Monetary Policy Transmission in Emerging Markets and Developing Economies

Central banks in emerging and developing economies (EMDEs) have been modernizing their monetary policy frameworks, often moving toward inflation targeting (IT). However, questions regarding the strength of monetary policy transmission from interest rates to inflation and output have often stalled progress. We conduct a novel empirical analysis using Jordà’s (2005) approach for 40 EMDEs to shed a light on monetary transmission in these countries. We find that interest rate hikes reduce output growth and inflation, once we explicitly account for the behavior of the exchange rate. Having a modern monetary policy framework—adopting IT and independent and transparent central banks—matters more for monetary transmission than financial development.

Abstract

Central banks in emerging and developing economies (EMDEs) have been modernizing their monetary policy frameworks, often moving toward inflation targeting (IT). However, questions regarding the strength of monetary policy transmission from interest rates to inflation and output have often stalled progress. We conduct a novel empirical analysis using Jordà’s (2005) approach for 40 EMDEs to shed a light on monetary transmission in these countries. We find that interest rate hikes reduce output growth and inflation, once we explicitly account for the behavior of the exchange rate. Having a modern monetary policy framework—adopting IT and independent and transparent central banks—matters more for monetary transmission than financial development.

I. Introduction

Many central banks in emerging markets and other developing economies (EMDEs) have been modernizing their monetary policy frameworks. In particular, many large emerging markets have moved to using interest rates as the primary operational targets, often in an inflation targeting (IT) regime with floating exchange rates.

However, some central banks remain reluctant to transition to a price-based approach to monetary policy because of the perception that policy rates are not able to influence output and inflation. Substantial dollarization and underdeveloped credit markets (in part because of insufficient bank competition) may weaken standard monetary policy transmission. Similarly, low central bank credibility may hamper the ability to manage expectations, and weaken transmission (Frankel, 2010). The academic literature on these issues is limited, however. A few academic studies have found support for the view of weak transmission due to underdeveloped financial markets (e.g., Mishra, Montiel, and Spilimbergo, 2012),1 while others (Berg and others, 2013) have argued that failure to detect transmission in these economies is often driven by data and methodological problems. However, the quantitative relevance of these arguments has, to our knowledge, not yet been systematically explored.

This paper systematically analyzes the strength of monetary transmission from interest rates in EMDEs. We identify monetary policy (i.e., interest-rate-) shocks by removing the influence of current macroeconomic conditions and expected future GDP growth and inflation. We then estimate the responses of output and prices to such shocks for a sample of 39 EMDEs using Jordà’s (2005) local projections method. We explore the extent to which the strength of the transmission mechanism depends on monetary policy frameworks, the level of financial development, and other structural characteristics, such as financial dollarization.2

Our first key result is that transmission through interest rates generally does work in EMDEs. The estimates of the impact of monetary policy shocks resemble those for advanced economies once we take the exchange rate channel—a key part of transmission in EMDEs—properly into account. On average, output falls by 1.1 percent in response to a 100bp monetary policy shock. Furthermore, once we condition the response of prices to an interest rate shock on the magnitude of the contemporaneous change in the nominal exchange rate, the estimated response is in line with what is expected in theory. That is, Sims’s (1992) and Eichenbaum’s (1992) price puzzle disappears. Although more muted than the estimated response of output, following a contractionary monetary policy shock, prices decline by around 0.3 percent if the nominal exchange rate appreciates at the same time. The finding on the effectiveness of monetary policy is consistent with results in Abuka et al (2019), Berg et al (2013) and Willems (2018), who report evidence of monetary transmission in developing countries following large monetary contractions.

Our second key result is that monetary policy frameworks matter more than other structural country characteristics, including financial development, for the transmission of interest rate shocks to output. The estimates show that the response of output to such shocks is stronger in countries that have adopted IT, and where the central bank is more independent or more transparent. This finding is robust to controlling for competing explanations (such as overall quality of country governance) and to addressing the endogeneity of IT adoption.3 To our knowledge, this is a novel result which has not yet been documented systematically for EMDEs. Transmission to output appears to be somewhat stronger in more financially developed economies, but the opposite is true for the transmission to prices. Other country characteristics, such as capital account and trade openness or bank competition, do not seem to have significant effects on the transmission mechanism, either. Consistent with a few previous studies covering small groups of countries (e.g., Armas and Grippa 2005, Acosta, Omaechea, and Coble 2014), we also find that monetary transmission does work in countries with high levels of financial dollarization.

The rest of the paper is organized as follows. Next, we briefly discuss the channels of monetary policy transmission. In Section III we describe the data and empirical approach. We discuss the results and their robustness in Section IV. Section V concludes.

II. Determinants and Channels of Monetary Transmission

Monetary transmission works through various channels (Figure 1). For example, according to the interest rate channel, an increase in nominal interest rates translates into an increase in real rates and the user cost of capital, given some degree of price stickiness. Higher real rates, in turn, lead to a postponement of consumption and a reduction in desired investment, exerting downward pressure on prices.

Figure 1.
Figure 1.

Channels of Monetary Transmission

Citation: IMF Working Papers 2020, 035; 10.5089/9781513529738.001.A001

If the economy is open, as is the case of most emerging and developing countries, the exchange rate channel is another important part of monetary transmission.4 An increase in the domestic interest rate leads to a stronger currency (ceteris paribus), which puts downward pressure on the prices of tradable goods in the consumer price basket. Moreover, a stronger exchange rate typically leads to a reduction in both net exports and the overall level of aggregate demand. In the presence of currency mismatches, however, a countervailing effect may be important: an appreciating currency may strengthen borrowers’ and lenders’ balance sheets, increasing their ability to borrow and extend credit, and thereby stimulate the economy (see, for example, Céspedes, Chang, and Velasco, 2004, or Avdjiev and others 2019, among many others). Conceivably, the interactions between the exchange-rate- and interest-rate effects are nonlinear.

Although empirically less relevant than the interest-rate- and exchange-rate channels, other monetary policy channels can be relevant in certain circumstances. For example, under the bank lending channel, tight monetary policy drains liquidity and deposits from the banking system and induces cuts in lending if banks face frictions in issuing uninsured liabilities to replace the shortfall in deposits. In addition, high short-term rates (together with a downward-sloping yield curve) depress bank profits and reduce their net worth, further hindering their ability to issue non-deposit liabilities (IMF 2016).

Country characteristics such as financial market development or institutional policy frameworks should affect the transmission of monetary policy through various channels. Differences in the liquidity, the structure of interbank money markets, and overall financial development are likely to matter for the transmission of policy rates to the economy through the interest rate channel. Similarly, market segmentation, lack of access to financing, dollarization, or the presence of dominant state banks may reduce transmission of policy rates to lending rates. Moreover, the bank lending (or narrow credit) channel may be more important in less developed economies because many households and firms rely heavily on bank lending if they have access to credit at all, even if it may be impaired in some cases (e.g., because of excess bank liquidity due to remittances; see Barajas and others, 2018).

III. Empirical Methods and Data

A. Data

We study a sample of 40 emerging and developing economies. All data used in the benchmark analyses are monthly. The countries and data sources used in each case are described in Appendix I. Summary statistics of the dependent and explanatory variables are in Tables 1 and 2.

Table 1.

Summary Statistics

The table shows summary statistics for all the variables used in the regressions presented in the paper. 1PC stands for the first principal component obtained from a principal component decomposition of a set variable. See Table A.1.1 for details.

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Table 2.

Summary Statistics by Country

The table shows summary statistics by country for some of the variables used to capture country characteristics influencing the transmission of monetary policy. Panel A shows statistics for variables representing financial sector development: Sahay and others’ (2015) index of financial development, the credit-to-GDP ratio from the World Bank’s Global Financial Development Database, and a measure of financial inclusion (number of ATMs per 10,000 people), also from the World Bank’s Global Financial Development Database. Panel B shows statistics for the variables representing monetary policy frameworks: the adoption of inflation targeting, Garriga’s (2016) index of central bank independence, and Dincer and Eichengreen’s (2014) index of central bank transparency. S.D. stands for standard deviation. 25 percent and 75 percent are the 25th and 75th percentiles, respectively.

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Our main variables of interest are industrial production, the all-items consumer price index (CPI),5 and a monetary-policy or other short-term interest rate. We take special care in choosing the appropriate interest rate. All countries in the sample have some form of interbank market and report interbank rates. Many central banks, including all inflation targeters, are aiming with their open market operations to closely align a specific short-term money market rate (the operating target) with their policy rate. However, in countries where “policy rates” are not market clearing or may not present arbitrage opportunities with other short-term interest rates, they sometimes contain little, if any, information on short-term funding costs. In these cases, we pick a short-term interest rate for each country that represents a relatively liquid money market and appears representative of broader funding costs after a cross-checking with other short-term rates, such as T-bills. We only include those EMDEs in our sample where we could identify such a rate. In most cases, these rates are also relatively well aligned with the respective policy rate.

We use several variables to capture a range of relevant country characteristics that could be important for the transmission of policy/interest rate shocks, focusing in particular on the level of financial development and the type of monetary policy framework.

We consider three main measures of financial development. First, we use Sahay and other’s (2015) index of overall financial development.6 Second, we employ total private credit by banks and nonbank financial intermediaries as a percentage of GDP to measure the depth of credit markets. Finally, we use the number of ATM’s per 100,000 inhabitants to gauge financial inclusion.

We differentiate across three key dimensions according to which monetary policy frameworks can differ: whether the country has adopted inflation targeting (IT) or not, the level of central bank independence, and its level of transparency. Specifically, we use data from the IMF’s AREAER database to build a dummy variable indicating the adoption of IT.7 We also use indices of central bank independence (Garriga, 2016), and an index of augmented central bank transparency (Dincer and Eichengreen, 2014). In our sample, the first and the third measures are significantly correlated, while the second one is not correlated with the others.

We also explore other country characteristics such as the quality of country governance, the degrees of financial dollarization, capital account openness, and trade openness, the importance of food in the consumption basket, the degree of bank competition, and the importance of foreign banks. Most of these characteristics are captured by fairly standard measures sourced from the literature and public data sources. An exception is the degree of financial dollarization, which is captured by a new index of deposit and credit dollarization based on restricted IMF data. Appendix Table A.1 describes the data sources.

B. Statistical Methods

Empirically assessing the impact of monetary policy on economic activity and prices requires exogenous (controlled) variation in the policy variables. Another difficulty is that the results on the propagation of macroeconomic shocks to output and prices can be sensitive to the modelling of the transmission mechanism. In this paper, we model the transmission mechanism using Jordà’s (2005) local projection method and identify monetary policy (interest rate-) shocks with the help of a Taylor rule in the spirit of Romer and Romer (2004). High-frequency identification, a popular alternative method, is only possible for a smaller set of countries in our sample but is nevertheless explored in the robustness section below.

We use a simple Taylor-rule model to identify monetary policy (interest rate-) shocks for each country in our sample, as follows:8

Δiit=α0i+α1iEtΔyit+12+α2iEtπit+12+Σj=12α3ijΔyiti+Σj=12α4ijΔpitj+Σj=12α5ijΔneeritj+Σj=12α6ijiitj+ϵit,(1)

where Et∆yt+12 and Et∆pt+12 are the 12-month-ahead market forecasts of GDP growth and inflation, as measured by Consensus Forecasts. Ideally, we would use central bank forecasts as in Romer and Romer (2004), but these are generally not available. Implicitly, we assume that central banks and markets have the same information set.9 The variables y, p, i, and neer denote output, prices, a short-term interest rate, and the nominal effective exchange rate (in logs), respectively. The monetary policy (interest rate) shock is captured by the residual ε.10 In other words, deviations from the Taylor-type rules are intended to capture the non-systematic and unexpected part of monetary policy actions.11 Since the overall magnitude of the shocks varies considerably across countries, we standardize the residuals on a country-by-country basis. Therefore, a unit monetary policy shock signifies a one standard deviation shock.12 The Taylor rule residual may not always capture true monetary policy shocks, especially if the country does not use an interest rate as its main monetary policy tool. For those countries in our sample where central banks do not yet actively target a short-term interest rate and/or do not systematically adjust their policy rate to changes in their output/inflation forecasts, the residual ε simply measures exogenous interest rate variation (purged from any impact of lagged variation in output, prices and the exchange rate). This variation could reflect adjustments in other monetary policy instruments, such as reserve requirements or unsterilized foreign exchange interventions, but potentially also other exogenous factors.

Overall, the estimated Taylor rules display coefficients with the expected signs for inflation and output growth expectations (Table 3). The estimated coefficients on inflation expectations tend to be larger than those of expected output growth and are more often statistically significant, but monetary policy seems to be reacting similarly to both in many countries. About a quarter of the countries in the sample tighten monetary policy in response to a currency depreciation. Finally, the fit of estimated Taylor rules shows significant variation across the EMDEs in our sample, and is usually (but not always), better for IT countries (e.g., Brazil, Colombia, Mexico, and Turkey) than for other countries that have used multiple policy instruments and that are less focused on inflation and output forecasts when setting their policy instruments.

Table 3.

Taylor Rule Regressions

The table shows the estimates of the country-by-country regressions of a Taylor rule with the following specification.

Δiit=α0i+α1iEtΔyit+12+α2iEtπit+12+Σj=12α3ijΔIPiti+Σj=12α4ijΔCPIitj+Σj=12α5ijΔNEERitj+Σj=12α6ijiitj+ϵit,

where i is a short-term interest rate, Et∆yt+12 and Et∆pt+12 are the 12-month ahead market forecasts of GDP growth and inflation as measured by Consensus Forecasts, and IP, CPI, and NEER are the logs of industrial production, a consumer price index, and the nominal effective exchange rate. Heteroscedasticity and autocorrelation-robust standard errors are in parentheses. *, **, *** signify statistical significance at the 10, 5, and 1 percent level, respectively.

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Table 3.

Taylor Rule Regressions (Continued)

The table shows the estimates of the country-by-country regressions of a Taylor rule with the following specification.

Δiit=α0i+α1iEtΔyit+12+α2iEtπit+12+Σj=12α3ijΔIPiti+Σj=12α4ijΔCPIitj+Σj=12α5ijΔNEERitj+Σj=12α6ijiitj+ϵit,

where i is a short-term interest rate, Et∆yt+12 and Et∆pt+12 are the 12-month ahead market forecasts of GDP growth and inflation as measured by Consensus Forecasts, and IP, CPI, and NEER are the logs of industrial production, a consumer price index, and the nominal effective exchange rate. Heteroscedasticity and autocorrelation-robust standard errors are in parentheses. *, **, *** signify statistical significance at the 10, 5, and 1 percent level, respectively. In the table, L1 and L2 mean first and second lags of a given variable

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Table 3.

Taylor Rule Regressions (Continued)

The table shows the estimates of the country-by-country regressions of a Taylor rule with the following specification.

Δiit=α0i+α1iEtΔyit+12+α2iEtπit+12+Σj=12α3ijΔIPiti+Σj=12α4ijΔCPIitj+Σj=12α5ijΔNEERitj+Σj=12α6ijiitj+ϵit,

where i is a short-term interest rate, Et∆yt+12 and Et∆pt+12 are the 12-month ahead market forecasts of GDP growth and inflation as measured by Consensus Forecasts, and IP, CPI, and NEER are the logs of industrial production, a consumer price index, and the nominal effective exchange rate. Heteroscedasticity and autocorrelation-robust standard errors are in parentheses. *, **, *** signify statistical significance at the 10, 5, and 1 percent level, respectively. In the table, L1 and L2 mean first and second lags of a given variable.

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Table 3.

Taylor Rule Regressions (Continued)

The table shows the estimates of the country-by-country regressions of a Taylor rule with the following specification.

Δiit=α0i+α1iEtΔyit+12+α2iEtπit+12+Σj=12α3ijΔIPiti+Σj=12α4ijΔCPIitj+Σj=12α5ijΔNEERitj+Σj=12α6ijiitj+ϵit,

where i is a short-term interest rate, Et∆yt+12 and Et∆pt+12 are the 12-month ahead market forecasts of GDP growth and inflation as measured by Consensus Forecasts, and IP, CPI, and NEER are the logs of industrial production, a consumer price index, and the nominal effective exchange rate. Heteroscedasticity and autocorrelation-robust standard errors are in parentheses. *, **, *** signify statistical significance at the 10, 5, and 1 percent level, respectively. In the table, L1 and L2 mean first and second lags of a given variable.

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We then estimate the responses of output and prices to monetary policy shocks using local projections (Jordà, 2005). The local projections method directly estimates the response of macroeconomic variables to properly identified policy shocks. In doing that, it does not require the specification and estimation of the unknown true multivariate dynamic data-generating process, and is therefore more robust to misspecification than vector autoregression (VAR) models, even if it entails some loss of efficiency. Furthermore, local projections are more amenable to highly non-linear and flexible specifications—such as the interactive effects with specific country characteristics which we are interested in—than VARs.

Since virtually all the countries in our sample are small open economies, the quantification of policy/interest rate transmission—especially to prices—in EMDEs benefits from explicitly accounting for the exchange rate channel. In line with this notion, previous studies have highlighted the importance of accounting for monetary policy transmission through exchange rates in small open economies as a form of avoiding “puzzling dynamic responses” (Cushman and Zha, 1997, Bjørnland, 2008).13

Moreover, a priori it seems important to allow for potential nonlinearities in the specification that may be associated with the exchange rate channel. For example, if FX mismatches are low, the traditional interest- and exchange rate channels reinforce each other in a particularly strong manner in small open economies. Exports may be boosted not only through price effects, but also through cheaper access to trade credit. On the other hand, import compression effects may be strengthened through effects on intermediate imports. Similarly, in the presence of FX mismatches at the borrower- or lender level, it is far from clear that balance-sheet effects of exchange rate movements counteract traditional effects in linear ways. We therefore make the response of output or prices to a monetary policy shock also dependent on the contemporaneous change in the NEER by interacting the policy/interest rate shock with the change in the exchange rate. The specification is as follows.

yit+h=μih+Σj=02γjhϵ^itj+δ0hΔneerit*ϵ^it+Σj=02β1jhZitj+Σj=12β2jhiitj+Xitλh+ωith,(2)

where μih is a country fixed effect, ϵ^ is the estimated (and standardized) country-specific policy

shock, the vector Z includes contemporaneous and lagged values for y, p, and neer. The vector x contains global and country-specific controls, including the VIX, a commodity price index, the first principle component of the United States’, euro area’s, and Japan’s shadow policy rates, and country-level monthly temperature and precipitation anomalies.14 A separate regression is estimated for each horizon (h) The impulse response function for prices is obtained in the same fashion.

In (2), the coefficient associated with the contemporaneous shock (γ0h) is the response of output (or prices) when the exchange rate channel is shut down, and γ0h+σδ0h is the total output (or price) response when we also consider the amplifying effect of exchange rates. For the latter we assume that a one standard-deviation change in the NEER (a) occurs simultaneously with the policy/interest rate shock, which is somewhat comparable to a sign restriction assumption in VARs (Uhlig 2005). In addition, (2) imposes a recursiveness assumption as it assumes that Z is predetermined and that the shock has no contemporaneous effect on output or prices.15 In Appendix III, we explore simpler versions of (2), where the monetary policy shock enters without interactions with the exchange rate change.

We estimate equation (1) with country-by-country OLS and equation (2) with the fixed-effects within estimator (FE), and calculate standard errors using the Newey and West (1987) estimator where the bandwidth expands with the horizon h of the impulse response.16

IV. Results

A. Benchmark Regressions

Output strongly declines after a contractionary monetary policy shock (Figure 2.1). The estimated impulse response function shows output falling by about 0.4 percent following a contractionary one-standard deviation shock to monetary policy, regardless of the behavior of the exchange rate (Table 5). The responses are statistically significant at the 1 percent significance level, peak after about 7 months when the exchange-rate channel is active, and at 10 months when it is not. Since the shocks are standardized at the country level, a one-standard deviation shock does not mean the same across countries in terms of basis points. For the median country in our sample in terms of shock volatility, a 100-basis point rise in interest rates lowers output by 1.15 percent when considering the contemporaneous effect of the exchange rate and 1.05 when not.17 These dynamics are broadly in line with Ramey’s (2016) results for the U.S. using similar identification methods.18

Table 4.

Estimates from Benchmark Regressions

The table shows the estimated coefficients of the monetary policy shock (ε), the change in the nominal effective exchange rate (neer), and their interaction obtained based on the following specifications for output (y) and prices (p), respectively, for each quarter ahead (h).

yit+h=Σj=02γ1jhϵ^itj+γ2hΔneerit*ϵ^it+βih+Σj=02β1jhZiti+Σj=12β4jhiitj+Xitλh+ωith,andpit+h=Σj=02θ1jhϵ^itjθ2hΔneerit*ϵ^it+φih+Σj=02φ1jhZiti+Σj=12φ4jhiitj+Xitπh+ηith,θ100=0.

where the vector Z includes contemporaneous and lagged values for y, p, and neer, and i is a short-term interest rate. The vector x contains global and country-specific controls, including the VIX, a commodity price index, the first principle component of the United States’-, euro area’s-, and Japan’s shadow policy rates, and country-level monthly temperature and precipitation anomalies. Heteroscedasticity and autocorrelation-robust standard errors are in parentheses. *, **, *** signify statistical significance at the 10, 5, and 1 percent level, respectively.

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Table 5.

Effect of Monetary Policy Tightening on Output and Prices

The table shows the estimated peak effect of a monetary policy tightening shock on output and prices, in percent. The results are based on the estimates of the following specifications for output and prices, respectively.

yit+h=Σj=02γ1jhϵ^itj+γ2hΔneerit*ϵ^it+βih+Σj=02β1jhZiti+Σj=12β4jhiitj+Xitλh+ωith,andpit+h=Σj=02θ1jhϵ^itjθ2hΔneerit*ϵ^it+φih+Σj=02φ1jhZiti+Σj=12φ4jhiitj+Xitπh+ηith,θ100=0.

where ϵ^ is the estimated (and standardized) country-specific policy shock the vector Z includes contemporaneous and lagged values for y, p, and neer, and i is a short-term interest rate. The vector x contains global and country-specific controls, including the VIX, a commodity price index, the first principle component of the United States’-, euro area’s-, and Japan’s shadow policy rates, and country-level monthly temperature and precipitation anomalies. The effect without exchange rate channel measures the effect assuming in the policy shock while the exchange rate does not change. Conversely, the effect with the exchange rate channel assumes an increase in the policy shock contemporaneous to a one standard-deviation appreciation in the exchange rate (about 2.2 percent). Column (1) shows the effect on output and prices of a one standard-deviation policy shock. Column (2) shows the effect of a 100-basis point increase in the policy rate which is equivalent to a one standard-deviation increase in the non-standardized policy shock for the median country (Serbia) ranked by the volatility of the residual of the country-by-country regressions with the following specification.

Δiit=α0i+α1iEtΔyit+12+α2iEtπit+12+Σj=12α3ijΔyiti+Σj=12α4ijΔpitj+Σj=12α5ijΔneeritj+Σj=12α6ijiitj+ϵit,

where Et∆yt+12 and Et∆pt+12 are the 12-month ahead market forecasts of GDP growth and inflation as measured by Consensus Forecasts. The volatility of the residual is 35 basis points for the median country, which implies that a 100-basis points increase in interest rates is equivalent to a 2.8-standard deviation policy shock. Column (3) shows the month after the policy shock when the largest decline in output or prices is experienced. Heteroscedasticity and autocorrelation-robust p-values in parentheses. *, **, *** signify statistical significance at the 10, 5, and 1 percent level, respectively. S.D. stands for standard deviation.

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The effects on prices of a contractionary monetary policy shock are more muted, and only significant when we account for the exchange-rate channel (Figure 2.2). A one-standard-deviation hike in the policy shock accompanied by a one-standard-deviation appreciation in the NEER (about 2.2 percent increase) is associated with a 0.12 percent decline in prices after 11 months. This effect is statistically significant, but only at the 10 percent significance level. The equivalent effect of a 100-basis point interest-rate hike is roughly 0.33 percent. There is no measurable effect of tighter monetary policy when holding the exchange rate constant.

The results from these benchmark regressions suggest that the behavior of the exchange rate could be a major reason behind countries’ heterogenous responses to monetary policy shocks (e.g., Ehrmann 2000, and Kim and Roubini 2000), and potentially accounts for the price puzzle (see also Appendix III for a discussion of the price puzzle and dynamic heterogeneity). Most individual regressions of output and prices up to 20 months ahead show statistically significant estimates of the individual coefficients of the policy shock, the change in the NEER, and their interaction (Table 4). Importantly, the transmission of a monetary policy shock to output and prices is statistically different from estimates obtained when excluding the amplification mechanism through exchange rates (Figure 2).

Figure 2.
Figure 2.

Impulse Responses of Output and Prices to A Monetary Policy Shock

The charts show impulse responses of output and prices estimated with Jorda’s (2005) local projections method using panel data and country fixed effects. The dotted lines represent the lower and upper limit of 90 percent significance confidence bands and the solid lines represent the point estimate. Square markers indicate that the difference between the solid red line and the solid blue line is statistically significant at least at the 10 percent significance level (panels 5 and 6 only). Standard errors are robust to heteroscedasticity and autocorrelation.

Citation: IMF Working Papers 2020, 035; 10.5089/9781513529738.001.A001

B. Financial Development, Monetary Policy Frameworks, and Other Country Characteristics

We now test if country characteristics affect the transmission of monetary policy, assuming the exchange rate amplification channel is active. We do this by interacting of the policy shock with variables which capture financial development, policy frameworks, and other structural characteristics. The generic specification is as follows:

yit+h=μih+Σj=02γjhϵ^itj+δ0hΔneerit*ϵ^it+δ1hWit+δ2hϵ^i,t×Wit+Σj=02β1jhZitj+Σj=12β2jhiitj+Xitλh+ωith,(3)

where W is a variable representing financial sector development or some other country characteristic.

Our results show that the transmission of monetary policy to output is somewhat stronger with higher levels of financial development. The impulse responses of output to a monetary policy shock of an emerging market economy at the top 25th percentile of the index financial development or of financial inclusion is significantly different than that of one of an emerging market economy at the bottom 25th percentile (Figure 3), but there is no difference when we use the size of credit market instead, in line with Saizar and Chalk’s (2008) findings. Surprisingly, regarding the price response, at least in the near term, prices in emerging market economies at the top 25th percentile of financial development respond significantly less than in less financially developed economies (at the bottom 25th percentile). Overall, the results are somewhat sensitive to the choice of variable representing financial development.

Figure 3.