Chapter

Chapter 7. How Strong Is the Monetary Transmission Mechanism in the East African Community?

Author(s):
Paulo Drummond, S. Wajid, and Oral Williams
Published Date:
January 2015
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Author(s)
Hamid R. Davoodi, Shiv Dixit and Gabor Pinter 

The leaders of the five partner states of the East African Community (EAC) decided in 2007 to fast track agreements on key protocols of the East African Monetary Union (EAMU) by 2012. A successful EAMU depends, among other things, on effective harmonization of existing monetary policies and operations across the EAC in transition to a future common monetary policy. The EAMU is expected to enhance the benefits of the EAC Customs Union and the EAC Common Market and deepen integration. It is also expected to reduce the costs and risks of conducting business transactions across national boundaries, as well as make way for a single currency, remove the costs of transactions in different currencies, and reduce the risk of adverse exchange rate movements in intra-EAC trade.

An important issue for each country is the effectiveness of their monetary transmission mechanisms (MTM)—the policy instruments used in each country and the channels through which changes in these instruments are transmitted into changes in real GDP and inflation, and the relative importance of each channel.1 In particular, we need to understand the extent to which the MTM differs across EAC countries and reasons for such differences.

A potential finding of significant heterogeneity would pose challenges for the harmonization of monetary policies and for the design and conduct of a common monetary policy for monetary union. A common monetary policy would dictate the use of the same instrument across all countries and then an expansionary monetary policy—an exogenous positive shock to reserve money, for example—that should not be expansionary in one country and contractionary in another.

Studying the MTM is not only important for the design and effectiveness of monetary policy in countries in transition to a monetary union, but continues to be relevant in countries already in monetary unions, such as the euro area, the Eastern Caribbean Monetary Union, and two monetary unions in sub-Saharan Africa: the West African Economic and Monetary Union and the Central African Economic and Monetary Community.2

The vast empirical literature on monetary transmission has primarily focused on developed economies. The most distinguishing characteristic of the MTM in developed countries is the focus on prices (interest rates, exchange rates, and other asset prices) rather than quantities (money, credit, base money, bonds, foreign assets, and so on).3 In contrast, the prevailing orthodoxy of the MTM in low-income countries has been its focus on quantities rather than prices. This difference is often attributed to weak institutional frameworks, oligopolistic banking structures, shallow financial markets, and extensive central bank intervention in foreign exchange markets in low-income countries.

Mishra, Montiel, and Spilimbergo (2010) revisited the prevailing orthodoxy of the MTM in low-income countries. They provide theoretical arguments about why bank lending channels might be more effective in low-income countries than other channels and find this channel to be either weak or unreliable. Specifically, they provide cross-country evidence of a weak interest rate pass-through4—from central bank lending rates to money market rates and from money market rates to commercial banks’ lending rates—though they do not empirically investigate the impact of changes in the interest rate or other monetary policy instruments on prices and real output in low-income countries.

On the other hand, a recent study of sub-Saharan Africa finds that monetary policy is perhaps more effective in this region than commonly believed (IMF, 2010). The study, based on a panel vector autoregression of sub-Saharan African countries in the past decade, finds that a contractionary monetary policy—defined either by lower reserve money growth or a higher central bank discount rate—decreases output growth significantly, but the impact on inflation and its statistical significance depends on the measure of the monetary policy instrument. A decline in reserve money (the operating target for many sub-Saharan African countries) reduces inflation as expected, though the decline is not statistically different from zero. However, an increase in the central bank discount rate or policy rate (the operating target for a small number of sub-Saharan African countries) has a statistically significant impact on inflation, but, surprisingly, it increases inflation—the so-called price puzzle.

These disparate findings on the effects of monetary policy may show the presence of different operating targets across countries and the need to conduct country-specific studies of MTMs that control for heterogeneities. In contrast to these cross-country studies, little is known about the MTM in EAC countries; studies have so far used a narrow set of methodologies and data sets. Moreover, no literature review has been conducted covering all EAC countries. On the latter, Davoodi, Dixit, and Pinter (2013) conduct an extensive review of the literature that shows the MTM is strong in Kenya, though only compared to prices, but that it is generally weak in the rest of the EAC for output or prices.

This chapter makes two contributions to a study of the MTM in the EAC:

  • We apply the latest methodologies from time-series analysis to each EAC country, including Bayesian vector autoregression and factor-augmented vector autoregression, two techniques that have not been used in studies of the MTM in the EAC.
  • We use a methodology that quantifies the relative importance of various channels of the MTM in each EAC country.

The outline of this chapter is as follows: The next section describes the conduct of monetary policy and the existing institutional framework, which is the starting point for harmonizing the conduct of monetary policy. This section shows how reserve money targeting, the dominant monetary policy framework in the EAC, is implemented in the vector autoregression. After that, we describe six channels of the MTM. Then, we describe the various vector autoregression methodologies. The next section describes the data. The penultimate section presents the empirical results, including an evaluation of the relative strengths and weaknesses of various channels of the MTM in each EAC country. Finally, we offer some conclusions.

Conduct of Monetary Policy in the East African Community

Instruments, Targets, and Goals

EAC central banks use open market operations as the main instrument of monetary policy implementation, but also rely on standing facilities, changes in reserve requirements, required reserve averaging, and foreign exchange operations. But differences exist in the application of these instruments among member countries’ central banks, most notably in the computation of the cash reserve requirement. Reserve money is the operating target for monetary policy and broad money is the intermediate target. Price stability is the overriding goal for monetary policy, but central banks also support economic growth and financial stability (Figure 7.1).

Figure 7.1Monetary Policy: Instruments, Targets, and Goals

Source: Authors.

In July 2011, the Central Bank of Uganda declared inflation-targeting lite as its monetary policy framework. Under an inflation-targeting framework, inflation forecasts are often the intermediate target by which a central bank attempts to anchor inflation expectations. In November 2011, the Central Bank of Kenya adopted a new monetary policy framework that gives more prominence to its policy interest rate though, unlike Uganda, it did not declare a shift to inflation-targeting lite. Practices can differ, though, as countries gain experience in the conduct of a new monetary policy framework. The empirical work in this chapter excludes these periods of marked shift in the monetary policy framework.

Monetary Policy Framework

For the sample period used in this chapter, all central banks use reserve money targeting—widely known as the reserve money program (RMP) for countries with an IMF program—as their monetary policy framework. There are two building blocks of monetary policy formulation in an RMP. The first involves setting an intermediate target for broad money. This is not under the direct control of the central bank, but provides a useful signal about current or prospective movements in inflation and output, and the final monetary policy goals. The second relates the intermediate target to an operating target, which is reserve money. It is under the effective control of the central bank but further from policy goals; in other words, it has a longer policy lag than broad money. The target for broad money is set to be consistent with macroeconomic policy goals regarding economic growth and inflation; hence, income velocity. The target for reserve money is set taking into account assumptions about the money multiplier (i.e., relating broad money to reserve money) and seasonality.

In practice, the implementation of the RMP has departed from the standard textbook quantity theory of money and become more flexible during the implementation of monetary policy. This is sometimes referred to as flexible RMP. This can be done by accommodating shifts in money multipliers and velocity (e.g., money demand shocks, financial deepening), two factors in part determined by portfolio decisions of individuals, and by incorporating unanticipated shocks to output and inflation (e.g., better-than-projected agriculture activities, large shifts in global food and fuel prices). This may cause monetary aggregates to deviate substantially from ex ante monetary targets. Uganda conducted a flexible RMP from September 2009 to June 2011. The increasing use of a small set of high frequency data and regular and sometimes more frequent meetings of monetary policy committees also enable central banks in the region to help fine-tune the monetary policy stance.

What is often called the MTM is depicted in Figure 7.2. The figure is a stylized look at the main channels of an MTM. Some are present in the EAC and some not. Some indicators may also not apply at this stage, such as a market-determined or timely survey-based measure of inflation expectations, but some channels may require the availability of high-frequency data, such as monthly indexes of real economic activities. Figure 7.2 also shows the feedback rules from output and inflation to monetary policy, thus enabling systemic responses of monetary policy to developments in inflation and output. The empirical challenge is to disentangle this endogenous monetary policy response from an exogenous monetary policy. Different models of MTMs essentially use different identification criteria to address this challenge. How each channel in the MTM could work is shown in the next section.

Figure 7.2Inside the Monetary Transmission Mechanism

Source: Authors.

Channels of the Monetary Transmission Mechanism

Regardless of the monetary policy framework used in practice, central bankers want to know how changes in monetary policy instruments affect inflation and output, and the timing and size of such effects.

Traditionally, the effects of monetary policy actions are thought to be transmitted via money or credit channels—the so-called money versus credit view of monetary policy. In the former, changes in the nominal quantity of money affect spending directly, whereas in the latter open market operations induce changes in interest rates that affect spending. In some models, credit rationing and financial accelerators can have additional effects on output and prices. Most models rely on some form of nominal price or wage rigidity to draw the hypothesized links between money, interest rates, and output. We now cover in more detail how each channel works.

Money Channel

This channel is perhaps the oldest one that effectively assumes changes in reserve money are transmitted to broad money via the money multiplier (i.e., that banks are in the business of creating inside money). This argument also assumes a role for individuals holding components of broad money, currency in circulation, and various forms of deposits. The money view of monetary policy assumes aggregate demand moves in line with money balances used to finance transactions and affect the split of nominal GDP between real GDP and the price level. It is this idea that forms the basis for broad money representing the intermediate target in many central bankers’ money-focused monetary policies (Mishkin, 1998).

Interest Rate Channel

The interest rate channel has been the traditional channel of monetary policy since the first developments in macroeconomic theory. This channel can be summarized in the standard Keynesian Investment/Saving-Liquidity Preference Money Supply framework, whereby an expansionary monetary policy leads to a fall in the real interest rate, thus decreasing the cost of capital and stimulating investment. This then results in an increase in aggregate demand and output. It is important to note that real spending decisions are only affected by changes in real interest rates and that monetary policy authorities only have direct control over short-term nominal interest rates. The crucial factor linking the monetary base with real interest rates—and ultimately determining the effectiveness of the interest rate channel—is the slow adjustment of the price level. “Price stickiness” causes movements in the monetary policy rate, which has a significant effect on short-term real interest rates. In addition, the rational expectations hypothesis of the term structure suggests that long-run real interest rates are determined by expectations about future short-term real interest rates. Monetary policy authorities are therefore able to use short-term policy rates to influence long-run real interest rates through price stickiness and the term structure, which then affect the real economy.

Exchange Rate Channel

In small, open economies, one of the most important monetary policy channels is the exchange rate channel. The extent to which monetary policy can affect movements in the exchange rate is largely influenced by the theory of uncovered interest rate parity. This simple theoretical relationship suggests that the expected future changes in nominal exchange rates are related to the difference between the domestic and foreign interest rates. In theory, the uncovered interest rate parity enables the monetary policy authority to influence the exchange rate, which in turn affects the relative prices of domestic and foreign goods, thus affecting net exports and output. For example, a cut in the monetary policy rate would make domestic deposits less attractive compared to foreign deposits, leading to a fall in the demand for domestic currency. As a result, the domestic currency would depreciate, making domestic goods cheaper compared to foreign goods and leading to an increase in net exports and total output. The effectiveness of the exchange rate channel is determined by the uncovered interest rate parity condition, but its empirical validity has often been criticized. As a result, many experts suggest that this should be augmented with a risk-premium term, implying that foreign investors, upon buying domestic financial assets, require compensation not only for expected depreciation but also for holding domestic assets.

Credit Channel

Asymmetric information in financial markets provides the basis for the credit channel of monetary transmission. Bernanke and Gertler (1995) offer a detailed description of how imperfections in credit markets may cause a monetary contraction to lead to an increase in the external finance premium faced by borrowers and to a decrease in the loan supply. It is important to note that the credit channel is often referred to as an amplifier of traditional monetary channels rather than a stand-alone mechanism. Economists usually distinguish between two types of credit channels stemming from imperfections in financial markets: the bank-lending channel and the balance-sheet channel. The bank-lending channel is based on the assumption that a monetary contraction, which decreases bank reserves and bank deposits, lowers the quality of bank loans available. The balance-sheet channel is related to the effects monetary policy can exert on the net worth of businesses and households. A monetary contraction decreases the net worth of firms through its cash flows and the value of collateral, thus leading to a higher external finance premium associated with more severe moral hazard problems. This in turn would reduce the level of lending, investment, and output.

Asset Price Channel

Traditional monetary theory suggests that monetary contraction, through an increase in the discount rate of financial assets, may lead to a fall in asset prices, which will then further affect the real economy. Mishkin (1995) singles out two main mechanisms through which monetary policy shocks are propagated by changes in equity prices. First, the theory of Tobin’s q suggests that when equities are cheap relative to the replacement cost of capital, firms do not want to issue new equities to purchase investment goods, leading to a decline in investment. Second, equity prices may have substantial wealth effects on consumption because of the permanent income hypothesis. A rise in stock prices increases the value of financial wealth, thus increasing the lifetime resources of households as well as the demand for consumption and output. A similar mechanism is applied to prices of other assets such as housing, which is a substantial component of wealth. Therefore, the MTM also operates through land and housing price channels.

Expectation Channel

Because modern monetary policy analysis is based on forward-looking and rational economic agents, the expectation channel is in effect fundamental to the working of all channels of the MTM. In practice, this channel is mainly operational in developed economies with well-functioning and deep financial markets. For example, expectations of future changes in the policy rate can immediately affect medium- and long-term interest rates. Monetary policy can also guide economic agents’ expectations of future inflation and thus influence price developments. Inflation expectations matter in two important areas. First, they influence the level of the real interest rate and thus determine the impact of any specific nominal interest rate. Second, they influence price and money wage-setting behavior and feed through into actual inflation in subsequent periods. Similarly, changes in monetary policy stances can influence expectations about the future course of real economic activities by affecting inflation pressures expectations and the ex ante real rate and guiding the future course of economic activities.

Empirical Methodology

Vector autoregression models are the most widely used methodology to analyze the MTM. Their use for monetary policy analysis started with the seminal work of Sims (1980) and his recursive methodology has been used widely. In fact, most studies of the MTM in low-income countries, as reviewed by Mishra, Montiel, and Spilimbergo (2010), have used vector autoregressions with the majority of studies using recursive vector autoregressions. Studies of the MTM in developed economies also continue to use vector autoregressions and their variants, as reviewed by Christiano and others (1999) for the United States; Weber, Gerke, and Worms (2009) for the euro area; and more recently by Boivin, Kiley, and Mishkin (2011) for the United States and other Group of Seven economies.5

We use three variants of structural vector autoregression to study the MTM in the EAC: standard recursive structural vector autoregressions, Bayesian vector autoregressions, and factor-augmented vector autoregressions. Recursive structural vector autoregressions assume a recursive relationship between errors of a reduced-form vector autoregression and remain the most widely used methodology in the literature on the MTM. However, this method may suffer from problems of overparameterization and misspecification, which undermine the robustness of the empirical results. To tackle these problems, two additional methods are applied. First, the standard ordinary least squares estimation of the recursive structural vector autoregression is replaced by Bayesian estimation techniques (Litterman, 1986). Bayesian methods provide an effective treatment for problems of overparameterization by the use of prior information.6

Second, factor methods are used that allow for the use of information contained in other variables while simultaneously reducing the number of parameters in the vector autoregression. Each variant is estimated for each country separately, allowing for country-specific dynamics in the evolution of the MTM. Factor-augmented vector autoregressions are estimated following Bernanke, Boivin, and Eliasz (2005). These methods assume a larger information set is being used by central bankers and different estimation methods that provide useful checks on the robustness of the results from the recursive structural vector autoregression models. We refer readers to Davoodi, Dixit, and Pinter (2013) for a detailed discussion of these vector autoregression methodologies.

Data

Estimation of a monthly vector autoregression model requires the compilation of measures of money, price level, asset prices, and GDP at monthly frequencies. Data on the first three indicators were obtained from IMF databases, national authorities, and staff estimates. However, GDP data are available only at quarterly frequencies for all EAC countries except Burundi.

At best, there may be 10 years of quarterly data for each country except Burundi. The starting date for each country’s quarterly national accounts is as follows: Kenya (2000 first quarter), Rwanda (2006 first quarter), Uganda (1999 fourth quarter), and Tanzania (2001 first quarter). This data set amounts to 40 observations at the maximum, which may not offer sufficient degrees of freedom for statistical inference given the nature of time lags and the number of variables needed even for a small, low-order vector autoregression.7

We need to generate proxies for real GDP at quarterly frequency for Rwanda before 2006 first quarter and for Burundi for all periods. Our strategy is as follows: For Rwanda, in the years before 2006 for which quarterly GDP data are not available, seasonality factors of quarterly data post-2006 are applied to annual real GDP to interpolate to quarterly frequency. For Burundi, because its production structure is similar to Rwanda’s, we generate a quarterly series of real GDP by applying Rwanda’s quarterly seasonality factors to Burundi’s annual GDP data. Monthly estimates of GDP are then derived for all EAC countries by interpolating quarterly GDP data using a cubic spline, a widely used technique.8 Finally, the monthly estimates are seasonally adjusted using the X-12 ARIMA method.

The benchmark model is estimated from January 2000 to December 2010 on log levels, except for interest rate series, which are in percent. This is a widely used specification in the literature. Use of levels rather than first differences preserves any long-run relationship, if present, and does not affect statistical inference (Sims, Stock, and Watson, 1990).9

A structural vector autoregression model consisting of six endogenous and four exogenous variables is estimated for each country. The endogenous variables are real GDP, the consumer price index (CPI), reserve money, short-term interest rate, credit to private sector, and the nominal effective exchange rate. The four exogenous variables that affect endogenous variables are a global oil price index, a global food price index, U.S. federal funds rate, and U.S. industrial production. The latter two are proxies for global demand conditions, whereas global food and fuel prices are expected to affect, among other things, inflation and output beyond external demand factors.

To check for the robustness of our results, the Bayesian vector autoregression model is applied to the dataset explained previously. In addition, factor-augmented vector autoregression methods are used by adopting principal component methods as follows: The first principal component of the four exogenous variables is used as an exogenous variable. The first principal component of two endogenous variables (credit and nominal effective exchange rates) and additional variables, M1, M2, M3,10 are constructed. This essentially leads to the estimation of a vector autoregression with five endogenous variables and one exogenous variable, hence reducing the parameter space and mitigating problems of over-parameterization. Finally, the benchmark structural vector autoregression model specification will be estimated on a longer sample for each country going back to the mid-1990s.

Empirical Results

The benchmark results for each EAC country are obtained by estimating country-specific structural vector autoregression models from January 2000 to December 2010, using recursive identification methods. For all countries, we chose the vector autoregression lag length using the standard lag length selection criteria (Akaike, Shwarz, Hannan-Quinn), final prediction error, and so on. We found a maximum lag length of three, which was also sufficient to generate serially uncorrelated vector autoregression errors. In contrast, most empirical work on MTM in advanced countries uses 6 to 12 lags for monthly data, or two to four quarters for quarterly data. While some may expect monetary policy to take time to have an effect in the EAC, this view seems to be based entirely on the experience of advanced economies and the conventional wisdom, driven in part from Milton Friedman’s early work that “lags in monetary policy are long and variable.” Adding more lags beyond three months, which we also did, results in increasing problems of overparameterization, associated with larger confidence bounds for impulse responses, reflecting the increase in noise and imprecision.

That said, we should also point out that the effects of monetary policy in some EAC countries do last beyond three months because cumulative effects are only built up over time and show up in cumulative impulse responses. However, the main difference with impulse responses in advanced countries is that effects of monetary policy in the EAC are short lived. If a confidence interval for impulse responses includes zero, then monetary policy has no statistically significant effect on either prices or output. In other words, the MTM is weak. As the horizon is expanded beyond six months, impulse responses become wider, rendering either economically insignificant results, statistically insignificant results, or both. We could choose to have a weaker inference standard than the conventional confidence intervals of plus and minus two standard errors (a 95 percent confidence interval).

If we opt, for example, for a 90 percent confidence interval, it could increase the Type II error (probability of accepting a false hypothesis that MTM is strong when it is in fact weak). Davoodi, Dixit, and Pinter (2013) report the 95 percent confidence interval, showing that the MTM is weak in the EAC. The results reported subsequently use a 90 percent confidence interval, showing a somewhat stronger MTM.

Burundi

The first row of Figure 7.3 shows the impulse responses of output and prices to a one standard deviation positive shock in reserve money (monetary loosening) in our baseline recursive model, whereas the second row depicts those to a positive shock in the policy rate (monetary tightening). Because of the lack of a structured interbank market, we use the Treasury bill rate as the policy interest rate in the baseline vector autoregression for Burundi. One lag was selected by the Akaike, Schwarz, and Hannan-Quinn information criteria. However, we proceeded with two lags to avoid serial correlation of residuals at the first lag order.

Figure 7.3Structural Vector Autoregression Impulse Responses for Burundi

Source: Authors’ calculations.

Note: Shocks are one standard deviation; vertical axes are percentages, horizontal axes are months, and the shaded areas denote the 90 percent confidence bands. CPI = consumer price index.

We find that output responds positively and significantly to a shock in reserve money, albeit with a considerable lag that spans almost a year. Its response to a shock in the Treasury bill rate is prompt (peaking in six months) and sustained over a longer period though not statistically significant. Nesting our recursive vector autoregression into a factor-augmented vector autoregression model, however, generates relatively muted impulse responses of output to shocks in both reserve money and the Treasury bill rate and a more pronounced response of prices to an interest rate shock. This perhaps illustrates the instability of the MTM in Burundi.

To ascertain the relative strength of the interest rate channel, we re-run the vector autoregression with the Treasury bill rate exogenized (i.e., lagged values of the Treasury bill rate are treated as exogenous variables in a smaller vector autoregression involving GDP, CPI, reserve money, credit, and the nominal effective exchange rate [Figure 7.4]). Such a procedure generates a vector autoregression identical to the original, except that it prevents any responses within it that pass through the interest rate.11 Activating the interest rate channel in this fashion morphs a negative impulse response of output to innovations in reserve money to a positive (and statistically significant) response. The channel also has a positive impact on prices. We note that this considerable swing is only captured when using the Treasury bill rate as opposed to the indicative discount rate. Similar dynamics can be observed even when the credit and exchange rate channels are inactivated, demonstrating the robustness of the interest rate channel in Burundi. We conclude that movements in interest rates amplify the impact of reserve money on output and the price level. Interest rates therefore appear to be a transmission channel, but the effect is not statistically significant, given the direct impact of interest rates on either output or the price level.

Figure 7.4Testing the Interest Rate Channel in Burundi

Source: Authors’ calculations.

Note: The solid lines show the impulse responses from the benchmark structural vector autoregression with six endogenous variables; the dashed lines show the impulse responses from the structural vector autoregression with five endogenous variables. CPI = consumer price index.

Kenya

The results of the benchmark model with recursive identification are shown in Figure 7.5, which displays the impulse responses of GDP and CPI to a one standard deviation shock to reserve money and the repo rate.12 Similar to Cheng (2006), the results suggest that a positive shock to the policy rate has a significant and persistent effect on CPI, which peaks 9 to 11 months after the shock. A positive shock to reserve money has a positive impact on CPI that is in line with economic theory and peaks later at 15 months, later than an interest rate shock. Both types of shocks have no statistically significant effect on GDP.

Figure 7.5Structural Vector Autoregression Impulse Response for Kenya

Source: Authors’ calculations.

Note: Shocks are one standard deviation; vertical axes are percentages, horizontal axes are months, and the shaded areas denote the 90 percent confidence bands. CPI = consumer price index.

Davoodi, Dixit, and Pinter (2013) show that a shock to both reserve money and the policy rate has a significant impact on the nominal effective exchange rate and credit, whereas a shock to the nominal effective exchange rate corresponding to an unexpected nominal exchange rate appreciation and a shock to credit corresponding to an unexpected credit expansion both have a significant impact on CPI. Since the two shocks are by construction orthogonal, these results can be interpreted as indirect evidence on the existence of an exchange rate and credit channel of monetary policy.

To undertake a direct assessment of the nominal effective exchange rate and credit channels, we analyze the effects of a monetary policy shock in a vector autoregression in which the target variable associated with the given channel is endogenous, and then compare these results to those from running a vector autoregression in which the same target variable is exogenous. This exercise essentially involves comparing the effects of monetary policy shocks in a five-variable vector autoregression including GDP, CPI, reserve money, interest rate, and credit with the those obtained by a four-variable vector autoregression whereby credit is made exogenous. Figure 7.6 shows these results and confirms that allowing for the endogenous presence of the credit variable increases the impact of a monetary policy shock on CPI.

Figure 7.6Testing the Credit Channel in Kenya

Source: Authors’ calculations.

Note: The solid lines show the impulse responses from the benchmark structural vector autoregression with six endogenous variables; the dashed lines show the impulse responses from the structural vector autoregression with five endogenous variables. CPI = consumer price index.

The results associated with the exchange rate channel are shown in Figure 7.7. The endogenous presence of the nominal effective exchange rate magnifies the impact of both types of monetary policy shock, though the presence of the exchange rate channel seems more pronounced in a policy shock. Studies (Cheng, 2006) have shown that Kenya’s nominal exchange rate is highly sensitive to changes in the short-term interest rate, which then affects the overall price level through import prices. The right panel of Figure 7.7 can be seen as direct evidence for this.

Figure 7.7Testing the Exchange Rate Channel in Kenya

Source: Authors’ calculations.

Note: The solid lines show the impulse responses from the benchmark structural vector autoregression with six endogenous variables; the dashed lines show the impulse responses from the structural vector autoregression with five endogenous variables. CPI = consumer price index; NEER = nominal effective exchange rate.

This exogeneity–endogeneity exercise has been used to test for other channels of monetary policy transmission as well, but none of them had any significant impact on the way monetary policy shocks affect the CPI.

Rwanda13

A reserve money shock induces a positive and statistically significant output effect, but no price effect. A positive response of prices to a shock in the key repurchase rate14 is counterintuitive, providing a price puzzle that lasts for quite some time (Figure 7.8). The factor-augmented vector autoregression approach not only confirms the positive response of output to a shock in the monetary base, but also reduces the extent of the price puzzle in our recursive specification. This alludes to the success of the factor-augmented model in extracting pertinent information from the expanded dataset of macroeconomic variables.

Figure 7.8Structural Vector Autoregression Impulse Response for Rwanda

Source: Authors’ calculations.

Note: Shocks are one standard deviation; vertical axes are percentages, horizontal axes are months, and the shaded areas denote the 90 percent confidence bands. CPI = consumer price index.

A shock to private sector credit has a significant effect on output for the first 5 months and on the price level for the first 15 months, both of which are statistically significant. The significant influence of reserve money on the private sector credit sector also suggests that it may be an important channel in monetary transmission (Davoodi, Dixit, and Pinter, 2013). To examine this further, we re-ran the vector autoregression with lags of private sector credit exogenized, and compared it to our baseline results (Figure 7.9). This exercise confirms the credit channel in Rwanda is indeed strong. The credit channel seems to be stronger statistically for the response of GDP to a reserve money shock than the response of CPI to a reserve money shock.

Figure 7.9Testing the Credit Channel in Rwanda

Source: Authors’ calculations.

Note: The solid lines show the impulse responses from the benchmark structural vector autoregression with six endogenous variables; the dashed lines show the impulse responses from the structural vector autoregression with five endogenous variables. CPI = consumer price index.

Tanzania

In a recursive vector autoregression model estimated for 2000–10, a positive shock to reserve money increases the CPI in the first year and half, though the effect is not statistically significant (Figure 7.10). However, this impact becomes highly significant when the vector autoregression is estimated over the longer period (January 1993 to December 2010) in our baseline recursive structure. We reach similar conclusions using Bayesian vector autoregression and factor-augmented vector autoregression. Under the latter, a shock to reserve money has statistically significant positive output effects consistent with the Bank of Tanzania, the central bank, using a much larger information set, including credit and broad money aggregates, commodity prices, than that in the baseline vector autoregression for the conduct of monetary policy.

Figure 7.10Structural Vector Autoregression Impulse Responses for Tanzania

Source: Authors’ calculations.

Note: Shocks are one standard deviation; vertical axes are percentages, horizontal axes are months, and the shaded areas denote the 90 percent confidence bands. CPI = consumer price index.

Using either sample period, a positive shock to the interest rate increases the CPI, the price puzzle that was also evident in Rwanda and partly in Kenya, but the impact is not statistically significant. In fact, the confidence interval for all impulse responses for output and price level include zero, which indicates weak monetary transmission.

These findings are similar to those of Montiel and others (2012) for Tanzania, who employ recursive and nonrecursive vector autoregressions. These authors attribute the weak MTM to the shallowness of financial markets and the oligopolistic structure of the banking system. Although these factors may play a role, the weak MTM can also result despite a stable velocity (Adam and others, 2012) if the money multiplier is unstable in the short run, a result found by Adam and Kessy (2010).

However, other possibilities should not be ruled out. For example, stability of the money multiplier and velocity as reported in Davoodi, Dixit, and Pinter (2013) showed that the money multiplier is relatively stable but velocity is relatively unstable, the opposite of that found by Adam and Kessy (2010). Our findings are consistent with the interpretation that shocks to reserve money are transmitted to money. But transmission from money to prices or output is weak because shifts in velocity, caused perhaps by financial innovations, may attenuate any aggregate demand effects. The finding of a strong effect of money on prices in the short and long run, as reported by Adam and others (2012), could be due to the addition of error correction terms or disequilibrium in various markets to an otherwise standard money demand that may have corrected shifts in velocity, thus restoring the role of reserve money as an inflation anchor in Tanzania’s RMP.15

Other reasons exist for the weak MTM in Tanzania, given our vector autoregression results and those of Montiel and others (2012). For example, the exchange rate channel could play a role in Tanzania, as in Kenya, but the presence of capital controls may be limiting its usefulness. Removal of capital controls by 2015, an objective of the Tanzanian authorities, should strengthen the role of the exchange rate and the interest rate channels.

Uganda

As in Rwanda, Uganda’s output responds significantly to positive shocks to reserve money over the short term, while leaving prices unfettered (Figure 7.11). This mechanism is sustained even when exogenous variables are dropped from the model. Conversely, a positive shock to the policy rate has an ambiguous effect on output, but a persistent deflationary impact on prices. Estimating the vector autoregression with five lags, as opposed to three in the baseline specification, generates a more pronounced impulse response of CPI to innovations in the policy rate. The results of the factor-augmented vector autoregression specification generally mimic the dynamics of the baseline specification, with one important difference: the interest rate increases reduce inflation under a factor-augmented vector autoregression more than a simple structural vector autoregression or Bayesian vector autoregression. This perhaps shows that the Central Bank of Uganda uses a much larger information set in deciding interest rate changes.16 Our findings are in marked contrast to those of Mugume (2011), who finds that all monetary transmission channels are inactive in Uganda.

Figure 7.11Structural Vector Autoregression Impulse Responses for Uganda

Source: Authors’ calculations.

Note: Shocks are one standard deviation; vertical axes are percentages, horizontal axes are months, and the shaded areas denote the 90 percent confidence bands. CPI = consumer price index.

Results from Variance Decomposition

It is important to quantify the relative importance of shocks in variability of inflation and output and not just the mean dynamics, which is what the impulse responses suggest. Results from variance decomposition of the estimated vector autoregressions show (Table 7.1):

  • Changes in output are more due to shocks to reserve money than to the interest rate. This is largest in Uganda and Rwanda, a finding that is consistent with our impulse response analysis.
  • Inflation is more due to shocks to the interest rate than shocks to reserve money. This is more pronounced in Tanzania and Uganda. The result is surprising for Tanzania because it runs counter to the findings from impulse response analysis, but it is consistent with the impulse response analysis for Uganda.
Table 7.1Variance Decomposition
GDPCPI
Reserve MoneyInterest RateReserve MoneyInterest Rate
Burundi8.37.64.50.3
Kenya5.43.819.312.8
Rwanda12.73.13.94.4
Tanzania0.70.71.411.1
Uganda11.82.70.37.2
Source: Authors.Note: CPI = consumer price index.
Source: Authors.Note: CPI = consumer price index.

Summary of Main Results

Using a 90 percent confidence interval in evaluating statistical inference of impulse responses, we summarize and interpret our results as follows:

  • An expansionary monetary policy (i.e., a positive shock to reserve money) increases output significantly in Burundi, Rwanda, and Uganda, but has no statistically significant effect on prices in any EAC country. This result is consistent with the presence of a flat, short-run, aggregate supply curve in the EAC. An unstable money multiplier or unstable income velocity of money does not seem to have affected the MTM in Burundi, Rwanda, and Uganda, at least compared to output. Even if velocity is unstable, the finding of strong output effects from shocks to reserve money shows that shifts in velocity have not been large enough and/or in the wrong direction to offset the expansionary effects of reserve money on output.
  • Monetary policy, as measured by shocks to reserve money, has short lags in Uganda (statistically significant output effects for the first 6 months only), but long lags in Burundi and Rwanda (statistically significant output effects from 6 to 15 months).
  • An expansionary monetary policy (a negative shock to the policy rate) increases prices significantly in Kenya and Uganda and output in Burundi, Kenya, and Rwanda.
  • Monetary policy, as measured by shocks to the policy rate, has long lags for prices and output for all EAC countries (varying from 5 months in Burundi to 36 months in Uganda). Rwanda has the shortest lag (three to five months).
  • Channels of the MTM differ across the EAC, with exchange rate and credit channels being important in Kenya, credit in Rwanda, and interest rate in Burundi.
  • The policy rate seems to matter more to the evolution of prices in countries with deeper financial markets and more competitive banking systems, such as Kenya and Uganda.
  • Interest rates typically decline in response to a positive shock to reserve money, an effect that is statistically significant at a 90 percent confidence interval in three countries (Burundi, Kenya, Uganda). So movements in money and interest rates are consistent with each other.

What explains the lack of significance in the other EAC countries (Tanzania and Rwanda)? The negative response of interest rates to a positive shock in reserve money should strengthen the contractionary (expansionary) effect of a negative (positive) shock to reserve money. But we find that a shock to reserve money and the policy rate sometimes move in directions that exert expansionary and contractionary impulses, resulting in a statistically insignificant impact of either reserve money or policy rate on prices. This interpretation seems to explain our findings for Tanzania and Rwanda. This may also indicate that attempts to choose simultaneously prices (interest rates) and quantities (reserve money) that are inconsistent with each other can weaken the MTM, the role of interest rates, and the development of market-determined interest rates and exchange rates.

Conclusions

As in emerging or frontier market economies, some EAC countries have begun conducting monetary policy through prices (interest rates) rather than quantities (monetary aggregates), although adherence to any stated targeting rule varies across countries. As in developed countries, the shift away from a money-focused monetary policy seems to be taking place as a result of, among other things, possible structural shifts in money demand and money multipliers, as well as a deepening of the financial sector and openness of the economy to international flows.

Some EAC countries have found it operationally relevant to work with a flexible RMP, which takes into account shifts in velocity and multipliers and other exogenous shocks that affect monetary targets. Uganda, for example, implemented a flexible RMP from September 2009 through June 2011 that delinked short-term liquidity from structural liquidity management. Uganda has formally shifted to an inflation-targeting lite monetary policy framework with a direct focus on prices (short-term interest rate as the operating target) rather than on quantities (reserve money) while continuing to monitor developments in monetary and credit aggregates, external environment, output gap, and inflationary expectations.

In some EAC countries and during particular episodes, monetary policy may have been conducted by simultaneously choosing both prices and quantities. Such an approach, common in countries with shallow financial markets and limited experience with competitive auctions of central bank liquidity papers, tends to undermine development in the interbank markets and reduces the role of interest rates and exchange rates in the MTM.

Kenya and, to some extent, Tanzania and Rwanda have also started relying more on changes in the policy rate to guide monetary policy while continuing to use direct instruments (e.g., changes in reserve requirement ratio) to alter monetary policy conditions. Complicating the problem has been the role of commercial banks, as their lending rates tend to be sticky and not responsive to changes in the policy rate. The credit channel will take time as banks learn to work within a new monetary policy framework.

Strengthening the MTM in the EAC requires, among other things, addressing the following factors and policies, some of which are developmental in nature and would take time to implement:

  • Ensure that monetary targets and interest rate policies are consistent with each other. Interest rates play a supporting role in a money-focused monetary policy if reserve money continues to be the operating target. In theory, interest rates are endogenous when money is being targeted and should be allowed to play such a role.
  • A high share of currency in circulation in reserve money reduces the role a central bank can play in affecting cost conditions in the economy. As a result, regulating a small part of reserve money, namely, bank reserves, will not be as effective. An interest rate–focused monetary policy may not suffer from the same fate.
  • A large informal economy reduces the role monetary policy can play in influencing cost conditions in financing economic activities, a factor that could go hand in hand with a higher share of currency in circulation. The size of an informal economy and share of currency in circulation could become less of a binding constraint if interest rates fully reflect liquidity conditions and monetary policy and the public becomes aware of the cost of holding idle money balances.
  • A low financial depth or low access to finance reduces the scope and reach of monetary policy.
  • A shallow and limited integration of interbank foreign exchange and money markets reduces the effectiveness of the exchange rate and interbank interest rates in transmitting changes in monetary policy.
  • Limited competition in the banking sector can reduce interest rate pass-through because actions by monetary authorities may not be fully transmitted to changes in credit availability, loan rates, or deposit rates.
  • High commercial bank excess reserves reduce the role a central bank can play in regulating the market for bank reserves, and, hence, liquidity in the economy. Banks can simply draw on these balances to lend, which may undo central bank actions.
  • Capital controls can weaken the MTM. The presence of significant capital controls can make the exchange rate channel or interest rate channel ineffective because exchange rates and interest rates may not respond to changes in market fundamentals, and capital flows may cease to operate effectively in both directions.
  • High-quality and high-frequency data can help. The noise in the data-generating process for the EAC and low-income countries may be responsible for a weak MTM. This may make it harder to isolate the effects of monetary policy and lack of timely data may lead to policy errors. Clearly, improvements in data availability, particularly at high frequency, can give meaningful signals to central bankers and the public; hence, allowing timely responses to developments in economic activities.
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We wish to thank Peter Allum, Michael Atingi-Ego, Andy Berg, Jorge Canales Kriljenko, David Dunn, EAC central banks, Etibar Jafarov, Nils Maehle, Catherine McAuliffe, Peter Montiel, Rafael Portillo, Rogelio Morales, Steve O’Connell, Catherine Pattillo, Oral Williams, Mary Zephirin, and seminar participants in the IMF’s African Department Monetary Policy Network for helpful discussions and comments. Shiv Dixit and Gabor Pinter were at the IMF when this research was initiated.
1The focus of MTM is on how it affects output and inflation (Taylor, 1995), though monetary policy also affects other macroeconomic indicators and is influenced by them (Bernanke and Gertler, 1995; Ireland, 2008).
2See Peersman and Smets (2001) and European Central Bank (2010) for the European Monetary Union, Laurens (2005) for the Eastern Caribbean Currency Union, van den Boogaerde and Tsangarides (2005) for the West African Economic and Monetary Union, and Iossifov and others (2009) for the Central African Economic and Monetary Community.
3This view is evolving, however. For example, the global financial crisis of 2007–08 led some to argue for a credit-focused monetary policy in advanced economies and output and inflation goals for monetary policy (Christiano, Illut, Motto, and Rostagno, 2010) and for supplementing monetary policy with an active role for macroprudential policies (Bean and others, 2010; Issing, 2011).
4See also IMF (2010) for evidence of weak interest rate pass-through in Africa.
5There are nevertheless alternative methods for monetary policy analysis such as dynamic stochastic general equilibrium models that impose a more theoretically motivated structure on the data; see Christiano, Trabandt, and Walentin (2010) for a recent review. Owing to increasing computational capacity, these models have become widely used among central bankers and have produced some promising results for low-income countries as well (Berg and others, 2010; O’Connell, 2011).
6See Chapter 10 of Canova (2007) for a detailed review of Bayesian vector autroregressions and how these methods may be useful for shrinking the parameter space of the model.
7As suggested earlier, a six-variable vector autrogression with a constant, a time trend, two lags of each variable, plus contemporaneous values of our four exogenous variables results in estimation of 18 parameters, leaving only 22 degrees of freedom at maximum.
8Many studies of MTM in advanced countries also use interpolated monthly GDP data. See Bernanke, Boivin, and Eliasz (2005).
9Results of Johansen’s cointegration tests were inconclusive. Moreover, the cointegration vectors are hard to interpret given the number of variables involved.
10M3 is not used in the factor-augmented vector autoregression for Burundi because the data are not available.
11See Morsink and Bayoumi (2001) for this approach.
12The time series switches to reverse the repo rate from June 2009 onward. Re-running the vector autroregression that ends in May 2009 does not change the results.
13Sensitivity tests employed to check for robustness include accounting for historical periods, including lags of exogenous variables, excluding exogenous variables, replacing policy rate with the lending rate. See Davoodi, Dixit, and Pinter (2013).
14Time-series spliced with discount rate prior to January 2007.
15This result is not unique to Tanzania. For example, covering a group of 17 sub-Saharan African countries, Barnichon and Peiris (2008) show that the real money gap has a statistically significant contemporaneous impact on inflation.
16It is also worth noting that fitting our baseline recursive vector autoregression on a longer time frame (September 1995 to December 2010) increases the magnitude and life of the shocks drastically, suggesting that transmission channels have withered over time.

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