## 3.1 Data Description ## Source details **Canonical URL:** [3.1 Data Description](https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022227-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2022/english/wpiea2022227-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2022/english/wpiea2022227-print-pdf.pdf.json) --- ### Sample and scope - Panel dataset of 68 countries over the period 1990-2019. - Includes 32 advanced economies and 36 major emerging market and developing economies (IMF WEO classification). - As of 2019, there are 32 inflation targeting countries, accounting for about 47 percent of all sample countries. - According to the WEO data, the sample countries accounted for about 93 percent of the global nominal output in 2019. - Hyperinflation periods are dropped for regression analysis. ### Country coverage and note - Advanced Economies (32) and Emerging Markets and Developing Economies (36) listed in the source. - Note in source: countries in red are inflation targeting countries as of 2019; those in bold red are inflation targeting countries with more than ten years of track record measures. ### Data sources and variable frequency - Monetary policy framework classification: IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) (Annual). - IT targets and bands: Central Bank Websites (Annual). Zhang (2021) assembles public records; dataset extended to 2019. - Real GDP: WEO, IFS, Haver Analytics (Quarterly). - Consumer Price Index (CPI): IFS, WEO (Quarterly). - Primary Commodity Index: IMF (Quarterly). - Nominal Effective Exchange Rate (NEER): IMF IFS (Quarterly). - Institutional Quality Index: International Country Risk Guide (ICRG) — simple average of bureaucracy quality, corruption, law and order (Annual). - Human Capital Index: Penn World Table 10.0 (Annual). - Terms of Trade and Population Growth: IMF WEO (Annual). - Table 2 in source summarizes variable names, data frequency, and data sources. ### Constructed measures and purpose - Three novel inflation targeting (IT) track record measures constructed for 32 major IT countries, covering entire history through 2019: - Percent of the time that inflation stays within the band. - Duration of the most recent spell that inflation remains in the band. - Maximum time span of consecutive quarters that inflation falls outside the target range during a given period. - Measures are time-varying and country-specific to capture dynamic and heterogeneous characteristics of monetary policy frameworks. - Measures are computed over horizons N = 1, 3, or 5 years (N = 1, 3, or 5). - Measures are normalized to lie between 0 and 1. - Measures are not available for inflation targeters who do not have formal inflation target ranges. ### Summary statistics for main variables (as reported) - Real GDP Growth (Observations 1,739): Mean 3.20 percent, Std. Dev. 3.92 percent, Min -20 percent, Max 34.08 percent. - CPI Inflation (Observations 1,950): Mean 5.87 percent, Std. Dev. 10.95 percent, Min -5.75 percent, Max 161.52 percent. - Terms of Trade Change (Observations 1,956): Mean 0.52 percent, Std. Dev. 6.58 percent, Min -55.90 percent, Max 94.61 percent. - Population Growth (Observations 2,006): Mean 0.88 percent, Std. Dev. 1.09 percent, Min -6.51 percent, Max 6.49 percent. - Human Capital Change (Observations 2,030): Mean 0.82 percent, Std. Dev. 0.63 percent, Min -0.92 percent, Max 4.74 percent. - Primary Commodity Price Change (Observations 1,836): Mean 4.41 percent, Std. Dev. 15.71 percent, Min -29.83 percent, Max 27.93 percent. - NEER Change (Observations 1,999): Mean -1.73 percent, Std. Dev. 11.86 percent, Min -98.78 percent, Max 94.59 percent. - Institutional Quality Change (Observations 1,957): Mean 0.55 percent, Std. Dev. 9.19 percent, Min -37.50 percent, Max 200.00 percent. ### Role of the data in the paper - Data underpin: - Documentation of IT track records and dynamic adjustments of target bands. - Empirical analysis of contemporaneous and future effects of IT track records on real GDP growth and CPI inflation. - Robustness checks and heterogeneity analysis across countries and frameworks. *Source: wpiea2022227-print-pdf (3.1 Data Description).* ### 3.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ### 3.1 Data Description ### Sample and scope - Panel dataset of 68 countries over the period 1990-2019. - Includes 32 advanced economies and 36 major emerging market and developing economies (IMF WEO classification). - As of 2019, there are 32 inflation targeting countries, accounting for about 47 percent of all sample countries. - According to the WEO data, the sample countries accounted for about 93 percent of the global nominal output in 2019. - Hyperinflation periods are dropped for regression analysis. ### Country coverage - Advanced Economies (32) and Emerging Markets and Developing Economies (36) listed in the source. - Note in source: countries in red are inflation targeting countries as of 2019; those in bold red are inflation targeting countries with more than ten years of track record measures. ### Data sources and variable frequency - Monetary policy framework classification: IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) (Annual). - IT targets and bands: Central Bank Websites (Annual). Zhang (2021) assembles public records; dataset extended to 2019. - Real GDP: WEO, IFS, Haver Analytics (Quarterly). - Consumer Price Index (CPI): IFS, WEO (Quarterly). - Primary Commodity Index: IMF (Quarterly). - Nominal Effective Exchange Rate (NEER): IMF IFS (Quarterly). - Institutional Quality Index: International Country Risk Guide (ICRG) — simple average of bureaucracy quality, corruption, law and order (Annual). - Human Capital Index: Penn World Table 10.0 (Annual). - Terms of Trade and Population Growth: IMF WEO (Annual). - Table 2 in source summarizes variable names, data frequency, and data sources. ### Constructed measures and purpose - Three novel inflation targeting (IT) track record measures constructed for 32 major IT countries, covering entire history through 2019: - Percent of the time that inflation stays within the band. - Duration of the most recent spell that inflation remains in the band. - Maximum time span of consecutive quarters that inflation falls outside the target range during a given period. - Measures are time-varying and country-specific to capture dynamic and heterogeneous characteristics of monetary policy frameworks. ### Summary statistics for main variables (as reported) - Real GDP Growth (Observations 1,739): Mean 3.20 percent, Std. Dev. 3.92 percent, Min -20 percent, Max 34.08 percent. - CPI Inflation (Observations 1,950): Mean 5.87 percent, Std. Dev. 10.95 percent, Min -5.75 percent, Max 161.52 percent. - Terms of Trade Change (Observations 1,956): Mean 0.52 percent, Std. Dev. 6.58 percent, Min -55.90 percent, Max 94.61 percent. - Population Growth (Observations 2,006): Mean 0.88 percent, Std. Dev. 1.09 percent, Min -6.51 percent, Max 6.49 percent. - Human Capital Change (Observations 2,030): Mean 0.82 percent, Std. Dev. 0.63 percent, Min -0.92 percent, Max 4.74 percent. - Primary Commodity Price Change (Observations 1,836): Mean 4.41 percent, Std. Dev. 15.71 percent, Min -29.83 percent, Max 27.93 percent. - NEER Change (Observations 1,999): Mean -1.73 percent, Std. Dev. 11.86 percent, Min -98.78 percent, Max 94.59 percent. - Institutional Quality Change (Observations 1,957): Mean 0.55 percent, Std. Dev. 9.19 percent, Min -37.50 percent, Max 200.00 percent. ### Role of the data in the paper - Data underpin: - Documentation of IT track records and dynamic adjustments of target bands. - Empirical analysis of contemporaneous and future effects of IT track records on real GDP growth and CPI inflation. - Robustness checks and heterogeneity analysis across countries and frameworks. *Source: wpiea2022227-print-pdf (3.1 Data Description) from the provided IMF PDF content.* ### 3.2 Stylized Facts ### 3.2 Stylized Facts ### Key comparative patterns (IT vs non-IT) - Average real GDP growth: - 1990s: roughly the same between IT and non-IT countries. - 2000s: IT countries’ average growth slightly underperforms. - 2010s: IT countries modestly over-perform. - Average CPI inflation: - Early IT adopters (a few developed countries) had significantly lower inflation in the 1990s. - As more countries adopted IT, the inflation differential narrowed in the 2000s and reversed in the 2010s. - Overall implication: - On an unconditional basis, IT countries do not have better macroeconomic outcomes on average; further econometric analysis is required. ### 3.3 Inflation Targeting Track Record Measures ### Construction and purpose - Objective: systematically evaluate how inflation targeters manage inflation relative to their stated policy objectives. - Three novel, rule-based track record measures (normalized to lie between 0 and 1) computed over horizons N = 1, 3, or 5 years (N = 1, 3, or 5): - A measure (average in-band probability): percent of actual inflation that stays within the central bank’s announced band in the last N years. - R measure (recent in-band duration): duration of the most recent spell that actual inflation stays within the announced band in the last N years. - M measure (maximum out-of-band duration): maximum time span of consecutive quarters actual inflation falls outside the target corridor in the last N years. - Notes: - Measures are not available for inflation targeters who do not have formal inflation target ranges. - When comparing across countries, actual dates matter (e.g., easier to meet targets during the Great Moderation); time-fixed effects are included in regressions. ### Illustrative hypothetical example (Figure 4) - Central bank band: lower bound 1 percent, upper bound 3 percent. - Over 12 quarters (3 years): - A measure = 8/12 = 0.667 (8 periods in-band). - R measure = 4/12 = 0.333 (most recent consecutive in-band spell = 4 quarters). - M measure = 3/12 = 0.25 (maximum consecutive out-of-band miss = 3 periods). ### Summary patterns and correlations - Averages and directions: - Average in-band probability measures (A) are larger than recent in-band duration measures (R) because A considers all in-band episodes while R considers only the last episode. - The M measure has the opposite direction (it captures the extent of misses). - Correlations (3-year measures): - A and R: positive correlation. - M and A or R: negative correlations. - Time-horizon effect: - As the horizon expands from 1 to 5 years, all three measures decline in value. - Income-group patterns (3-year backward-looking A measure): - High degree of synchronization since the late 2000s across income groups, with relatively low track records around 2010 and high track records around 2019. - Advanced economies on average have better track records than emerging markets and developing economies. - Substantial heterogeneity exists within each income group. ### Relationship to macroeconomic performance - Scatter-plot evidence: - No clear unconditional correlation between the probability of inflation staying within bands and average real GDP growth (Figure 6). - No clear unconditional correlation between the probability of inflation staying within bands and average CPI inflation (Figure 7). ### 3.4 Dynamic Inflation Target Bands ### Key facts on band dynamics and heterogeneity - Central banks frequently change inflation targets and bands; changes are not limited to initial adoption periods. - Band sizes are critical to the track record measures (wider bands mechanically raise in-band probabilities). - Empirical findings: - Historical band sizes range from 1 percentage point to 5 percentage points across inflation targeters. - As of 2019, the majority of inflation targeters use a 2-percentage-points band size. - Other common band sizes include 3 percentage points, followed by 4 and 1 percentage point. - Income-group differences in average band size: - Advanced economies: average band size stable and moves around 2 percentage points. - Emerging markets and developing economies: - Had an average band size of 4 percentage points in 1999 (driven by a limited number of early adopters). - Average band size fell below 2.5 percentage points before the Great Financial Crisis. - Average band size rose above 2.5 percentage points after the Great Financial Crisis. - Interpretation: larger band sizes in many emerging and developing economies may reflect structural preconditions not being met and the need to accommodate greater/more frequent inflation overshoots. ### 4.1 Results for All Countries (Empirical Setup and Key Findings) ### Dataset and model - Sample: panel dataset of all 68 countries over the period of 1990-2019. - Estimation framework: - Dynamic panel model: y_it = η y_{i,t−1} + β IT_it + λ X + α_i + θ_t + ε_it - y_it: annual change in real GDP or CPI for country i in year t. - η: coefficient on the lagged dependent variable. - IT_it: inflation targeting dummy (IT = 1 if country adopts inflation targeting, IT = 0 otherwise). - X: vector of standard control variables. - For real GDP growth regressions: X includes annual growth rates in terms of trade, population, human capital index, and institutional quality. - For CPI inflation regressions: X includes annual growth rates of commodity prices, nominal effective exchange rate, and institutional quality. - α_i and θ_t: country and time fixed-effects. - Estimation method: Generalized Method of Moments (GMM). - Lagged dependent variable used as GMM instrument. - Time dummy variables used as IV instruments. ### Main empirical findings - Persistence: - Lagged dependent variables are positive and statistically significant (growth and inflation exhibit inertia). - Controls: - Terms of trade and population growth: lift economic growth. - Commodity price increases: raise inflation. - Nominal effective exchange rate appreciation and better institutional quality: reduce inflation. - Inflation targeting dummy: - Estimated coefficients on IT dummy variables are statistically insignificant in all regressions once time fixed-effects are controlled for. - Point estimates and standard errors on the IT dummy variables are large, which may reflect limitations of a binary IT classification. - Interpretation and caveats: - Results are consistent with literature that IT adoption is not a panacea for promoting growth or controlling inflation. - Black-or-white classification of IT adoption cannot fully capture how monetary policy is actually conducted (e.g., many IT countries lack complete exchange rate flexibility; some non-IT countries are influenced by IT practices). - Motivation: need for continuous and dynamic measures to profile inflation targeters more accurately. *Source: wpiea2022227-print-pdf - 3.2 Stylized Facts* ### 4.2 Results for Inflation Targeting Countries ### 4.2 Results for Inflation Targeting Countries ### Sample and track record measures - There are 32 inflation targeting countries in 2019; the analysis selects 21 inflation targeters that have at least 10 years of IT experience with explicitly stated IT target bands. - The subsample excludes periods when countries only have inflation point targets (example: Czech National Bank currently targeting a 2 percent inflation point; only before 2006 there was a target band). - ITTR it denotes time-varying inflation targeting track record measures for country i in year t. Track record measures include A, R, and M variants and are computed over 1-year, 3-year, 5-year windows and the entire IT period (the entire IT period measure is static). ### Dynamic panel (contemporaneous) results for inflation targeters - Modified dynamic panel model: yit = η y i,t−1 + β ITTR it + λX + αi + θt + εit, estimated by GMM with country and time fixed effects. - Table 8 regressions (inflation targeting subsample, 21 countries) find: - The estimated coefficients on IT track record measures (A measure, 1-year; 3-year; 5-year; within-band time) are not statistically significant for contemporaneous real GDP growth or CPI inflation across all horizons. - No. of observations in Table 8 regressions: 352, 315, 275, 403 (RGDP columns) and 354, 316, 275, 404 (CPI columns). - No. of countries: 21 for all regressions in Table 8. - Interpretation: - Better IT track records do not affect contemporaneous economic growth or inflation in this sample. ### Local projections (future-period effects) - Local projection specification: yi,t+h = βh ITTR it + λh X + αh i + θh t + εh i,t. - Figures 11 and 12 show local projection responses of real GDP growth and CPI inflation with 95% confidence intervals. - The response of economic growth or inflation to an increase in the IT track record measure does not statistically differ from zero: the 95% confidence intervals never fall outside the horizontal line y = 0 in the next 5 years. - Conclusion: - Better IT track records do not enhance economic growth or decrease inflation over the next 5 years. Results are robust across track record measures (A, R, M) and horizons (contemporaneous and future). - Short-term impacts of IT track records on real GDP growth are possibly negative at a lower significance level (consistent with IT prioritizing inflation control over growth). ### Band-size adjustment - Rationale: IT band sizes vary across countries; a wide band makes it easier for actual inflation to remain within range. - Adjusted track record measure: previous IT track record measure divided by the corresponding IT band size for country i in year t. - Dynamic panel with band-size-adjusted measures (Table 9) finds: - Estimated coefficients for all adjusted track record measures are not statistically significant for short-term macroeconomic outcomes. - Conclusion: - No empirical evidence that heterogeneity in IT band sizes explains the lack of effect of IT track records on short-term growth and inflation. ### Robustness checks - Alternative track record measures: - Replacing A measures with R and M measures yields consistent findings: estimated coefficients remain statistically insignificant for 1-year, 3-year, and 5-year backward-looking windows (Tables 10 and 11). - Alternative dependent variables: - Inflation deviation from target (Table 12): IT track record measures are not statistically significant in affecting deviation from the inflation target. Note: limited number of observations (No. of observations: 111 or 94; No. of countries: 15 or 16) requires caution. - Inflation deviation from the world average (Table 13): subtracting world average inflation from a country’s inflation, IT track records do not affect the inflation differential vis-à-vis the world average (No. of observations: 316 or 275; No. of countries: 21). - Diagnostic test outcomes reported across tables (examples): - Arellano-Bond AR1 test (p-value) often significant (e.g., 0.009, 0.014, 0.017 in some Table 8 columns). - Arellano-Bond AR2 test (p-value) generally not significant (e.g., 0.949, 0.601, 0.451 in Table 8). - Hansen test of overid. restrictions (p-value) reported as 1.000 in many inflation-targeter regressions. ### Key empirical findings (summary bullets) - The 21 inflation targeters with at least 10 years of explicit IT bands show heterogeneous track records in keeping inflation within announced bands. - Better IT track records do not translate into statistically significant improvements in contemporaneous real GDP growth or CPI inflation. - Local projections reveal no statistically significant effect of better IT track records on growth or inflation up to 5 years ahead (95% confidence intervals include zero throughout). - Adjusting track record measures for IT band size does not change the null finding. - Results are robust to alternative IT track record constructions (A, R, M) and to alternative dependent variables (inflation deviation from target; inflation deviation from world average). ### Interpretation, implications, and research agenda - Possible reasons for heterogeneity and limited short-term macroeconomic benefits: - Some countries, particularly developing ones, may lack preconditions for successful IT (e.g., shallow financial markets, fiscal dominance). - Insufficient exchange rate flexibility and “fear of floating” can limit de-jure IT implementation from delivering de-facto outcomes. - Dynamic updating of IT bands complicates evaluation of track records and may distort links between track records and macroeconomic performance. - The IT framework is less effective at handling commodity price shocks; commodity price changes are found significant for inflation outcomes in this study. - Policy and research considerations: - Short-run benefits of better IT track records on growth and inflation are not evident; however, other potential benefits merit study, including effects on growth and inflation volatilities, inflation expectations, financial markets, and central bank credibility and accountability. - Long-run effects of solid IT track records are unknown and present an open research question as longer time-series data accumulate. - Future research directions suggested in the paper: - How central banks dynamically choose IT point targets and band sizes; reasons behind changing IT objectives. - Long-term macroeconomic impacts of IT track records using low-frequency/long-horizon samples. - Implications of IT track records on inflation expectations, macroeconomic volatility, and financial markets. *Italic: Source: 4.2 Results for Inflation Targeting Countries, wpiea2022227-print-pdf* ### References ### wpiea2022227-print-pdf - References ### References list - Acemoglu, D., Johnson, S., Robinson, J., and Thaicharoen, Y. (2003). Institutional causes, macroeconomic symptoms: volatility, crises and growth.Journal of Monetary Economics, 50(1):49–123. - Ambrocio, G., Ferrero, A., Jokivuolle, E., and Ristolainen, K. (2022). What should the inflation target be? views from 600 economists.Working Paper. - Andrade, P., Gal ́ı, J., Le Bihan, H., and Matheron, J. (2018). The optimal inflation target and the natural rate of interest. Technical report, National Bureau of Economic Research. - Ardakani, O. M., Kishor, N. K., and Song, S. (2018). Re-evaluating the effectiveness of inflation targeting.Journal of Economic Dynamics and Control, 90:76–97. - Athey, S., Atkeson, A., and Kehoe, P. J. (2005). The optimal degree of discretion in monetary policy.Econometrica, 73(5):1431–1475. - Ball, L. (2010). The performance of alternative monetary regimes. InHandbook of Monetary Economics, volume 3, pages 1303–1343. Elsevier. - Ball, L. M. and Sheridan, N. (2004). Does inflation targeting matter? Inthe Inflation- Targeting Debate, pages 249–282. University of Chicago Press. - Barro, R. J. et al. (2003). Determinants of economic growth in a panel of countries.Annals of Economics and Finance, 4:231–274. - Barro, R. J. and Gordon, D. B. (1983). A positive theory of monetary policy in a natural rate model.Journal of Political Economy, 91(4):589–610. - Berg, A., Ostry, J. D., Tsangarides, C. G., and Yakhshilikov, Y. (2018). Redistribution, inequality, and growth: new evidence.Journal of Economic Growth, 23(3):259–305. - Bhalla, S., Bhasin, K., and Loungani, P. (2022). Inflation targeting: Vaccine or placebo? Working Paper. - Bordo, M. D. and Schwartz, A. J. (1999). Monetary policy regimes and economic perfor- mance: the historical record.Handbook of Macroeconomics, 1:149–234. - Brito, R. D. and Bystedt, B. (2010). Inflation targeting in emerging economies: Panel evidence.Journal of Development Economics, 91(2):198–210. - Calvo, G. A. and Reinhart, C. M. (2002). Fear of floating.The Quarterly Journal of Economics, 117(2):379–408. - Clayton, C. and Schaab, A. (2021). A theory of dynamic inflation targets.Working Paper. - Cottarelli, M. C. (1998).The nonmonetary determinants of inflation: A panel data study. International Monetary Fund. - De Mendon ̧ca, H. F. and e Souza, G. J. d. G. (2012). Is inflation targeting a good remedy to control inflation?Journal of Development Economics, 98(2):178–191. - Doppelhofer, G., Miller, R. I., et al. (2004). Determinants of long-term growth: A bayesian averaging of classical estimates (bace) approach.American Economic Review, 94(4):813– 835. - Ehrmann, M. (2021). Point targets, tolerance bands or target ranges? inflation target types and the anchoring of inflation expectations.Journal of International Economics, 132:103514. - Fratzscher, M., Grosse-Steffen, C., and Rieth, M. (2020). Inflation targeting as a shock absorber.Journal of International Economics, 123:103308. - Friedman, B. M. (1990). Targets and instruments of monetary policy.Handbook of Monetary Economics, 2:1185–1230. - Gon ̧calves, C. E. S. and Salles, J. M. (2008). Inflation targeting in emerging economies: What do the data say?Journal of Development Economics, 85(1-2):312–318. - Honig, A. (2009). Dollarization, exchange rate regimes and government quality.Journal of international Money and Finance, 28(2):198–214. - Ilzetzki, E., Reinhart, C. M., and Rogoff, K. S. (2019). Exchange arrangements entering the twenty-first century: Which anchor will hold?The Quarterly Journal of Economics, 134(2):599–646. - Jord`a, ` O. (2005). Estimation and inference of impulse responses by local projections.Amer- ican Economic Review, 95(1):161–182. - Klein, M. W. and Shambaugh, J. C. (2015). Rounding the corners of the policy trilemma: sources of monetary policy autonomy.American Economic Journal: Macroeconomics, 7(4):33–66. - Lin, S. and Ye, H. (2007). Does inflation targeting really make a difference? evaluating the treatment effect of inflation targeting in seven industrial countries.Journal of Monetary Economics, 54(8):2521–2533. - Lin, S. and Ye, H. (2009). Does inflation targeting make a difference in developing countries?Journal of Development Economics, 89(1):118–123. - Mishkin, F. S. (2000). Inflation targeting in emerging-market countries.American Economic Review, 90(2):105–109. - Mishkin, F. S. and Schmidt-Hebbel, K. (2007). Does inflation targeting make a difference? - Svensson, L. E. (1999). Inflation targeting as a monetary policy rule.Journal of Monetary Economics, 43(3):607–654. - Svensson, L. E. (2000). Open-economy inflation targeting.Journal of International Eco- nomics, 50(1):155–183. - Svensson, L. E. (2010). Inflation targeting. InHandbook of Monetary Economics, volume 3, pages 1237–1302. Elsevier. - Vega, M. and Winkelried, D. (2005). Inflation targeting and inflation behavior: a successful story?International Journal of Central Banking, 1(3):153–175. - Walsh, C. E. (1995). Recent central bank reforms and the role of price stability as the sole objective of monetary policy.NBER Macroeconomics Annual, 10:237–252. - Zhang, Z. (2021).Stock Returns and Inflation Redux: An Explanation from Monetary Policy in Advanced and Emerging Markets. International Monetary Fund. *References section from wpiea2022227-print-pdf* --- _Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022227-print-pdf.pdf_