INFLATION, INFLATION UNCERTAINTY AND OUTPUT GROWTH IN THE WAMZ: IMPLICATIONS FOR CURRENCY UNION Abiodun O. FOLAWEWO, PhD & Oluwafemi M. ADEBOJE Department of Economics University of Ibadan, Ibadan, Nigeria Paper Presented at the 9th Annual Conference on Regional Integration in Africa (ACRIA 9) July 8-11, 2018, Banjul, The Gambia

Presentation Outline Background to the Study Motivation of the Study Brief Literature Review Methodology Empirical Resu Conclusion

Background to the Study Different countries of the world (even in Africa) have embraced economic regionalism in order to meet the challenges of the emerging world economic order. This is also reflected in the increasing interest in establishing monetary union. UEMOA and CEMAC in Africa. WAMZ: The heads of state and government of five countries - The Gambia, Ghana, Guinea, Nigeria and Sierra Leone- formally launched, signed and established WAMZ in Dec. 2000. The ultimate expectations of economic policy makers is to ensure the achievement of macroeconomic goals. This is because any distortion in any of the macroeconomic variables could cause adverse economic effect. For instance, distortion in inflation could result into inflation uncertainty, which causes an adverse output effect as uncertainty about future inflation distorts the allocative efficiency aspect of price mechanism (Fountas et al., 2006).

Background to the Study Contd. Thus, the inter-relationship between inflation, its uncertainty and output growth is fundamental in taking decision on whether to create the monetary union or not and in taking monetary policy decision after the creation of the union. Lack of uniform evidence on the transmission channel from inflation to output through inflation uncertainty could signal national disparities among the member countries and render common monetary policy less effective stabilization policy tool (Fountas, 2004). A non-uniformity of the relationships could also signal that inflation rates arising from common monetary policy decision, will result in asymmetrical real effects on the countries.

Motivation for the Study Different studies have been done to examine the interrelationship between inflation and its uncertainty and/or including economic growth across African countries. Nigeria: Bamanga et al. (2016) Tunisia: Hachicha (2013) South Africa: Nasr et al.(2015) Ghana: Oteng-Abayie and Doe (2013); Barimah and Amuakwa-Mensah (2014) 4 African rich countries ( Algeria, Gabon, Congo Rep. & Libya): Ndoricimpa (2014) West Africa Monetary Zone as a whole? Therefore, this study investigates the relationship between inflation, its uncertainty and output growth by observing monthly inflation and output growth data of the five WAMZ member countries for the 1999:01 to 2016:12 period.

The Literature The relationship between inflation and inflation uncertainty has attracted a lot of empirical/theoretical studies globally. The resultant positive impact of higher average inflation on higher uncertainty was first suggested by Okun (1971) and Friedman (1977). Ball (1992) later formalised Friedman’s idea. During inflationary period, monetary authority may be tempted to adopt tight monetary policy measure but may fear the consequential effect of recession. Thus, they are faced with dilemma which creates uncertainty about future monetary policy and hence makes monetary policy less stable. Also in the midst of inflation uncertainty, central bank seeks to respond more sharply to shocks than when there is no uncertainty, in order to avoid undesirable outcomes in the future (Soderstrom, 2002).

The Literature Cont’d The negative relationship between inflation uncertainty and economic growth has been suggested by Grier and Perry (2000), Fountas et al. (2002) and Apergis (2004). The reverse causation from inflation uncertainty to inflation was suggested by Cukierman and Meltzer (1986). Positive unidirectional relationship from inflation to inflation uncertainty (Fountas et al, 2002; Bamanga et al, 2016); Positive unidirectional relationship from inflation uncertainty to inflation (Wilson, 2006; Balaji et al., 2016).

Methodology Variance from GARCH model as measure of uncertainty This study employs a two-step estimation approach: Variance from GARCH model as measure of uncertainty Granger causality tests: inflation and its uncertainty; nominal uncertainty and output growth. Measure of Inflation Uncertainty This study adopts the asymmetric exponential-GARCH (EGARCH) model. The choice of this model among various GARCH models is based on the proposition that GARCH model sets symmetry restrictions on the conditional variance which are not coherent with notion of inflation uncertainty in Friedman hypothesis (Brunner and Hess, 1993).

Methodology Cont’d. The GARCH model for the estimation of the time-varying conditional variance is given by : Where πt is the rate of inflation at time t and εt is the shocks to the inflation process that cannot be forecasted with information known at time t. εt is also assumed to be normally distributed with zero mean with a time-varying conditional variance ht.

Methodology Contd. Granger Causality Tests Causality between inflation and its uncertainty: Equation (3) is used to test whether inflation (πt ) causes inflation uncertainty while equation (4) tests whether inflation uncertainty causes inflation. The tests allow us to know whether inflation uncertainty (ht) precedes output growth (yt) and vice versa.

Methodology Contd. Causality between Inflation Uncertainty and Output Growth: The tests allow us to know whether inflation uncertainty (ht) precedes output growth (yt) and vice versa.

Empirical Results Descriptive Statistics The dataset are monthly observations (Jan. 1999 to Dec. 2016 ) of the inflation and output growth of the five countries. The inflation series for Guinea range from January 2005 to December 2016 while that of Sierra Lone range from January 2007 to December 2017. All the data were sourced from International Financial Statistics database. Table 1: Summary Statistics of Distribution of Inflation Series Source: Authors’ Computations   Gambia Ghana Guinea Nigeria Sierra Leone Mean 6.128 15.908 16.930 11.551 7.085 Median 5.591 14.732 13.842 11.356 6.890 Maximum 21.073 41.944 42.615 28.232 17.412 Minimum 0.177 5.312 1.474 -2.486 3.367 Std. Dev. 4.151 7.356 9.883 5.240 2.087 Skewness 1.575 1.627 0.824 0.261 1.662 Kurtosis 5.495 5.745 2.797 3.643 8.521 Jarque-Bera 145.282 163.039 16.544 6.167 207.659

Empirical Results Contd. Guinea has the highest average monthly inflation rate of about 15.9% and also experienced the highest inflation rate of over 42% a month. Sierra Leone has the lowest average inflation with a monthly rate of 7.09% and also characterised with the lowest standard deviation. Table 2: Summary Statistics of Distribution of Output Growth   Gambia Ghana Guinea Nigeria Sierra Leone Mean 3.756 6.116 3.684 6.816 6.183 Median 4.961 5.076 3.772 5.297 5.366 Maximum 8.598 14.517 6.829 36.054 28.989 Minimum -5.154 3.639 -2.010 -0.327 -10.112 Std. Dev. 3.527 2.591 1.907 7.089 8.210 Skewness -1.030 1.719 -0.925 3.069 0.728 Kurtosis 3.084 5.539 4.039 12.249 3.813 Jarque-Bera 38.225 164.366 40.504 1108.919 25.011 Source: Authors’ Computations

Empirical Results Contd. Unit Root Tests Most of the tests rejected the null hypothesis of presence of unit root in all the five countries. Table 3: Unit Root Tests Notes:* indicates significance at the 1% level; ** indicates 5% significance level and *** indicates 10% level of significance The results indicate that the series is stationary over time. ADF (constant term ) DF-GLS P-P KPSS Gambia -2.70* -2.47*** -2.18 0.13*** Ghana -2.02 -2.02*** -2.73* 0.46*** Guinea -2.14 1.52*** 0.58 Nigeria -3.22** -3.02 -3.41** 0.09*** Sierra Leone 1.16 -1.11*** -0.02 0.17***

Empirical Results Contd. Estimates of Inflation Uncertainty The asymmetric effects of inflation on uncertainty are indicated by the γ coefficients. The coefficients are positive and significant at the 1% level for all the WAMZ countries, indicating that an increase in inflation causes more inflation uncertainty and vice versa. Table 4: GARCH Models for Inflation and Inflation Uncertainty Gambia Ghana Guinea Nigeria Sierra Leone Inflation Equation Intercept 0.279 (0.000) 0.027 (0.792) -0.035 (0.764) 0.360 (0.196) 0.278 (0.001) Πt-1 0.962 (0.000) 0.998 (0.000) 0.984 (0.000) 0.963 (0.000) Variance Equation α0 0.044 (0.021) -1.034 (0.000) -0.780 (0.000) -0.130 (0.000) -1.987 (0.000) α1 -0.096 (0.005) 2.301 (0.000) 2.242 (0.000) 0.209 (0.000) 1.446 (0.000) ARCH (β) 0.085 (0.000) 0.120 (0.233) -0.011 (0.935) 0.094 (0.008) -1.102 (0.561) GARCH (γ) 1.000 (0.000) 0.238 (0.000) 0.296 (0.000) 0.974 (0.000) 0.370 (0.000)

Empirical Results Contd. Causal Relationship between Inflation and its uncertainty The results of the Granger causality tests as stated in equations (3) and (4) are shown in Table 5. We reject the null hypothesis that inflation does not granger cause inflation uncertainty for all the countries and establish that inflation granger causes inflation uncertainty in all the WAMZ member countries. There is bidirectional positive causality between inflation and inflation uncertainty in Gambia and Guinea while there is unidirectional positive causality only from inflation to inflation uncertainty in Ghana, Nigeria and Sierra Leone. Table 5: Granger Causality between Inflation and Inflation Uncertainty using AR(p):EGARCH variance series: 1990-2016 Notes: The optimal lag length is determined by the Akaike Information Criterion *** indicates significance at the 1% level ** indicates significance at the 5% level H0: Inflation does not granger cause inflation uncertainty H0: Inflation uncertainty does not granger cause inflation Optimal lag length Gambia 362.410*** 8.517** 3 Ghana 86.567*** 0.108 Guinea 29.213*** 11.088*** 2 Nigeria 120.926*** 2.516 7 Sierra Leone 20.257*** 1.900 6

Empirical Results Cont’d. Causal Relationship between Inflation Uncertainty and Output The results of the Granger causality tests as defined in equations (5) and (6) are shown in Table 6. We cannot reject the null hypothesis that inflation uncertainty does not Granger cause output growth for all the countries. Output growth Granger causes inflation uncertainty only in Sierra Leone while for the other four countries; output growth does not Granger cause inflation uncertainty. Table 6: Granger Causality tests between inflation uncertainty and output growth using AR(p):EGARCH variance series: 1990-2016 Notes:The optimal lag length is determined by the Akaike Information Criterion *** indicates significance at the 1% level H0: Inflation uncertainty does not granger cause output growth H0: Output growth does not granger cause inflation uncertainty Optimal lag length Gambia 0.667 1.482 5 Ghana 0.063 0.808 3 Guinea 0.806 1.925 Nigeria 0.777 Sierra Leone 0.017 11.710*** 4

Conclusion Evidence that inflation raises inflation uncertainty provides support for Holland’s stabilization hypothesis that if inflation causes inflation uncertainty, then monetary authorities will make attempt to put inflation uncertainty in check by taking restrictive monetary policy actions to lower inflation. Therefore, the proposed common monetary policy for all the central banks in WAMZ might lead to a symmetric real effects. Hence, the integration of the five countries as a monetary union seems feasible. However, there is need for better monetary stabilisation through central bank independence in all the WAMZ member countries.

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