 1970s under the BCEAO’s histor- ical monetary policy, which is a historical period coinciding with booming commodity prices, whereas the path of money base growth dictated by equation (3.18) shows a more constant and steady path for money base growth; except in 1995, which is a year preceding a 38 percent growth in nominal GDP caused by the devaluation of CFA franc. Also, we observe weak money base growth during the late 1980s under the BCEAO’s historical monetary policy, which is a period coinciding with falling commodity prices and economic downturn, whereas our benchmark rule shows an expansionary path of money base growth at that time.

We can clearly see where the main difference lies between the historical policy stance of the BCEAO and the proposed benchmark rule: the BCEAO’s historical monetary policy has been more pro-cyclical while our benchmark rule would have been more counter-cyclical. The historical monetary policy stance of the BCEAO has been much more restrictive under crises and more expansionary under booms. Under 115 the proposed benchmark rule, the central bank will not accommodate a boom by allowing excessive money base growth, but it will intervene aggressively to stimulate the economy during a major economic crisis.

In any case, it is clear that the monetary base has been more volatile under the historical monetary policy stance of the BCEAO, which is not an unexpected outcome because the BCEAO’s operational target has never been a monetary aggregate.

Historical money base growth Benchmark rule 30 20 10 Percent 0

-10 Monetary response factor = 1 -20 1970 1974 1978 1982 1986 1990 1994 1998 2002

Data Source: IMF, author's calculations

Figure 3.13: Time Series Plot of the Historical Path Money Base Growth Versus Benchmark Rule

Finally, in order to gauge the effectiveness of the benchmark rule in stabilizing nominal GDP, we follow a slightly different approach taken by McCallum (1987). We state the problem of the central bank as minimizing the sum of the squared errors (SSE)

n X n∗ n 2 min (4y − 4yt ) (3.19) i=1 We first compute the sum of the squared errors between the nominal GDP growth target and the simulated growth rate of nominal GDP given by the benchmark rule and then we compute the same statistic between the nominal GDP growth target and the actual historical growth rate of nominal GDP influenced by the historical fixed exchange rate policy of the BCEAO. If the sum of the squared errors calculated between the nominal GDP growth target and the simulated growth rate of nominal GDP 116 is smaller than the sum of the squared errors calculated between the nominal GDP growth target and the historical growth rate of nominal GDP, then we can conclude that the benchmark rule would have improved the macroeconomic performance of the WAEMU better than the historical fixed exchange rate policy implemented by the BCEAO.

We estimate the following macroeconomic condition which measures the link between nominal GDP and the monetary base

n n 4yt = .02 + .294yt−1 + .274mbt + et (3.20) (1.11) (2.47) (2.57)

R2 = .22,σ ˆ = .07, DW = 1.78

The et are the estimated shocks for the economy. We use the benchmark rule specified in equation (3.18) to derive the simulated path of money base growth, then we fit the simulated path of money base and the estimated shocks of the economy into equation (3.20) to obtain the simulated path of nominal

GDP growth. The simulated values for money base growth and nominal GDP growth are calculated for

31 years. We report the sum of the squared errors associated with our benchmark rule and the sum of the squared errors associated with the historical monetary policy of the BCEAO in Table 3.10.

Using different nominal GDP growth targets in Table 3.10, we find that the benchmark rule given by equation (3.18) minimizes the sum of squared errors. Therefore, if the objective is to minimize nominal

GDP fluctuations for the WAEMU, then we can conclude that using McCallum’s (1987) rule as a guideline is useful for attaining this objective. There are benefits from exchange rate pegs, but clearly when the objective is to stabilize nominal GDP fluctuations, a fixed exchange rate regime is clearly less effective in satisfying this task; that is of course a standard finding in the literature.

In the previous chapter, we compared various monetary policy regimes in order to identity the optimal monetary policy regime for the WAEMU. The conclusion was that under certain conditions, nominal GDP targeting was to superior to a rigid peg, inflation targeting, and monetary aggregate targeting. In this section, the main objective is to examine the usefulness of using McCallum’s (1987) rule in implementing nominal GDP targeting for the WAEMU. Given the results presented in Table 3.10, we can conclude that McCallum’s (1987) rule can be useful as a guideline for implementing nominal GDP targeting for the WAEMU. 117

λ Benchmark rule (SSE) Historical fixed exchange rate rule (SSE) 4y∗ 1 .166 .243 6 % 1.25 .166 .243 6 % 1.5 .169 .243 6 % 2 .180 .243 6 % λ Benchmark rule (SSE) Historical fixed exchange rate rule (SSE) 4y∗ 1 .167 .218 8 % 1.25 .168 .218 8 % 1.5 .171 .218 8 % 2 .182 .218 8 % λ Benchmark rule (SSE) Historical fixed exchange rate rule (SSE) 4y∗ 1 .174 .217 10 % 1.25 .174 .217 10 % 1.5 .175 .217 10 % 2 .185 .217 10 % λ Benchmark rule (SSE) Historical fixed exchange rate rule (SSE) 4y∗ 1 .186 .241 12 % 1.25 .183 .241 12 % 1.5 .183 .241 12 % 2 .188 .241 12 %

Table 3.10: The Sum of the Squared Errors: Benchmark Rule Versus Historical Fixed Exchange Rate Rule

3.4.1 Selected Case Studies Cote d’Ivoire

The link between nominal GDP and the monetary base for Cote d’Ivoire is estimated as

n n 4yt = .02 + .354yt−1 + .224mbt + e1t (3.21) (2.03) (2.84) (2.05)

R2 = .43,σ ˆ = .07, DW = 2.26 using annual data from 1970 to 2015. We report the sum of the squared errors associated with the benchmark rule specified in equation (3.18) and the sum of the squared errors associated with the historical monetary policy stance of the BCEAO in Table 3.11.20

20We plot the actual path of money base growth and the simulated path of money base growth for Cote d’Ivoire in Figure C.3. 118

λ Benchmark rule (SSE) Historical fixed exchange rate rule (SSE) 4y∗ 1 .232 .386 10% 1.25 .227 .386 10% 1.50 .224 .386 10% 2 .226 .386 10%

Table 3.11: The Sum of the Squared Errors: Benchmark Rule Versus Historical Fixed Exchange Rate Rule (Cote d’Ivoire)

For Cote d’Ivoire, Table 3.11 shows that the benchmark rule given by equation (3.18) minimizes the sum of the squared errors. Therefore, if the objective is to minimize nominal GDP fluctuations for Cote d’Ivoire, then we can conclude that using McCallum’s (1987) rule as a guideline is useful for attaining this objective.

Senegal

The estimated relationship between nominal GDP and the monetary base for Senegal is found to be

n n 4yt = .03 + .244yt−1 + .154mbt + e2t (3.22) (2.77) (1.92) (2.12)

R2 = .43,σ ˆ = .07, DW = 1.90

We report the sum of the squared errors associated with the benchmark rule specified in equation

(3.18) and the sum of the squared errors associated with the historical monetary policy of the BCEAO in Table 3.12 for Senegal.21

λ Benchmark rule (SSE) Historical fixed exchange rate rule (SSE) 4y∗ 1 .144 .183 10% 1.25 .142 .183 10% 1.50 .140 .183 10% 2 .138 .183 10%

Table 3.12: The Sum of the Squared Errors: Benchmark Rule Versus Historical Fixed Exchange Rate Rule (Senegal)

21We plot the actual path of money base growth and the simulated path of money base growth for Senegal in Figure C.4. 119

For Senegal, Table 3.12 shows that the benchmark rule given by equation (3.18) minimizes the sum of the squared errors. Therefore, if the objective is to minimize nominal GDP fluctuations for

Senegal, then we can conclude that using McCallum’s (1987) rule as a guideline is useful for attaining this objective.

3.4.2 Historical Counterfactual Simulation

During the late 1980s and early 1990s, the BCEAO failed to prevent the money supply, domestic credit, and fiduciary circulation from collapsing at a time when the members of the WAEMU were experiencing difficult times due to the overvaluation of the CFA franc and falling commodity prices. In

Figure 3.14, we show that the domestic credit gap, fiduciary circulation gap, and the overall money supply gap collapsed from 1988 to 1993.

An interesting question that one could ask is what would have happened to nominal GDP during the crisis period of 1988-1993 if the BCEAO had followed the benchmark rule specified in equation

(3.18)? Could the macroeconomic performance of the WAEMU have been better? Could the BCEAO have prevented the economic depression that followed from 1988-1993? We can attempt to answer these questions by using counterfactual simulation. .1 0 -.1 -.2 -.3 1985 1990 1995 2000

Domestic credit gap Fiduciary circulation gap Monetary mass gap

Data Source: BCEAO, author's calculation

Figure 3.14: Domestic Credit Gap, Fiduciary Circulation Gap, and Monetary Mass Gap (1986-1990s)

Note: The cyclical components were calculated by using the HP-filter 120

It is a stylized fact that the external position of the BCEAO tends to worsen in proportion to falling commodity prices. Under these circumstances, the BCEAO must often to tighten so as to prevent interest rates from falling, prompting the overall money supply, including other key monetary aggregates such as domestic credit and fidicuary ciruclation to contract. We examine the external position of the

BCEAO from 1985 to 1995 in Figure 3.15. As mentioned by Nascimento (1994), the deterioration of the external position from 1988 until the early 1990s was a direct result of falling commodity prices.

.2 Before the devaluation .1 0 -.1 External Position Gap -.2 -.3 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

Data Source: WDI, author's calculations

Figure 3.15: The External Position Gap of the BCEAO (1985-1995)

Note: The external position gap was calculated by using the HP-filter

Although the BCEAO cannot control what is happening to commodity prices or rainfall, never- theless it can certainly react to mitigate their effects on the economy. The benchmark rule that we are proposing calls for a counter-cyclical monetary policy stance, which means that whenever a commodity price shock causes nominal GDP growth to fall below its target, the central bank must react to stabilize the economy. We remain convinced that the economy would have performed better and the collapse of key monetary aggregates such as domestic credits or fiduciary circulation could have been avoided had the

BCEAO followed the benchmark rule proposed in equation (3.18) during the crisis period of 1988-1993. 121

Nonetheless, proponents of hard pegs would argue that the fixed exchange rate policy used by the

BCEAO during the crisis period of 1988-1993 would have been superior, because a flexible exchange rate regime such as nominal GDP targeting would have called for a sharp depreciation of the exchange rate during the crisis, which would have been detrimental to the economy. Nonetheless, we know that the peg did not isolate the WAEMU from exchange rate volatility. In fact, the peg made the situation worse, because the CFA franc was extremely overvalued by 1993 as compared to what it was at the beginning of 1988.22

Furthermore, there is no evidence that a depreciation would have been detrimental to the economy.

There is also no evidence that a large depreciation would have induced severe balance sheets effects for the WAEMU, because historically it certainly did not do so in 1994 when the CFA franc was sharply devalued. Also, the vast majority of households, domestic producers, and farmers borrow domestically and do not hold their financial assets denominated in foreign currencies, so a depreciation would not have impacted their ability to borrow domestically, as long as the overall external position of the BCEAO remained in good condition. This was certainly the case after the devaluation of 1994, as the external position of the WAEMU significantly improved.

We shall rely on counterfactual simulation to test our hypothesis. As discussed earlier, domestic credit represents an important channel for transmitting the effects of monetary policy into the economies of the WAEMU, especially during economic downturns. We use annual data from 1970 to 2012 to estimate a macroeconomic model linking domestic credit to nominal GDP as follows

n n 4yt = .04 + .164yt−1 + .264crt + e3t (3.23) (2.36) (2.62) (2.09)

R2 = .21,σ ˆ = .06, DW = 2.02 where crt denotes the log of domestic credit and e3t are the estimated shocks buffeting the economy. Next, we use equation (3.18) to derive the simulated path of domestic credit growth. We plot the simulated path of domestic credit growth and the historical path of domestic credit growth during the crisis period of 1988-1993 in Figure 3.16.

22Please refer to Figure C.5 for empirical evidence. 122

As expected, domestic credit growth would have been more expansionary had the BCEAO adopted our benchmark rule during the crisis period of 1988-1993, but instead the economy experienced a painful contraction of domestic credit under the historical fixed exchange rate policy. 20 10

Monetary response factor = 1 Percent 0 -10 1988 1989 1990 1991 1992 1993

Benchmark rule (simulated domestic credit growth path) Historical path (domestic credit growth)

Data Source: BCEAO, author's calculations

Figure 3.16: Simulated Path of Domestic Credit Growth Versus Historical Path of Domestic Credit Growth (1988-1993)

We report the sum of the squared errors associated with the benchmark rule and those associated with the historical monetary policy rule of the BCEAO during the crisis period of 1988-1993 in Table

3.13. Clearly, we can see that the macroeconomic performance of the WAEMU would have been superior to the historical fixed exchange rate policy had the BCEAO adopted a flexible exchange rate regime, most specifically nominal GDP targeting. Certainly, nominal GDP would have been kept more stable.

λ Benchmark rule (SSE) Historical fixed exchange rate rule (SSE) 4y∗ 1 .023 .060 10% 1.25 .020 .060 10% 1.50 .017 .060 10% 2 .014 .060 10%

Table 3.13: Sum of the Squared Errors: Benchmark Rule Versus Historical Fixed Exchange Rate Rule(1988-1993) 123

3.5 Nominal GDP Forecast Targeting Model

Previously, we have shown empirically the expediency of using nominal trade data in forecasting nominal GDP. In this section, we show how nominal trade can be incorporated into this relevant context.

We begin by specifying the forecast of nominal GDP as follows

n n g yt+1 = yt + vt + nt + mbt + Θ (3.24)

n where yt+1 is the BCEAO’s one year ahead forecast of nominal GDP, Θ is the forecast error, which is a linear combination of multiple shocks happening between the periods of t and t + 1 that the central bank

g cannot observe. nt is the nominal trade gap, mbt is the monetary base, which is the operational target. n vt is the velocity of money and is not assumed to be constant. yt is nominal GDP and all variables are expressed in logs.

The BCEAO releases an annual forecast of nominal GDP every year. This means that nominal

GDP forecast targeting is quite feasible for the WAEMU. However, the effectiveness of this regime would depend on the reliability of the internal forecast. Nonetheless, even if the internal forecasts lack accuracy, there is always the possibility of relying on external forecasts. We follow an approach similar to that of

Svensson (1997) by specifying the intermediate target as the one-year ahead forecast of nominal GDP based on the set of information available at present time (t). This can be expressed as

n n g yt+1|t = yt + vt + nt + mbt (3.25)

n where yt+1|t is the BCEAO intermediate target. This intermediate target depends on the current level of nominal GDP, the velocity of money, the current level of money base, and the current state of the nominal trade gap. Note that the forecast of nominal GDP is based on its fundamentals derived from the equation of exchange, including nominal GDP itself and the nominal trade gap as an additional exogenous factor. The problem of the BCEAO lies in minimizing the gap between the nominal GDP target and the one-year ahead forecast of nominal GDP.

Since the target is specified in nominal terms, it should be much easier to achieve it. Given the

BCEAO’s annual forecast of nominal GDP, the BCEAO’s monetary policy committee would be aware of what the level of nominal GDP is forecast to be at time t + 1. Therefore, the BCEAO would adjust its operational target at time t based on the annual forecast of nominal GDP such that, for period t + 1, the annual forecast of nominal GDP equals the nominal GDP target. The central bank problem can be 124 expressed as

n n∗ 2 minL(yt+1|t − y ) (3.26) mbt and the central bank reaction function can be derived as follows

n∗ n g mbt = λ1(y − yt ) − λ2nt − vt (3.27)

where λ1 and λ2 are monetary response factors to be determined by the BCEAO. Nonetheless, if the nominal GDP forecast exceeds the nominal GDP target, the BCEAO must to tighten at time t and in the opposite scenario it must ease at time t. The central bank would follow this reaction function to make sure that the nominal GDP forecast equals the nominal GDP target. We understand that shocks can occur within the control lag, but the BCEAO would only be held responsible for its ability to minimize the difference between the nominal GDP target and the nominal GDP forecast. Therefore, the best the central bank can do is to do a good job in making forecasts of nominal GDP as close as possible to the target.

3.6 Conclusion

In this chapter, the main objective was to identify a strategy for implementing nominal GDP targeting for the WAEMU by taking into consideration the existing practical constraints that the BCEAO could face in doing so. First, we found that a monetary aggregate would be appropriate as the operational target given the structure of the economy. Secondly, we showed that McCallum’s (1987) rule could be a useful guideline for keeping nominal GDP growth on a 10 percent annual path (or any similar rates).

Given the unavailability of quarterly estimates of nominal GDP measured in current prices, we proposed to rely on nominal trade data as a proxy for nominal GDP for two reasons. First, nominal trade growth was found to be useful in forecasting nominal GDP growth. Second, trade data could be rendered accessible on a quarterly basis. Therefore, the key policy recommendation was to rely on quarterly estimates of nominal trade as a means to approximate trends in nominal GDP so that the

BCEAO can react to nominal GDP fluctuations coming from nominal trade on a timely basis.

The gains from nominal GDP targeting were largely due to preventing the monetary base from collapsing under economic downturns and preventing it from excessively growing during economic booms.

As a result, the economy performed better under McCallum’s (1987) rule as compared to under the historical fixed exchange rate policy of the BCEAO, which imposed a more restrictive monetary policy 125 stance. It is important to acknowledge that there are some limitations to the results of this study. For example, based on McCallum’s (1987) rule, it is known that nominal GDP growth reacts positively to changes in reserve money, but there is no clarity on how this expansion of nominal GDP growth is split between inflation and output.

On a positive note, we have estimated the short-run elasticity of supply for the WAEMU in chapter

2, and found a fairly inelastic response of domestic inflation to changes in the output gap occasioned by changes in the money supply. Therefore, it is likely to suspect that gains in nominal GDP growth as a result of changes in reserve money may be more driven by output than by inflation. In any case, based on McCallum’s (1987) rule, it is categorically impossible to know how nominal GDP growth gains are split between inflation and output.

Another limitation is how to reach or keep the economy on a six percent real growth target. Mon- etary policy can only do so much to affect growth in the long run. Therefore, we acknowledged the need to create structural programs and take on fiscal reforms to boost domestic investment in key sectors of the economy. For example, developing key special economic zones, adding value added to agricul- tural exports, improving infrastructure construction in Guinea-Bissau, increasing electricity production in Burkina Faso, increasing military expenditures in Mali, sustaining agricultural production in the Sa- hel part of the WAEMU, and improving financial intermediation in Cote d’Ivoire, Benin, and Togo are some viable policy recommendations to boost growth for the WAEMU. In any case, it is important to acknowledge that nominal GDP targeting has never been considered in context of the WAEMU. We are contributing to the economic literature by providing the first study to show how McCallum’s rule could be useful in implementing nominal GDP targeting for the members of the WAEMU. 126

APPENDIX A

APPENDIX A

Sample (adjusted): 2004M02 2017M05 Trend assumption: Linear deterministic trend Lags interval (in first differences): 1 to 12

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.441194 335.9083 159.5297 0.0000 At most 1 * 0.359112 242.7959 125.6154 0.0000 At most 2 * 0.293087 171.6118 95.75366 0.0000 At most 3 * 0.270860 116.1162 69.81889 0.0000 At most 4 * 0.184043 65.57400 47.85613 0.0005 At most 5 * 0.123370 33.03096 29.79707 0.0205 At most 6 0.068348 11.96375 15.49471 0.1587 At most 7 0.003970 0.636466 3.841466 0.4250

Trace test indicates 6 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Table A.1: Johansen Cointegration Test Results: Price Convergence Evidence 127

Picture Source: www.bceao.int Figure A.1: The Operational Framework of the BCEAO’s Financial Payment System 128

APPENDIX B

APPENDIX B

0

-1 D i c k e y - F

u -2 l l e r

T - S t a

t -3 i s t i c s

-4

-5 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Figure B.1: France’s Inflation (CPI), Unit Roots Test with Break Points Results 129 11 1 1 1 1 1 1 1 1 1 1 2.10 .63 3.44 2 .93 2.17 .0331111.0009782.0042049 .0331111 .0040121 .0124661 .0331111 .0012831 .0331111 .0099894 .0331111 .0052909 .0331111 .0034033 .0210767 .0083944 .002146 .014763 Table B.1: Central Bank Quadratic Loss Functions: Parameters Calibration 2 2 2 e µ v α σ σ σ c Parameters WAEMU-7b Burkina Faso Cote d’Ivoire Niger Senegal Togo 130

Table B.2: Augmented Dickey Fuller Tests: Inflation Proxies

INF RW D Test Statistic 5 Percent Critical Value P-value WAEMU-7 -8.177 -2.955 0.000 Burkina Faso -7.586 -2.955 0.000 Cote d’Ivoire -7.667 -2.955 0.000 Niger -7.667 -2.955 0.000 Senegal -7.917 -2.955 0.0000 Togo -8.594 -2.955 0.000 INF ARMAD Test statistic 5 percent critical value P-value WAEMU-7 -6.027 -2.955 0.000 Burkina Faso -5.847 -2.955 0.000 Cote d’Ivoire -6.215 -2.955 0.000 Niger -5.513 -2.955 0.000 Senegal -5.549 -2.955 0.000 Togo -7.979 -2.955 0.000 INF MAD Test statistic 5 percent critical value P-value WAEMU-7 -6.027 -2.955 0.000 Burkina Faso -7.314 -2.955 0.000 Cote d’Ivoire -7.391 -2.955 0.000 Niger -7.462 -2.955 0.000 Senegal -7.312 -2.955 0.000 Togo -8.493 -2.955 0.000 INF RW Test statistic 5 percent critical value P-value WAEMU-7 -8.177 -2.955 0.000 Burkina Faso -9.286 -2.955 0.000 Cote d’Ivoire -7.707 -2.955 0.000 Niger -6.900 -2.955 0.000 Senegal -8.187 -2.955 0.000 Togo -7.816 -2.955 0.000 INF ARMA Test statistic 5 percent critical value P-value WAEMU-7 -6.027 -2.955 0.000 Burkina Faso -6.851 -2.955 0.000 Cote d’Ivoire -6.273 -2.955 0.000 Niger -6.036 -2.955 0.000 Senegal -5.272 -2.955 0.000 Togo -6.192 -2.955 0.000 INFMA Test statistic 5 percent critical value P-value WAEMU-7 -7.551 -2.955 0.000 Burkina Faso -7.972 -2.955 (0.00) Cote d’Ivoire -6.945 -2.955 0.000 Niger -6.306 -2.955 0.000 Senegal -6.994 -2.955 0.000 Togo -7.144 -2.955 0.000 131

Table B.3: Augmented Dickey Fuller Tests: Other Variables

mmsDt or mmst Test Statistic 5 Percent Critical Value P-value WAEMU-7 -3.351 -2.952 0.0127 Burkina Faso -4.998 -2.952 (0.00) Cote d’Ivoire -3.782 -2.952 0.0031 Niger -6.189 -2.952 0.000 Senegal -4.579 -2.952 0.0001 Togo -5.927 -2.952 0.000 msDt or mst Test Statistic 5 Percent Critical Value P-value WAEMU-7 -3.205 -2.952 0.0197 Burkina Faso -4.705 -2.952 0.0001 Cote d’Ivoire -3.962 -2.952 0.0016 Niger -3.316 -2.952 0.0142 Senegal -4.955 -2.952 0.000 Togo -5.118 -2.952 0.000 gsDt or gst Test statistic 5 percent critical value P-value WAEMU-7 -3.670 -2.952 0.0046 Burkina Faso -4.827 -2.952 0.000 Cote d’Ivoire -4.684 -2.952 0.0001 Niger -3.889 -2.952 0.0021 Senegal -4.103 -2.952 0.0010 Togo -5.363 -2.952 0.000 rainst Test Statistic 5 percent Critical Value P-value WAEMU-7 -6.469 -2.952 0.000 Burkina Faso -7.151 -2.952 0.000 Cote d’Ivoire -6.084 -2.952 0.000 Niger -6.389 -2.952 0.000 Senegal -7.563 -2.952 0.000 Togo -6.684 -2.952 0.000 ogapt Test Statistic 5 Percent Critical Value P-value WAEMU-7 -3.011 -2.952 0.0339 Burkina Faso -5.233 -2.952 0.0000 Cote d’Ivoire -3.237 -2.952 0.0179 Niger -3.690 -2.952 0.0043 Senegal -5.424 -2.952 0.000 Togo -3.734 -2.952 0.0037 compexDt or compext Test Statistic 5 Percent Critical Value P-value WAEMU-7 -3.123 -2.952 0.0249 Burkina Faso -3.291 -2.952 0.0153 Cote d’Ivoire -2.656 -2.952 0.0820 Niger -2.966 -2.952 0.0381 Senegal -3.409 -2.952 0.000 Togo -3.132 -2.952 0.0243 cocoasDt or cocoast Test Statistic 5 Percent Critical Value P-value WAEMU-7 -3.795 -2.952 0.0030 Burkina Faso -3.878 -2.952 0.0022 Cote d’Ivoire -3.688 -2.952 0.0043 Niger -3.668 -2.952 0.0046 Senegal -4.146 -2.952 0.0008 Togo -3.656 -2.952 0.0048 tabasDt or tabast Test Statistic 5 Percent Critical Value P-value WAEMU-7 -3.952 -2.952 0.0017 Burkina Faso -4.038 -2.952 0.0012 Cote d’Ivoire -3.578 -2.952 0.0062 Niger -4.021 -2.952 0.0013 Senegal -4.033 -2.952 0.0012 Togo -4.161 -2.952 0.000 wogapDt or wogapt Test Statistic 5 Percent Critical Value P-value WAEMU-7 -3.796 -2.952 0.0030 Burkina Faso -4.047 -2.952 0.0012 Cote d’Ivoire -3.377 -2.952 0.0118 Niger -3.838 -2.952 0.0025 Senegal -3.788 -2.952 0.0030 Togo -3.908 -2.952 0.0020 goilDt or goilt Test Statistic 5 Percent Critical Value P-value WAEMU-7 -3.796 -2.952 0.0030 Burkina Faso -7.385 -2.952 0.0000 Cote d’Ivoire -7.089 -2.952 0.0000 Niger -7.472 -2.952 0.000 Senegal -7.383 -2.952 0.0000 Togo -7.141 -2.952 0.000 Notes: when a variable is is followsed by (D), it implies that it was deflated by the GDP deflator. Otherwise, it is assumed to be deflated by the CPI 132

APPENDIX C

APPENDIX C

The VAR system can be expressed as follows

exgt v I(0)

imgt v I(0)

          ex exgt γ10 γ11(L) γ12(L) exgt−1 et   =   +     +   (C.1)          im imgi γ20 γ21(L) γ22(L) imgt−1 et where (exgt) is the aggregate growth rate of exports and (impgt) is the aggregate growth rate of imports. if γ12 = 0, it would imply that impgt does not granger causes expgt and if γ21 = 0, it would imply that expgt does not granger causes impgt. We use aggregate imports and exports data from 1967 to 2016, collected from the World Development Indicators (WDI). We report the Granger causality test results in Table C.1. In the case of the WAEMU, we can safely reject the null hypothesis that export growth does not Granger causes import growth and conclude that the growth rate of export revenues are useful in forecasting or explaining the growth rate of imports.

Table C.1: Granger Causality Test Results: Export Growth and Import Growth

Null hypothesis: F-satisitics P-value Imports growth does not granger causes exports growth .31 0.73 Exports growth does not granger causes imports growth 3.49 .03 Notes: The sample size is from (1967-2016) and we used a 1 year lag. 133

Table C.2: Augmented Dickey Fuller Tests: IS and LM Equation (Annual Data)

Key Variables Test Statistic 5 Percent Critical Value P-value (y − y¯)t -3.408 -2.961 0.0107 (r − r¯)t -5.300 -2.955 0.0000 (reer − reer¯ )t -3.766 -2.955 0.0033 4(m − p)t -6.189 -2.952 0.000 4yt -3.342 -3.682 0.0131 4it -9.565 -2.978 0.000 τ -0.890 -2.978 0.7913

Table C.3: Augmented Dickey Fuller Tests: LM Equation (Quarterly Data)

Key Variables Test Statistic 5 Percent Critical Value P-value 4(m − p)t -7.705 -2.964 0.000 4yt -6.124 -2.964 0.000 4it -7.856 -2.964 0.000 τ -3.351 -2.961 0.0128

Roots of the companion matrix 1 .5 0 Imaginary -.5 -1 -1 -.5 0 .5 1 Real Data Source: IMF, WDI, author's calculations

Figure C.1: Panel VAR: Eigenvalues of the Companion Matrix 134

Sample (adjusted): 1972 2015 Included observations: 44 after adjustments Trend assumption: Linear deterministic trend n Series: yt and nt Lags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.232131 14.03795 15.49471 0.0819 At most 1 0.053428 2.415962 3.841466 0.1201

Trace test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.232131 11.62199 14.26460 0.1257 At most 1 0.053428 2.415962 3.841466 0.1201

Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Table C.4: Johansen Cointegration Test: The Log of Nominal Trade (nt) and the Log of Nominal GDP (yt).

Table C.5: Augmented Dickey Fuller Tests: Nominal Trade Growth and Nominal GDP Growth (WAEMU)

Key Variables Test Statistic 5 Percent Critical Value P-value n 4yt -4.439 -2.944 0.0003 4nt -5.627 -2.955 0.0000 135

Forecast Error 2 0 -2 Percent -4 -6 1995 2000 2005 2010 2015

Data Source: WDI, author's calculations

Figure C.2: Forecast Errors: Nominal GDP Growth

.6 Benchmark rule Actual money base growth .4 .2 0 -.2 -.4

1970 1980 1990 2000 2010 2020

Data Source: IMF, WDI, authors' calculations

Figure C.3: Benchmark Rule Against Actual Money Base Growth (Cote d’Ivoire). 136

Benchmark rule .6 Actual money base growth .4 .2 0 -.2 -.4 1970 1980 1990 2000 2010 2020

Data Source: IMF, author's calculations

Figure C.4: Benchmark Rule Against Actual Money Base Growth (Senegal)

(French franc/US dollar) index 120 (CFA franc/ US dollar) index 100

Pre exchange rate misalignment 80 Exchange rate index (local currency/ US dollar) 60

1984 1986 1988 1990 1992 1994

Data Source: WDI, author's calculations

Figure C.5: Currency Fluctuations: (CFA Franc/$) Index and (French Franc/$) Index, (Base Year=1985) 137

Nominal GDP growth 40 10 % nominal GDP growth target 30 20 Percent 10 0 -10 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014

Data Source: WDI, author's calculations

Figure C.6: Actual Nominal GDP Growth Versus Nominal GDP Growth Target (10 Percent) 138

Extension to McCallum’s Rule

Previously we proposed equation (3.18) as the only benchmark rule with nominal GDP as the intermediate target, but alternatively one may also be interested in knowing if using the inflation rate as the intermediate target could lead to an improvement in the macroeconomic performance of the economy, specifically by reducing the sum of the squared errors specified in equation (3.19). This case can be examined by expressing the new benchmark rule as follows

∗ ∗ 4mbt = π − 4vt + λ(π − 4πt−1) (C.2) λ>0

∗ Where π is the inflation target and πt−1 is the previous year’s inflation rate measured by taking the first difference of the log of the GDP deflator. We compute the sum of the squared errors between the nominal GDP growth target and the simulated growth rate of nominal GDP given by the new benchmark rule specified in equation (C.2). If the sum of the squared errors associated with the new benchmark rule given by equation (C.2) is smaller than the sum of the squared errors calculated between the nominal

GDP growth target and the historical growth rate of nominal GDP, then we can conclude that the new benchmark rule specified in equation (C.2) would have improved the macroeconomic performance of the

WAEMU better than the historical fixed exchange rate policy used by the BCEAO.

Furthermore, If the sum of the squared errors associated with the new benchmark rule given by equation (C.2) is smaller than the sum of the squared errors calculated between the nominal GDP growth target and the simulated growth rate of nominal GDP given by equation (3.18), then we can also con- clude that the new benchmark rule specified in equation (C.2) would have improved the macroeconomic performance of the WAEMU better than the original McCallum’s (1987) rule specified in equation (3.18).

We report the sum of the squared errors associated with each of the three rules in Table C.6. In

Table C.6, we find that the original benchmark rule specified in equation (3.18) outperforms all the other rules and improves the macroeconomic performance of the WAEMU when the nominal GDP growth target is below 10 percent. Whereas, the new benchmark rule specified in equation (C.2) outperforms all the other rules when the inflation target is significantly high, at 10 percent and above; note that this is an interesting finding. First, because Table C.6 shows that strict inflation targeting would be detrimental for the WAEMU, but not as bad as using a fixed exchange rate regime. Second, Table C.6 reinforces chapter 1’s findings which showed stronger growth rates associated with moderate inflation rates for those

Sub-Saharan African countries that allowed some degree of exchange rate flexibility. 139

λ Benchmark rule (3.18) (SSE) Historical fixed exchange rate rule (SSE) New benchmark rule (C.2) (SSE) 4y∗ or π∗ 1 .179 .368 .198 2 % 1.25 .174 .368 .193 2 % 1.5 .173 .368 .190 2 % 2 .179 .368 .188 2 % λ Benchmark rule (3.18) (SSE) Historical fixed exchange rate rule (SSE) New benchmark rule (C.2) (SSE) 4y∗ or π∗ 1 .166 .243 .176 6 % 1.25 .166 .243 .175 6 % 1.5 .169 .243 .176 6 % 2 .180 .243 .181 6 % λ Benchmark rule (3.18) (SSE) Historical fixed exchange rate rule (SSE) New benchmark rule (C.2) (SSE) 4y∗ or π∗ 1 .167 .218 .172 8 % 1.25 .168 .218 .172 8 % 1.5 .171 .218 .173 8 % 2 .182 .218 .179 8 % λ Benchmark rule (3.18) (SSE) Historical fixed exchange rate rule (SSE) New benchmark rule (C.2) (SSE) 4y∗ or π∗ 1 .1743 .217 .1746 10 % 1.25 .174 .217 .173 10 % 1.5 .175 .217 .173 10 % 2 .185 .217 .178 10 % λ Benchmark rule (3.18) (SSE) Historical fixed exchange rate rule (SSE) New benchmark rule (C.2) (SSE) 4y∗ or π∗ 1 .186 .241 .181 12 % 1.25 .183 .241 .177 12 % 1.5 .183 .241 .175 12 % 2 .188 .241 .178 12 %

Table C.6: The Sum of the Squared Errors (SSE): Benchmark Rule (3.18), New Benchmark rule (C.2), and Historical Fixed Exchange Rate rule 140

REFERENCES

Abubakar, Mika’ilu and Baba N. Yaaba (2014). “Monetary policy in Nigeria: Any role for McCallum rule?” American Journal of Economics 4(2), 114–123.

Amin, Aloysius Ajab (2000). “Long-term growth in the CFA franc zone countries.” UNU/WIDER 25.

Azariadis, Costas, James Bullard, Aarti Singh, and Jacek Suda (2015). “Optimal monetary policy at the zero lower bound.” Federal Reserve Bank of St. Louis.

Badolo, Felix and Romuald Somlanare Kinda (2014). “Climatic variability and food security in developing countries.” CERDI 201405.

Ball, Laurance (1997). “Efficient rules for monetary policy.” NBER Working Paper 5952.

Bean, Charles R (1983). “Targeting nominal income: an appraisal.” Economic Journal 93, 806–19.

Bhandari, Pranjul and Jeffrey A. Frankel (2015). “Nominal GDP targeting for developing countries.” NBER Working Paper 20898, 3–32.

Billi, Roberto M (2015). “A note on nominal GDP targeting and the zero lower bound.” Sveriges Riksbank Working Paper Series 270, 5–20.

Blanchard, Olivier (2005). Macroeconomics. Prentice Hall.

Brito, Ricardo D and Brianne Bystedt (1997). “Inflation targeting in emerging economies: Panel evi- dence.” Journal of Development Economics 91(2), 198–210.

Broda, Christian (2013). “Terms of trade and exchange rate regimes in developing countries.” Journal of International Economics 63, 2–23.

Calvo, Guillermo Antonio and Carmen Minos Reinhart (2002). “Fear of floating.” The Quarterly Journal of Economics CXVII (2).

Cavallo, Michelle, Kate Kisselev, Fabrizio Perri, and Nouriel Roubini (2005). “Exchange rate overshooting and costs of floating.” Federal Reserve Bank of San Francisco 07.

Clark, Todd E (1994). “Nominal GDP targeting rules: Can they stabilize the economy?” Economic Review Federal Reserve Bank of Kansas City 79, 11–20.

Coulibaly, Issiaka (2017). “Costs and benefits of the CFA franc.” https://worldpolicy.org/2017/02/28/costs-and-benefits-of-the-cfa-franc/ .

Creamer, Kenneth and Robert Tregathen Botha (2017). “Assessing nominal GDP targeting in the South African context.” Tand Robert Trehe Economic Journal 17, 1–10. de Mendon¸ca,Helder Ferreira and Gustavo Jos´ede Guimar˜aese Souza (1997). “Is inflation targeting a good remedy to control inflation?” Journal of Development Economics 98, 178–91. 141

Dembo, Toe (2012). “Calcule de l’indice des conditions mon´etairesdans L’UEMOA.” Document d’´etude et de recherche DER/12/01. Devarajan, Shantayanan and Jaime de Melo (1987). “Evaluating participation in African monetary unions: A statistical analysis of the CFA zones.” World Development 15, 483–496. Eduardo, Carlos S and Joao M Salles (1997). “Inflation targeting in emerging economies: What do the data say?” Journal of Development Economics 85(1-2, 312–318. Edwards, Sebastian and Eduardo Levi Yeyati (2005). “Flexible exchange rate as shock absorbers.” European Economic Review 49(8), 2079–2105. Frankel, Jeffrey (2013). “Exchange rate and monetary policy for Kazakhstan in light of resource exports.” Harvard Kennedy School 2, 1–39. Frankel, Jeffrey (2014). “Nominal GDP targeting for middle-income countries.” Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey 14(3), 1–14. Frankel, Jeffrey and Menzie Chinn (1995). “The stabilizing properties of a nominal GNP rule.” Journal of Money, Credit and Banking 27, 318. Frankel, Jeffrey Alexander, Carlos A Vegh, and Guillermo Javier Vuletink (2012). “On graduation from fiscal procyclicality.” Journal of Development Economics, Elsevier 100(1), 32–47. Friedman, Milton (1953). “The case for flexible exchange rates.” University of Chicago Press 88, 170–200. Garin, Julio, Robert Lester, and Eric Sims (2015). “On the desirability of nominal GDP targeting.” NBER Working Paper No. 21420. Gordon, David and Robert Barro (1983). “A positive theory of monetary policy in a natural rate model.” Journal of Political Economy 91, 589–610. Gulde, Anne Marie and Charalambos G Tsangarides (2008). The CFA Franc Zone : Common Currency, Uncommon Challenges. The International Monetary Fund. Hall, Robert E and N. Gregory Mankiw (1993). “Nominal income targeting.” NBER Working Paper 4439. Hodrick, Robert and Edward Prescott (1997). “Postwar US business cycles: An empirical investigation.” Journal of Money, Credit, and Banking 29, 1–16. International Monetary Fund (2011). “Managing volatility: A vulnerability exercise for low-income countries.” IMF Working Paper. International Monetary Fund (2016). “Sub-Saharan Africa multispeed growth.” International Monetary Fund 0258-7440. Jensen, Robert (2000). “Agricultural volatility and investments in children.” The American Economic Review 90, 399–404. Kakpo, Fiacre E. (2017). “Cˆoted’Ivoire: le gouvernement r´eduitles d´epenses publiques de 10 % en rai- son du r´eculedes prix du cacao.” http://www.agenceecofin.com/finances-publiques/2104-46784-cote- divoire-le-gouvernement-reduit-les-depenses-publiques-de-10-en-raison-du-recul-des-prix-du-cacao. Kinda, Tidiane (2011). “Modeling inflation in Chad.” IMF Working Paper 11/57. Lane, Christopher (1989). “The effectiveness of monetary policy in Cˆoted’Ivoire.” Overseas Development Institute. Love, Inessa and Lea Zicchino (2006). “Financial development and dynamic investment behavior: Evi- dence from panel VAR.” The Quarterly Review of Economics and Finance 46(2), 190. 142

McCallum, Bennett (1988). “Robustness properties of a rule for monetary policy.” Carnegie-Rochester Conference Series on Public Policy 29, 173–204. McCallum, Bennett (1997). “The alleged instability of nominal income targeting.” NBER Working Paper 6291. McCallum, Bennett T. (1984). “Monetarist rules in the light of recent experience.” American Economic Review 74, 388–91. McCallum, Bennett T. (1987). “The case for rules in the conduct of monetary policies : a concrete example.” Weltwirtschaftliches Archive 123, 415–429. Meade, James (1978). “The meaning of internal balance.” The Economic Journal 88(351)(2), 423–435. Mentz, Markus and Stephen P Sebastian (2003). “Inflation convergence after the introduction of the euro.” CFS Working Paper 30. Mishkin, Frederic S. and Adam S. Posen (1997). “Inflation targeting: Lessons from four countries.” NBER Working Paper 6126. Mundell, Robert (1970). “African trade, politics, and money.” In Tremblay, R., ed., Africa and Monetary Integration 63, 11–67. Mundell, Robert A (1961). “A theory of optimum currency areas.” The American Economic Review 51(4), 657–665. Nascimento, Jean Claude (1994). “Monetary policy in unified currency areas: The cases of the CAMA and ECCA durung 1976-90.” IMF Working Paper 94/11. Ngouana, Constant Lonkeng (2012). “Exchange rate volatility under peg: Do trade patterns matter?” IMF Working Paper 12/73. Poole, William (1970). “Optimal choice of monetary policy instruments in a simple stochastic macro model.” Quarterly Journal of Economics 84, 197–216. Risler, Laurent (2016). “Three essays on the impact of inflation targeting in developing and emerging countries.” PhD dissertation, American University, Washington, DC.. Rudebusch, Glen D (2002). “Assessing nominal income rules for monetary policy with model and data uncertainty.” The Economic Journal 112, 402–432. Sanogo, Issouf (2016). “La s´echeresse fait grimper le prix des l´egumes `a Bouak´e.” http://news.abidjan.net/h/578841.html. Simwaka, Kisu (2010). “Choice of exchange rate regimes for African countries: Fixed or flexible exchange rate regimes?” MPRA Paper 23129. Sumner, Scott B. (2014). “Nominal GDP targeting: A simple rule to improve Fed performance.” Cato Journal 34(2), 315–337. Svensson, Lars (1997). “Inflation forecast targeting: Implementing and monitoring inflation targets.” European Economic Review 41(6). Taylor, John B (1993). “Discretion rules versus policy in practice.” Carnegie-Rochester Conference Series on Public Policy 39, 200–210. Thiam, Ibrahima (2011). “Mixed exchange rate regime in the West African Economic and Monetary Union (WAEMU).” Journal of Economics and International Finance 3(16), 787–792. Tobin, James (1980). “Stabilization policy ten years after.” Brookings Papers on Economic Activity 1. 143

Udom, Solomon I. and Baba N. Yaaba (2015). “Determining the optimal monetary policy instrument for Nigeria.” CBN Journal of Applied Statistics 6. UEMOA (2017). “Trait´emodifi´ede l’Union Economique´ et Mon´etaireOuest Africaine (UEMOA).” http://www.uemoa.int. Weisbrot, Mark, Lara Merling, Alexander Main, and David Rosnick (2017). “The French economy, European authorities, and the IMF: Structural reform or increasing employment?” Center for Economic and Policy Research. Woodford, Michael (2012). “Methods of policy accommodation at the interest-rate lower bound.” Federal Reserve Bank of Kansas City.