The long-run constancy behavior of the real interest rate is often examined in the framework of the Fisher relationship, Fisher (1930)

ON THE FISHER EFFECT AND INFLATION DYNAMICS IN LOW-INCOME COUNTRIES: AN ASSESSMENT OF SUB-SAHARAN AFRICA ECONOMIES NANDWA, Boaz^* Abstract

Controlling for structural breaks, this study examines whether the relationship between the interest rate and infla tion exhibits common stochastic trends in a sample of Sub- Saharan Africa (SSA) economies. The results indicate that while the Fisher effect does not hold for the entire sample period, 1980:I-2005:II nor in pre-economic reforms period, this relationship holds for the post-economic reforms (deregulated financial markets and exchange rate float regime) period, 1995:I-2005:II. Further, from the vector error- correction model (VECM), we find a less than proportionate response of short-term adjustment of the nominal interest rate to expected inflation. This implies that, compared to the long-term, in the short-term the nominal interest rate are poor predictors of inflation and the monetary policy in these countries is unlikely to impact on ex ante real interest rates in the long-term.

JEL Classification: C32, E43, E47, E58, 055

Key Words: Monetary policy, the Fisher effect and Sub-Saharan Africa

1.Introduction

The Fisher effect, which links the interest rate to expected inflation, has been one of the most important outcomes of the classical economic theory. According to this theory, in the long-term equilibrium, changes in the growth of money supply leads to fully perceived changes in inflation and consequently adjustment of the nominal interest rate.

The long-run constancy behavior of the real interest rate is often examined in the framework of the Fisher relationship, Fisher (1930). In this context, the real interest rate is wholly determined by real factors in the economy such as; the investor’s time preference and marginal productivity of capital. The Fisher hypothesis posits that the nominal interest rate has a one-for-one correspondence with inflation in the long-run, and thus, changes in the prices should have no effect on the level of the real interest rate.

Otherwise if the real interest rate is related to the expected inflation rate, ceteris paribus, movements in the inflation rates will not be fully absorbed by adjustments in the nominal interest rate, Fama (1975 and 1990), Bernake (1990) and Mishkin (1992). So far, the empirical evidence on temporal and cross-country validation of the Fisher effect has not been conclusive, with the findings not supportive of the full adjustment of the nominal interest rate to inflation (see Rose, 1988; Moazzami, 1989; Mishkin, 1990a; 1990b and 1992; Engsted, 1995 and Evans and Lewis, 1995)¹. In these studies, the nominal interest moves less or greater than one-for-one with inflation across time (short- or long-run) and

* Boaz Nandwa is a Post-doctoral research fellow at the Economic Growth Center, Yale University, E-mail:

[email protected]

Acknowledgement: I am indebted to Prof. Christopher Udry for his generosity in facilitating my research work at EGC. Valuable comments from Prof. Andoh and Otsi are greatly acknowledged. All the errors are mine.

1For further details, refer to Lahiri (1972), Gibbon (1972), Gruen and Wilkinson (1994), King and Watson (1997) and Koustas and Serletis (1999).

countries. However, most of these studies on the Fisher relationships have been conducted on high-income countries.

Studies carried out on low-income countries are few and the evidence is mixed. Payne and Ewing (1997) using Johansen and Juselius (1990) cointegration technique evaluated the Fisher effect for nine low-income countries. They found evidence of long-run relationship between nominal interest rate and inflation for Sri Lanka, Malaysia and Pakistan, but there was no evidence of the Fisher effect for Argentina, India, Thailand and Niger. Garcia (1993) found that inflationary expectations explained 99% of the movements in the nominal interest rate for Brazil over 1973-1990 period. Similarly, Blake and Phylaktis (1993) concluded that there was existence of a long-run unit correspondence between inflation and nominal interest rate for Argentina, Brazil and Mexico. Finally, Mendoza (1992) and Thornton (1996) found evidence in support of the Fisher effect for Mexico and Chile respectively.

Studies on the Fisher effect for Sub-Saharan Africa (SSA) economies are absent. In addition, none of these previous studies on low-income countries took into account the structural changes occasioned by the shift in monetary policy regimes (for instance, a shift in the exchange rate regime from fixed to float and financial deregulation in the domestic financial (interest rates) markets). This is because, in a multicountry study, Bosner-Neal (1990) found that changes in monetary policy regimes influenced the movements in interest rates in the economy. Therefore, using the Johansen and Juselius (1990) cointegration methodology, this study is aimed at incorporating these structural changes in analyzing the implication of the Fisher effect on inflation dynamics in a sample of SSA countries. This approach is relevant in the study of these countries, characterized by lack of credible inflation forecasting framework, since it forms the basis of understanding the relationship between interest rates and inflation.

The rest of the paper is organized as follows. In section 2 we outline the empirical model of the Fisher effect. Data sources are presented in section 3. Section 4 presents and discusses the empirical results followed by conclusion in section 5.

2.Modelling the Fisher Effect

The simple Fisher relationship is given by the following log-linear expression:

i_t = +r_t E_tπ_t+1 (1) Where it is the logarithm of one plus the nominal interest rate, rt is the logarithm of one plus ex ante real interest rate, Et is the expectation parameter conditional on information available at period t and pt + 1 is the expected changes in prices from period t to t + 1.

Further, if inflation expectations are fully formed contingent upon the information set available at time t, then ₁

.

t

e t t

Eπ ₊ =π Where, for instance, Etyt = E(yt|Ot) is the m-period ahead expectation given the current information set, Ot, available to the agents in the economy at time t. Hence, assuming ex ante interest rate is generated by a stationary

process and that the actual and expected inflation differ by a stationary zero mean process, then:

t

e

t t

π = ∆ +π ε (2) Where ∆ is the actual change in inflation (consumer price index) and eπ_t t is a vector of independent and identically distributed (iid) Gaussian white noise errors.

Thus equation (1) can be rewritten as:

i_t = + ∆ +φ θ π ε_{t t} (3) Therefore, given constant ex ante real interest rate( )φ , equation (3) shows that if inflation expectations in the economy are accurate, then changes in inflation should be reflected by equal changes in the nominal interest rate. Here, according to the Fisher hypothesis,

θ

is expected to be positive and equal to one (or closer to unity). This is the crux of the Fisher effect framework².

2.1 Dynamic implication of the Fisher effect in SSA Economie.- Equation (3) has popularly been used in examining the neutrality of the Fisher effect by focusing on the long-term equilibrium. Carmichael and Stebbing (1983) and Moazzami (1989) contends that regression of equation (3) is susceptible to misspecification error by the virtue of assuming that there are no deviations of the interest rates in the short-term from the long- term equilibrium. Thus in order to assess the short- and long-term dynamics of interest rate and inflation for SSA economies, we model (3) using the Johansen and Juselius (1990) cointegration methodology, whereby the variables in their levels are allowed to be endogenous. In addition, according to this approach, all possible causal relationships among the variables are specified and simultaneous testing of the number of cointegrating relationships is permitted. Then, depending on the order of integration of each series, the dynamics of the variables are modelled within a vector error-correction model (VECM).

In essence, a cointegration analysis allows testing for the existence of an equilibrium relationship between two or more time series variables. Two series, yt and xt, are said to be cointegrated of the order (b,d) if they are both integrated of order b and has to be differenced d times in order to be stationary. Thus, yt and xt are said to be cointegrated

2 2. Some of the theoretical and empirical propositions advanced to explain the less or greater than unity coefficient are couched in the Mundell-Tobin “Wealth effect” by Mundell (1963) and Tobin (1965) who suggested that when the opportunity cost of holding money increases (as a result of higher inflation) then money holding decreases and capital stock increases. Because of the decreasing returns to scale, the interest rate decreases with the lower levels of marginal productivity of capital. Thus the coefficient on inflation expectation will be positive but less than one. The second proposition is the “tax effect” by Darby (1975) who pointed out that when the nominal interest rate is taxed, the Fisher effect implies that the change in the nominal interest rate will be greater than the change in expected inflation in order to maintain the ex ante real interest rate (see Evans and Lewis, 1995; Crowder and Hoffman, 1996 and King and Watson, 1997 for further details).

and they tend to move together over the long-term, Engle and Granger (1987). Consider the following vector autoregressive (VAR) model:

1 1 2 1

... , 1,...,

t t t k t k t t

Y =δY₋ +δ Y₋ + +δY₋ +γD +ε t= T (4) Where Yt is a g x 1 vector of nominal interest rate and inflation, integrated of the order one, I(1), and Dt is a vector matrix containing constant and dummy variables – denoting the periods of the structural breaks and seasonal dummies. Supposing that there exists a comovement of the variables which trend together to the long-term equilibrium, then the Engle and Granger (1987) representation of (4) which constitutes the VECM can be reparameterized as a k-lag difference equation:

∆ = Γ ∆Y_{t 1} Y_t−1+ Γ ∆₂ Y_t−1 + + Γ ∆

...

_k−1 Y_{t k− +1}+ ΨY_t−1+γW_t +ε_t (5)

Where,

1 k

i i

I ψ

=

 

Ψ = − +

∑

 (6)

and

1

1

; ( 1,..., 1)

k i i

I i k

i ⁻ψ

=

 

Γ = − −

∑

 = − (7)

The Γ_i are linear combination of Ψi’s parameters to be estimated and Ψ is the long- run parameter matrix. Hence cointegration among interest rate and inflation implies a reduced rank of Ψ. This can be factorized as Ψ=αβ´, such that, β is a g x r matrix, whose r columns represent the cointegrating vectors among the variables in Yt while a is a g x r matrix whose g rows represent the adjustment coefficients (error-correction coefficients) that measures the strength of the cointegrating vectors in the VECM. The Johansen and Juselius (2000) trace and maximum tests are implemented to test for cointegration (order of the rank). These test statistics are sensitive to the choice of the lag length (k) – which are selected based on the Akaike Information Criteria (AIC) and Schwartz Bayesian Criteria (SBC) to eliminate serial correlation. The cointegration relations are then estimated as the eigenvectors corresponding to r non-zero eigenvalues in (5), with the likelihood-maximizing solution forθ

ˆ

obtained by solving the eigenvalue objective function:

|λs_{k k} −(s s s_{k0 00}⁻¹ _k′₀) | 0= (8) Where Skk is the residual matrix from the OLS regression of Yt on ∆ Yt-k+1, Sk0 is the cross-product matrix and the residuals from OLS regression of ∆ Yt on ∆ Yt-1,..., ∆ Yt-k+1

are contained in S00. From (8), we obtain the eigenvalues;

ˆ ˆ ˆ ... ˆ

1 2 3

m

λ >λ >λ > >λ and the associated eigenvectors, V

ˆ .

The size of the eigenvalues is a measure of how strong the cointegration relation is correlated with the linear combination of the stationary process. This is then verified through testing the null hypothesis on the cointegrating

vectors in the model. The eigenvectors are normalized such that V S V

ˆ

′_kk

ˆ

=

1.

^To distinguish between zero, non-zero and the largest eigenvalues (i.e. determining the number of cointegrating vectors (r)), we implement the likelihood ratio maximum and trace eigenvalue test statistics of Johansen and Juselius (1990) (with a Chi-squared distribution):

2ln ln(1 ˆ )

| 1 1

Q T

max r r r

λ = − + = − −λ + (9)

2ln ln(1 ˆ) 1

k

Q T

Trace r i

i r

λ = − = − ∑ −λ

= + (10) Where

ˆ

λiis the eigenvalues obtained from estimating θ and T is the sample size. The statistics test the null hypothesis that there are at most r cointegrating vectors (against the alternative that there are r + 1 vectors) and the null of r cointegrating vectors (against the alternative that the cointegration vectors are to equal or less than r + 1) respectively.

3. Data Sources

The data used in this study is quarterly seasonally unadjusted interest rate and inflation series obtained from International Financial Statistics (IFS) CD-ROM of the International Monetary Fund. For missing values, these were filled by information obtained from the respective central bank’s websites. Centered seasonal dummies are incorporated to account for seasonal variations in the time series. The sample period is divided into three sub-samples; the full sample period, 1980:I-2005:II, pre-economic reforms period (under regulated domestic financial markets and fixed exchange rate regime), 1980:I-1994:IV and post-economic reform period (deregulated financial markets and exchange rate float³ regime), 1995:I-2005:II³. The countries covered under this sample are: Cameroon, Ghana, Kenya, Malawi, Nigeria and Zambia. The yield on the government bond is a proxy for nominal interest rate. The inflation rate is calculated as the first difference of the natural logarithm of the consumer price index (CPI). Distributed lag on past inflation is used as proxy of expected inflation, Cagan (1956).

4. Empirical Results

Unit Roots Test: Before testing for cointegration, a critical conside-ration of stochastic process generating the time series properties of the data and estimation methodology is imperative to the rejection or acceptance of the Fisher effect. Mishkin (1992) observed that the rejection of the Fisher effect estimates in some studies is because of the possibility of spurious regression that arise due to the nonstationarity of the interest rate

33. Float is used here loosely to represent managed and flexible exchange rate regimes because in most instances, where we have flexible exchange rate regime in these countries, it is still subject to intervention by the central bank to stabilize the exchange rate. Chow test was used in testing for the structural changes over the sample periods (results not reported here but available from the author upon request).

and inflation. We therefore perform the Augmented Dickey Fuller (ADF) unit roots test based on the following OLS regression outlined by Dickey and Fuller (1981):

0 1 1

1 k

y y t y

t t i t i t

i

δ δ ϑ ϕ ε

∆ = + − + + ∑= ∆ − + (11)

Where ₁

1

1,

k i i

δ ρ

=

=

∑

− ^{, Yt} is the time series variable under consideration and t is the time trend. We also have ∆ as the difference operator, such that ∆yt = yt – yt-1, and et is a stationary random error. The test is performed on both the levels and first difference of the variables. The null hypothesis of the presence of unit root, H0: δ1=0 is tested against the alternative of stationarity, H1: δ1<0. The null hypothesis is rejected when λ1 is negative and significant. The results of the ADF, in levels and first difference are presented in table 1. From table 1 we find that most of the variables are integrated of order one, I(1), in their levels and becomes stationary, I(0), after first differencing. Since the values of ADF t-statistics are well above the 95% significance level in the three sample periods, the null hypothesis that the variables contains unit root in their first difference is rejected. Further, Engle and Granger (1987) observed that, if two variables are cointegrated, then Granger causality must exist in at least one direction.

This relationship is outlined in the VECM, in the sense that, since the variables share a common trend then either the first difference of interest rate or inflation must be Granger caused by lagged values of the VECM terms, which themselves are a function of the lagged values of the level variables. That is, if it-k and pt-k share common trends, then the current change, say in πt (∆πt ), is partly the result of pt moving into alignment with the trend values of it, Engle and Granger (1987). The inflation-interest rate causality tests are presented in table 2. The results show that the causality direction runs from inflation to interest rate⁴, consistent with the classical theory that a change in the money supply leads to changes in inflation and consequently, causes adjustments in the nominal interest rates.

But in the long-run, ceteris paribus, ex ante real interest rate will not respond to changes in expected inflation, Fisher (1930).

4Gruen and Wilkenson (1990) pointed out that the Fisher relationship should lead us to expect inflation to be weakly exogenous. Thus the weak exogeneity test conducted (not reported here but available from the author upon request) is rejected for the interest rate variable, but accepted for inflation.

Table 1 ADF Unit Roots Test

Nominal interest rate Inflation Countries Periods Level Diff Level Diff Cameroon 1980:I-2005:II

1980:I-1994:IV 1995:I-2005:II

-1.30(2) -1.79(2) -1.34(1)

-4.23(1)*

-3.35(1)*

-6.74(0)*

-1.03(2) -0.83(2) -1.12(2)

-4.33(0)**

-6.01(1)*

-5.31(1)*

Ghana 1980:I-2005:IV 1980:I-1994:IV 1995:I-2005:II

-1.13(2) -0.92(2) -0.87(2)

-4.12(1)**

-3.36(0)**

-4.29(0)*

-1.23(2) -0.55(2) -1.10(2)

-2.98(4)**

-4.71(2)*

-5.28(2)*

Kenya 1980:I-2005:II 1980:I-1994:IV 1995:I-2005:IV

-1.87(3) -1.18(3) -1.91(2)

-3.22(4)**

-5.52(2)*

-6.21(2)*

-1.45(3) -1.37(2) -0.84(2)

-3.56(4)**

-4.03(2)**

-6.65(1)*

Malawi 1980:I-2005:II 1980:I-1994:IV 1995:I-2005:II

-1.21(2) -0.59(2).

-1.13(2)

-3.98(1)**

-4.98(1)*

-5.01(1)*

-1.61(2) -1.33(2) -0.96(2)

-5.18(2)*

-3.42(1)**

-5.85(1)*

Nigeria 1980:I-2005:II 1980:I-1994:IV 1995:I-2005:II

-1.74(3) -1.24(3) -0.79(3)

-3.72(4)**

-3.36(2)**

-4.15(2)*

-1.38(3) -1.19(3) -0.85(2)

-4.11(4)**

-5.43(2)*

-4.49(2) Zambia 1980:I-2005:II

1980:I-1994:IV 1995:I-2005:II

-1.02(2) -1.78(2) -1.11(2)

-4.23(1)**

-5.19(0)*

-8.27(0)*

-0.82(2) -1.25(2) -1.52(2)

-3.34(1)**

-4.16(2)**

-5.11(2)*

Notes: Centered seasonal dummies have been included to control for seasonal integration. The ADF statistics were obtained using OLS regression with a constant term only. The critical ADF values are taken from MacKinnon (1991), where ** and * indicate 5% and 1% significance levels respectively. The lag length, based on AIC and SBC are in brackets – selected to eliminate serial correlation. Results for full sample period, pre-economic reforms period and post-economic reforms period respectively.

Table 2 Granger-Causality Tests

Null hypothesis F-test 5% critical value Inflation does not Granger cause interest rate 4.03 2.89 Interest rate does not Granger cause inflation 1.75 1.47

Testing for Cointegration: The cointegration maximum and trace eigenvalue statistic tests are reported in table 3.

Table 3. Cointegration Tests: Panel A 1980:I-2005:II

Countries Rank λTrace λMax

Cameroon r = 0 ; r = 1 8.34 ; 3.12 7.59 ; 3.43 Ghana r = 0 ; r = 1 7.42 ; 5.38 9.86 ;5.53 Kenya r = 0 ; r = 1 8.09 ; 4.71 7.31 ; 4.55 Malawi r = 0 ; r = 1 7.25 ; 3.14 6.33 ; 3.29 Nigeria r = 0 ; r = 1 3.75 ; 4.18 8.01 ; 4.29 Zambia r = 0 ; r = 1 6.17 ; 6.03 9.87 ; 5.97

Panel B 1980:I-1994:IV

Cameroon r = 0 ; r = 1 8.32 ; 3.96 11.72 ; 8.29 Ghana r = 0 ; r = 1 10.31 ; 5.17 7.02 ; 3.78

Kenya r = 0 ; r = 1 9.03 ; 4.11 9.27 ; 3.92 Malawi r = 0 ; r = 1 7.89 ; 5.20 6.28 ; 5.21 Nigeria r = 0 ; r = 1 8.57 ; 3.85 8.19 ; 4.38 Zambia r = 0 ; r = 1 6.92 ; 4.04 10.47 ; 7.19

Panel C 1995:I-2005:II

Cameroon r = 0 ; r = 1 14.91** ; 8.43 25.01* ; 4.50 Ghana r = 0 ; r = 1 29.52* ; 7.29 34.11* ;3.12 Kenya r = 0 ; r = 1 45.12* ; 9.47 26.32* ; 9.08 Malawi r = 0 ; r = 1 27.81* ; 8.31 15.27** ; 3.83 Nigeria r = 0 ; r = 1 31.74* ; 8.73 23.56* ; 5.57 Zambia r = 0 ; r = 1 25.49* ; 7.73 28.34* ; 6.72

Notes: The cointegration tests λTrace and λMax are trace-test and maximal eigenvalue tests respectively. The critical values for ?Trace and λMax were obtained from Osterwald-Lenum (1992) table 1. Finally, ** and * denotes 5% and 1% significance levels respectively.

Using both the maximum and trace test statistics, the results for the full sample in panel A and pre-economic reforms period in panel B shows absence of cointegrating vectors over the two sample periods. This indicates absence of the Fisher effect in both panels A and B. However, for the post-economic reforms period in panel C, the hypothesis of no cointegrating vector is rejected in favor of the alternative that at least one cointegrating vector is present at the 5% and 1% significance levels. This implies that, for post- economic reforms period, there is one statistically significant cointegrating vector between inflation and interest rate at the 5% and 1% significance levels and thus the Fisher relationship holds.

Table 4 reports the normalized maximum likelihood estimates of θ and the likelihood ratio test of the Fisher hypothesis, θ= 1.

Table 4 Testing for the Fisher Hypothesis Countries θ

ˆ

χ2 test statistic H₀: θ ^{= 1} Cameroon 0.89 2.32 Ghana 0.86 3.20 Kenya 1.04 2.27 Malawi 0.87 1.29 Nigeria 1.12 2.36 Zambia 0.87 1.78

Notes: Under the null hypothesis of the the Fisher effect, _θ = 1, the likelihood ratio statistic is Chi-squared distributed with one degree of freedom (with critical value of 3.784 at the 5%

significance level).

The estimated parameters presented in table 4 (from the normalizad cointegration results in table 3, panel C) are closer to unity and the results shows that the Fisherian unity null hypothesis can not be rejected. This indicates that after controlling for structural breaks, the Fisher relation holds in the long-run for the post-economic reforms period. This period was characterized by the floating exchange rate regime and

deregula tion of the financial markets, which affected the nominal interest rate and inflation in the sampled countries.

Dynamics of Short-Run Adjustments: Given that we have established a long-run relationship between interest rate and inflation for the countries in our sample, we next explore the dynamic relationship between these two variables in a VECM framework.

This is implemented in order to examine the speed of adjustment of the nominal interest rate to unanticipated changes in inflation. Table 5 shows that the ECt-1 results on interest rate and inflation is significant for all the countries implying that, in the short-run, the interest rate adjusts to eliminate long-run disequilibrium.

Table 5 VECM Results of Short-Run Adjustments

Cameroon Ghana Kenya

Variables it

∆ ∆π_t ∆i_t ∆π_t ∆i_t ∆π_t Constant 0.512**

(3.031)

1.261**

(2.412)

1.012*

(4.812)

0.028**

(3.461)

2.015*

(5.027)

1.028*

(7.812) ECt-1 -0.112**

(3.281)

-0.162**

(2.421)

-0.112*

(5.161)

-0.122*

(3.711)

-0.152*

(4.362)

-0.172**

(2.901)

R² 0.62 0.49 0.58 0.53 0.67 0.74

Normality (2) 0.106 0.219 0.034 0.213 0.071 0.218 LM(2) 2.141 2.319 1.204 0.942 2.046 1.921 ARCH(1) 0.801 0.458 0.609 0.592 0.194 0.524

Malawi Nigeria Zambia

Variables

∆i_t ∆π_t ∆it ∆πt ∆it ∆πt

Constant 1.015**

(2.027)

0.928**

(2.812)

1.031*

(3.027)

0.901*

(3.613)

0.015 (1.027)

0.028**

(2.522) ECt-1 -0.412**

(2.671)

-0.162**

(2.341)

-0.185*

(3.562)

-0.162*

(7.954)

-0.412**

(3.231)

-0.162**

(3.314)

R² 0.49 0.65 0.71 0.82 0.64 0.57

Normality (2) 0.931 1.034 0.482 0.519 0.237 0.469 LM(1) 1.102 2.049 2.120 1.148 1.082 2.162 ARCH(2) 0.641 0.375 0.452 0.817 0.363 0.457

Notes: The t-statistics, corrected for heteroscedacticty, are given in brackets, where * and ** denotes significance at the 1% and 5% levels respectively. Under their respective null hypotheses, the test statistics for normality, serial correlation and autocorrelation are asymptotically distributed. The significant lag levels are in brackets. We fail to reject the hypotheses at the 5% level, thus, the residuals are normal and not serially correlated nor autocorrelated. Dummies for structural breaks and seasonal variations were also significant (not reported here but can be obtained from the author upon request).

For instance, in Kenya and Nigeria, we find that there is a 15.2% and 18.5% adjustment in the nominal interest rate to eliminate disequilibrium in the long-term respectively.

However the adjustment coefficients indicate a slow adjustment of the nominal interest rate to the unanticipated inflation. This implies that in the short-term, nominal interest rates are poor predictors of inflation. This finding maybe as a result that other monetary policy variables such as growth in money supply and exchange rate, which shows wide variability in the short-run, play significant role in determining interest rate and

forecasting of the inflation rate in these economies. The Lagrange Multiplier (LM) and autocorrelation conditional heteroscedasticity (ARCH) test statistics indicates absence of serial correlation and autocorrelation respectively

5 Conclusion

In this study, by taking into consideration the structural break, we found a cointegrated relationship between inflation and interest rate, which confirmed the existence of the Fisher effect among the selected SSA countries. This implies that changes in the nominal interest rate are influenced by variation in the inflation expectations, and therefore, changes in monetary policy in these countries is unlikely to impact on the ex ante real interest rate. This has implications in terms of saving-investment, capital flows, asset price determination and other intertemporal decision-making process.

The finding of the Fisher effect in post-economic reforms period should not come as a suprise because, under this period, these economies experienced relatively stable macroeconomic environment which lowered their inflation rates. Concurrently, with deregulation of the financial markets and floating of exchange rate, the interest rates became aligned with inflation expectations in the economy. Given the limited time series data on the tax structures in low-income countries, future area of research should focus on whether the differences in the tax structures between low-income countries and high- income countries could account for the differences in the Fisher effect.

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