5 The Monetary Transmission Mechanism

Andrew Berg, and Rafael Portillo
Published Date:
April 2018
  • ShareShare
Show Summary Details
Andrew Berg, Jan Vlcek, Luisa Charry and Rafael Portillo 

1 Introduction

In this chapter, we attempt to learn about the transmission mechanism by looking closely at a set of related cases in which policymakers in four countries (Kenya, Uganda, Tanzania, and Rwanda—hereafter the EAC4) suddenly and unexpected ly tightened monetary policy to varying degrees. Methodologically, we take inspiration from Summers (1991: 130), who suggests that ‘Skillful exploitation of natural experiments that provide identifying variation in important variables represents the best hope for increasing our empirical understanding of macro-economic fluctuations. While lacking the scientific pretension of an explicit probability model, careful historical discussions of events surrounding particular monetary changes, such as those provided by Friedman and Schwartz (1963), persuade precisely because they succeed in identifying relevant natural experiments, and describing their consequences’.

We thus apply a version of what Romer and Romer (1989) call the ‘narrative approach’ to identifying the effects of monetary policy: ‘The central element of this approach is the identification of monetary shocks through non-statistical procedures … The method involves using the historical record … to identify episodes when there were large shifts in monetary policy or in the behavior of monetary policy that were not driven by developments on the real side of the economy’.1 To the best of our knowledge, this is the first attempt to apply a case-study methodology to monetary policy transmission in low-income countries.

Some of the same features of the monetary policy environment in low-income countries that make the VAR-based analyses discussed in Chapter 6 extremely challenging also greatly complicate our approach here. In part because of the rapid evolution of monetary policy regimes, we are not able to identify several independent and comparable tightening events, so that we cannot conduct statistical analyses. And it is hard to argue, as do Romer and Romer (1989), that the tightening episodes are unrelated to recent economic developments, notably supply shocks.2 Thus, more along the lines of the seminal work of Friedman Schwartz (1963), which preceded and motivated the R&R approach but did not rely on explicit statistical inference, we rely on a close reading of the narrative and the data to ‘provide identifying variation’, in the language of Summers (1991), with regard to the macroeconomic effects of a tightening in monetary policy.

On the positive side, we are able to draw on variations across the four countries studied—notably in terms of economic structure and policy regime—to shed light on the influences of these factors on monetary policy transmission. Two ideas emphasized in the recent literature are salient. Mishra et al. (2012) argue that structural features of the LIC environment, notably underdeveloped and monopolistic financial systems and inflexible exchange rates, are likely to make trans mission weak and unreliable, because policy rates do not transmit to lending rates in underdeveloped and monopolistic banking systems, and in any event these rates do not matter that much to the real economy. In addition, as we have stressed in this book, policy regimes themselves will influence the transmission mechanism.

Our narrative centres on a significant tightening of monetary policy that took place—to varying degrees and in different ways—in October 2011 in the EAC4. In 2010–11 there was a major commodity price shock, and inflation took off in the EAC4, echoing the events of 2007–08. Throughout 2010 and most of 2011, monetary policies remained fairly loose in Kenya, Uganda, and Tanzania, with only cautious and ineffective efforts to tighten, perhaps encouraged by the experience of gradually moderating inflation without policy tightening during the earlier episode in 2009. The commodity price shocks turned out to be much more persistent this time, and they combined with vigorous economic activity, a negative balance of payments shock, and accommodative policy to further accelerate inflation and de-anchor expectations, weakening the exchange rate in an inflationary spiral.

Some of the countries began to respond, to varying degrees. In July 2011, Uganda announced a new inflation targeting (IT) ‘lite’ policy regime, and in August began a still-somewhat gradual tightening of policy. Kenya enacted fitful, partial, and ineffective tightening measures, but did clarify its regime in September 2011. In Rwanda, by contrast, tighter monetary policy and a stable exchange rate throughout the period kept inflation from taking off.

Finally, the governors of the four central banks agreed at an unusual October 2011 meeting that policy needed to be tightened significantly in order to bring inflation under control, even at a cost to output, and they acted immediately.3 This tightening and surrounding events are our topic here. While the tightening took place in response to economic events, this does not make it entirely endogenous and thus does not invalidate our narrative approach to identifying the monetary transmission mechanism. Throughout 2011, concerns about the adequacy of the policy stance were increasingly widespread. However, it was unclear when a tightening might come or how strong it would be. Indeed, the narrative suggests that some observers were becoming concerned that it might not come at all. Thus, when it came, it was at least partly unexpected—unusual, in the language of Friedman and Schwartz (1963). We can thus ask, what did this large monetary policy tightening shock do?

We find some evidence consistent with a clear transmission mechanism. In some of the four countries, after a large policy-induced rise in the short-term interest rate, lending and other interest rates rose, the exchange rate tended to appreciate, output tended to fall, and inflation declined.

The variation in experiences among the four cases is informative. Most importantly, the cross-country variation in transmission seems to depend sharply on the policy regime in place. In particular, we find the clearest transmission in Uganda, where the IT-lite regime itself was simpler and more transparent, and in Kenya, particularly once the authorities explicitly signalled the monetary policy stance with a short-term interest rate and described their intentions in terms of their inflation objective. In regimes where the stance of monetary policy was harder to assess, such as Tanzania, which conducted monetary policy under a de jure monetary targeting regime, and Rwanda, which had a de facto exchange rate peg, the transmission of monetary policy to lending rates is less evident. Nonetheless, in Tanzania, the exchange rate seemed to respond strongly to adjustments of the monetary policy stance.

We see mixed signs of the importance of financial development. All four countries, like other LICs, have relatively small, concentrated, and bank-dependent financial systems, but to varying degrees. In particular, Kenya’s large financial sector makes it an outlier, and it also had perhaps the most complete and unambiguous transmission. However, Uganda, which also clearly demonstrated the main elements of monetary policy transmission, has a relatively small financial sector compared to those of the other three countries.

While the shock was not entirely expected, it was not isolated from outside influences, particularly shocks to global risk appetite and commodity prices, which directly affected exchange rates and prices of traded goods. A close reading of the timing and some statistical evidence suggests that such shocks do not explain most of the exchange rate and price movements around the time of the tightening event, such that the residual unexplained component is consistent with our emphasis on the role of monetary policy itself. Moreover, Uganda’s somewhat earlier policy tightening—starting after its regime change in July—matches an earlier if also somewhat more gradual turnaround in its exchange rate and inflation.

The chapter proceeds as follows. We first briefly present the stylized facts of the countries under study, including structural features of the economy, the financial system, prices, and the policy regimes. We then proceed to the event study, identifying the policy shock and tracing out the effects of these shocks on the main macroeconomic variables: interest rates, credit aggregates, the exchange rate, output, and inflation. We then analyse more closely the role of important exogenous shocks to capital flows commodity prices that complicate interpretation of the events. Finally, we draw some tentative lessons.

2 The Environment for Monetary Policy

2.1 Structure of the Economy and the Financial Sector

The four countries of interest here are in many ways typical of SSA LICs, making their experiences of general interest. Moreover, they share a recent macroeconomic history that is sufficiently stable that the recent monetary policy contraction is a salient event. They are among SSA’s many ‘success stories’ since the mid-1990s, with the achievement of macroeconomic and political stability and rapid economic growth (Table 5.1). Their economic structure is also broadly characteristic of SSA LICs: low-trade shares, mostly commodity exports, high though falling aid dependence, service sector-led growth, and large agricultural sectors and rural populations. The terms of trade have been fairly stable or rising in recent years.4

Table 5.1.Basic Economic Indicators, 2011
CountryPopulation (Millions)Real GDP Per Capita (USD, PPP)Average Real GDP Growth (Per cent, 2001–11)Public Debt/GDP (Per cent, 2011)
Sources: IMF and the World Bank.
Sources: IMF and the World Bank.

Inflation in the EAC4 is volatile and highly correlated across countries, mostly explained by the high share of food items in the overall CPI. The weight of food prices in the CPI is highest in Tanzania (47 per cent), followed by Kenya (36 per cent), Rwanda (35 per cent), and Uganda (27 per cent).5 Domestic food crops yields in the region are highly dependent on weather patterns, which are characterized by a bimodal annual rainfall cycle. The existence of trade barriers to protect the domestic agricultural production has increased the sensitivity of the domestic food supply to unfavourable weather conditions.

Financial systems also share the characteristics that have been associated with weak transmission, though with important cross-country variation. Financial development has been rapid in recent years, but these countries still generally have small and concentrated private-bank-dominated financial systems, a large informal financial sector, shallow capital markets (except Kenya), short yield curves (except Kenya and more recently Uganda), and substantial dollarization (Table 5.2). In all four countries, commercial banks maintain substantial excess reserve deposits at the central bank. Additionally, the four economies exhibit different degrees of financial openness, with Uganda and Kenya being the more financially integrated of the four and Rwanda and Tanzania the least. Looking across the four countries, Kenya stands out for its relatively developed financial sector, with a larger and less concentrated banking system.

Table 5.2.Financial Sector Indicators, 2011
GroupsCredit to the Private Sector (Per cent of GDP)Bank Credit to the Private Sector (Per cent of GDP)Five-Bank Asset Concentration (Per cent)aStocks Traded, Total Value (Per cent of GDP)Dollarization bChinn-Ito Financial Openness Indexc
Average EAC422.719.175.40.918.20.4
Low-Income Countries19.618.880.04.912.8—0.4
Emerging Economies60.949.169.626.64.00.3
Advanced Economies145.3133.784.870.20.52.2

Assets of five largest banks as a share of total commercial banking assets.

Foreign currency deposits as a share of total deposits in the banking system.

Index values are for 2010. The index takes a maximum value of 2.5 for the most financially open economies and a minimum of —1.9 for the least financially open. See Chinn and Ito (2008).

Sources: IMF estimates and the World Bank.

Assets of five largest banks as a share of total commercial banking assets.

Foreign currency deposits as a share of total deposits in the banking system.

Index values are for 2010. The index takes a maximum value of 2.5 for the most financially open economies and a minimum of —1.9 for the least financially open. See Chinn and Ito (2008).

Sources: IMF estimates and the World Bank.

As in most SSA countries with managed floats, all four (except Uganda after July 2011) conducted monetary policy under a de jure monetary aggregate targeting framework, in principle adjusting the money supply to achieve inter mediate targets in terms of broad money growth. However, these three regimes were much more flexible, and complex, in practice (Appendix 5A: Table 5A.1).

The broader experience with such de jure money targeting regimes is that target misses are frequent, and at least in relatively low-inflation environments, such deviations are not associated with misses of inflation objectives. Rather, central banks tend to make judgements on an ongoing basis as to whether targets should be achieved, and if not, how they should be revised for the next quarter, depending on outcomes in money and exchange rate markets and a broader sense of whether inflation and output (and other) objectives are being achieved. This is for various reasons, not least because to adhere to the targets would generate excessive volatility of short-term interest rates in the face of money demand shocks. This makes the stance of policy hard to grasp and in particular very hard to infer from monetary aggregates themselves.6

Tanzania and especially Rwanda adhered most closely to their de jure money targeting regime, though even here, economically meaningful deviations were frequent.7 Kenya had over time paid less and less attention to monetary aggregates, culminating in September 2011 with a clear announcement of a move to use the short-term interest rate as its main policy instrument, with the objective of achieving its inflation objectives (Chapter 15). Uganda too had undergone an important evolution in this regard, moving in October 2009 from quite strict to flexible money targeting with substantial attention to interest rates as the operating target, to an explicit inflation targeting ‘lite’ regime in July 2011 with use of short-term interest rates in pursuit of its inflation objectives.

The different degree of attention to monetary aggregates in practice has corresponded to varying degrees of interbank interest rate volatility. This is clearest in Tanzania and least in Uganda and Kenya, consistent with their de facto use of interest rates as operating targets and indicators of the stance of policy.

In these hybrid regimes, it is often difficult to know how to interpret any particular interest rate. Sometimes ‘policy rates’ are not market-clearing, present no arbitrage opportunities with other short-term interest rates, and contain no signal of policy intention. But this can change suddenly as the details of central bank operations change. Pressures from fiscal authorities can encourage these central banks to create deviations between ‘policy rates’ and some market rates, particularly those at which the Treasury finances its activities, sometimes creating further opacity with respect to interest rates.

In assessing the stance of policy, we in general refer to the interbank rate as an indicator of the market short-term interest rate, with due reference to ‘policy rates’, exchange rate interventions, and money aggregates as appropriate. Other instruments, such as changes in reserve requirements, may have some independent signalling import but also are likely to work at least in part through their influence on interbank rates.

3 The Event Study

We define a shock as an episode in which a central bank undertakes overt and unusual—large and substantially unexpected—actions to exert a contractionary influence on the economy in order to reduce inflation. The tightening we consider here took place mainly in October 2011 around the time of a meeting of EAC Central Bank governors at which it was stated that inflation was getting out of control and that monetary policy needed to be tightened. This meeting and the resulting sharp policy actions represent the distinct monetary policy shock that allows us to trace the transmission mechanism. We now take a closer look at the tightening episode.

3.1 The Run-Up

Through mid-2011, international prices of food and energy shot up by more than 30 per cent and 40 per cent, respectively (Figure 5.1, Panel 1). Meanwhile, economic activity in the EAC4 was generally recovering from the earlier effects of the global financial crisis (Panel 2). Direct evidence suggests that the monetary policy stance was mostly accommodative, with nominal rates fairly flat and real interest rates mostly negative in all three countries (Panels 3 and 4). Partly reflecting this policy stance, but also at times owing to pressures on the capital account from swings in global risk aversion, nominal real exchange rates were generally weakening (Panels 5 and 6).8

Figure 5.1.The Run-Up

Sources: IMF estimates, Haver, national authorities. See footnote 9 for detailed calculation.

By 2011 Q3, these factors were reflected in headline inflation in Kenya, Uganda, and Tanzania that surpassed the common inflation target of 5 per cent (Panel 7).9 Even though most of the increase in headline inflation during this period is explained by the acceleration in food and fuel inflation, core inflation also increased substantially in all countries, almost doubling during the course of a year, and in all cases considerably overshooting the common inflation target (Panel 8).10

Consistent with the negative real interest rates, the monetary policy authorities were generally ‘behind the curve’ in responding to the building inflationary pressures.11 Moreover, policy responses were generally poorly signalled both in terms of the statements of the authorities and in that different instruments, such as different short-term interest rates, gave different signals.

As inflation in Kenya continued to increase and the nominal exchange rate depreciated by about 10 per cent in the period from March until September 2011, the Central Bank of Kenya (CBK) responded fitfully and opaquely. For example, a March increase of the Central Bank Rate (CBR) by 25 basis points to 6 per cent, reversing a lowering of 25 basis points in January 2011, was accompanied by mixed signals as to the intent of the CBK, and had no discernible effect on lending rates or the exchange rate.12 In August 2011, the central bank resorted to stronger moves, including restricting access to the discount facility and restricting liquidity provision through open market operations, still keeping the CBR unchanged. These moves resulted in a brief intra-month 2,200 basis point spike in interbank rates, which had little apparent effect on treasury bill rates and none on lending rates or the exchange rate, which continued to depreciate.

In September, the CBK clarified its operating regime, emphasizing that it would use the CBR as its main policy instrument with the objective of achieving its inflation objectives. However, while the central bank raised the CBR from 6.25 per cent to 7 per cent, at the same time, and in contradiction, it provided liquidity to the interbank market at 5.75 per cent. Meanwhile, policy statements from the CBK were ambiguous and lacked a clear announcement of policy tightening.13

Tanzania began acting as early as 2010 Q4, by some measures. A sharp con traction in the real growth rate of money in late 2010 caused a jump in the interbank rate, with a hint of pass-through to T-bill rates, but this proved short-lived even as the growth rate of money continued to slow. As real money growth reached nearly zero in mid-2011, interbank (but not T-bill) rates again spiked. There was no discernible effect of these actions on lending rates, the exchange rate, or credit.

Uganda, in contrast, began effective tightening earlier. Interbank rates increased by some 500 basis points during the first half of 2011, though with little apparent effect; for example, lending rates remained unchanged. The BOU tightened much more consistently and coherently after the July 2011 announcement of a new ‘IT-lite’ regime and the introduction of the CBR to signal the policy stance.14 The central bank raised the CBR in two steps, from 13 per cent in July to 16 per cent in September. In this case, the lending rate increased by 340 basis points from July until September 2011, and the exchange rate began to stabilize, with the real exchange rate appreciating in September. We return to the effects of these measures in the next section.

Rwanda presents a contrasting picture: through 2010–11, output was below trend, the real exchange rate and real interest rates were stable (and the latter positive). This may have reflected a tighter monetary policy stance, as well as the de facto crawling peg exchange rate regime. It is hard to infer much about the stance of policy during this period; for example, real reserve money growth varied sharply in a way that seems unrelated to short-term interest rates and the exchange rate or for that matter inflation and the output gap.

4 The Event and its Aftermath

As 2011 unfolded, inflation accelerated on the strength of higher food and oil inflation, strong demand, weakening exchange rates, and still-negative interest rates (Rwanda was the exception). The EAC4 came to the realization by their October meeting that action was needed to stabilize the situation.

On 5 October, the CBK increased the CBR by 400 basis points and on 1 November by a further 550 basis points to 16.5 per cent. During this period, it also increased the cash reserve requirement by 75 basis points to 5.25 per cent, and adjusted the discount window rate by more than 20 per cent, ‘as decisive and immediate action is required from the monetary policy side to stem these inflationary expectations’.15 In addition, the Ministry of Finance lowered the foreign exchange exposure limit for commercial banks to 10 per cent of core capital from 20 per cent.16

The Bank of Uganda (BUG) followed up early substantial tightening, and the introduction of its new regime in July, by raising its policy interest rate by 400 basis points in October and a further 300 basis points in November, to 23 per cent, and stepped up its intervention in the foreign exchange market to contain the depreciating pressures on the Shilling, stating that ‘the upside risks to inflation have increased, it is necessary to tighten monetary policy further … [this] should be seen as a clear signal of the BOU’s determination to bring inflation under control. However, should the upside risk to inflation continue in the months ahead, then monetary policy will be tightened further’.17

In Tanzania, the central bank increased its policy rate by 200 basis points in October and a further 202 basis points in November to 11.00 per cent, and on 26 October augmented the minimum reserve requirements on government deposits held by banks from 20 per cent to 30 per cent, reduced commercial banks’ limit on foreign currency net open positions from 20 per cent to 10 per cent of core capital, tightened capital controls, and increased sales of foreign exchange in the interbank market.18 This shift was more decisive than earlier efforts, perhaps partly because this time the authorities put more emphasis on the policy rate, as well as on the quantitative actions.

Finally, the National Bank of Rwanda took a variety of much more moderate tightening measures, consistent with the much lower degree of disequilibrium throughout 2011. Again, it is hard to point to any specific measurable action with respect to money aggregates, but the central bank’s policy rate was increased by 50 basis points to 6.5 per cent, as ‘the Central Bank finds it appropriate to review its policy rate in order to keep the monetary aggregates at optimal levels to limit inflation pressures while continuing to support economic growth’.19

Having identified the policy-tightening event and the variations across the four countries, we now turn to an assessment of the various channels of transmission of monetary policy across our group of countries. The variety of instruments and (time-varying) differences in regimes can make it difficult to characterize in a simple way the stance of policy itself. We generally use the interbank rate as the best single measure of the policy shock itself. As a measure of the short-term market rate in all four countries, it captures aggregates of the effects of policy rates and quantitative policies (quantitative interventions, reserve requirements) and distils the divergent effects of potentially inconsistent actions taken with various not-necessarily-market-clearing ‘policy rates’.

4.1 The Interest Rate Channel

In Uganda and Kenya, the pass-through from policy rates to interbank rates was fast and complete (Figure 5.2, Panels 1 and 2) (and to T-bill rates, shown in the working paper). Tanzania’s battery of measures also transmitted quickly to money market rates.

Figure 5.2.The Monetary Policy Contraction and its Aftermath

Source: IMF and authors’ calculations.

The relevance of the policy regime is evident in the transmission to banking rates (Panel 3). Lending rates, in particular, responded swiftly—though partially—to the monetary policy contraction in Kenya and Uganda. Uganda’s lending rates began to respond somewhat earlier, corresponding to the August/September tightening. There is little sign of transmission to lending rates in Tanzania and Rwanda. The lack of response of lending rates in Tanzania and Rwanda, despite the increases in the interbank rates in these two countries, is reminiscent of the non-response of lending rates to Kenya’s August spike in interbank rates.

Even in Kenya and Uganda, the pass-through from short-term market to lending rates was partial. This may reflect lack of competition or other structural weaknesses in the financial system, as argued in Mishra et al. (2012). However, lending rates are longer-term rates, so partial pass-through to (at least somewhat temporary) tightening is also consistent with fully functioning markets, by which we mean arbitrage across returns of different assets, and an expectation that the period of high short rates will be somewhat shorter than the tenor of the loans.20 Moreover, standard data in these countries (such as we use here) report average, not marginal lending rates. Finally, as we have already argued above, the pass-through of policy rates to short-term market and lending rates will depend on the clarity of the regime and in particular the ability of market participants to infer that the increase reflects policy intent and will not be quickly reversed.21 It may be that there was still some uncertainty about whether the authorities in Kenya and Uganda would stay the course.

4.2 The Bank Lending Channel

The data indicate the existence of the credit channel in Kenya, Uganda, and Tanzania. In these three countries growth in credit to the private sector peaked soon after the policy contraction started and decelerated substantially as the monetary authorities stepped up the pace of tightening. Accordingly, during the 2011 Q3 to 2012 Q3 period, credit to the private sector growth in Kenya, Uganda, and Tanzania decelerated. Again, Uganda’s contraction began a month or two before the others. There are also signs of credit rationing in the case of Tanzania: even though lending rates did not respond to the tightening, there was a meaningful impact on the quantity of credit extended to the economy. In Rwanda, there is little sign of slower credit growth, perhaps reflecting the much less significant tightening.

Abuka et al. (2015) provide important supportive evidence for the bank lending channel in the case of Uganda. A unique dataset of loan-level data spanning the period in question allows them to control for demand effects through region-industry dummies and aggregate variables such as GDP, directly. This allows them to interpret the effects of the policy shock as causal for credit supply and as not due to demand effects. They find that higher short-term market interest rates are associated with an increase in banks’ lending rates and reductions in loan volume at the extensive and intensive margins. The strength of this bank lending channel is significant, albeit about half of that observed in advanced economies studied with similar data and techniques.22

4.3 The Exchange Rate Channel

In Kenya, Uganda, and Tanzania, the increase in short-term interest rates was associated with a contemporaneous appreciation of the currency. Notably, this took place during the decisive tightening phase in 2011 Q4, but not earlier when policy was more cautious and less clearly signalled. Uganda is again a partial exception insofar as the exchange rate stabilization began two months earlier, corresponding to its earlier tightening phase.

4.4 Output

The tightening episode is associated with a contraction of output in Uganda and to a lesser extent in Tanzania (Panel 7). The absence of a visible decline in the output gap in Kenya is notable. Of course, factors other than monetary policy such as fiscal policy and foreign demand also influence the output gap, and it is difficult to measure the output gap in the context of frequent supply shocks.23

The Abuka et al. (2015) analysis based on loan-level data provides supportive analysis of the effects of this particular monetary policy shock on output in Uganda. By identifying differential loan supply effects in districts with varying banking sector conditions, and measuring real effects through night-time light output measured from satellites, they can plausibly identify the real effects of monetary policy acting through the bank balance sheet channel. They find that output does indeed contract more in those districts where banks have balance sheets, suggesting a strong balance sheet channel for the monetary contraction.

4.5 Inflation

Finally, the inflation rate came down sharply with the monetary contraction. Headline inflation began turning around rather quickly, within a month or two, presumably reflecting the rapid pass-through of exchange rate movements. The turnaround was sharpest in Uganda and Kenya but was apparent in Tanzania as well. Again, Rwanda shows a much more gradual pattern, reflecting the fact that inflation was never far from target. Core inflation followed, albeit much more gradually (Panel 9).

5 On The Role of Global Risk Appetite and Supply Shocks

The backdrop against which the coordinated October 2011 tightening took place was challenging in that the countries faced both a negative balance of payments shock and a negative supply shock in the context of accommodative policies and growing imbalances. We now discuss the direct role of these two factors, concluding that, while they were important, they cannot provide an alternative explanation of the events we have documented.

First, can shifts in capital flows and global risk aversion explain the exchange rate dynamics during the run-up and following the coordinated tightening? The year 2011 was one of increased global risk aversion, with the rising political tensions in the Middle East associated with the Arab Spring, the sovereign debt crisis in Europe, and the downgrading of the credit rating of major industrial economies. This surely contributed to exchange rate pressures on the EAC4, but cannot plausibly explain the timing and magnitude of the real depreciations observed. The currencies of Kenya, Uganda, and Tanzania started to weaken in 2010, well in advance of the episode, standing during 2010 and 2011 amongst the most depreciated currencies in the emerging and frontier markets world (excluding fixed exchange rate regimes, Figure 5.3).

Figure 5.3.Emerging and Frontier Markets Real Exchange Rates (Index, Jan. 2010=100, increase means appreciation24)

Sources: IMF and authors’ computation.

Taking a closer look, swings in global risk appetite can partly explain the sharp depreciation during the July–September period and perhaps some of the appreciation that followed the monetary policy tightening. However, the timing of the turnaround indicates a strong independent role for the monetary policy contraction. Global risk appetite, as proxied by the VIX Index, deteriorated markedly in August and September as the credit ratings of the United States, Japan, and Italy were downgraded and Europe’s debt crisis intensified (Figure 5.4). Tensions in international capital markets eased by late September.

Figure 5.4.VIX (Index) and EAC4 Exchange Rates

Source: Bloomberg.

However, the currencies of Kenya and Tanzania reached their lowest levels weeks later.25 The Kenyan shilling strengthened immediately on the second day of the policy-tightening announcement, after staying near low levels despite improved global sentiment. The Tanzanian shilling, on the other hand, continued to weaken even after the announcement of the coordinated policy, interrupting its slide only after a further battery of measures was announced in the week commencing 24 October.

A second alternative narrative emphasizes the role of the increase in global commodity prices in the region’s inflationary dynamics. On this view, the inflationary pressures were not a reflection of vigorous economic activity and loose monetary policy but a result of the higher food and fuel prices. Two points are worth noting here. First, as shown earlier, core inflation (mainly excluding food and energy prices) also increased substantially through 2011, almost doubling and overshooting the target, before beginning to come back down, Second, movements in food and fuel inflation are themselves influenced by the monetary policy stance. Monetary policy influences the domestic price of imported food and fuel through the exchange rate. For locally produced (and not fully traded) food, monetary policy can work through aggregate demand. Thus, disentangling the contribution of monetary policy from that of supply shocks cannot solely be based on separating food/fuel and core inflation. Chapter 15 discusses these issues and applies a simple structural model to events in Kenya during 2011 to find that monetary policy accounts for much of the inflation dynamics, including the behaviour of domestic food prices.

We can provide a more quantitative if less nuanced characterization of the role of global risk appetite and global commodity prices by seeing how much of the month-to-month variation in the exchange rates in the EAC4 can be associated statistically with movements in the VIX and global interest rates. This is a simple regression to interpret: we assume that any policy actions that are correlated with these global shocks are reactions to these events; any residual policy reactions can thus be left in the error term. This assumption gives maximum weight to these global shocks. It permits us to use OLS to regress nominal exchange rate level on the VIX, the US interest rate, and several lags, country by country. We interpret the residual as the candidate variation to be explained by other factors, such as monetary policy.

The regression explains a fair amount of the variance of the exchange rate (from 56 per cent in Uganda to 46 per cent in Rwanda, presumably low owing to its quasi-managed regime). Most of this is due to the importance of the lagged exchange rate itself, though the exogenous variables are highly significant. In Figure 5.5, we plot the predicted value of the exchange rate based only on the exogenous shocks and associated endogenous dynamics (the ‘counterfactual’, which we interpret as reflecting the dynamics of the exchange rate in the absence of unexpected monetary policy decisions).26 As Figure 5.5 shows, in all but Rwanda the exogenous factors go in the right direction in explaining the depreciations in Q3 and subsequent appreciations, but much less than observed and with not quite the right timing. The peak depreciation of the predicted exchange rate in all countries occurs around September, but the actually (much weaker) bottom occurs in October in Tanzania and Kenya and in August in Uganda, more consistent with the timing of the monetary policy shock.

Figure 5.5.Nominal Exchange Rate Depreciation, MoM Annualized in Per cent

Source: Authors’ computation.

The results for headline inflation paint a similar story. Inflation peaks coincide or follow with a one-month lag in the nominal exchange rate depreciation and they cannot be explained by the counterfactual (Figure 5.6).

Figure 5.6.CPI Inflation, MoM Annualized in Per cent

Source: Authors’ computation.

To summarize this section, the swings in global risk aversion and the food and fuel supply shock during 2011 did play an important role during the episode under study, they are only part of the story, both in terms of magnitude and timing, and do not overturn the conclusion that monetary policy seems to have played a decisive role.

6 Summary and Interpretations

We have identified a moment when three of the EAC4 broke from previous behaviour and executed more-or-less clearly signalled monetary policy contractions with the explicit intent of reducing inflation. We find clear evidence of most elements of the standard transmission mechanism in most of the countries.

The transmission was clearest in Kenya and Uganda, where market and lending rates followed the policy rate with little lag, the exchange rate appreciated sharply on the policy announcement, credit growth (and, at least in Uganda, the output gap) began to decline immediately. Both headline and core inflation also began to decline almost immediately. Transmission was less clear in Tanzania, where the effects on some interest rates, activity, the exchange rate, and inflation were still broadly evident, but lending rates failed to respond and the effects on output were barely evident. Rwanda presents a control along several dimensions: initial imbalances were much smaller, the tightening much less significant, and the various components of transmission much more muted or invisible.

Based on and summarizing the preceding narrative, we can now evaluate the two hypotheses discussed in the instruction to explain the variation in cross-country experience. In the episode under study, we find substantial explanatory power in the idea that the nature of the policy regime conditions transmission.

In the cases of clearest transmission, Kenya and Uganda, the regimes in October 2011 most resembled inflation targeting in that the authorities prioritized inflation, emphasized the role of the policy rate, allowed the exchange rate a large degree of flexibility, and broadly avoided multiple objectives. Earlier tightening efforts by Kenya, e.g. in August 2011, were more incoherent in terms of the consistency across different instruments and communications and did not trans late into lending rates or the exchange rates. Uganda’s earlier tightening efforts were more coherent and stronger following its July 2011 move to ‘IT lite’ in July, and indeed had some effect on lending rates, credit, the exchange rate, and inflation about two months before Kenya and Tanzania.

In Tanzania the money targeting regime led to highly volatile short-term interest rates, a variety of instruments were used in not always consistent ways, and overall there was less clear signalling of the policy stance.

Rwanda’s regime was the most complex, with a quasi-pegged exchange rate, direct influence on private sector credit, monetary aggregate targets, and a policy rate. The emphasis on the exchange rate left little room for monetary policy itself to act, and insofar as it could, the regime did not provide a clear signal. In the event, there was apparently less tightening, and less need to tighten.

The second hypothesis is that transmission worked better in countries with greater financial depth and more open capital accounts. We find mixed support for this story in this episode. It remains plausibly the case that countries with more liquid and deeper financial markets will observe stronger transmission from policy rates to the macroeconomy.27 However, in this particular case measures of financial depth do not seem determinative for the clarity of transmission. A glance back at Table 5.2 reminds us that Kenya is the clear outlier for all measures of financial depth, with the other three countries remarkably similar. And yet, as we have argued, the evidence for transmission looks fairly strong, and similar, for Uganda and Kenya, in contrast to Tanzania and Rwanda.

On the other hand, the narrative is consistent with the view that the lower degree of financial openness in Tanzania and Rwanda (Table 5.2) may have contributed to obscuring or impairing transmission in these two countries. The exchange rate did seem to respond in Tanzania, but less dramatically than in Kenya and Uganda.

Finally, it is often asserted that the high levels of excess reserves usually observed in SSA countries prevent the operation of the monetary transmission mechanism, for example because tightening policy may amount to ‘pushing on a string’, as banks respond to a contraction by withdrawing excess reserves.28 In the episode we examine here, excess reserves did not seem to impair the transmission mechanism. As Figure 5.7 shows, there are indeed substantial reserves in excess of required levels in all four countries, with large variations across time and countries but with no evident influence on transmission. While in Kenya, excess reserves fell during 2011, they remained above 6 per cent of required reserves even in October, and they rose in Uganda and varied around 20 per cent of required reserves during the peak of the tightening phase, higher than they had been since 2007.29

Figure 5.7.Excess Reserves

Source: IMF and authors’ computation.

7 Conclusions

The identification of monetary policy transmission is difficult under any circumstances, and especially so in countries with poor data, obscure and time-varying policy regimes, and frequent supply shocks. As emphasized by Summers (1991) among others, the analysis of dramatic events such as the Great Depression and the Volker disinflation in the United States has played a critical role in forming professional opinion and framing the discussion in advanced countries. However, such analyses are scarce in developing countries.

We have taken advantage of a dramatic tightening of monetary policy in four countries in East Africa in October 2011 to trace through the effects of this tightening on interest rates, credit, the exchange rate, output, and inflation. We find clear evidence of a working transmission mechanism in two of the countries: after a large policy-induced rise in the short-term interest rate in Kenya and Uganda, lending rates rose, the exchange rate appreciated, output growth tended to fall, and inflation declined. The other two countries represent a contrast to varying degrees. In Tanzania, some but not all market rates and the exchange rate seem to respond to adjustments of the monetary policy stance, and we see some signs of the effects of policy in output (possibly through credit rationing); in Rwanda, the initial disequilibrium was much smaller, any tightening much less evident, and the effects obscure.

These case studies provide many illustrations of the role that the policy framework itself plays in governing the strength of transmission. Most importantly, Kenya and Uganda by October had clarified their inflation objective and the centrality of the policy rate as the main signal. In this context, they were able to clearly articulate that they were raising rates to bring inflation down. Earlier efforts in Kenya in August, before this clarification, were ineffective. Tanzania represents an intermediate case, in which a continued focus on money targeting and some inconsistency across policy instruments coexisted with a somewhat less clear transmission.

The proliferation of policy instruments was common across all four countries. This put a premium on coherence and communication in signalling the policy stance. The difficulty in interpreting the different measures of policy may impact not only our ability as researchers to discern events but the capacity of interest rates to clear markets and signal policy. From a methodological point of view, this active use of a wide set of instruments under differing policy regimes with multiple objectives, along with the suggested quasi-contemporaneous nature of the trans mission mechanism, suggest that extra care should be applied when using standard statistical procedures, such as VARs, to measure the effects of policy, especially when those studies are conducted for country groups.

In contrast, we found some role for financial openness, but little for financial depth or the degree of excess reserves in explaining the cross-country patterns of transmission in these particular cases. We should not overemphasize or over-generalize this result. It remains plausible that countries with less developed financial markets and less open capital accounts will observe weaker transmission. However, this does not seem to have been a determinative feature here.

Clearly, in general, shocks other than those to monetary policy are the main drivers of macroeconomic outcomes in countries such as those we examine here, as various chapters of this book (Chapters 4, 11, 15, 17, 18, and 20) make clear. And here as elsewhere, monetary policy was not made in a vacuum, and identification of shocks and their effects is challenging. It is always a great leap of faith to suppose that a residual in an estimated monetary policy reaction represents such a shock and not a misspecification (e.g. an omitted variable or a nonlinearity) in the reaction function. In our cases, we have used the historical narrative to attempt to argue that much of the shock was a surprise, and we have tried to exclude simple alternative hypotheses about the drivers of some of the key variables. But ultimately the results cannot be definitive.

Much remains unknown about the transmission mechanism in these countries. The role of supply shocks, food prices, the banking system and limited financial participation, fiscal policy, limited capital account openness, and many other features deserve further exploration. Nonetheless, the results are consistent with the view that getting monetary policy broadly right may nonetheless be an important contributor to macroeconomic stability and thus eventually financial development and growth. They should be encouraging for the central banks considering moving toward forward-looking monetary policy frameworks such as inflation targeting. The transmission mechanism appears to be working, but it functions best when signals are clear and the regime simple and coherent. This point is shown analytically in Chapter 9 of this book.

Table 5A.1.Policy Regimes as of 2011
CountryExchange Rate ArrangementaMonetary Policy Frameworkb
National Bank of Kenya (NBK)Free float

Limited foreign exchange intervention, open capital account
Flexible money targeting

De jure money targets. Central Bank Rate (CBR), announced monthly, important policy signal. In September 2011 the CBK clarified operating procedures and emphasized the CBR as the reference rate.
Bank of Uganda (BOU)Float

Occasional f/x intervention to ‘maintain stability in the foreign exchange market’ and build up reserves. Open capital account.
Inflation targeting lite (as of July 2011)

Moved from flexible money targeting in July 2011. Interest rate operating target, set monthly. A corridor around the CBR is also defined, with its width frequently adjusted.
Bank of Tanzania (BOT)Float

Substantial f/x intervention. In 2012, the Tanzania shilling traded in a 2 per cent range against the dollar. Widespread capital account controls yield a de facto fairly closed capital account.
Money targeting

The BOT’s Monetary Policy Committee (MPC) sets—and Parliament approves—an operational target on reserve money and intermediate targets on the growth in M2 and M3. Economically meaningful deviations are common. The bank also indicates the stance of policy through movements in the Bank Rate.
National Bank of Rwanda (NBR)Crawling-peg-like arrangement

Regular f/x intervention. Restrictions to capital account transactions are not prevalent, but the absence of capital inflows is consistent with the view that the capital account is de facto closed.
Flexible money targeting

Reserve money as operational target and a floor on net foreign assets as an intermediate target. The relatively closed capital account likely allows a certain degree of independence between exchange rate management and monetary policy. The MPC also sets the Key Repo Rate (KRR), which guides liquidity operations. Some direct controls on credit.

a According to IMF (2012).

b According to Chapter 15 (Kenya), Chapter 18 (Rwanda), Bank of Uganda (2011a) and Chapter 2 (Uganda), and IMF (2015) (for Tanzania).

a According to IMF (2012).

b According to Chapter 15 (Kenya), Chapter 18 (Rwanda), Bank of Uganda (2011a) and Chapter 2 (Uganda), and IMF (2015) (for Tanzania).


    AbukaC.AlindaR.MinoiuC.PeydroJ. andPresbiteroA. (2015). Monetary Policy in a Developing Country: Loan Applications and Real Effects. IMF Working Paper 15/270. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    AdamC.MaturuB.Ndung’uN. andO’ConnellS. (2010). Building a Monetary Regime for the 21st Century. In C.AdamP.Collier andN.Ndung’u (Eds.) Kenya: Policies for Prosperity. Oxford: Oxford University Press.

    • Search Google Scholar
    • Export Citation

    Bank of Uganda. (2011aJuly). Monetary Policy Statements.

    Bank of Uganda. (2011bOctober). Monetary Policy Statements.

    BergA.VlcekJ.CharryL. andPortilloR. (2013). The Monetary Transmission Mechanism in the Tropics: A Narrative Approach. IMF Working Paper 13/197. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    BulirA. andVlcekJ. (2015). Monetary Transmission: Are Emerging Market and Low Income Countries Different. IMF Working Paper 15/239. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    Central Bank of Kenya. (2011aMarch). Monetary Policy Committee Meeting [Press release].

    Central Bank of Kenya. (2011bMarch). Monetary Policy Committee Meeting [Press release].

    Central Bank of Kenya. (2011cSeptember). Monetary Policy Committee Meeting [Press release].

    Central Bank of Kenya. (2011dOctober). Monetary Policy Committee Meeting [Press release].

    ChinnM. andItoH. (2008). A New Measure of Financial Openness. Journal of Comparative Policy Analysis10 (3) 30922.

    ChristianoL. J.EichenbaumM. andEvansC. L. (1999). Monetary Policy Shocks: What Have We Learned and to What End? Handbook of Macroeconomics (Vol. 1 pp. 65148). Elsevier.

    • Search Google Scholar
    • Export Citation

    DotseyM. andReidM. (1992July–August). Oil shocks, monetary policy, and economic activity. Federal Reserve Bank of Richmond Economic Review781427.

    • Search Google Scholar
    • Export Citation

    FriedmanM. andSchwartzA. (1963). A Monetary History of the United States. Princeton: Princeton University Press.

    GurkaynakR.LevinA.MardarA. andSwansonE. (2007). Inflation Targeting and the Anchoring of Inflation Expectations in the Western Hemisphere. Economic Review. San Francisco: Federal Reserve Bank of San Francisco.

    • Search Google Scholar
    • Export Citation

    International Monetary Fund. (2012). Annual Report on Exchange Arrangements and Exchange Restrictions. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    International Monetary Fund. (2015). Evolving Monetary Policy Frameworks in Low-Income and Other Developing Countries. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    MishraP.MontielP. andSpilimbergoA. (2012). Monetary Transmission in Low-Income Countries: Effectiveness and Policy Implications. IMF Economic Review60270302.

    • Search Google Scholar
    • Export Citation

    MontielP.AdamC.MboweW. andO’ConnellS. (2012). Financial Architecture and the Monetary Transmission Mechanism in Tanzania. Working Paper. International Growth Center.

    • Search Google Scholar
    • Export Citation

    National Bank of Rwanda. (2011). The BNR Key Repo Rate Increased From 6% to 6.5%.

    RomerC. andRomerD. (1989). Does Monetary Policy Matter? A New Test in the Spirit of Friedman and Friedman. In NBER Macroecononomics Annual412184. National Bureau of Economic Research.

    • Search Google Scholar
    • Export Citation

    SaxegaardM. (2006). Excess Liquidity and Efectiveness of Monetary Policy: Evidence from Sub-Saharan Africa. IMF Working Paper 06/115. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    ShapiroM. D. (1994). Federal Reserve Policy: Cause and Effect. In N. G.Mankiw (Ed.) Monetary Policy. NBER Working Paper 4342. Chicago: University of Chicago Press.

    • Search Google Scholar
    • Export Citation

    SummersL. (1991). The Scientific Illusion in Empirical Macroeconomics. Scandinavian Journal of Economics93 (2) 12948.

    WoodfordM. (2001 August 30–September 1). Monetary Policy in the Information Economy. Proceedings from the Federal Reserve Bank of Kansas City Annual Symposium Conference: Economic Policy for the Information Economy1297370. Jackson Hole, Wyoming.

    • Search Google Scholar
    • Export Citation
2The exogeneity of Romer and Romer’s policy events is challenged in Shapiro (1994) and Dotsey and Reid (1992). See the discussion in Christiano et al. (1999).
3See the communique from the 12 October 2011 meeting at
4Berg et al. (2013) present much more background information and references.
5The Tanzania CPI survey includes rural households, unlike the surveys in Kenya, Uganda, and Rwanda.
6See Chapters 1, 8, and 9, and Adam et al. (2010) for discussions of this issue, and Chapter 16 for Kenya specifically.
7As discussed in Chapter 8, deviations that occur roughly once per year correspond in magnitude, by back-of-the-envelope calculations, to interest rate deviation of 5 percentage points in Rwanda and 20 percentage points in Tanzania. Rwanda’s close control of the nominal exchange rate created some tensions with the money targets in practice.
8Rwanda was an exception to these generalizations, with output below potential (though rising), a stable nominal exchange rate, and positive real interest rates. Section 5 examines the role of exogenous external shocks more closely to disentangle them from that of policy.
9Output gaps are estimated with a Hodrick-Prescott filter on the four-quarter cumulative real GDP in Uganda and Tanzania and Non-Agricultural GDP in Kenya and Rwanda. The estimation sample includes data up to 2012 Q3 to correct for end-of-sample bias. Real interest rates are calculated using the twelve-month backward-moving average CPI-based inflation rate. The real exchange rates are CPI-based bilateral rates with the US dollar.
10For details on the construction of these series, see Berg et al. (2013).
11In part, this may have reflected the experience of the earlier episode of external price shocks in 2007/08, when temporarily rising inflation was followed by the commodity price and external demand collapse of the global financial crisis, obviating the need for a monetary policy response in that case, as discussed in Berg et al. (2013).
12In March 2011, the CBK suggested somewhat contradictorily that ‘this tightening will provide a solution to inflationary pressure and will stabilize the exchange rate while still protecting economic activity’ (Central Bank of Kenya, 2011a). In July, they still considered this action sufficient to mitigate soaring inflation (Central Bank of Kenya, 2011b).
13At an extraordinary meeting on 14 September 2011, the CBK announced that ‘The high overall inflation environment is mainly a consequence of high food prices and high fuel and energy prices… The CBK will pursue the inflation objective through a continuation of the gradual tightening of monetary conditions’ (Central Bank of Kenya, 2011c).
14With the introduction of the IT-lite framework also came the release of a monetary policy statement signed by the governor and providing forward guidance to market participants.
16In October 2011, the CBK clarified its communications by stating that ‘this upward adjustment of the CBR was expected to provide a signal to banks that interest rates should rise and therefore reduce the expansion in credit to the private sector’ (Central Bank of Kenya, 2011d).
18IMF (2011c).
20The average maturity of loans in the cleaned loan-level Uganda dataset of Abuka et al. (2015) is 1.5 years. Moreover, it is a characteristic of fully credible regimes that very long rates do not move much with short rates, because inflation expectations are well anchored (Gurkaynak et al., 2007). See also Bulir and Vlcek (2015).
21While not necessarily relevant in this episode of dramatic tightening, this point is closely related to the argument, e.g., in Woodford (2001), that the substantial smoothing seen in advanced-country monetary policy reaction functions implies a large pass-through to lending rates. A lesser degree of smoothing thus implies lower pass-through.
22They also identify a bank balance sheet channel in which balance sheet conditions of banks influence these effects, with for example poorly capitalized banks transmitting the interest rate changes more strongly.
23In Uganda, the output gap increased from 2010 Q3 to 2011 Q3 despite positive real interest rates for the first half of that period. Fiscal policy was expansionary during the first part of this period, with a fiscal impulse (the change in the primary balance adjusted for the cyclical position, estimated at 1 per cent of GDP for the 2010 Q3–2011 Q2 period (IMF, 2011b)).
24REER is downloaded from IMF database. The median and percentiles are calculated from REER of Brazil, Chile, Colombia, Czech Republic, Estonia, Ghana, Hungary, India, Indonesia, Israel, Korea, Mauritius, Mexico, Pakistan, Peru, Philippines, Poland, Romania, Serbia, Slovenia, South Africa, Sri Lanka, Thailand, and Turkey.
25Uganda stabilized somewhat earlier, in August, in this case coinciding more closely with the levelling off of the VIX but also with the earlier beginning of the monetary policy tightening phase, which also peaked in October.
26That is, we create a ‘counterfactual’ based on the predictions of the estimated model given the exogenous variables, rather than using actual lagged values of the endogenous variable.
27Mishra et al. (2016) find evidence to this effect in a large sample of developing countries.
29This evidence suggests that these ‘excess’ reserves may be an equilibrium phenomenon reflecting factors such as risk aversion on the part of banks and structural deficiencies in the functioning of the interbank market. Addressing these deficiencies that create excess liquidity is likely important for the promotion of financial development but, judging from this episode, it does not appear necessary for the transmission of monetary policy.

    Other Resources Citing This Publication