Martin Stojanovikj
email: martin_stojanovik@yahoo.com

Goran Petrevski

Ss Cyril

Inflation Targeting and Disinflation Costs in Emerging Market Economies 1

Keywords: Inflation Targeting, Sacrifice Ratio, Disinflation, Emerging Market Economies. JEL codes: E52, E58 1

In this paper, we study whether adopting Inflation Targeting (IT) in Emerging Market Economies (EMEs) affects the output costs of disinflation, controlling for a number of additional factors. Based on a sample of 170 disinflation episodes in 44 EMEs over the 1970-2017 period, we provide strong evidence that adopting IT is associated with a higher sacrifice ratio in EMEs. In addition, we find that gradual disinflation may be less costly in EMEs. Moreover, we show that trade openness is associated with a lower sacrifice ratio, while both central bank independence (CBI) and external shocks have adverse effects on the sacrifice ratio. Our main findings are robust to alternative classifications of the IT regime, alternative definitions of disinflation episodes, different thresholds for high inflation, different peak levels of trend inflation rate, across various specifications of the empirical model, as well as for both the entire sample and the sub-sample covering the post-1990s period.

Introduction

During the past three decades, an increasing number of industrialized countries and emerging market economies have adopted IT as a monetary policy strategy. Within this regime, the central bank focuses on price stability as its predominant policy goal, by announcing explicit numerical targets for medium-term inflation. The proponents of this monetary policy strategy argue that it offers several benefits for EMEs: it enhances central bank credibility, it reduces inflation persistence, it helps anchoring inflation expectations, it contains a high degree of flexibility, it enables policy makers to cope with short-run circumstances and adverse shocks, and it involves lower economic costs in the face of policy failures [START_REF] Bernanke | Inflation Targeting: A New Framework for Monetary Policy?[END_REF][START_REF] Mishkin | A Decade of Inflation Targeting in the World: What Do We Know and What Do We Need to Know?[END_REF][START_REF] Batini | Under What Conditions Can Inflation Targeting Be Adopted? The Experience of Emerging Markets[END_REF][START_REF] Mishkin | Monetary Policy under Inflation Targeting: An Introduction[END_REF]. As a result, the above features suggest that IT may be superior compared to alternative monetary policy strategies in the sense that it reduces the inflation-output volatility and leads to lower disinflation costs.

This paper investigates the relationship between IT and the sacrifice ratio in EMEs, controlling for a number of additional factors, which are referred to as common determinants of the sacrifice ratio in the empirical literature. We provide strong evidence that adopting IT might have adverse effects on the costs of disinflation in EMEs. In calculating sacrifice ratios, we employ a slightly modified version of the approach used in [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF]. Based on this methodology, we have identified 170 disinflation episodes in 44 EMEs, i.e., 78 disinflation episodes during and 92 disinflation episodes in the 1990-2017 period. The main findings from our study can be summarized as follows: first, we provide strong evidence that adopting IT is associated with a higher sacrifice ratio in EMEs; second, we find that gradual disinflation may be less costly in EMEs; third, we show that trade openness is associated with a lower sacrifice ratio, while more independent central banks and adverse external shocks tend to increase the sacrifice ratio. Our main findings are robust to alternative classifications of the IT regime, alternative definitions of disinflation episodes, different thresholds for high inflation, different peak levels of trend inflation rate, across various specifications of the empirical model, as well as for both the entire sample and the sub-sample covering the post-1990s period. Yet, we find that the results are sensitive to the filtering method employed for estimating trend output.

The rest of the paper is structured as follows: Section 2 reviews the relevant literature; Section 3 explains the issues related to the data collection process and calculation of the sacrifice ratio; Section 4 presents the baseline specification of the empirical model and discusses the main findings; the robustness analysis is presented in Section 5; and all findings are summarized in Section 6.

Literature Review

Over time, a considerable empirical literature has been accumulated on the determinants of sacrifice ratios in both OECD and developing countries. In these regards, there is much less evidence on the relationship between IT and the sacrifice ratio, especially for the recent inflation targeters among the EMEs.

The surge in the empirical literature on the real costs of disinflation has been initiated by [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF], who provided a modified measure of the sacrifice ratio. Based on several assumptions with regards to the behavior of trend output in the sample of OECD countries, he proposed a simple measure of the sacrifice ratio calculated by dividing the cumulated output loss (the sum of deviations of actual output from trend output) by the change in trend inflation during each disinflation episode. He estimated the sacrifice ratios for a sample of 19 OECD countries during 1960-1991, finding that its main determinants are the speed of disinflation and nominal wage rigidity. Later on, most of the empirical literature has basically followed the Ball's approach in measuring the sacrifice ratio. The non-exhaustive list of studies incudes: [START_REF] Chortareas | Does Monetary Policy Transparency Reduce Disinfation Costs[END_REF], Temple 2002[START_REF] Brumm | The Sacrifice Ratio and Central Bank Independence Revisited[END_REF], Down 2004[START_REF] Diana | Central bank independence, speed of disinflation and the sacrifice ratio[END_REF][START_REF] Daniels | Openness, central bank independence, and the sacrifice ratio[END_REF][START_REF] Daniels | Exchange-rate pass through, openness, and the sacrifice ratio[END_REF][START_REF] Caporale | Political Regimes and the Cost of Disinflation[END_REF][START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF][START_REF] Senda | Inflation History and the Sacrifice Ratio: Episode-Specific Evidence[END_REF][START_REF] Caporale | Government Ideology, Democracy and the Sacrifice Ratio: Evidence from Latin American and Caribbean Disinflations[END_REF][START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF][START_REF] Roux | Sacrifice ratios and inflation targeting: the role of credibility[END_REF]).

As mentioned above, the main features of the IT regime suggest that it promises to offer a less costly device for controlling inflation. However, the empirical support to this proposition seems to be rather limited. For instance, [START_REF] Mishkin | Inflation Targeting: Lesson from four Countries[END_REF] analyze the experiences with IT in New Zealand, Canada, and the UK, pointing out that IT might have served to lock-in the gains from previous disinflation rather than to facilitate the disinflation itself. [START_REF] Bernanke | Inflation Targeting: Lessons from the International Experience[END_REF] and Ball and Sheridan (2003) show that there is no credibility bonus for the countries that have adopted IT as their monetary strategy. They conclude that IT appears to have no significant effect on the disinflation process though their findings might be potentially sensitive with respect to the definition of the starting date of the implementation of IT. Similarly, based on a sample of OECD countries, [START_REF] Roux | Sacrifice ratios and inflation targeting: the role of credibility[END_REF] find evidence that IT only matters if disinflations are slow, while fast disinflations make IT irrelevant in terms of the sacrifice ratio. [START_REF] Chortareas | Does Monetary Policy Transparency Reduce Disinfation Costs[END_REF], too, provide evidence that IT does not affect sacrifice ratios in OECD countries. [START_REF] Gonçalves | Inflation targeting and the sacrifice ratio[END_REF] examine the effect of IT on the sacrifice ratio in both OECD and developing countries. Their study shows that, generally, IT lowers the sacrifice ratio, but this effect is stronger in OECD countries, i.e. the results for the developing countries are not robust to the model specification. In a subsequent study on a sample of 25 OECD countries, [START_REF] Gonçalves | Inflation targeting matters: Evidence from OECD economies' sacrifice ratios[END_REF] confirm that adopting IT reduces the disinflation costs. However, Brito (2010) criticizes the methodological approach in Goncalves and Carvalho (2009) and finds that IT isn't a determinant of the sacrifice ratio in OECD countries. Moreover, employing the Philips curve-based approach for a sample of EMEs, [START_REF] Brito | Inflation targeting in emerging economies: Panel evidence[END_REF] provide evidence on the negative relationship between IT and sacrifice ratios. Based on a large sample of 189 countries over 1969[START_REF] Bowdler | Openness, exchange rate regimes and the Phillips curve[END_REF][START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] suggests that the determinants of sacrifice ratios vary between OECD and non-OECD countries but provides evidence that adopting IT is found to be statistically insignificant in both the OECD and developing countries.

A number of papers attempt to identify the importance of some specific factors for the size of sacrifice ratio. For instance, [START_REF] Jordan | Disinflation costs, accelerating inflation gains, and central bank independence[END_REF]Jordan ( , 1999)), Baltensperger and Kugler (2000), [START_REF] Brumm | The Sacrifice Ratio and Central Bank Independence Revisited[END_REF], [START_REF] Diana | Central bank independence, speed of disinflation and the sacrifice ratio[END_REF][START_REF] Diana | Central bank independence, speed of disinflation and the sacrifice ratio[END_REF][START_REF] Daniels | Openness, central bank independence, and the sacrifice ratio[END_REF] study the relationship between CBI and the sacrifice ratio; Temple (2002), [START_REF] Daniels | Openness, central bank independence, and the sacrifice ratio[END_REF], Daniels and VanHoose (2006[START_REF] Bowdler | Openness, exchange rate regimes and the Phillips curve[END_REF][START_REF] Daniels | Exchange-rate pass through, openness, and the sacrifice ratio[END_REF][START_REF] Bowdler | Openness, exchange rate regimes and the Phillips curve[END_REF] investigate the interaction between openness and the sacrifice ratio; [START_REF] Chortareas | Does Monetary Policy Transparency Reduce Disinfation Costs[END_REF] explore how central bank transparency affects sacrifice ratio: while Daniels et al. (2006) and [START_REF] Bowdler | Labor market structures and the sacrifice ratio[END_REF] focus on the effects of labor market institutions on sacrifice ratios.

Methodology and Data

Calculating the Sacrifice Ratio

The sacrifice ratio provides an approximation of the real output costs associated with disinflation. In this regard, there are several approaches to measure disinflation costs. For instance, Hutchison and Walsh (1998) measure the sacrifice ratio based on the Phillips curve. One issue with this particular method is that it limits the inflation-output trade-off to be equal during the periods of accelerating inflations and the periods of disinflation, which is not the case for our sample of countries where different factors are responsible for the movement of inflation in opposite directions. [START_REF] Cecchetti | Structural estimates of the US sacrifice ratio[END_REF] employ three different structural VAR models to calculate the sacrifice ratio. On the other hand, [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] relies on the trend inflation to identify peaks and troughs, so that the period from a particular peak to the trough is identified as a disinflation episode. Furthermore, given the tendency of the Hodrick-Prescott filter to minimize the deviations of actual output from the trend, [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] proposes a log-linear method of deriving these deviations. Summing them for all the years comprising a particular disinflation episode and dividing by the change in trend inflation during the same period results in the sacrifice ratio. This method, although not unsusceptible to criticism, has been widely used in the empirical literature. Besides the original form as proposed by [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF], two additional variations have emerged in the subsequent literature. Zhang (2005) argues that Ball's measure does not include the long-lasting effects that accompany each disinflation episode. Therefore, he proposes a slightly modified measure of the sacrifice ratio, relying upon the Hodrick-Prescott filter, and assuming that potential output grows throughout the particular episode at the rate implied by the Hodrick-Prescott filter at the beginning of the disinflationary episode. Further on, [START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF] builds upon this approach, with the additional assumption that output is at its trend level one year prior to the year the disinflation episode starts, thus trying to capture an even larger portion of the longer-lasting effects of disinflation. In our study, we follow [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] in calculating the sacrifice ratio with a slight modification, which better reflects the specific features of disinflation episodes in EMEs, and potentially increases the computational power and accuracy within our sample.

Working with annual data, particularly for the inflation rate, provides a challenge in adopting the original approach of [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF]. Though providing estimates for both quarterly and annual data for OECD countries, [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] bases the calculations of sacrifice ratios on the availability of quarterly data of inflation, which are not available for the majority of EMEs in our sample. Therefore, we depart from his method slightly in the initial stage and follow [START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] in using annual data for inflation, and then calculating trend inflation as a 3-year centered moving average. From an economic standpoint this methodological tweak suits our analysis, since it provides a theoretically adequate setup for estimating the effect of adopting IT on the sacrifice ratio resting on the premise that inflation targets are implemented in the medium-term of two to three years [START_REF] Hammond | State of the Art of Inflation Targeting[END_REF]. In this way, we allow for the central bank behavior in each period to be both forward and backward looking and more adapted to the focal period of the central bank policy actions (the medium-term). In addition, [START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] provides evidence of the proximity of the sacrifice ratio measurements from this approach to the original sacrifice ratios in [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF].

In our baseline model, we follow [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] and identify disinflation episodes as periods in which trend inflation rate falls from peak to trough for at least 1.5 percentage points. Subsequently, a peak occurs in the year in which the trend inflation rate is higher than the rate in both the preceding and succeeding years, while a trough occurs in the year in which the trend inflation is lower than the rate in both the preceding and succeeding years. Using this methodology, we have identified 170 disinflation episodes in 44 EMEs, which are listed in Table A1. Specifically, there are 78 disinflation episodes during 1970-1990 and 92 disinflation episodes in the 1990-2017 period. The average rate of change in trend inflation per episode, as calculated by the 3-year moving average trend inflation rate, is 75.62% during 1970-1990, and 37.28% for the 1990-2017 period. This evidence suggests that the macroeconomic conditions have become more favorable for the EMEs over the past three decades, so that although there has been a larger number of disinflation episodes, they have been less challenging. The comparison with [START_REF] Mishkin | Monetary Policy Strategies for Emerging Market Countries: Lessons from Latin America[END_REF], [START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF][START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] shows that we are able to identify the majority of disinflation episodes across the same countries in all three datasets.

The sacrifice ratio is calculated as the sum of the deviations in actual output from trend output divided by the change in trend inflation rate over each disinflation episode. [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] estimates trend output based on the following three assumptions: 1) output is at its trend level at the start of a disinflation episode, 2) output is at its trend level four quarters after an inflation trough, and 3) output grows log-linearly between these two points when trend and actual output are equal. We identify our trend output level following the first two assumptions. We suspect that the approximation of a log-linear growth rate in trend output between these two points provides smaller output values throughout the disinflation period for the sample of EMEs, thus producing a downward bias on the estimates. This is indeed the case in our results in Tables 123456, where the estimates by the Ball's method always exhibit smaller magnitude, although, with respect to the statistical significance of the coefficients, the results are identical to our baseline estimates. The empirical literature points to the well-known stylized fact that output volatility in EMEs is much higher compared to OECD countries [START_REF] Ramey | Cross-Country Evidence on the Link between Volatility and Growth[END_REF][START_REF] Blanchard | The Long and Large Decline in U.S. Output Volatility[END_REF], Kose et al. 2003a, 2003b). A logarithmic transformation of the sort provided by [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] tends to offer a more adequate approximation of smaller values, while potentially underestimating the size of the output gap in our sample characterized with higher output volatility. Hence, we measure the sacrifice ratio by calculating the output gap for each year during a disinflation episode as the difference between trend and actual output over trend output, instead of working with log-levels of output. Although it potentially improves the accuracy of our calculations of the sacrifice ratio, the results provided in Sections 4 and 5 show that this modification does not alter the results in any significant way. However, the sacrifice ratios calculated by the aforementioned modification and the original Ball's method are not robust when experimenting with the Hodrick-Prescott and Hamilton (2018) filters for estimating trend output.

Data Issues

We work with annual data, primarily due to data availability, and our sample comprises of 44 EMEs during 1970-2017. 2 The macroeconomic conditions across the countries within the sample have varied from being highly volatile during the first two decades to the relatively stable macroeconomic conditions prevailing since the mid-1990s. The average output volatility, as measured by the standard deviation of real GDP growth, was 0.5 during , and then it declined to 0.4 in the post-1990 period. Despite the distinctive heterogeneity, we proceed working with the entire sample in order to exploit all available information. At the same time, in order to control for the potential factors behind this volatility, our regression model includes several variables, commonly present in empirical studies, such as: trade openness, domestic and external shocks, and political factors [START_REF] Broner | Globalization and Risk Sharing[END_REF][START_REF] Kraay | Comparative Advantage and the Cross-Section of Business Cycles[END_REF], Loayza and Raddartz 2007). In addition, as a robustness check, we provide estimates for the 1990-2017 sub-sample, too.

In the regression model, IT is measured as a binary variable (equal to 1 if country i is an inflation targeter in period t, and 0 otherwise). Here, we rely on the classification provided by [START_REF] Hammond | State of the Art of Inflation Targeting[END_REF] in selecting the year in which a particular country decided to adopt this monetary strategy, notwithstanding the actual month in which the decision was officially implemented. The official dates of IT adoption for the 17 EMEs are presented in Table A2. [START_REF] Calvo | The mirage of exchange rate regimes for emerging market countries[END_REF] argue that, when faced with weak fiscal and financial institutions, low credibility of monetary institutions, currency substitution and liability dollarization, and vulnerability to sudden stops of capital inflows, EMEs need a strong commitment to IT in order to exploit its benefits. In this regard, having a clear institutional commitment to the inflation target along with transparent and accountable monetary framework is what distinguishes fully-fledged targeters from the other countries adopting this regime. Hence, in the baseline model, we rely on the implementation of full-fledged IT, while in the robustness checks we provide estimates based on the more flexible IT regime.

In order to calculate the index of central bank independence (CBI), we use the data provided by [START_REF] Gariga | Central Bank Independence in the World: A New Data Set[END_REF]. The weighted CBI-index is more suitable to our analysis as it covers all the countries in our sample for the entire time period. The political variable included in the regression model is a categorical variable taking the values of 0, 1 or 2, referring to governments controlled by left-wing parties, centrist governments, and right-wing governments, respectively. The data for this variable has been taken from [START_REF] Beck | New tools and new tests in comparative political economy -the database of political institutions[END_REF], while all the remaining data have been extracted from the World Bank World Development Indicators.

Model Specification

The baseline specification of our model is as follows:

(1), where:

is the sacrifice ratio for a particular disinflation episode; is the constant; is a dummy variable for the IT regime whose values equal 1 if the country i is an inflation targeter during a particular disinflation episode, and 0 otherwise; are the control variables; while is the disturbance, following the normality assumption, . We estimate the specification with OLS, using robust standard errors, which are consistent in the potential presence of heteroskedasticity and serial correlation. [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] identifies the speed of disinflation (Speed) as the main determinant of the sacrifice ratio. The sign and the significance of the regression coefficient in front of this variable theoretically differ between the sharp regime-shift approach and the gradualist approach to disinflation. On the one hand, the traditional view is that gradual disinflation is less costly because it allows for an adjustment of wages and prices [START_REF] Taylor | Union Wage Settlements during a Disinflation[END_REF]. On the other hand, [START_REF] Sargent | Stopping Moderate Inflations: The Methods of Poincare and Thatcher[END_REF] argues that sharp shifts in monetary regimes decrease disinflation costs due to the enhanced policy credibility and the accompanying quick adjustment of inflation expectations. We measure Speed by the change in trend inflation over the duration of an inflation episode. Further on, we follow [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] and augment the baseline regression by decomposing the Speed variable into the change in trend inflation during a particular episode (Change) and the duration of each disinflation episode (Length). For OECD countries, the empirical studies generally confirm that the faster the disinflation process, the lower the output costs, i.e., credible disinflations are associated with smaller output losses [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF][START_REF] Boschen | The Ex Ante Credibility of Disinflation Policy and the Cost of Reducing Inflation[END_REF][START_REF] Diana | Central bank independence, speed of disinflation and the sacrifice ratio[END_REF][START_REF] Daniels | Openness, central bank independence, and the sacrifice ratio[END_REF], Zhang 2005[START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF], [START_REF] Daniels | Trade openness, capital mobility, and the sacrifice ratio[END_REF][START_REF] Daniels | Exchange-rate pass through, openness, and the sacrifice ratio[END_REF][START_REF] Gonçalves | Inflation targeting matters: Evidence from OECD economies' sacrifice ratios[END_REF][START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF][START_REF] Roux | Sacrifice ratios and inflation targeting: the role of credibility[END_REF][START_REF] Katayama | What determines the sacrifice ratio? A Bayesian model averaging approach[END_REF]). On the other hand, Andersen and Wascher (1999) show that speed of disinflation does not matter in OECD countries; [START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] confirm this conclusion for non-OECD countries; [START_REF] Gonçalves | Inflation targeting and the sacrifice ratio[END_REF] obtain similar results for both developed and developing countries; while [START_REF] Caporale | Government Ideology, Democracy and the Sacrifice Ratio: Evidence from Latin American and Caribbean Disinflations[END_REF] finds that the speed of disinflation may, in fact, increase sacrifice ratios.

A priori, the association between CBI and disinflation costs is ambiguous. On the one hand, more independent central banks are capable of anchoring inflation expectations, which reduces disinflation costs. On the other hand, higher CBI should be associated with lower average inflation and lower inflation variability, which leads to less frequent price and wage adjustments, thus making disinflation more costly due to the flatter Phillips curve [START_REF] Walsh | Central bank independence and the costs of disinflation in the European Community[END_REF]. The early empirical evidence (Debelle and Fisher 1994[START_REF] Jordan | Disinflation costs, accelerating inflation gains, and central bank independence[END_REF][START_REF] Bernanke | Inflation Targeting: Lessons from the International Experience[END_REF][START_REF] Posen | Central Bank Independence and Disinflationary Credibility: A Missing Link?[END_REF] suggest that higher CBI increases the output loss of disinflation (the so-called "Credibility-Sacrifice Ratio Puzzle"). Nevertheless, some recent empirical studies (Baltensperger and Kugler 2000[START_REF] Brumm | The Sacrifice Ratio and Central Bank Independence Revisited[END_REF][START_REF] Diana | Central bank independence, speed of disinflation and the sacrifice ratio[END_REF][START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] find that CBI reduces output losses of disinflation in OECD countries, whereas [START_REF] Daniels | Openness, central bank independence, and the sacrifice ratio[END_REF], Daniels andVanHoose (2009, 2013) provide opposite evidence.

The rationale for including trade openness (Openness) in the regression is due to [START_REF] Romer | Openness and Inflation: Theory and Evidence[END_REF], who argues that openness makes the Phillips curve steeper, i.e. it worsens the output-inflation trade-off. Specifically, in open economies, monetary contraction exerts a larger direct pressure on the price level through the exchange-rate appreciation. Consequently, following a negative monetary shock, the decline in inflation is larger in open economies, leading to lower sacrifice ratios. On the other hand, Daniels and VanHoose (2006) show that, in the presence of nominal rigidities, openness may increase the sacrifice ratio. As for the empirical evidence, [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF], Temple (2002), [START_REF] Daniels | Openness, central bank independence, and the sacrifice ratio[END_REF], and Daniels and VanHoose (2013) find a negative and/or insignificant relationship between openness and sacrifice ratio in OECD countries, while [START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] supports this result for the developing countries. [START_REF] Daniels | Trade openness, capital mobility, and the sacrifice ratio[END_REF] provide evidence on the positive association between openness and sacrifice ratios.

In small open economies, foreign exogenous shocks (Shocks) may have strong effects on domestic inflation, thus making the calculation of the sacrifice ratio biased. For instance, during a particular disinflation episode, a favorable supply shock may lead to a larger decline in inflation for a given monetary policy action. In this case, the reduced sacrifice ratio is partially caused by the favorable supply shock and not by monetary policy. [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] estimates the sacrifice ratio employing a method which smooths out supply shocks, identifying them as part of the error term. Nevertheless, deemed an important aspect in the model, he performs two types of estimations to check for their presence in the data and fails to find any strong evidence which would indicate that they bias the results. As our sample consists of EMEs, we consider foreign shocks as an important determinant of the sacrifice ratio. It is well-known that these countries operate under unfavorable macroeconomic environment compared to the advanced economies, i.e. they are frequently exposed to adverse external shocks, which result in large inflation-output volatility (Fraga et al. 2003). Therefore, we control for foreign shocks in two ways: first, by including the changes in oil prices as a regressor in the baseline specification, and second, by estimating the model for the period of 1990-2017, which is arguably characterized by a more stable macroeconomic environment. Andersen and Wascher (1999), [START_REF] Boschen | The Ex Ante Credibility of Disinflation Policy and the Cost of Reducing Inflation[END_REF] and [START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF], too, include oil prices among the determinants of the sacrifice ratio, serving as a proxy variable that controls for the external shocks. Yet, the empirical evidence provided in these papers seems to be rather inconclusive.

Initial inflation is included as a standard determinant of the sacrifice ratio. Both Lucas (1973) and [START_REF] Ball | The New Keynesian Economics and the Output -Inflation Trade-off[END_REF] show that trend inflation influences the output-inflation trade-off in the sense that higher inflation reduces the degree of downward nominal rigidity and thus steepens the Phillips curve. Therefore, higher initial inflation should be associated with lower output costs of disinflation. [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF], Andersen and Wascher (1999), Temple (2002), Zhang (2005), [START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF], Gonçalves andCarvalho (2008, 2009), and [START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] all provide empirical support to the aforementioned proposition for both developed and developing countries. On the other hand, [START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF], [START_REF] Daniels | Trade openness, capital mobility, and the sacrifice ratio[END_REF], and Caporale (2011) fail to confirm these findings.

Nominal wage rigidity is another variable we control for, in order to isolate the effects of IT on the sacrifice ratio. As mentioned above, within New Keynesian models, nominal rigidities exert critical influence on the inflation-output trade-off, the speed of disinflation, and the sacrifice ratio. In these regards, initial inflation may serve as a proxy for the extent of nominal rigidity since wage contracts tend to be shorter at high inflation rates. Due to the lack of available labor market data for EMEs, we adopt the convention put forth by [START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF], who uses the 10year inflation history as a proxy for nominal wage rigidity, expecting a negative association between this variable (Nominal Rigidity) and the sacrifice ratio.

In principle, one should expect to find a positive relationship between government debt (Debt) and the sacrifice ratio. Indeed, [START_REF] Durham | Sacrifice ratios and monetary policy credibility: do smaller budget deficits, inflation-indexed debt, and inflation targets lower disinflation costs?[END_REF], [START_REF] Brito | Inflation targeting does not matter: another look at OECD sacrifice ratios[END_REF] and [START_REF] Roux | Sacrifice ratios and inflation targeting: the role of credibility[END_REF] provide empirical evidence that supports this proposition for the developed countries, though [START_REF] Durham | Sacrifice ratios and monetary policy credibility: do smaller budget deficits, inflation-indexed debt, and inflation targets lower disinflation costs?[END_REF] and [START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] find opposite results for the developing economies. Finally, following [START_REF] Caporale | Political Regimes and the Cost of Disinflation[END_REF] and [START_REF] Caporale | Government Ideology, Democracy and the Sacrifice Ratio: Evidence from Latin American and Caribbean Disinflations[END_REF], in order to control for the effects of political factors, we include a dummy variable (Party), which takes three values: zero if the government is controlled by left-wing parties, one in case of a centrist government, and two for right-wing parties. As right-wing governments are known to be inflation-averse, the expected coefficient in front of this variable is negative.

Estimation Results

We follow [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] as our initial estimation strategy. Table 1 contains some basic estimates (columns 1 and 2) along with the estimation of the full specification of equation ( 1), presented in the last column. As it can be seen, in all cases, the effect of IT on the sacrifice ratio is positive and highly significant. Hence, this finding suggests that the real costs of disinflation in EMEs that have adopted IT are larger compared to EMEs with different monetary strategies. As it will be shown in the following section, this result remains valid in a variety of different specifications and subsampling. In column (1) the variable Speed has the expected sign, but it is not statistically significant. We expand this variable in column (2) by decomposing it into the difference in trend inflation during a disinflationary episode (Change) and the duration of an episode (Length). This has resulted in a slight improvement in the explanatory power of the regression, as evidenced by the adjusted R-squared coefficient, which has increased from 0.056 to 0.079. In columns ( 2) and (3), we find a negative association between the length of disinflation episodes and the sacrifice ratio. Therefore, we provide empirical support to gradualism as a viable pathway for making disinflation less costly [START_REF] Taylor | Union Wage Settlements during a Disinflation[END_REF]. The magnitude of Length is -0.0017 in column (3) which, given its standard deviation of 2.7 years, implies that the prolongation of a disinflationary episode by one additional year has an economically important influence on sacrifice ratio. In what follows, we provide a brief comment on the control variables included in the regression model (column 3). As for CBI, we find a positive and highly significant effect on the sacrifice ratio, which implies that disinflation seems to be more costly in EMEs with more independent central banks. This result reaffirms previous claims in the empirical literature put forth by Debelle and Fisher (1994), [START_REF] Jordan | Disinflation costs, accelerating inflation gains, and central bank independence[END_REF][START_REF] Posen | Central Bank Independence and Disinflationary Credibility: A Missing Link?[END_REF], among others. According to [START_REF] Walsh | Central bank independence and the costs of disinflation in the European Community[END_REF], with more independent central banks, nominal contracts tend to be longer reflecting the lower inflation expectations. As a result, higher CBI both shifts the Phillips curve inwards and makes it flatter, which increases the sacrifice ratio. Further on, the coefficient of Openness is negative suggesting that EMEs which are more open to international trade experience lower disinflation costs. Finally, we find that external shocks exert statistically significant and economically important effects on the sacrifice ratio, thus supporting our choice to include this variable in the regression model. As it can be seen, adverse external shocks, as proxied by changes in the oil price, make disinflation in EMEs more costly.

Robustness Analysis

The main finding from Table 1 is that adopting IT is associated with higher disinflation costs in EMEs. In this section, we check the robustness of this conclusion, initially by including additional control variables commonly found in the empirical literature, which could possibly add to the explanatory power of the regression model. In this exercise, first we retain the same time period of analysis , while further on we estimate the empirical model on a shorter sub-sample . In column (1) of Table 2, we estimate the same specification as before, while allowing for a less formal implementation of the IT regime, following [START_REF] Bernanke | Inflation Targeting: Lessons from the International Experience[END_REF]. As it can be seen, both the sign and statistical significance of all regression coefficients remain virtually the same as in our base specification (presented in column 3 of Table 1). In other words, according to our results, the form of implementing the IT regime (full-fledged versus lite-IT), does not in any way alter its effect on the sacrifice ratio. In columns ( 2), (3), and (4) of Table 2, we proceed by augmenting the baseline regression model with additional variables extracted from the empirical literature, while retaining our initial measure of IT, i.e., considering only full-fledged IT regimes.

In column (2) we control for the fiscal position as proxied by the debt/GDP ratio. As expected, the coefficient of Debt has a positive sign, indicating that a higher debt level during a disinflation episode increases the sacrifice ratio in EMEs. Yet, note that this effect is not statistically significant. Initial inflation, too, has the expected coefficient, as implied by economic theory, but neither is this effect significant (column 3). Regarding nominal rigidities, we find a positive and significant effect, but we are rather cautious in its interpretation (column 4). Since the data on nominal wage rigidity is not available for EMEs, we employ the proxy variable used in [START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF] and [START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF], namely the 10-year inflation history prior to each disinflation episode. The rationale behind this approach is that the countries with long history of high inflation tend to develop some mechanisms, allowing prices and wages to adjust frequently (for instance, indexation of nominal contracts). Therefore, these countries are characterized by a lower degree of nominal rigidities. Yet, in column (4), the regression coefficient before inflation history is counterintuitive, i.e. contrary to both theory and empirical evidence, thus potentially raising concerns with respect to the validity of this proxy variable. Simultaneously, there is room for an alternative interpretation of the above finding: the long history of high inflation in EMEs is associated with deeply rooted inflation expectations and low credibility of disinflation policies, which results in high sacrifice ratios. As for the rest of the control variables, we obtain similar results as above.

In Table 3, we provide various estimates for the sub-sample over the 1990-2017 period. It is well known that EMEs experienced a highly volatile macroeconomic environment in the 1970s and 1980s, followed by a period of greater stability. Since the regression estimates presented above are based on the whole sample, comprising the highly volatile period, they could suffer from problems related to sample heterogeneity or potential heteroskedasticity. Therefore, in order to check for the potential sensitivity of our results, we have re-estimated our base specification for the post-1990s sample (column 5). All results are fundamentally unaltered. Again, in column (1) we allow for a less formal implementation of the IT regime. Notwithstanding the definition, the relation between IT and the sacrifice ratio remains positive and statistically significant. Also, the magnitude of the regression coefficient before IT remains stable across the two samples. In columns (2)-(4), we augment the baseline specification by including three control variables, respectively. The results obtained are basically equivalent to the ones in Table 2, once again confirming the robust relationship between IT and the sacrifice ratio. An apparent pattern emerging from Table 3 refers to the effect of external shocks. While the regression coefficient in front of Shocks remains positive, as expected, it is not statistically significant anymore. These results are probably related to the more stable macroeconomic environment in the post-1990s period, making the effect of external shocks negligible. Additional peculiarity is the relation between Party and the sacrifice ratio, which remains negative, but now it is statistically significant across Table 3. Therefore, for the sub-sample covering the recent period we are able to provide evidence that disinflation costs in EMEs are lower under right-wing governments, which is consistent with the findings by [START_REF] Caporale | Political Regimes and the Cost of Disinflation[END_REF] and [START_REF] Caporale | Government Ideology, Democracy and the Sacrifice Ratio: Evidence from Latin American and Caribbean Disinflations[END_REF]. Note that the statistical significance of Party in the post-1990s period partly reflects the profound political changes within our sample. Specifically, it was only after 1990s that ring-wing political parties emerged in the former communist countries. Consequently, the divide between right and left-wing governments is more evident in the post-1990s period. Compared to developed countries, disinflation episodes in EMEs are characterized by both higher peak inflation and trend inflation. For instance, initial inflation has a mean of 77.5% in our sample. In what follows, we check for the sensitivity of our results to various peak levels of trend inflation. Dornbusch and Fischer (1993) and [START_REF] Burton | Ending Moderate Inflations[END_REF] define episodes of moderate inflation as periods with inflation rates in the range of 15% -30%, lasting at least 3 years. [START_REF] Fischer | Modern Hyper -and High Inflations[END_REF] term an inflation episode in the range of 25% -50% as moderate to high, while [START_REF] Bruno | Inflation Crisis and Long-Run Growth[END_REF] identify all two-year periods with inflation rates beyond 40% as inflation crises. Table 4 provides the estimates from the regression based on different peak levels of trend inflation during disinflation episodes, both for the entire sample and for the post-1990s sub-sample subsequently. Column (1) considers only disinflation episodes with trend inflation at the peak of each disinflation episode less than (or equal to) 20%. In this way, we identify 84 and 58 disinflation episodes for the two samples, respectively. As it can be seen, taking different thresholds for the peak trend inflation does not affect our findings, i.e., the relation between IT and the sacrifice ratio remains positive and statistically significant. In columns ( 2)-( 4), we set the threshold for trend inflation at the peak of disinflation episodes at 30%, 40% and 50%, respectively. As we allow for higher peak trend inflation, the magnitude of the coefficient of IT remains remarkably stable. Additionally, note that, with higher trend inflation, external shocks become statistically significant in the whole sample (columns 3 and 4), which points to the potentially close relationship between disinflation episodes and external shocks in EMEs during the highly volatile pre-1990s period. Since the formula for calculating the sacrifice ratio includes the deviations from potential output in the numerator, this procedure clearly leaves the possibility for any errors in the estimates of potential output open, which would translate in the magnitude of sacrifice ratios. Table 5 provides evidence on the sensitivity of our results with respect to the filter used in detrending output. In columns 1-2, we employ the original [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] method for calculating the sacrifice ratio, for the two samples, respectively. By comparing the results in these two columns with those reported previously, we cannot detect any fundamental differences. Again, we find that adopting the IT regime in EMEs is associated with larger sacrifice ratios. Similarly, the coefficients of the remaining variables not only retain their signs and levels of significance, but they follow the same patterns as those reported in Tables (2-4). We proceed checking for the potential sensitivity of our results to the estimation of potential output by employing the Hodrick-Prescott filter (columns 3-4) and the Hamilton ( 2018) filter (columns 5-6). As [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] argues the Hodrick-Prescott filter has the tendency of minimizing the deviations between trend and cyclical output, thus exerting a downward bias on the obtained sacrifice ratio values. Indeed, Table 5 confirms that the effect of IT on the sacrifice ratio is sensitive on the method selected for measuring the output gap. Specifically, when potential output is measured by applying both the Hodrick-Prescott and the Hamilton filter, the coefficient of IT loses its statistical significance. Also, note that the adjusted R-squared deteriorates sharply as we move from left to right across Table 5, supporting the findings in [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF]), Temple (2002), and [START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] that the alternative methods for calculating the sacrifice ratio may be less accurate. Temple ( 2002), Gonçalves andCarvalho (2008, 2009), [START_REF] Brito | Inflation targeting does not matter: another look at OECD sacrifice ratios[END_REF][START_REF] Mazumder | Determinants of the Sacrifice Ratio: Evidence from OECD and non-OECD Countries[END_REF] follow [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] in identifying as disinflation episodes only the periods in which trend inflation has fallen by at least 1.5 percentage points from peak to trough (2 percentage points when using quarterly data). In Table 6, following Cecchetti's comments in [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF] as well as [START_REF] Cecchetti | Structural estimates of the US sacrifice ratio[END_REF] and [START_REF] Hofstetter | Disinflations in Latin America and the Caribbean: A free lunch?[END_REF], we examine the sensitivity of our baseline estimation (reported in Table 1, column 3) to alternative definitions of disinflation episodes. In column (1) we consider only disinflation episodes with a change in trend inflation from peak to trough in a disinflationary episode equal to or greater than 2 percentage points. By applying this threshold, we have identified 130 disinflation episodes in total. Again, the coefficient of IT is positive and statistically significant, indicating that the relation between IT and the sacrifice ratio remains stable under this restriction. The apparent pattern of stability of this coefficient is evident in columns ( 2) and (3), too in which we define disinflation episodes as the periods with declines in trend inflation equal to or greater than 3 and 5 percentage points, respectively. As we only restrict our attention to the episodes in which the change in trend inflation is equal to or greater than 10 percentage points (column 4), we observe that IT does not affect the sacrifice ratio. However, we consider this result dubious, given the low number of identified disinflation episodes (49), and the ambiguous specification of this model as shown by the negative adjusted R-squared coefficient. Finally, note that, as the change in trend inflation throughout the inflationary episode is additionally restricted to greater than 3 percentage points (column 2) and greater than 5 percentage points (column 3), the length of the disinflation episode no longer matters for the sacrifice ratio. In other words, it seems that, when the disinflation is larger, i.e., trend inflation is reduced substantially, it does not matter whether the disinflation is quick or gradual.

Conclusion

In this paper, we study the relationship between IT and the sacrifice ratio in EMEs, controlling for a number of additional factors, which may affect the cost of disinflation. In calculating the sacrifice ratio, we employ a slightly modified version of the approach used in [START_REF] Ball | What Determines the Sacrifice Ratio?[END_REF]. Using this methodology, we have identified 170 disinflation episodes in 44 EMEs, i.e. 78 disinflation episodes during 1970-1990 and 92 disinflation episodes in the 1990-2017 period. We provide strong evidence that adopting IT in EMEs is associated with higher output costs during disinflation episodes. In addition, we find that gradual disinflation may be less costly in EMEs. Also, we show that trade openness is associated with lower sacrifice ratios, while both more independent central banks and adverse external shocks lead to higher sacrifice ratios. Our main findings are robust to alternative classifications of the IT regime (full-fledged versus lite-IT), alternative definitions of disinflation episodes (different thresholds for the cumulative decline in trend inflation during an episode), different thresholds for high inflation, different peak levels of trend inflation rate, across various specifications of the empirical model, as well as for both the entire sample and the sub-sample covering the post-1990s period. Yet, we find that the results are sensitive to the filtering method employed for estimating trend output, i.e., the regression estimates lose their statistical significance when we apply both the Hodrick-Prescott and the Hamilton filter.

Table 1 .

 1 Determinants of sacrifice ratio inEMEs, 1970EMEs, -2017

		(1)	(2)	(3)
	IT	0.0175***	0.0186***	0.016***
		(0.006)	(0.006)	(0.006)
	Speed	0.00005	-	-
		(0.0011)
	Change	-	0.0003	0.0002
			(0.0002)	(0.0002)
	Length	-	-0.0016*	-0.0017**
			(0.0008)	(0.0007)
	CBI	-	-	0.029***
				(0.0107)
	Party	-	-	-0.0025
				(0.0017)
	Openness	-	-	-0.011*
				(0.006)
	Shocks	-	-	0.014*
				(0.008)
	Constant	-0.00013	0.0076**	0.0016
		(0.0019)	(0.0033)	(0.005)
	Sample size	164	164	140
	Adjusted	0.056	0.079	0.216
	Notes: Sacrifice ratio is the dependent variable; OLS estimates with HAC standard errors in parentheses; ***, **,
	and * indicate 1%, 5% and 10% level of significance, respectively.

Table 2

 2 : Sacrifice ratio is the dependent variable; OLS estimates with HAC standard errors in parentheses; ***, **, and * indicate 1%, 5% and 10% level of significance, respectively.

	. Robustness checks, 1970-2017
		(1)	(2)	(3)	(4)
	Inflation Targeting	0.0116**	0.0161***	0.0160***	0.0141**
	(IT)	(0.005)	(0.006)	(0.006)	(0.006)
	Change	-0.0005	0.0002	0.0005	-0.0002
		(0.0002)	(0.0002)	(0.0005)	(0.0002)
	Length	-0.0018**	-0.0017**	-0.0017**	-0.0016**
		(0.0008)	(0.0008)	(0.0008)	(0.0007)
	CBI	0.029***	0.029***	0.029***	0.033**
		(0.011)	(0.011)	(0.011)	(0.0129)
	Party	-0.003	-0.0025	-0.0025	-0.0047**
		(0.002)	(0.0017)	(0.0017)	(0.0022)
	Openness	-0.011*	-0.0115*	-0.0115*	-0.0154**
		(0.006)	(0.006)	(0.006)	(0.0072)
	Shocks	0.0144*	0.0150	0.0143*	0.009
		(0.008)	(0.010)	(0.008)	(0.0124)
	Debt	-	0.0008	-	-
			(0.004)
	Initial Inflation	-	-	-0.0003	-
				(0.0005)
	Nominal Rigidity	-	-	-	0.0023*
					(0.0012)
	Constant	0.0019	0.0011	0.0018	0.0031
		(0.005)	(0.006)	(0.0054)	(0.007)
	Sample size	140	140	140	112
	Adjusted	0.191	0.210	0.210	0.243

Notes

Table 3

 3

	. Robustness checks, 1990-2017
		(1)	(2)	(3)	(4)	(5)
	Inflation	0.011*	0.015**	0.014**	0.012*	0.015**
	Targeting (IT)	(0.0057)	(0.0063)	(0.0065)	(0.0064)	(0.0062)
	Change	0.0003	0.0003	0.043	-0.0008*	0.0003
		(0.0005)	(0.0005)	(0.0381)	(0.0004)	(0.0004)
	Length	-0.002**	-0.002**	-0.002**	-0.002**	-0.002**
		(0.0009)	(0.0009)	(0.0009)	(0.0009)	(0.0009)
	CBI	0.031**	0.031*	0.030**	0.032**	0.031**
		(0.0153)	(0.0161)	(0.0151)	(0.0160)	(0.0152)
	Party	-0.005**	-0.004*	-0.005*	-0.007**	-0.004*
		(0.0025)	(0.0023)	(0.0024)	(0.0025)	(0.0023)
	Openness	-0.022**	-0.022**	-0.023**	-0.022**	-0.022**
		(0.0091)	(0.0093)	(0.0092)	(0.0093)	(0.0092)
	Shocks	0.016	0.016	0.0164	0.012	0.015
		(0.0216)	(0.0228)	(0.0208)	(0.0236)	(0.0208)
	Debt	-	0.002	-	-	-
			(0.0069)
	Initial Inflation	-	-	-0.043	-	-
				(0.0383)
	Nominal	-	-	-	0.003**	-
	Rigidity				(0.0012)
	Constant	0.014	0.012	0.018	0.015	0.0132
		(0.0105)	(0.0123)	(0.0118)	(0.0108)	(0.0100)
	Sample size	79	79	79	73	79
	Adjusted	0.244	0.263	0.270	0.279	0.273
	Notes: Sacrifice ratio is the dependent variable; OLS estimates with HAC standard errors in parentheses; ***, **,
	and * indicate 1%, 5% and 10% level of significance, respectively.

Table 4 .

 4 Estimates with different peak levels of trend inflation rate

			1970-2017
		(1)	(2)	(3)	(4)
	Inflation Targeting	0.015**	0.017***	0.016***	0.016***
	(IT)	(0.0065)	(0.0062)	(0.0059)	(0.0060)
	Change	0.011	0.018	0.002	0.004
		(0.0722)	(0.0380)	(0.0102)	(0.0211)
	Length	-0.002	-0.002*	-0.002**	-0.002*
		(0.0021)	(0.0011)	(0.0009)	(0.0010)
	CBI	0.033**	0.029**	0.030**	0.029**
		(0.0134)	(0.0123)	(0.0120)	(0.0119)
	Party	-0.002	-0.003	-0.003	-0.003
		(0.0031)	(0.0024)	(0.0022)	(0.0021)
	Openness	-0.013**	-0.013**	-0.012**	-0.012**
		(0.0062)	(0.0061)	(0.0062)	(0.0061)
	Shocks	0.022	0.017	0.017*	0.017*
		(0.0137)	(0.0110)	(0.0105)	(0.0099)
	Constant	0.004	0.003	0.003	0.003
		(0.0093)	(0.0068)	(0.0065)	(0.0063)
	Sample size	84	109	116	119
	Adjusted	0.251	0.228	0.219	0.221
			1990-2017
		(1)	(2)	(3)	(4)
	Inflation Targeting	0.014*	0.015**	0.014**	0.014**
	(IT)	(0.0072)	(0.0066)	(0.0064)	(0.0064)
	Speed	-0.057	-0.029	-0.043	-0.020
		(0.0760)	(0.0451)	(0.0445)	(0.0222)
	Length	-0.001	-0.002	-0.001	-0.002*
		(0.0019)	0.0012	(0.001)	(0.0010)
	CBI	0.035**	0.033**	0.035**	0.033**
		(0.0171)	(0.0166)	(0.0168)	(0.0159)
	Party	-0.005	-0.005*	-0.005*	-0.005*
		(0.0033)	(0.0027)	(0.0027)	(0.0159)
	Openness	-0.025**	-0.024**	-0.023**	-0.022**

Table 5 .

 5 Robustness checks with alternative methods for estimating trend output

		(1)	(2)	(3)	(4)	(5)	(6)
		Ball	Ball	Hodrick-	Hodrick-	Hamilton	Hamilton
		1970-2017	1990-2017	Prescott	Prescott	1970-2017	1990-2017
				1970-1990	1990-2017
	Inflation	0.007***	0.006**	0.009	0.004	0.011	0.015*
	Targeting (IT)	(0.0025)	(0.0027)	(0.0089)	(0.0100)	(0.008)	(0.0089)
	Change	0.00007	0.0001	-0.0002	-0.0003	0.0001	0.0004
		(0.0008)	(0.0004)	(0.0002)	(0.0008)	(0.0001)	(0.0008)
	Length	-0.0008**	-0.0008**	0.0011	0.0012	-0.0005	-0.0009
		(0.0003)	(0.0004)	(0.0011)	(0.0012)	(0.0008)	(0.0008)
	CBI	0.012***	0.013*	0.001	-0.020	0.009	0.032
		(0.0046)	(0.0066)	(0.0205)	(0.0268)	(0.0167)	(0.0238)
	Party	-0.001	-0.002**	0.0008	0.0009	-0.002	-0.003
		(0.0007)	(0.0010)	(0.0050)	(0.0060)	(0.0029)	(0.0036)

Table 6 .

 6 Sacrifice ratio and different rates of difference in trend inflation from peak to trough, : Sacrifice ratio is the dependent variable; OLS estimates with HAC standard errors in parentheses; ***, **, and * indicate 1%, 5% and 10% level of significance, respectively.

	1970 -2017
		(1)	(2)	(3)	(4)
	Inflation Targeting	0.016**	0.013***	0.014**	0.0085
	(IT)	(0.0062)	(0.0051)	(0.0067)	(0.0120)
	Change	0.0002	0.0001	0.0002	0.0002
		(0.0002)	(0.0002)	(0.0002)	(0.0002)
	Length	-0.0017**	-0.001	-0.0011	-0.0009
		(0.0008)	(0.0008)	(0.0008)	(0.0008)
	CBI	0.026**	0.017*	0.017	0.0115*
		(0.0109)	(0.0102)	(0.0120)	(0.0058)

Notes

Specifically, the sample consists of the following countries: Argentina, Algeria, Brazil, Botswana, China, Costa Rica, Cote d'Ivoire, Chile, Colombia, Czech Republic, Croatia, Dominican Republic, Ecuador, Egypt, El Salvador, Ghana, Guatemala, Hungary, Israel, Indonesia, India, Jordan, Lebanon, Malaysia, Morocco, Mexico, Nigeria, Pakistan, Panama, Peru, Philippines, Poland, Russia, Serbia, Singapore, South Africa, South Korea, Tanzania, Thailand, Turkey, Tunisia, Uruguay, Ukraine, and Venezuela.

 Zhang, L. (2005). Sacrifice Ratios with Long-Lived Effects. International Finance, 8, 231-262.

APPENDIX