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### I. Objective, identification, and data
- Objective: Systematically analyze the strength of monetary transmission from interest rates in EMDEs.
- Identification strategy: Monetary policy (interest-rate) shocks identified by removing influence of current macroeconomic conditions and 12-month-ahead market forecasts of GDP growth and inflation (E_t Δy_{t+12} and E_t Δp_{t+12}) using a country-level Taylor-rule specification; high-frequency identification explored in robustness for a smaller subset.
- Shock normalization: Taylor-rule residual ε standardized country-by-country; a unit shock = one standard deviation.
- Estimation method: Jordà’s (2005) local projections method with country fixed effects; Newey and West (1987) standard errors with bandwidth expanding with horizon h.
- Benchmark sample length: 15 years (2003–17); extended-sample checks back to 1995 conditional on data availability.
- Data frequency: monthly for benchmark analyses.
- Core variables: industrial production (IP), all-items consumer price index (CPI), a monetary-policy or short-term interest rate, and nominal effective exchange rate (NEER, in logs).
- Sample scope: "a sample of 39 EMDEs" for main estimation of responses; "We study a sample of 40 emerging and developing economies" for data description (monthly data used).

### II. Key empirical findings and magnitudes
- Aggregate impulse-response magnitudes:
  - Output: "Output falls by 1.1 percent in response to a 100bp monetary policy shock."
  - Benchmark output responses reported: output falls by about 0.4 percent following a contractionary one-standard deviation policy shock; for the median country a 100-basis point rise in interest rates lowers output by:
    - 1.15 percent when considering the contemporaneous effect of the exchange rate.
    - 1.05 percent when not considering the contemporaneous effect of the exchange rate.
  - Prices: Conditional on a contemporaneous nominal exchange rate appreciation, "prices decline by around 0.3 percent" following a contractionary monetary policy shock.
- Peak effects (Table 5):
  - Output:
    - Without exchange rate channel: -0.3709*** (p-value 0.0001); 1 S.D. -1.0450; Peak month 10
    - With exchange rate channel: -0.4087*** (p-value 0.0005); 100 bp-equivalent -1.15157; Peak month 7
  - Prices:
    - Without exchange rate channel: -0.0111 (p-value 0.8374); 1 S.D. -0.0311; Peak month 15
    - With exchange rate channel: -0.1180* (p-value 0.0513); 100 bp-equivalent -0.3324; Peak month 11
  - Volatility note: The volatility of the residual is 35 basis points for the median country, implying that a 100-basis points increase in interest rates is equivalent to a 2.8-standard deviation policy shock.
- Exchange-rate channel and the price puzzle:
  - The "price puzzle" disappears once the response of prices is conditioned on contemporaneous change in the nominal exchange rate.
  - A one-standard-deviation 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 (statistically significant at the 10 percent level); equivalent 100-basis point effect on prices roughly 0.33 percent.
  - No measurable effect on prices when holding the exchange rate constant.
- Statistical significance:
  - Output responses statistically significant at the 1 percent level in benchmark regressions.
  - Many individual regressions up to 20 months ahead show statistically significant coefficients for the policy shock, ΔNEER, and their interaction.

### III. Heterogeneity: frameworks, financial development, and country characteristics
- Monetary policy frameworks and institutions:
  - Transmission to output is stronger in countries that have adopted inflation targeting (IT).
  - Transmission stronger where central bank is more independent (CBI index) or more transparent (CBT index).
  - Only in IT countries do prices show a significant decline in response to a tightening monetary policy shock (when exchange-rate channel active).
  - Central bank transparency results are weaker than for IT and CBI.
- Financial development and inclusion:
  - Transmission to output is somewhat stronger in more financially developed economies (top 25th percentile vs bottom 25th percentile show significantly different impulse responses).
  - Near-term price responses: economies at the top 25th percentile of financial development respond significantly less than bottom 25th percentile.
  - No significant difference when using size of credit market as the financial development measure; results sensitive to choice of FD measure.
- Interaction of IT and financial development:
  - Total effect of IT adoption does not depend strongly on the level of financial development, credit market depth, or financial inclusion.
  - For a given FD index, IT adopters show statistically significantly stronger transmission of policy shocks to output regardless of FD.
  - IT is more important for effective transmission to prices in countries with low financial development.
  - Correlation in sample: correlation between IT and the FD index is 0.28; standard deviation of FD is 0.17 for IT=0 and 0.15 for IT=1.
- Other country characteristics with no robust effects:
  - No statistically significant differences in responses by degree of capital account openness, trade openness, degree of dollarization, importance of food in CPI, degree of bank competition, or importance of foreign banks — with two exceptions:
    - Countries with greater capital account openness have more transmission to prices (noted).
    - Having more foreign banks is associated with stronger transmission to output (noted).

### IV. Mechanisms and channels
- Primary channels emphasized:
  - Interest-rate channel: higher nominal interest rates → higher real rates and user cost of capital → lower consumption and investment → downward pressure on prices.
  - Exchange-rate channel (especially in open EMDEs): higher domestic interest rate → currency appreciation (ceteris paribus) → downward pressure on tradable prices and reduced net exports.
    - Countervailing effect: currency appreciation can strengthen balance sheets in the presence of currency mismatches and stimulate credit and activity.
- Additional channels: bank lending channel (liquidity drain, deposit effects, bank profits/net worth), wealth channel, broad credit channel, real balances channel, expectations channel.
- Nonlinearities: exchange-rate and interest-rate effects can interact nonlinearly; specification interacts policy shock with contemporaneous ΔNEER and treats Z (contemporaneous and lagged y, p, neer) as predetermined.

### V. Identification, robustness, and methodological checks
- Taylor-rule residual details:
  - Country-by-country OLS estimation of Taylor rule; two lags of policy shock included in local-projection specification.
  - For countries with currency board (Bulgaria) or official dollarization (Ecuador), monthly change in policy rate of reference currency used.
  - Assuming AR(1) for Taylor-rule errors: of 40 countries, only six show evidence of autocorrelated shocks at the 5 percent significance level (Ljung and Box Q statistic).
- Robustness checks and alternative identifications:
  - Benchmark regressions tried with core CPI; results similar.
  - Subsample of countries with interest-rate-based monetary policy: results broadly align though less precise.
  - High-frequency identification (HFI) using changes in two-year government bond yields on announcement days produced IRFs that mostly show no significant response and often wrong sign; HFI not pursued further.
  - Adding contemporaneous ΔNEER to Taylor rule, time-varying Taylor rule (5-year rolling window), and extending sample back to 1995 produce qualitatively similar patterns though statistical significance and smoothness of IRFs can weaken; price puzzle may reemerge with longer sample.
  - Replacing NEER with bilateral USD rate, purging NEER for FX interventions, and controlling for systemic crises yield very similar benchmark results.
- Endogeneity of IT adoption:
  - Instrumental-variables pooled logit first stage using excluded instruments: ratios of recent volatility (output, inflation, NEER), level of political accountability, and cumulative number of sovereign defaults since 1960.
  - Fitted IT dummy = 1 when predicted probability > 0.5.
  - Two-step estimation implemented in bootstrap with 399 replications to obtain correct standard errors.
  - Result: fitted IT supports causal effect—adopting IT strengthens transmission to output; non-IT countries show no transmission to output.
- Inference and figures:
  - Local projections preferred over VARs for flexibility and robustness to misspecification.
  - Figures use 90 percent confidence bands; square markers indicate statistical differences at least at the 10 percent level; bias-corrected bootstrapped confidence intervals used for some inference.

### VI. Dataset summary and selected descriptive statistics (exact reported values)
- Consumer Price Index (log): N 6,030; Mean 4.6020; Standard deviation 0.2793; Minimum 3.225; Median 4.636; Maximum 6.066
- Industrial Production Index (log): N 6,030; Mean 4.6670; Standard deviation 0.2553; Minimum 3.597; Median 4.638; Maximum 5.682
- Nominal Effective Exchange Rate (log): N 6,030; Mean 3.8612; Standard deviation 2.294; Minimum -5.902; Median 4.581; Maximum 5.187
- Policy Rate: N 6,030; Mean 0.064; Standard deviation 0.045; Minimum -0.001; Median 0.058; Maximum 0.453
- Inflation Targeting Dummy: N 6,030; Mean 0.442; Standard deviation 0.497; Minimum 0; Median 0; Maximum 1
- Financial Development Index: N 4,295; Mean 0.395; Standard deviation 0.164; Minimum 0.040; Median 0.383; Maximum 0.745
- Private Credit by Deposit Banks: N 5,185; Mean 48.694; Standard deviation 32.404; Minimum 9.27; Median 39.42; Maximum 150.21
- ATMs per 100,000 Adults: N 4,821; Mean 43.265; Standard deviation 30.814; Minimum 0.53; Median 7.19; Maximum 185.17
- Inflation Forecast: N 6,030; Mean 5.506; Standard deviation 3.518; Minimum -0.538; Median 4.948; Maximum 37.167
- GDP Growth Forecast: N 6,030; Mean 4.074; Standard deviation 2.327; Minimum -7.201; Median 4.10; Maximum 22.867
- CBOE VIX Index: N 6,030; Mean 2.885; Standard deviation 0.354; Minimum 2.328; Median 2.811; Maximum 4.093
- Commodity Price Index: N 6,030; Mean 8.417; Standard deviation 0.358; Minimum 7.609; Median 8.486; Maximum 9.265
- Global Monetary Policy (1PC): N 6,030; Mean -0.811; Standard deviation 1.458; Minimum -3.3; Median -0.786; Maximum 1.851
- Temperature Anomaly: N 6,030; Mean 0.91; Standard deviation 1.342; Minimum -7.612; Median 0.844; Maximum 9.093
- Precipitation Anomaly: N 6,030; Mean -0.009; Standard deviation 1.153; Minimum -6.51; Median -0.071; Maximum 1.33
- Change in 2Y yield on Announcement Dates: N 5,801; Mean -0.003; Standard deviation 0.061; Minimum -0.264; Median 0.00; Maximum 0.185
- Central Bank Independence Index: N 6,030; Mean 0.483; Standard deviation 0.167; Minimum 0.149; Median 0.489; Maximum 0.784
- Dollarization Index: N 4,884; Mean 67.615; Standard deviation 0.422; Minimum 0.123; Median 62.572; Maximum 224.264
- Bank Concentration (%): N 5,281; Mean 53.773; Standard deviation 14.537; Minimum 20.48; Median 52.31; Maximum 100

### VII. Policy implications and caveats
- Main policy implication: Modern monetary policy frameworks—particularly inflation targeting combined with central bank independence and transparency—strengthen transmission of interest-rate shocks to output and prices in EMDEs, and appear more important for effective transmission than financial development or other structural characteristics in this sample.
- Secondary implication: Less-than-fully developed financial markets, institutional deficiencies, and financial dollarization may be less important obstacles to adopting modern, interest-rate-based frameworks—provided the exchange rate can play its role in transmission.
- Caveats:
  - Causal interpretation depends on correct identification of monetary policy shocks; Taylor-rule residuals may still contain systematic components.
  - Except for IT (where endogeneity was modeled), other structural factors are treated as predetermined but may respond to monetary policy over time.
  - Further research needed to address selection problems from potentially endogenous structural characteristics and to explore structural changes affecting transmission.

*Source: wpiea2020035-print-pdf (excerpts from the IMF Working Paper chapter and appendices).*

### References .............................................................................................................

### wpiea2020035-print-pdf - References

### I. Introduction — purpose and approach
- Objective: Systematically analyze the strength of monetary transmission from interest rates in EMDEs.
- Identification strategy: Monetary policy (interest-rate) shocks are identified by removing the influence of current macroeconomic conditions and expected future GDP growth and inflation.
- Estimation method: Jordà’s (2005) local projections method.
- Sample scope:
  - "a sample of 39 EMDEs" for the main estimation of responses of output and prices to interest-rate shocks.
  - "We study a sample of 40 emerging and developing economies" for data description (monthly data used in benchmark analyses).
- Core variables: industrial production, all-items consumer price index (CPI), and a monetary-policy or other short-term interest rate chosen to represent a relatively liquid money market and broader funding costs.

### II. Key empirical findings
- General effectiveness of interest-rate transmission:
  - "Output falls by 1.1 percent in response to a 100bp monetary policy shock."
  - Conditional on a contemporaneous nominal exchange rate appreciation, "prices decline by around 0.3 percent" following a contractionary monetary policy shock.
  - The "price puzzle" (Sims 1992; Eichenbaum 1992) disappears once the response of prices is conditioned on the contemporaneous change in the nominal exchange rate.
- Role of monetary policy frameworks:
  - Transmission to output is stronger in countries that have adopted inflation targeting (IT).
  - Transmission to output is stronger where the central bank is more independent or more transparent.
  - These findings are robust to controls for competing explanations (such as overall quality of country governance) and to addressing the endogeneity of IT adoption.
- Financial development and other structural characteristics:
  - Transmission to output appears to be somewhat stronger in more financially developed economies.
  - The opposite is true for transmission to prices (i.e., greater financial development is associated with weaker transmission to prices).
  - Monetary transmission also works in countries with high levels of financial dollarization.
  - Other characteristics—capital account openness, trade openness, and bank competition—do not appear to have significant effects on the transmission mechanism.

### III. Mechanisms and channels of monetary transmission
- Primary channels highlighted:
  - Interest-rate channel: higher nominal interest rates → higher real rates and user cost of capital → lower consumption and investment → downward pressure on prices.
  - Exchange-rate channel (especially relevant in open EMDEs): higher domestic interest rate → currency appreciation (ceteris paribus) → downward pressure on prices of tradables and lower aggregate demand via reduced net exports.
    - Countervailing effect in presence of currency mismatches: an appreciating currency may strengthen balance sheets and stimulate credit and activity.
- Other channels noted:
  - Bank lending (narrow credit) channel: tight monetary policy drains liquidity and deposits → cuts in lending if banks face frictions issuing uninsured liabilities; high short-term rates with a downward-sloping yield curve depress bank profits and net worth.
  - Wealth channel, broad credit channel, real balances channel, expectations channel.
- Nonlinear interactions: exchange-rate and interest-rate effects can interact nonlinearly.

### IV. Data, variables, and country characteristics
- Data frequency: monthly for benchmark analyses.
- Interest rate selection:
  - Prefer short-term interbank or market rates that represent a relatively liquid money market and broader funding costs.
  - When official policy rates are not market clearing or lack arbitrage with short-term rates, an alternative representative short-term rate is chosen.
- Measures of financial development used:
  - Sahay and others (2015) index of overall financial development.
  - Total private credit by banks and nonbank financial intermediaries as a percentage of GDP.
  - Number of ATM’s per 100,000 inhabitants as a gauge of financial inclusion.
- Monetary policy framework indicators:
  - Dummy variable for adoption of inflation targeting (IT) from IMF’s AREAER database.
  - Central bank independence index (Garriga, 2016).
  - Augmented central bank transparency index (Dincer and Eichengreen, 2014).
  - In the sample, the IT dummy and the transparency index are significantly correlated; the independence index is not correlated with the other two.
- Additional country characteristics explored:
  - Quality of country governance.
  - Degree of financial dollarization (captured by a new index of deposit and credit dollarization based on restricted IMF data).
  - Capital account openness.
  - Trade openness.
  - Importance of food in the consumption basket.
  - Degree of bank competition.
  - Importance of foreign banks.

### V. Robustness and related literature notes
- Consistency with prior studies: findings align with Abuka et al (2019), Berg et al (2013), and Willems (2018) that find evidence of monetary transmission in developing countries following large monetary contractions.
- Conflicting prior evidence: some studies (e.g., Mishra, Montiel, and Spilimbergo, 2012) found weak transmission due to underdeveloped financial markets; others (Berg and others, 2013) argue detection failures are driven by data and methodological problems.
- Additional methodological checks mentioned:
  - Benchmark regressions were also tried with core CPI; results were similar, but all-items CPI is used to ensure a larger sample.
  - Endogeneity of IT adoption is addressed in robustness checks.

*Source: wpiea2020035-print-pdf - References (excerpts from the PDF chapter).*

### Appendix Table A.1 describes the data sources.

### Appendix Table A.1 describes the data sources (Appendix: Statistical Methods and Results)

### Statistical methods and identification
- Identification strategy
  - Monetary policy (interest rate) shocks are identified using a country-level Taylor-rule specification in the spirit of Romer and Romer (2004).
  - High-frequency identification is explored in robustness but is only possible for a smaller set of countries.
  - The monetary policy (interest rate) shock is captured by the residual ε from the Taylor-rule regression; deviations from the Taylor-type rule are intended to capture the non-systematic and unexpected part of monetary policy actions.
  - Residuals are standardized on a country-by-country basis; a unit monetary policy shock signifies a one standard deviation shock.
  - For countries with a currency board (Bulgaria) or officially dollarized (Ecuador), the monthly change in the policy interest rate of the reference currency (euro and dollar, respectively) is used.

- Taylor-rule inputs and assumptions
  - The Taylor-rule uses 12-month-ahead market forecasts of GDP growth and inflation (E_t Δy_{t+12} and E_t Δp_{t+12}) from Consensus Forecasts, assuming central bank and market forecasts share the same information set.
  - Variables: y (output), p (prices), i (short-term interest rate), neer (nominal effective exchange rate, in logs).
  - The Taylor-rule residual may not capture true policy shocks for countries that do not use an interest-rate instrument systematically; in such cases ε measures exogenous interest rate variation purged of lagged output, prices, and exchange rate effects.

- Estimation details
  - Equation (1) (Taylor rule) is estimated by country-by-country OLS.
  - Local projections (Jordà, 2005) are used to estimate responses of output and prices to identified shocks; this method directly estimates impulse responses without specifying the full multivariate dynamic process and is more robust to misspecification than VARs.
  - Equation (2) (local-projection specification) is estimated with fixed-effects within estimator (FE).
  - Standard errors use Newey and West (1987) estimator with bandwidth expanding with the horizon h.
  - Two lags of the policy/interest rate shock are included in (2); this does not affect the definition of IRFs.
  - In practice, assuming an AR(1) structure for the Taylor-rule errors, of the 40 countries in the sample, only six show evidence of having autocorrelated shocks at the 5 percent significance level (Ljung and Box Q statistic).

- Accounting for the exchange rate channel and nonlinearities
  - Given most sample countries are small open economies, the specification explicitly accounts for exchange rate transmission by interacting the policy/interest rate shock with contemporaneous change in the NEER.
  - The vector Z includes contemporaneous and lagged values for y, p, and neer and is treated as predetermined (recursiveness assumption); the shock is assumed to have no contemporaneous effect on output or prices.
  - The contemporaneous coefficient (γ^0_h) is the response when the exchange-rate channel is shut down; the total response when accounting for exchange-rate amplification is γ^0_h + σ δ^0_h, where σ denotes a one standard-deviation change in the NEER occurring simultaneously with the policy shock (the authors assume σ occurs simultaneously, akin to a sign restriction).
  - The vector x contains global and country-specific controls: VIX, a commodity price index, the first principal component of the United States’, euro area’s, and Japan’s shadow policy rates, and country-level monthly temperature and precipitation anomalies.

### Benchmark empirical results
- Output responses
  - Output falls by about 0.4 percent following a contractionary one-standard deviation monetary policy shock, regardless of exchange-rate behavior.
  - Responses are statistically significant at the 1 percent significance level.
  - Peak effects occur after about 7 months when the exchange-rate channel is active and at 10 months when it is not.
  - For the median country in 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.
    - 1.05 percent when not considering the contemporaneous effect of the exchange rate.
  - These dynamics are broadly consistent with Ramey (2016) results for the U.S.

- Price responses
  - Effects on prices are muted and only significant when the exchange-rate channel is active.
  - A one-standard-deviation 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 at the 10 percent significance level.
  - The equivalent effect of a 100-basis point interest-rate hike on prices is roughly 0.33 percent.
  - There is no measurable effect on prices when holding the exchange rate constant.

- Implications
  - Heterogeneous exchange-rate behavior across countries may explain heterogeneous responses to monetary policy shocks and partially account for the price puzzle.
  - Most individual regressions up to 20 months ahead show statistically significant coefficients for the policy shock, NEER change, and their interaction.
  - Transmission estimates that include the exchange-rate amplification mechanism are statistically different from those that exclude it.

### Heterogeneity: financial development, monetary policy frameworks, and country characteristics
- Financial development
  - Transmission of monetary policy to output is somewhat stronger with higher financial development.
  - Emerging market economies at the top 25th percentile of the financial development index or financial inclusion show impulse responses of output that are significantly different from those at the bottom 25th percentile.
  - No significant difference is found when using the size of credit market as the financial development measure.
  - Near-term price responses: emerging market economies at the top 25th percentile of financial development respond significantly less than less financially developed economies (bottom 25th percentile).
  - Results are sensitive to the choice of financial development measure.

- Monetary policy frameworks and institutional features
  - Presence of inflation targeting (IT), higher central bank independence (CBI), and greater central bank transparency (CBT) are associated with significantly stronger transmission of interest rate shocks to output.
  - Only in IT countries do prices show a significant decline in response to a tightening monetary policy shock (difference between IT and non-IT countries is not statistically significant for prices).
  - Central bank transparency results are weaker than for IT and CBI.

- Interaction of financial development and IT
  - A specification interacting a financial development measure (W1) with IT adoption (W2) and the policy shock finds:
    - The total effect of IT adoption does not depend strongly on the level of financial development, credit market depth, or financial inclusion.
    - For a given level of financial development (FD index), countries that have adopted IT show a statistically significantly stronger transmission of monetary policy shocks to output regardless of financial development or inclusion.
    - IT is more important for effective transmission of monetary policy shocks to prices in countries with low financial development.
  - Conclusion: having an IT framework appears more important for transmission than financial development in this sample.
  - Correlation note: In the sample, the correlation between IT and the FD index is 0.28; standard deviation of FD is 0.17 for IT=0 and 0.15 for IT=1.

- Other country characteristics
  - Monetary policy appears to affect output more strongly in jurisdictions with more robust governance (more accountable political systems, more effective governments, better regulation); no discernible difference for transmission to prices.
  - Results on IT hold once controlling for country governance; the transmission to output and prices remains stronger in IT adopters regardless of governance quality.

### Estimation robustness and specification notes
- Local projections preferred over VARs for robustness to misspecification and flexibility to allow nonlinear interactions.
- The authors explore simpler versions of the specification where the monetary policy shock enters without interactions with the exchange rate (Appendix III).
- Newey-West standard errors account for autocorrelation and heteroscedasticity; bandwidth expands with horizon h.
- The paper includes global controls (VIX, commodity price index, shadow policy rate principal component) and weather anomalies due to their influence on CPI in developing economies.

*Source: wpiea2020035-print-pdf - Appendix Table A.1 describes the data sources.*

### 1. Output responses by monetary policy framework and

### 1. Output responses by monetary policy framework and financial development

### Main findings on transmission of monetary policy
- When the role of exchange rates in monetary policy transmission is considered, there is significant transmission of interest-rate shocks to output and prices.
- Having a modern monetary policy framework (defined as IT, an independent central bank, and transparent monetary policy) is associated with stronger transmission to output and prices, and more so than financial development or other country characteristics.
- For countries that have not adopted inflation targeting, the authors find no transmission of monetary policy shocks to output in baseline estimates.
- For prices, no effect is found when the exchange rate channel is shut down, regardless of IT adoption; however, when the exchange rate channel is active, prices fall more under IT and the peak response is statistically different than zero.
- The paper fails to find statistically significantly different responses of output or prices to interest-rate shocks according to:
  - degree of capital account openness,
  - trade openness,
  - degree of dollarization,
  - importance of food in the consumption basket,
  - degree of bank competition,
  - importance of foreign banks.
- Two noted exceptions:
  - Countries with greater capital account openness have more transmission to prices (Figure A.2.3, panel 2).
  - Having more foreign banks is associated with stronger transmission to output (Figure A.2.4, panel 5).

### Endogeneity of inflation-targeting (IT) adoption — robustness check
- Concern: IT adoption may be endogenous (countries adopt IT in response to macro conditions and perceived policy effectiveness), which could bias estimates through reverse causality.
- Instrumental-variables approach:
  - First stage: pooled logit for adoption of IT using excluded instruments:
    - ratio of output volatility in the past three years relative to that in the preceding three years,
    - analogous ratios for volatilities of inflation and NEER,
    - level of accountability of the political system,
    - cumulative number of sovereign defaults since 1960.
  - Predicted probability used to create a fitted IT dummy: takes value one whenever the fitted probability is greater than 0.5.
  - Two-step estimation implemented in a bootstrap with 399 replications to obtain correct standard errors.
- Result: Estimates using fitted IT support a causal effect of IT on transmission — adopting IT strengthens the transmission of interest-rate shocks to output even after accounting for endogenous adoption. For non-IT countries, no transmission to output is found.

### Alternative identification of monetary policy shocks — robustness checks
- Concern: The Taylor-rule residual may not capture true monetary policy shocks, especially where policy is not interest-rate-based.
- Subsample check: Results for countries with interest-rate-based monetary policy broadly align with the larger sample, though less precise due to smaller sample and lower heterogeneity (Figure A.2.6).
- High-frequency identification (HFI) approach: Using changes in two-year government bond interest rates on announcement days (per Gilchrist and others 2015 and Appendix V) produced IRFs that mostly show no significant response of output and prices and often the wrong sign; authors do not pursue HFI further.
- Recursiveness/exchange rate assumptions: Adding contemporaneous change of the nominal effective exchange rate to the Taylor rule produced qualitatively similar results (not reported).
- Time-varying Taylor rule (5-year rolling window): flavor of results remains but statistical significance for importance of IT weakens (Figure A.2.7).
- Extending sample backwards to 1995 (conditional on data availability) with time-varying Taylor rules: responses of output similar to benchmark; only IT countries show statistically significant transmission; IRFs become less smooth, the price puzzle reemerges, and tests for frameworks or financial development lose statistical significance — structural changes and crises likely affect transmission.

### Other robustness exercises
- Exchange rate measure: Replacing the NEER with the bilateral exchange rate against the U.S. dollar does not change results (Avdjiev and others 2019).
- FX interventions: The authors purge NEER changes from the effect of FX interventions (using public intervention data or a proxy based on monthly changes in central banks’ net foreign assets adjusted for valuation changes) and re-estimate; results are very similar to benchmark (results available from authors).
- Financial crisis contamination: Re-estimating benchmark regressions controlling for systemic crises (Laeven and Valencia 2018 dates) yields results very similar to Figures 2, 3, and 4 (results available from authors).
- HFI and Taylor-rule approaches share the limitation of disregarding the central bank’s private information.

### Methodological and data notes with exact numeric values
- Benchmark sample length: 15 years (2003–17).
- Extended sample: back to 1995, conditional on data availability.
- First-stage fitted IT dummy threshold: fitted probability > 0.5.
- Bootstrap replications used to obtain correct standard errors: 399 replications.
- Cumulative number of sovereign defaults instrument uses defaults since 1960.
- Authors report lack of significance across many heterogeneity dimensions except the two exceptions noted above.

### Conclusion and policy implications
- Primary conclusion: Modern monetary policy frameworks—particularly inflation targeting combined with central bank independence and transparency—strengthen the transmission of interest-rate shocks to output and prices in EMDEs, more than financial development or other structural characteristics.
- Policy implication: Less-than-fully developed financial markets, institutional deficiencies, and financial dollarization are likely less important obstacles to adopting modern, interest-rate-based monetary policy frameworks than sometimes argued—provided the exchange rate is allowed to play its role in monetary policy transmission.
- Caveats:
  - Causal interpretation depends on correct identification of monetary policy shocks; shocks may still contain systematic components.
  - Except for IT (where endogeneity was modeled), other structural factors are assumed predetermined but may respond to monetary policy over time (e.g., financial development and dollarization).
  - Further research needed to address selection problems from potentially endogenous structural characteristics and to explore structural changes affecting transmission.

*Source: excerpt from the provided IMF chapter/section content.*

### REFERENCES

### REFERENCES

### Bibliographic sources (selection)
- Abuka, Charles, Ronnie K. Alinda, Camelia Minoiu, José-Luis Peydró, and Andrea F. Presbitero. 2019. “Monetary Policy and Bank Lending in Developing Countries: Loan Applications, Rates, and Real Effects.” Journal of Development Economics 139: 185–202.
- Acosta Ormaechea, Santiago, and David Coble. 2014. “Monetary Transmission in Dollarized and Non-Dollarized Economies: The Cases of Chile, New Zealand, Peru, and Uruguay,” in: Gelos, Gaston and Alejandro Werner (eds.): “Managing Economic Volatility in Latin America,” (Washington: International Monetary Fund).
- Adler, Gustavo, Noemie Lisack, and Rui Mano. 2019. “Unveiling the Effects of Foreign Exchange Intervention: A Panel Approach.” Emerging Markets Review, Vol. 40: 100620.
- Auerbach, Alan J., and Yuriy Gorodnichenko. 2012. “Measuring the Output Responses to Fiscal Policy.” American Economic Journal: Economic Policy 4, No. 2: 1–27.
- Avdjiev, Stefan, Valentina Bruno, Catherine Koch, and Hyun Song Shin. 2019. “The Dollar Exchange Rate as a Global Risk Factor: Evidence from Investment.” IMF Economic Review 67, No. 1: 151–173.
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### Key dataset summary (Table 1 — selected statistics)
- Consumer Price Index (log): N 6,030; Mean 4.6020; Standard deviation 0.2793; Minimum 3.225; Median 4.636; Maximum 6.066
- Industrial Production Index (log): N 6,030; Mean 4.6670; Standard deviation 0.2553; Minimum 3.597; Median 4.638; Maximum 5.682
- Nominal Effective Exchange Rate (log): N 6,030; Mean 3.8612; Standard deviation 2.294; Minimum -5.902; Median 4.581; Maximum 5.187
- Policy Rate: N 6,030; Mean 0.064; Standard deviation 0.045; Minimum -0.001; Median 0.058; Maximum 0.453
- Inflation Targeting Dummy: N 6,030; Mean 0.442; Standard deviation 0.497; Minimum 0; Median 0; Maximum 1
- Financial Development Index: N 4,295; Mean 0.395; Standard deviation 0.164; Minimum 0.040; Median 0.383; Maximum 0.745
- Private Credit by Deposit Banks: N 5,185; Mean 48.694; Standard deviation 32.404; Minimum 9.27; Median 39.42; Maximum 150.21
- ATMs per 100,000 Adults: N 4,821; Mean 43.265; Standard deviation 30.814; Minimum 0.53; Median 7.19; Maximum 185.17
- Inflation Forecast: N 6,030; Mean 5.506; Standard deviation 3.518; Minimum -0.538; Median 4.948; Maximum 37.167
- GDP Growth Forecast: N 6,030; Mean 4.074; Standard deviation 2.327; Minimum -7.201; Median 4.10; Maximum 22.867
- CBOE VIX Index: N 6,030; Mean 2.885; Standard deviation 0.354; Minimum 2.328; Median 2.811; Maximum 4.093
- Commodity Price Index: N 6,030; Mean 8.417; Standard deviation 0.358; Minimum 7.609; Median 8.486; Maximum 9.265
- Global Monetary Policy (1PC): N 6,030; Mean -0.811; Standard deviation 1.458; Minimum -3.3; Median -0.786; Maximum 1.851
- Temperature Anomaly: N 6,030; Mean 0.91; Standard deviation 1.342; Minimum -7.612; Median 0.844; Maximum 9.093
- Precipitation Anomaly: N 6,030; Mean -0.009; Standard deviation 1.153; Minimum -6.51; Median -0.071; Maximum 1.33
- Change in 2Y yield on Announcement Dates: N 5,801; Mean -0.003; Standard deviation 0.061; Minimum -0.264; Median 0.00; Maximum 0.185
- Central Bank Independence Index: N 6,030; Mean 0.483; Standard deviation 0.167; Minimum 0.149; Median 0.489; Maximum 0.784
- Dollarization Index: N 4,884; Mean 67.615; Standard deviation 0.422; Minimum 0.123; Median 62.572; Maximum 224.264
- Bank Concentration (%): N 5,281; Mean 53.773; Standard deviation 14.537; Minimum 20.48; Median 52.31; Maximum 100

### Country-level summary examples (Table 2 — selected entries)
- Argentina (Panel A): Financial Development Mean 0.42; S.D. 0.05. Credit-to-GDP Mean 11.67; S.D. 1.46. Financial Inclusion Mean 0.35; S.D. 3.75.
- Brazil (Panel A): Financial Development Mean 0.61; S.D. 0.08. Credit-to-GDP Mean 2.19; S.D. 16.95. Financial Inclusion Mean 11.88; S.D. 4.24.
- China (Panel A): Financial Development Mean 0.70; S.D. 0.06. Credit-to-GDP Mean 16.51; S.D. 11.64. Financial Inclusion Mean 109.71; S.D. 121.23.
- Mexico (Panel A): Financial Development Mean 0.48; S.D. 0.04. Credit-to-GDP Mean 1.68; S.D. 5.02. Financial Inclusion Mean 6.79; S.D. 4.64.
- Panel B reports country-level indicators on inflation targeting adoption and central bank transparency and independence for multiple countries (entries include N, Mean, Adoption date, Central Bank Transparency Mean, Central Bank Independence Mean, etc.).

### Taylor rule regression evidence (Table 3 — stylized findings)
- Country-by-country Taylor rule regressions use: short-term interest rate i; 12-month ahead market forecasts E_t Δy_{t+12} and E_t Δp_{t+12} from Consensus Forecasts; logs of IP, CPI, and NEER. Heteroscedasticity and autocorrelation-robust standard errors reported. *, **, *** denote significance at the 10, 5, and 1 percent levels.
- Examples of estimated coefficients (selected cells preserved exactly as reported):
  - Argentina (Panel A): Policy rate (lagged once) -0.3860*** (0.0738); Policy rate (lagged twice) 0.2991*** (0.0730); Observations 173; R-squared 0.235.
  - Brazil (Panel A): Policy rate (lagged once) 0.7556*** (0.0465); Policy rate (lagged twice) -0.7753*** (0.0444); Observations 171; R-squared 0.404.
  - India (Panel D): CPI forecast 0.0018** (0.0008); GDP forecast 0.0001** (0.0000); Observations 173; R-squared 0.401.
- Full table reports coefficients and standard errors for CPI forecast, GDP forecast, lagged policy rates (L1, L2), Δlog CPI (L1, L2), Δlog IP (L1, L2), Δlog NEER (contemporaneous and lags), constants, Observations, and R-squared across 40 country regressions.

### Benchmark regression estimates for policy shock, ΔNEER, and interaction (Table 4)
- Specification estimates reported for leads h = 1 to 20, for Output and Prices. Selected exact point estimates (policy shock; Δ NEER; policy shock x Δ NEER) for Output and Prices by lead:
  - Lead 1 (Output): Policy shock -0.0006 (0.0006); Δ NEER 0.0286 (0.0401); Policy shock x Δ NEER -0.0056 (0.0215)
  - Lead 1 (Prices): Policy shock 0.0002** (0.0001); Δ NEER -0.0028 (0.0172); Policy shock x Δ NEER -0.0088 (0.0065)
  - Lead 2 (Output): Policy shock -0.0014** (0.0006); Δ NEER -0.0652 (0.0424); Policy shock x Δ NEER 0.0047 (0.0238)
  - Lead 2 (Prices): Policy shock 0.0003** (0.0002); Δ NEER -0.0378* (0.0207); Policy shock x Δ NEER -0.0232* (0.0133)
  - Lead 3 (Output): Policy shock -0.0015** (0.0007); Δ NEER -0.0538 (0.0575); Policy shock x Δ NEER -0.0101 (0.0289)
  - Lead 3 (Prices): Policy shock 0.0005** (0.0002); Δ NEER -0.0419** (0.0201); Policy shock x Δ NEER -0.0289* (0.0152)
  - Lead 4 (Output): Policy shock -0.0015** (0.0007); Δ NEER 0.0302 (0.0406); Policy shock x Δ NEER 0.0452** (0.0193)
  - Lead 4 (Prices): Policy shock 0.0005* (0.0003); Δ NEER -0.0482** (0.0203); Policy shock x Δ NEER -0.0313** (0.0160)
  - (Table continues through Lead 20 with point estimates and robust standard errors for both Output and Prices; full entries are reported in the source.)
- Notes: Vector Z includes contemporaneous and lagged values for y, p, and neer; vector x includes VIX, commodity price index, first principal component of US-, euro area-, and Japan shadow policy rates, and country-level monthly temperature and precipitation anomalies. Heteroscedasticity and autocorrelation-robust standard errors shown in parentheses. *, **, *** signify significance at 10, 5, and 1 percent, respectively.

### Peak effects of a monetary policy tightening (Table 5)
- Peak effects reported in percent; columns show (1) effect of a one standard-deviation policy shock, (2) effect of a 100-basis point increase in the policy rate (median-country equivalent), and (3) month when the peak decline occurs.
- Output:
  - Without exchange rate channel: -0.3709*** (p-value 0.0001) ; 1 S.D. -1.0450 ; Peak month 10
  - With exchange rate channel: -0.4087*** (p-value 0.0005) ; 100 bp-equivalent -1.15157 ; Peak month 7 (implicitly reported in table layout)
- Prices:
  - Without exchange rate channel: -0.0111 (p-value 0.8374) ; 1 S.D. -0.0311 ; Peak month 15
  - With exchange rate channel: -0.1180* (p-value 0.0513) ; 100 bp-equivalent -0.3324 ; Peak month 11
- Additional notes: The volatility of the residual is 35 basis points for the median country, implying that a 100-basis points increase in interest rates is equivalent to a 2.8-standard deviation policy shock. Heteroscedasticity and autocorrelation-robust p-values in parentheses. *, **, *** signify statistical significance at the 10, 5, and 1 percent level, respectively.

*IMF Working Paper — references, tables, and summarized empirical results as presented in the source PDF.*

### Appendix I. Country List and Data Sources

### Appendix I. Country List and Data Sources

### Country sample
- Argentina
- Armenia
- Azerbaijan
- Bangladesh
- Bolivia
- Botswana
- Brazil
- Bulgaria
- Chile
- China
- Colombia
- Costa Rica
- Croatia
- Dominican Republic
- Ecuador
- Egypt
- Guatemala
- Honduras
- Hungary
- India
- Indonesia
- Jordan
- Kazakhstan
- Kenya
- Lebanon
- Macedonia
- Malaysia
- Mexico
- Pakistan
- Paraguay
- Peru
- Philippines
- Poland
- Romania
- Russia
- South Africa
- Serbia
- Sri Lanka
- Taiwan
- Thailand
- Tunisia
- Turkey
- Uganda
- Ukraine
- Uruguay
- Vietnam

### Table A.1 — Variables: definitions and sources
- Consumer Price Index (log)
  - Definition: Log of the all-items consumer price index
  - Sources: IMF International Financial Statistics
- Industrial Production Index (log)
  - Definition: Log of total industrial production index
  - Sources: IMF International Financial Statistics
- Nominal Effective Exchange Rate (log)
  - Definition: Log of a trade-weighted nominal exchange rate index
  - Sources: IMF International Financial Statistics
- Policy Rate
  - Definition: A monetary policy interest rate or the short-term interbank rate
  - Sources: IMF International Financial Statistics
- Inflation Targeting Dummy
  - Definition: Dummy variable which takes value 1 (0) when a country is (not) using inflation targeting as its monetary policy framework in a given month.
  - Sources: IMF AREAER database and central bank websites
- Financial Development Index
  - Definition: Summary of how developed financial institutions and financial markets are in terms of depth, access, and efficiency at the annual frequency.
  - Sources: Sahay and others (2015), available at https://data.imf.org/?sk=F8032E80-B36C-43B1-AC26-493C5B1CD33B
- Financial Development Index (Institutions)
  - Definition: Summary of how developed financial institutions are in terms of depth, access, and efficiency at the annual frequency.
  - Sources: Sahay and others (2015), available at https://data.imf.org/?sk=F8032E80-B36C-43B1-AC26-493C5B1CD33B
- Financial Development Index (Markets)
  - Definition: Summary of how developed financial markets are in terms of depth, access, and efficiency at the annual frequency.
  - Sources: Sahay and others (2015), available at https://data.imf.org/?sk=F8032E80-B36C-43B1-AC26-493C5B1CD33B
- Private Credit by Deposit Banks
  - Definition: Funding provided to the private sector by domestic money banks as a share of GDP.
  - Sources: Global Financial Development Database (World Bank)
  - Note: Domestic money banks are commercial banks and other financial institutions that accept transferable deposits.
- ATMs per 100,000 Adults
  - Definition: Number of ATMs per 100,000 adults.
  - Sources: Global Financial Development Database (World Bank)
- Inflation Forecast
  - Definition: The weighted average of current and next year’s inflation consensus forecast. Weights of current (next) year’s forecast decrease (increase) from 11/12 (1/12) to 1/12 (11/12) between January and December of each year.
  - Sources: Consensus Forecasts
- GDP Growth Forecast
  - Definition: The weighted average of current and next year’s GDP growth consensus forecast. Weights of current (next) year’s forecast decrease (increase) from 11/12 (1/12) to 1/12 (11/12) between January and December of each year.
  - Sources: Consensus Forecasts
- CBOE VIX Index
  - Definition: The Chicago Board of Exchange’s S&P500 implicit volatility index.
  - Sources: Thomson Reuters Datastream
- Commodity Price Index
  - Definition: Goldman Sachs Global Commodity Price Index.
  - Sources: Thomson Reuters Datastream
- Global Monetary Policy (1PC)
  - Definition: The first principal component of Krippner’s (2015) shadow policy rates for the euro area, Japan, United Kingdom, and the United States.
  - Sources: Krippner (2015), available at https://www.rbnz.govt.nz/research-and-publications/research-programme/additional-research/measures-of-the-stance-of-united-states-monetary-policy/comparison-of-international-monetary-policy-measures
- Temperature Anomaly
  - Definition: GISTEMP air temperature anomaly smoothed over global 250km-spaced grid. Temperature anomaly is assigned to each country-year by finding the point in the grid (with data) closest to the country’s capital using Robert Picard’s geonear procedure for Stata.
  - Sources: GISTEMP Team (2019) and Lenssen and others (2019). GISTEMP data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd/ (accessed on 1/15/2018)
- Precipitation Anomaly
  - Definition: NOAA’s precipitation anomaly smoothed over global 2.5o-latitude by 2.5o-longitude grid. Precipitation anomaly is assigned to each country-year by finding the point in the grid (with data) closest to the country’s capital using Robert Picard’s geonear procedure for Stata.
  - Sources: Chen and others (2002, 2004) and Chen and others (2003). PREC Precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd/.
- Change in 2Y yield on Announcement Dates
  - Definition: See Appendix III.
  - Sources: Central bank websites, Bloomberg, and Thomson-Reuters Datastream
- Central Bank Independence Index
  - Definition: Update to the Cukierman, Webb and Neyapty index. Definition in Cukierman, Webb, and Neyapti (1992).
  - Sources: Garriga (2016), available from https://sites.google.com/site/carogarriga/cbi-data-1. Since the data end in 2012, we use the average central bank independence in each country over the entire sample.
- Central Bank Transparency
  - Definition: Dincer and Eichengreen’s (2014) index of augmented central bank transparency
  - Sources: Dincer and Eichengreen (2014), available from https://eml.berkeley.edu/~eichengr/data.shtml. The dataset contains data from 1998 to 2014 and. For 2015-17 we assume central bank transparency to be the same as in 2014. Since the variable is slow moving, it is unlikely that this approximation causes significant biases.
- Voice and Accountability
  - Definition: As defined in Kaufmann and others (2010).
  - Sources: Worldwide Governance Indicators (World Bank)
- Government Effectiveness
  - Definition: As defined in Kaufmann and others (2010).
  - Sources: Worldwide Governance Indicators (World Bank)
- Regulatory Quality
  - Definition: As defined in Kaufmann and others (2010).
  - Sources: Worldwide Governance Indicators (World Bank)
- Chinn-Ito Index
  - Definition: Chinn and Ito’s (2006) index of de jure financial openness
  - Sources: Chinn and Ito (2006), available from http://web.pdx.edu/~ito/Chinn-Ito_website.htm
- Trade openness
  - Definition: Total exports and imports as a percentage of GDP (annual)
  - Sources: IMF World Economic Outlook
- Floating Exchange rate Dummy
  - Definition: Dummy variable which takes value 1 when a country is judged by the IMF as having a de facto floating, freely floating, or other managed exchange rate regime (and 0 otherwise).
  - Sources: IMF AREAER database
- Dollarization Index
  - Definition: The sum of deposit dollarization (share of foreign currency deposits in total bank deposits) and loan dollarization (share of foreign currency loans in total bank loans), at the monthly frequency.
  - Sources: IMF Monetary and Financial Statistics
- Food Weight in CPI Basket
  - Definition: Food weights in CPI basket.
  - Sources: Haver and EMDE database
- Bank Concentration ( percent)
  - Definition: Top five banks’ share of total bank assets in each country (annual).
  - Sources: Global Financial Development Database (World Bank)
- Foreign Banks Among Total Banks ( percent)
  - Definition: Number of foreign-owned banks (i.e., 50 percent or more of its shares are owned by foreigners) as a percentage of the total number of banks.
  - Sources: Global Financial Development Database (World Bank)
- Sovereign or Bank Crisis Dummy
  - Definition: Dummy variable which takes value 1 if there were a systemic banking crisis or sovereign debt crisis in a given year.
  - Sources: Definitions in Laeven and Valencia (2018). Laeven and Valencia (2018)

### Notes on Appendix Figures and empirical approach
- Estimation method:
  - Impulse responses estimated with Jordá’s (2005) local projections method using panel data and country fixed effects.
  - Standard errors are robust to heteroscedasticity and autocorrelation unless otherwise noted.
- Statistical inference and markers used in figures:
  - Dotted lines represent the lower and upper limit of 90 percent significance confidence bands.
  - 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 (or that the total effect is statistically significant at least the 10 percent significance level, depending on the figure).
  - In Appendix Figure A.2.5: Dashed lines show 90 percent bias-corrected confidence intervals. S = The square marks mean the responses in the two solid lines (blue and red) are statistically different at least at the 10 percent significance level. Inference is based on autocorrelation-robust bootstrapped confidence intervals.
- Figure themes (descriptive list of analyses presented in Appendix Figures A.2.1–A.2.8):
  - Transmission of monetary policy by country governance: voice and accountability, government effectiveness, regulatory quality (output and price responses).
  - Monetary policy frameworks vs. country governance: interaction of inflation targeting status with voice and accountability, government effectiveness, regulatory quality (output and price responses).
  - Transmission of monetary policy by capital account openness, trade openness, and financial dollarization (output and price responses).
  - Transmission by other country characteristics: food weight in CPI, bank competition, role of foreign banks (output and price responses).
  - Transmission with endogenous IT adoption: output and price responses with and without the exchange rate channel; note on inference and significance markers.
  - Transmission in countries using interest rate-based monetary policy: interactions with IT adoption, degree of dollarization, FD index (output and price responses).
  - Transmission with structural changes: time-varying Taylor-rule coefficients and interactions with IT adoption and FD index (output and price responses).
  - Transmission with long sample (1995-2017): time-varying Taylor-rule coefficients, IT adoption, FD index (output and price responses).

*Appendix I. Country List and Data Sources — source PDF: wpiea2020035-print-pdf*

### Appendix III.  Selection of Countries with an Interest-Based Monetary Policy

### Appendix III.  Selection of Countries with an Interest-Based Monetary Policy Framework

### Method for determining interest-based framework
- Historical reports examined around 2009 included IMF Article IV staff reports and central bank monetary policy reports.
- Criterion: If historical reports indicate that an EMDE in the sample was actively using the policy rate as the primary instrument in 2009, the country is classified as having an interest-based policy and included in the sample for the robustness exercise.
- Rationale: It is considered unlikely that a country reverts to using quantitative or administrative measures after having modernized its monetary policy framework and switched to a price-based tool.

### Exclusion rule
- If quantity-based tools (e.g., reserve requirements and quantitative targets) were used frequently in addition to (or instead of) the policy rate until at least 2009, the country is excluded from the sample.

### Countries selected as having an interest-rate based monetary policy framework
- Armenia
- Bolivia
- Botswana
- Brazil
- Chile
- Colombia
- Costa Rica
- Dominican Republic
- Egypt
- Guatemala
- Honduras
- Hungary
- Macedonia
- Malaysia
- Mexico
- Pakistan
- Paraguay
- Peru
- Philippines
- Poland
- Romania
- Russian Federation
- South Africa
- Sri Lanka
- Taiwan
- Thailand
- Turkey
- Ukraine
- Uruguay
- Vietnam

*Source: Appendix III.  Selection of Countries with an Interest-Based Monetary Policy Framework*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020035-print-pdf.pdf_