FACULTY OF COMMERCE, LAW, AND MANAGEMENT 

 

Essays on Inflation Targeting and Macroeconomic Performance 

 

Norbert Sfiso Buthelezi 

 

Supervisor 

Prof Christopher Malikane 

 

A thesis submitted to the Faculty of Commerce, Law and Management, University of the 

Witwatersrand, Johannesburg, in fulfilment of the requirements of the Doctor of Philosophy in 

Economics. 

10 June 2024 

  



ii 
 

Declaration 
I, Norbert Sfiso Buthelezi hereby submit this thesis for the degree of Doctor of Philosophy 

(Economics) at the University of Witwatersrand. I hereby declare that this thesis is my own work 

and has not been submitted by me for any degree or examination at any other university. 

 

Signed: ________________________ 

Name of the Student:  Norbert Sfiso Buthelezi 

10 June 2024 



iii 
 

Dedication 

I dedicate this doctoral thesis to my departed parents, Henry and Lydia Buthelezi and my beautiful 

sister, Lungile Ursula Buthelezi who also left this world too young. My parents sacrificed 

everything to ensure that I get the best available education during the dark days of apartheid South 

Africa.  

  



iv 
 

Acknowledgement 
I would like to profusely thank my supervisor, Professor Christopher Malikane for first accepting 

to supervise me, and understanding that my proposed topic will add value to the body of 

knowledge. His in-depth knowledge of the subject and intellectualism assisted me a great deal in 

completing this thesis.  

I also acknowledge Professor Carlos Correia who supervised me for my master’s thesis at UCT. I 

also thank Professor Brian Khan who was responsible for the BCom (Honours) Economics class 

when I registered at UCT immediately after my release from Robben Island, and all the lecturers 

who laid the foundation of this doctoral thesis. I am also grateful to all my high school and primary 

school teachers. I also thank my study mates on Robben Island, particularly, Dr Sbongiseni 

Dhlomo, Zamile Mazantsana, Peter-Paul Ngwenya and Kaobitsa Bushy Maape. A special word of 

gratitude to Matshidiso Molobi Dlamini and Lerato Ramatsui for the time they took to edit my 

thesis before submission. I also thank Dr Tshepo Mokoka and Tshepo Mashigo for the support and 

assistance they gave me as I was busy with this research.  

A special gratitude and appreciation to my late parents, Henry and Lydia Buthelezi for nurturing 

me, guiding me, praying for me and protecting me throughout their lives and made me realize the 

importance of knowledge and education. I also acknowledge the role played by my brothers, 

sisters, nephews, nieces, and grandchildren. Without their support and encouragement, I would not 

have reached this milestone. 

I am perpetually indebted to my wife, Thandeka MaXhakaza Buthelezi, for the support and love 

she gave me as I was busy with this research. Nodlela! Without her besides me, this task would 

have been insurmountable. A special mention and gratitude to our beautiful princess, Mandulo 

Qhawekazi. I thank everyone who assisted and pushed me along the way. 

  



v 
 

Table of Contents 
 

Declaration ...................................................................................................................................... ii 

Dedication ...................................................................................................................................... iii 

Acknowledgement ......................................................................................................................... iv 

List Of Figures .............................................................................................................................. vii 

List Of Tables............................................................................................................................... viii 

List Of Acronyms And Abbreviations ........................................................................................... ix 

Abstract ........................................................................................................................................... x 

1. Inflation Targeting as Monetary Policy Framework ............................................................... 1 

1.1. Background ...................................................................................................................... 1 

1.2. Research Problem ............................................................................................................. 3 

1.3. Contribution ..................................................................................................................... 3 

1.4. Research Questions .......................................................................................................... 3 

1.5. Structure of the study ....................................................................................................... 5 

2. Inflation Targeting and Macroeconomic Performance: A Taylor Rule Approach .................. 6 

2.1. Introduction ...................................................................................................................... 6 

2.2. Literature review .............................................................................................................. 9 

2.3. Methodology .................................................................................................................. 16 

2.3.1. Defining the degree of inflation targeting ............................................................... 16 

2.4. Empirical analysis .......................................................................................................... 19 

2.4.1. Data description ...................................................................................................... 19 

2.4.2. Effect of IT on bond yields ..................................................................................... 23 

2.4.3. Effect of IT on bond yields POST IT...................................................................... 27 

2.4.4. Effect of IT on economic growth ............................................................................ 28 

2.4.5. Effect of IT on economic growth: POST IT ........................................................... 32 

2.4.6. Effect of IT on inflation .......................................................................................... 36 



vi 
 

2.4.7. Effect of IT on inflation: Post IT ............................................................................ 39 

2.5. Conclusion ...................................................................................................................... 42 

3. Are Inflation Targets Optimal? ............................................................................................. 44 

3.1. Introduction .................................................................................................................... 44 

3.2. Literature review on the non-linear inflation-growth nexus .......................................... 46 

3.3. The non-linear growth-inflation relationship ................................................................. 49 

3.4. Empirical analysis .......................................................................................................... 52 

3.4.1. Optimal inflation and economic growth ................................................................. 52 

3.4.2. Optimal inflation and unemployment ..................................................................... 56 

3.4.3. Post-inflation targeting estimations. ....................................................................... 59 

3.5. Conclusion ...................................................................................................................... 63 

4. The Behaviour of Fiscal Policy Under Inflation Targeting ................................................... 65 

4.1. Introduction ................................................................................................................. 65 

4.2. Literature review ........................................................................................................ 67 

4.3. Theoretical framework ............................................................................................... 72 

4.3.1. The government’s budget constraint and the Taylor rule ....................................... 72 

4.3.2. Fiscal policy rules ................................................................................................... 74 

4.4. Empirical analysis ....................................................................................................... 75 

4.4.1. Estimating the basic fiscal rules .............................................................................. 75 

4.4.2. The role of the exchange rate in basic fiscal rules .................................................. 79 

4.4.3. The growth effect in basic fiscal rules .................................................................... 82 

4.5. Conclusion ..................................................................................................................... 85 

5. Conclusion ............................................................................................................................ 86 

6. References .............................................................................. Error! Bookmark not defined. 

 

  



vii 
 

List of Figures 
Figure 2.1 Evolution of IT Advanced economies ......................................................................... 21 

Figure 2.2 Evolution of IT Emerging Market ............................................................................... 21 

Figure 3.1 Inflation and economic growth .................................................................................... 55 

Figure 3.2 Nonlinear relation between inflation and unemployment ........................................... 58 

 

  



viii 
 

List of Tables 
Table 2.1 Augmented Dickey-Fuller unit root tests...................................................................... 22 

Table 2.2 Effect of inflation targeting on bond yields .................................................................. 24 

Table 2.3 Effect of inflation targeting on bond yields .................................................................. 27 

Table 2.4 Effect of inflation targeting on growth rate .................................................................. 29 

Table 2.5 Effect of inflation targeting on growth rate .................................................................. 32 

Table 2.6 Effect of inflation targeting on inflation ....................................................................... 36 

Table 2.7 Effect of inflation targeting on inflation: Post IT ......................................................... 39 

Table 3.1 Nonlinear relation between inflation and economic growth ......................................... 53 

Table 3.2 Nonlinear relation between inflation and unemployment ............................................. 56 

Table 3.3 Inflation and economic growth ..................................................................................... 60 

Table 3.4 Inflation and unemployment ......................................................................................... 61 

Table 4.1 Augmented Dickey-Fuller test ...................................................................................... 76 

Table 4.2 Estimation of the basic deficit fiscal rule (eq. 11, 𝜙𝑞 = 𝛾 = 0) .................................. 77 

Table 4.3 Estimation of the basic expenditure fiscal rule (eq. 15, 𝜙𝑞 = 𝛾 = 0) .......................... 78 

Table 4.4 Deficit rule with the real effective exchange rate ......................................................... 80 

Table 4.5 Expenditure rule with the real effective exchange rate ................................................. 81 

Table 4.6 Deficit rule with the real effective exchange rate and economic growth ..................... 82 

Table 4.7 Expenditure rule with the real effective exchange rate and economic growth ............. 83 

Table 4.8 Deficit rule with dollarized debt ................................................................................... 84 

 

  



ix 
 

List of Acronyms and Abbreviations  
IT - Inflation Targeting 

CB - Central Bank 

RBNZ- Reserve Bank of New Zealand 

FED - Federal Reserve Board 

BOE - Bank of England  

BCB - Banco Central do Brasil (Brazil’s Central Bank) 

FRED - Federal Reserve Economic Data 

NAIRU - Non- Accelerating Inflation Rate of Unemployment  

ARDL - Autoregressive Distributed Lag 

EU - European Union 

USA - United States of America  

OECD - The Organization for Economic Cooperation and Development  

UK- United Kingdom 

  



x 
 

Abstract 
This thesis focuses and investigates the impact of inflation targeting on macroeconomic 

performance, whether the level of the inflation target is consistent with optimal economic 

performance and finally, we investigate whether inflation targeting affects the behaviour of fiscal 

policy in such a way as to deliver fiscal sustainability. This is important because many central 

banks have adopted inflation targeting as their monetary policy framework. 

In chapter 2, we investigate the effect of inflation targeting on macroeconomic performance. We 

do so by formulating a measure of IT that is closely related to the degree of monetary policy 

activism that is used in the literature. Applying this to advanced and emerging market economies, 

we find that IT has an ambiguous effect on economic growth in advanced economies and it has 

negative effect in emerging markets. 

We also find mixed results on the effect of IT on inflation performance. Lastly, we find that IT 

tends to lower bond yields across economies. We argue that the financial market benefits of IT do 

not find expression in real economic activity because of the disconnect that may exist between 

financial markets and real economic activity. 

In chapter 3 we argue that there exists a non-linear relationship between inflation one hand and 

economic growth and unemployment rates on the other. IT requires an explicit announcement of 

a numerical target for inflation. However, it is not clear whether the announced targets are 

consistent with maximum economic growth and minimum unemployment rates. We derive a 

simple growth model in which economic growth and the unemployment rate are nonlinearly 

related to the inflation rate.  

Our findings are that there are some advanced economies that sacrifice growth to maintain low 

inflation rates. This sacrifice is more prevalent in emerging markets, and it ranges from 0.5 

percentage points to 3 percentage points. The same results hold for the unemployment rate, excess 

unemployment rate to maintain the low inflation targets ranges from 0.5 to 4.5 percentage points. 

We argue that policymakers should consider ways to align inflation targets to optimal levels in 

order to include more people into employment.  

In chapter 4 we investigate whether the implementation of fiscal policy is consistent with the 

monetary policy stance. A number of economies have adopted inflation targeting as an overall 



xi 
 

framework to guide monetary policy. However, a key requirement of this framework is that fiscal 

policy should not be implemented in a manner that is not consistent with inflation targeting. We 

investigate the behaviour of fiscal authorities under inflation targeting by estimating simple fiscal 

rules that incorporate the targets of monetary policy as normally specified in simple Taylor rules. 

Our results suggest that for many of the economies in our sample, fiscal authorities respond in a 

counter-cyclical manner. In advanced economies they do not restrain fiscal policy when inflation 

rises. This is in contrast to fiscal authorities in emerging markets. Lastly, we do not find uniform 

adherence to Bohn’s principle of fiscal sustainability across economies.    

Keywords: Inflation Targeting, Fiscal Policy Rules, Public Debt, Public Deficits, Taylor Rule. 

 

 



1 
 

1. Inflation Targeting as a Monetary Policy Framework 
1.1. Background  
 

The Reserve Bank of New Zealand is the first central bank to adopt inflation targeting (IT) as its 

new monetary policy framework. “That was the result of the Reserve Bank of New Zealand Act 

1989 and came into effect in February (1990)”, McDermott and Williams (2018).  Before this new 

approach, countries were mainly focusing on money supply, credit or exchange rate target as a 

way of controlling inflation.  Those approaches did not yield desired results as far as controlling 

inflation was concerned. Since this adoption of IT, by New Zealand, many other countries have 

followed. They include both developed and emerging economies. Australia, UK, Canada, Japan, 

Brazil, Chile, South Africa and many others. 

Inflation targeting refers to the target of inflation that is set either by government, the central bank 

or both, that must be achieved by the central bank as a way of protecting the value of currency, 

(price stability). Demertzis and Viegi (2008) define an inflation targeter as a central bank that that 

communicates a quantitative target, and they further say that a “central bank that does not try to 

explicitly quantify the term “price stability” and communicate it to the public is in turn a “non- 

inflation targeter”. 

 Pollin and Zhu (2014) say these targets are low, in most cases between 2% and 5%. 

It is interesting to note that when inflation targeting was adopted by RBNZ, it was less about 2 

percent, but more about indicating to the market commitment to low inflation. Lockyer (2022) 

says, “initially when 2 percent inflation targets were adopted, to indicate to the markets 

commitment to low inflation, it was not meant to be a level considered optimal in the long term”. 

The reality now is that many central banks consider it to be optimal. 

The proponents of IT, like Mishkin (2000) cites the following advantages of IT: (a) it allows 

monetary policy to respond better to shocks to domestic economy; (b) unlike monetary targeting, 

stable relationship between money and inflation is not essential for its success; (c) it is transparent 

and therefore easily understood by the populace; (d) it increases accountability by monetary 

authorities; (e) Clifton (2001) says it discourages inflationary opportunism because financial 

markets will be quick to spot it.  



2 
 

 

However, since IT adoption, empirical research has resulted in heterogeneous findings about the 

effectiveness of IT as a monetary policy framework to accomplish optimal macroeconomics 

outcomes. There are those who say whether you adopt or not, it does not matter. That is the 

argument of Ball and Sheridan (2005). They say the improvement of inflation performance may 

be because of mean reversion, and not necessarily because of IT. 

Rose (2006) argues that IT is beneficial to the economy. He says countries that adopt IT 

“experience lower exchange rate volatility”.  Goncalves and Salles (2008) also concluded that “the 

choice of the IT regime proved beneficial, especially for emerging markets. They say that countries 

that “chose to inflation target saw a greater reduction in growth volatility than those opting for 

alternative monetary policy arrangements”.  

Junankar and Wong (2020), using various estimation methods in a large sample countries found 

that, “growth rates increased for both inflation targeters and non-targeters at about the same time”. 

Therefore, growth could not be attributed to inflation targeting. 

Galindo (2005) says although IT could have succeeded in decreasing inflation, that was done at a 

cost of poor growth performance. Brito and Bystedt (2014) also do not agree that IT adoption 

results in positive macroeconomics outcomes. They say there are no statistical grounds to conclude 

that the IT framework improves performance in developing countries, as measured by the 

behaviour of inflation and output. 

In the light of these conflicting outcomes, this thesis contributes to the further understanding of 

the impact of IT on macroeconomic performance.  We adopt a methodology which is based on the 

Taylor rule. This is because inflation targeting central banks set their interest rates in order to target 

the inflation by following the Taylor rule. A related issue is whether the level of the inflation target 

is consistent with optimal economic performance.  We investigate this issue by specifying a model 

that postulates a non-linear relationship between inflation and other macro-economic variables. 

Lastly, we investigate whether inflation targeting affects the behaviour fiscal policy in such a way 

as to deliver fiscal sustainability.  

 



3 
 

1.2. Research Problem 

As we have demonstrated above, despite agreeing that high inflation is bad for the performance of 

the economy, there is still no agreement on whether inflation targeting is the correct monetary 

policy. As an example, Mishkin and Schmidt (2001) argue that IT has been successful in 

controlling inflation and improving economic performance. On the other hand, Ball and Sheridan 

(2004) say it is questionable whether the improvement in inflation performance in several countries 

was due to IT or not, because even with non-targeters, inflation performance improved. 

The question then arises as to whether the targets chosen by inflation targeters are appropriate 

ones. Do they deliver better outcomes as far as economic growth, bond yields, unemployment, etc. 

are concerned? We continue getting conflicting empirical results. This may be partly explained by 

differing methodologies employed by different scholars and researchers. Ball and Sheridan (2005) 

use difference in difference, while Goncalves use propensity score matching method, and Lin and 

Ye (2009) use panel data models. Thornton (2012) highlighted the problem that may arise because 

of the differences in the data used. All these may provide reasons for these heterogenous findings. 

1.3. Research Questions  
In chapter 2 we are asking whether inflation targeting results in superior macroeconomic 

performance as Goncalves et al. 2006 argue. We look at the impact of IT on economic growth, 

bond yields, and inflation rate. Are inflation targeters producing better outcomes compared to non-

targeters? Huang et al. 2019 argue that IT is associated with lower trade-off between output and 

inflation.  We also want to investigate whether the impact of IT is the same or different between 

advanced and emerging economies. Brito and Bystedt (2014) conclude that there is no evidence 

that IT regime improves economic performance, as measured by the behaviour of inflation and 

output growth in developing countries. 

In chapter 3 we investigate whether the current inflation targets are consistent with maximum 

economic growth and minimum unemployment rates? We also check if the actual inflation target 

is close to this optimal point or not and estimate the “unemployment costs” of the deviation of the 

inflation target from the optimal inflation rate. Horvath and Mateju (2011) found that there is no 

evidence that many central banks conducted studies before determining the existing threshold 

levels, they simply state the inflation target without explaining how they arrived at it. We therefore 

ask, “are inflation targets optimum”?  



4 
 

In chapter 4, we investigate whether the adoption of inflation targeting has affected the setting of 

fiscal policy. As pointed out by Woodford (2001) and Clarida et al. 1999, stability requires that a 

central bank adopts an activist monetary policy against inflation. The question that arises from this 

is whether fiscal authorities consider the targets of monetary policy in their decision-making?  

Is there monetary policy dominance, which is an important condition for inflation targeting, as 

pointed out by Bernanke and Mishkin (1997)? Is fiscal policy set in a manner which undermines 

monetary policy or not? Under inflation targeting, fiscal policy cannot be set in a way that 

undermines the ability of the central bank to achieve its inflation target (Sims, 2005). Is the 

behaviour of fiscal authorities in developed and emerging economies the same or not? Therefore, 

hypothetically, we expect that the targets of monetary policy to form an important part of fiscal 

policy.  

1.4. Contribution 

Firstly, our approach therefore differs with most existing literature in that we do not regard IT as 

a treatment effect. We directly measure IT based on estimating the time-varying reaction of the 

central bank to inflation and other target variables. In this way, our approach is closely related to 

the degree of policy activism proposed by Leeper (1991) and Sargent and Cogley (2005). We 

estimate a Taylor-type rule with time-varying coefficients and on this basis, construct a time-

varying measure of the degree of IT.  

Secondly, we investigate whether current inflation targets are consistent with maximum economic 

growth and minimum unemployment rates. We derive a simple model of long run economic 

growth based on the rate of capital accumulation. The interest rate again follows a simple IT rule 

in which the reaction coefficients vary non-linearly with the level of inflation, optimal inflation 

points. We then check if the actual inflation target is close to this optimal point or not and estimate 

the “unemployment costs” of the deviation of the inflation target from the optimal inflation rate.  

Lastly, we investigate whether the adoption of inflation targeting has affected the setting of fiscal 

policy. As pointed out by Woodford (2001) and Clarida et al. 1999, stability requires that a central 

bank adopts an activist monetary policy against inflation. In addition, Bonam and Lukkezem 

(2018), add that fiscal policy must be sustainable for macroeconomic stability to be achieved.  

 



5 
 

1.5. Structure of the study 

Chapter 2 investigates the effect of inflation targeting on macroeconomic performance, like 

economic growth, unemployment rate, bond yields and inflation.  In chapter 3 we ask the question 

whether inflation targeting is consistent with maximum economic growth and minimum 

unemployment rate. In chapter 4 we investigate the effect of IT on the conduct of fiscal policy. 

Chapter 5 concludes, summarizes key findings, and make some policy recommendations.  

 

  



6 
 

2. Inflation Targeting and Macroeconomic Performance: A Taylor 

Rule Approach 
 

2.1.  Introduction 
Since its adoption in 1990 by the Reserve Bank of New Zealand, followed by Canada (1991) and 

the UK (1992) several central banks have moved towards inflation targeting (IT) as their monetary 

policy framework. Jonsson (1999) argues that the popularity of IT was nourished by the consensus 

among policymakers and economists that there is no long-term trade-off between inflation and 

output, and that price stability fosters economic growth. 

Issing (2003) agrees with this sentiment and argues, " the awareness of the cost of inflation and of 

the absence of a long -run trade-off between inflation and real activity" are among the reasons for 

adopting IT. Huang et al. (2019) show that both in developed and developing countries, the output-

inflation trade-off is "significantly lower in IT countries" compared to those who do not adopt this 

monetary policy. Thus, Epstein and Yeldan (2008) mention that even the IMF encourages the 

adoption of IT, and it may consider making IT one of its conditions when offering financial 

assistance.  

According to Mishkin (2000) IT encompasses five elements: a) a public announcement of medium 

term numerical targets for inflation; b) an institutional commitment to price stability as the primary 

goal of monetary policy; c) an information inclusive strategy in which many variables are used for 

deciding the setting of policy instruments; d) increased transparency of the monetary policy 

strategy through communication with the public and the markets; and e) increased accountability 

of the central bank for attaining its inflation objectives. 

Mishkin (2000) then cites the following advantages of IT: (a) unlike exchange rate peg, IT allows 

monetary policy to respond better to shocks to domestic economy; (b) unlike monetary targeting, 

stable relation between money and inflation is not essential for its success; (c) IT is also easily 

understandable by the populace because of its transparency; (d) because there is a numerical point 

or band of inflation, IT increases accountability; (e) Mishkin further argues that IT "reduces the 

likelihood that the central bank will fall into the time inconsistency trap". 

 



7 
 

The latter advantage is also mentioned by Clifton et.al. (2001), who argue that “the transparency 

of IT is thought to reduce the inflationary bias of monetary policy since financial markets should 

quickly spot any inflationary opportunism”. Goncalves et al. (2006) cite the added advantages of 

IT to include, a) lower and less variable inflation and interest rates; b) more stable growth; and c) 

enhanced ability to respond to shocks without losing credibility. Mishkin and Posen (1997) say IT 

does have some disadvantages. 

They say, "because of the uncertain effects of monetary policy on inflation, monetary authorities 

cannot easily control inflation". They also cite the very long lag of monetary policy on inflation as 

a problem. Also, a negative supply shock may tempt monetary authorities to raise interest, and by 

so doing dampen output growth. 

The adoption of IT is also understood to mean low inflation rates, of about 2 percent, Roger and 

Stone, (2005). Krugman (2014) argue that this level was not arrived at scientifically, except to say 

it "made both economic and political sense". Blanchard et al. (2010) and Ball (2014) have argued 

for an inflation rate of at least 4 percent. This argument became louder with the experience of 

global financial crisis.  

When interest rates hit zero percent, monetary policy becomes very blunt in stimulating growth. 

In fact, Krugman (2014) argues that "there is growing evidence that economies entering a severe 

slump with low inflation can all too easily get stuck in an economic and political trap, in which 

there is a self-perpetuating feedback loop between economic weakness and low inflation". 

However, since its introduction, there have been divergent views about the impact of IT on 

macroeconomic performance. Early studies, e.g., Mishkin and Schmidt (2001) report that IT has 

been successful in controlling inflation and improving economic performance. On the other hand, 

based on their empirical finding, Ball and Sheridan (2004) argue that it is questionable whether 

the improvement in inflation performance in several countries was due to IT or not.  

Their argument is that non-inflation targeting countries also experienced improved inflation 

performance in that period. When looking at 20 OECD developed economies, they concluded 

that whether you adopt or not, it is irrelevant, entailing neither gains nor losses in terms of 

economic performance. However, Khan (2020), measured the causal relationship between 

inflation targeting adoption on economic growth. He concludes that, “when compared to the 



8 
 

countries that did not adopt inflation targeting, there is a significant reduction in the average 

growth rate among the inflation-targeting adopters by over 1/2 percentage point”. This proves 

that the debate about this relationship needs further research, hence this study. 

Duong, (2022) investigated 54 countries, with 15 being inflation targeters and concluded that, 

“the evidence of this study does not conclude that inflation targeting is the best monetary 

framework and that all countries must adopt it”. He says emerging economies may consider it 

when they face external shocks like the 2007 financial crisis. This challenges the widely held 

view of “one size fits all” approach when it comes to monetary policy. 

Coleman and Nautz (2022) were interested in the performance of inflation expectations when 

there are shocks like Covid 19 pandemic in Germany. It was found that consistently inflation was 

below 2 percent. This led to the decline of credibility of the central bank and consequently” de-

anchored” inflation expectations. 

In this paper, we revisit the question of the impact of IT on economic performance by formulating 

a time-varying measure of the degree of IT based on Taylor rule. We interpret the degree of IT as 

the weight of the central bank’s reaction to deviations of inflation from target. This measure can 

be motivated using the results from Svensson (1997, 1999) among others.  

As pointed out by Souza et al. (2016), most studies use the difference-in-difference method (Ball 

and Sheridan (2004), Goncalves and Salles (2008)), propensity score matching (Lin and Ye, 2009) 

and panel data models (Fouejieu, 2017), to measure IT as a treatment in which a dummy variable 

is used to separate pre-IT and post-IT adoption periods. 

Our approach therefore differs with most existing literature in that we do not regard IT as a 

treatment effect. We directly measure IT based on estimating the time-varying reaction of the 

central bank to inflation and other target variables. In this way, our approach is closely related to 

the degree of policy activism proposed by Leeper (1991) and Sargent and Cogley (2005). We 

estimate a Taylor-type rule with time-varying coefficients and on this basis, construct a time-

varying measure of the degree of IT.  

 



9 
 

The first advantage of our measure is that it is theory-based and has a direct link with seminal 

macro models of IT such as those by Svensson (1997), Ball (2000), Rudebusch and Svensson 

(1999) and Clarida et al. (1999). The second advantage of our measure is that it allows for the 

possibility that even if a central bank has not formally adopted IT, it is possible that we can detect 

some level of IT. 

Furthermore, a central bank that has formally adopted IT may, in some periods, be found to have 

decreased its degree of IT. The latter case is argued by Barbosa-Fihlo (2007) in Brazil as follows, 

"even though the BCB has officially abandoned exchange rate targeting in 1999, inflation targeting 

implies a disguised exchange rate targeting in Brazil". 

Our hypothesis is that IT does indeed improve macroeconomic performance. Although this 

hypothesis has been tested before, the difference is that here we directly measure IT based on 

estimating the time-varying reaction of the central bank to it and other target variables based on 

Taylor-type rule, as mentioned above. In the methodology section we show the percentage reaction 

of the central bank to inflation, relative to all other variables, see eq. (9) and eq. (10). We shall 

investigate both advanced and emerging economies, depending on the availability of data. 

The paper is structured as follows: section 2 is the literature review, section 3 derives and motivates 

our measure of the degree of IT, section 4 provides empirical results and interpretation and in 

section 5 we conclude with some policy recommendations. 

2.2. Literature review 
The move towards inflation targeting by many central banks across the globe is premised on the 

view that the inflation targeting framework delivers macroeconomic benefits. Yet, as has been 

pointed out, this premise is not empirically straightforward. The seminal paper by Ball and 

Sheridan (2005) opened a vast literature which seeks to evaluate the macroeconomic effects of 

inflation targeting and they found that IT does not matter. They found no significant differences 

in the performance of IT and non-IT economies. 

The heterogeneous findings about the effectiveness of IT in promoting macroeconomic 

performance poses a theoretical and policy challenge. To illustrate the problem in relation to 

emerging markets, Brito and Bystedt (2014) conclude, using panel GMM techniques, that there is 

no evidence that the inflation targeting regime (IT) improves economic performance as measured 

by the behaviour of inflation and output growth in developing countries.  



10 
 

 

Yet, Mendonca and Souza (2011), using a propensity score matching methodology, find that IT is 

an ideal monetary policy regime for developing countries because it reduces inflation volatility 

and helps drive inflation down to internationally acceptable levels in these countries by enhancing 

monetary policy credibility. In the light of these contradictory findings, it is not clear which 

recommendation should policymakers adopt. 

Huang et al. (2019) utilizing endogenous switching regression (ESR) approach, which they argue 

is more robust than techniques used before, concluded that adoption of IT "is associated with lower 

trade-off between output and inflation, both in developing and developed countries".  

Loewald et al. (2022) argue that the costs associated with inflation ratio are short term. Using trend 

analysis approach to calculate sacrifice ratio in South Africa, they conclude that, “the most recent 

reduction in trend inflation (2016 – 2019) was not associated with output losses from policy 

setting. They argue that the sacrifice ratio of post-apartheid South Africa is very low, averaging 

0.5. 

Ftiti (2010) using cohesion approach investigated whether IT led to a stable monetary 

environment. A stable monetary environment provides certainty, he argued. He concluded that IT 

policy “generates a good economic performance in developed countries". Ardakani et al. (2018) 

found that there was no difference as far as inflation level is concerned between targeters and non-

targeters, although IT led to improvement in Debt-GDP ratio in both the developed and developing 

economies. Lin and Ye (2009) argue that the performance of IT is dependent on "country 

characteristics such as government fiscal policy, central banks desire to limit the movements of 

exchange rates and the time length since the policy adoption". 

In addition, Brito and Bystedt (2014) further state that, in opposition to the previous views that IT 

adoption causes efficiency gains, we showed a negative significant relation between IT adoption 

and output growth, which must be considered for purposes of evaluating the IT policy. When we 

account for the output growth forgone in the IT disinflation process, there are no statistical grounds 

to conclude that the IT framework improves performance in developing countries, as measured by 

the behaviour of inflation and output.  

 



11 
 

This view is supported by Galindo (2005) where he concedes IT succeeded in moving decreasing 

inflation from 50 percent to 5 percent but argues that was done at the cost of poor growth 

performance. 

Yet in an earlier study, Goncalves and Salles (2008) concluded that, our results suggest that the 

choice of the IT regime proved beneficial for emerging economies. They find that: (i) the greater 

fall in inflation experienced by emerging market targeters can, to some extent, be attributed to the 

regime itself and not only to mean reversion; (ii) those choosing to inflation target saw a greater 

reduction in growth volatility than those opting for alternative monetary policy arrangements. 

They further state that the view that inflation targeting hinders economic growth is not supported 

by empirical evidence. Rose (2006) says IT is durable compared to other previous monetary policy 

regimes. In support of IT he argues, "countries that target inflation experience lower exchange rate 

volatility and fewer "sudden stops" of capital flows than their counterparts". 

These results by Goncalves and Salles (2008) have been questioned by Thornton (2016) on 

methodological grounds, who found that IT did not help reduce inflation and growth volatility in 

developing countries compared to the average experience under alternative monetary policy 

regimes. So, once again, policymakers are left without a clear policy choice from the literature. 

Perhaps a study that provides a more comprehensive assessment of the macroeconomic effects of 

IT is Mishkin and Schmidt (2007).  

These authors mention five key findings: i) ITers performed better than non-ITers as far as 

economic performance is concerned, ii) emerging market ITers achieved a significant reduction in 

output growth volatility and output gap volatility under IT, iii) the response to shocks, e.g. oil 

shocks, by ITers has led to a reduction in domestic inflation relative to their pre-IT experience, iv) 

the pass through effects from exchange rate shocks was better absorbed by ITers compared to non-

ITers and v) ITers experienced dramatic decline in interest rates sensitivity of output compared to 

non-ITers. 

One of the factors that complicates the assessment of the macroeconomic effects of IT is mean 

reversion. Ball and Sheridan (2004) argue that it is not conclusive whether the improvement in 

inflation in the ITers is because of the monetary policy or it is because of regression to the mean 



12 
 

or what they also call inflation convergence. Indeed, Arestis et al. (2014) report that inflation tends 

to converge across economies independent of the monetary policy framework.  

The convergence theory is supported by Bhala, et al. (2023) who argue that “it is not clear why 

IFIs should continue to advocate formal adoption of IT, since our results show that formal adoption 

is neither necessary nor sufficient to the attainment of beneficial inflation and growth outcomes”. 

They also say, “central banks and IFIs could …benefit from looking at the evidence on IT with a 

more critical eye, given the dangers of group thinking at these institutions as highlighted in some 

quarters.” The point made here is that the benefits of IT tend to be exaggerated. Balima et al., 

(2020) conclude that “empirical literature suffers from publication biases because authors, editors 

and reviewers prefer results featuring beneficial effects of IT adoption”. 

The method employed by Goncalves and Salles (2008) sought to identify the effect of IT as distinct 

from mean-reversion. They find that even after controlling for mean reversion, inflation fell more 

in the targeters group. Furthermore, Goncalves and Salles (2008) argue that the additional inflation 

reduction experienced by those adopting IT was not at all immaterial. 

Further evidence on the effect of IT on inflation and inflation expectations is provided by Levin et 

al. (2004), who found that the adoption of IT is not associated with a fall in inflation expectations 

over longer horizons. The subsequent study by Lin and Ye (2009) seems to arrive at a different 

result. It reports that IT has large and significant effects in lowering inflation and inflation 

volatility. Furthermore, Ftiti and Hichri (2014) conclude that the IT policy is relevant in achieving 

its primary goal of price stability and succeeds better than any other monetary policy in anchoring 

inflation expectations. 

However, Goncalves and Salles (2008) did not find strong evidence that the adoption of IT lowers 

inflation volatility relative to non-ITers. A favourable result is reported by Capistrin and Ramos - 

Francia (2010), who assessed the effects of IT on the dispersion of inflation expectations. They 

found that the dispersion of long run inflation expectations is smaller in targeting regimes after 

controlling for country-specific effects and other effects. Lastly, Gerlach and Tillman (2012) find 

that ITers tend to exhibit low inflation persistence compared to non-ITers in the Asia-Pacific 

region. They find no significant differences in the levels of inflation between ITers and non-ITers. 

 



13 
 

Linked to the effect of IT on inflation expectations is its effect on financial markets, especially 

long-term interest rates. Through the expectations theory of the term structure, the anchoring of 

inflation expectations means, to a large extent, that long term interest rates will be stabilized. Kose 

et al. (2012) find evidence that the IT regime adopted by the Central Bank of Turkey influenced 

real long-term interest rates and it ensured that realized inflation is close to target.  

A similarly favourable finding on Turkey is reported by Akyurek et al. (2011), who concluded that 

IT improved the transmission mechanism, and that inflation has become more predictable. 

 

In relation to economic growth, Ghosh and Phillips (1998) argue that very high inflation is bad for 

growth. However, there is less agreement about the effects of moderate inflation on economic 

growth. Souza et al. (2016) find that there is a positive effect of IT on economic growth especially 

for developing countries. They therefore argue that the adoption of IT implies gains in economic 

growth or at least non-sluggish economic growth.  

These results are in line with those of Mollick et al. (2011), who find that IT adoption results in 

higher output per capita in both industrial and emerging market economies. Corbo et al. (2002) 

compare sacrifice ratios, (defined as percentage output losses per percentage unit of inflation 

reduction) for targeters and non-targeters. They concluded that "IT contributed to lowering output 

costs of inflation stabilization". 

Although they find the impact of IT on economic growth to be minimal among developing 

countries, Ayres et al. (2014) also find that there is a statistically significant increase in real GDP 

among developing countries in certain regions only. However, in the case of Mexico, Carrasco 

(2011) notes that both the level and volatility of inflation began to fall before IT. He further notes 

that although lower volatility of economic growth is detected, growth itself has not been affected 

by the adoption of IT.  

Stronger results along these lines are reported by Brito and Bystedt (2010), who find a significant 

negative effect of IT adoption on economic growth. Along similar lines, Argitis (2008) finds that 

any restrictive monetary policy directly or indirectly associated with inflation targeting tends to 

reduce aggregate demand and employment. 



14 
 

 

Gerlach and Tilliman (2012) also found that there is declining persistence of inflation in most IT 

countries in the Asia Pacific region although in differing degrees. However, they notice that despite 

these countries saying they are ITers, some of their central banks continue to attach great weights 

to the exchange rate.  

Argitis (2008) after looking at 13 OECD observed that any restrictive monetary policy, directly or 

indirectly, associated with inflation targeting, tends to reduce aggregate demand and employment 

and therefore this will result in higher sacrifice ratio in terms of subdued growth rates and higher 

financial instability. Rowden (2010) also argues that IT is associated with big sacrifice ratios. He 

says International Monetary Fund sacrifices higher growth, employment, and public investment in 

health systems in order to keep inflation unnecessarily low. 

There are studies that investigate the impact of IT on financial stability, largely prompted by the 

global financial crisis of 2008 (see for example Kim and Mehrotra, 2017). The question is whether 

IT helped to cope better with the financial crisis. Woodford (2012) has called for the modification 

of IT in the light of financial crisis so that IT includes marginal crisis risks. In fact, he calls for 

leaning against the wind in order to reduce the probability of a financial crisis. 

In relation the stability of the banking systems, Fazio et al. (2015) argues that IT banking systems 

tended to be more stable and became less distressed during the global financial crisis compared to 

their non-IT counterparts. 

This conclusion seems however to be inconsistent with the finding by Fouejieu (2017), who reports 

that the financial sector of IT economies appears to be more fragile, thereby challenging the view 

that IT supports financial stability. Indeed, Soyoung and Mehrota (2017) raise the possibility of a 

trade-off between financial stability and price stability objectives. 

The existence of a trade-off between price stability and financial stability has been documented by 

Blot et al. (2015) for the case of the US and the Eurozone. In this context, Akram and Eitrheim 

(2008) find that financial stability can be advanced mainly through output stability and not through 

price stability. 



15 
 

Other authors have assessed whether the adoption of IT has an impact on inflation aversion by 

central banks. Dueker and Fischer (2006), ask the question whether IT imparts an aversion to 

inflation and inflation variability among inflation targeting countries above and beyond that 

displayed by non-inflation targeting countries? They conclude that the view that IT imparts more 

aversion to inflation and inflation variability in IT economies than in non-IT economies is not 

supported by empirical evidence in countries facing same circumstances. 

Alders et al. (1996) say the behaviour of central bankers in taking inflation seriously explains 

aversion of high inflation. They are of the view that this applies for both ITers and non-ITers. 

Bleich et al. (2012) found that the introduction of IT changed the willingness of central banks to 

fight inflation. He found that the inflation coefficient in the Taylor rule is significantly larger than 

unity. 

The ability of IT to cope with the challenges brought about by the global financial crisis of 2008 

has also been a subject of discussion. Svensson (2010) argues for flexible inflation targeting, 

especially after the global financial crisis. He describes flexible inflation targeting as “the 

monetary policy that aims to stabilize both inflation around the inflation target and the real 

economy.” The Assistant Governor of the Reserve Bank of Australia, Eday (2013) says, "one of 

the consequences of the recent financial crisis has been a rediscovery, or at least a renewed 

appreciation, of that role, (the stabilization of real economy)”.  

He continues to say that "this in many ways represents a return to the original rationale for central 

banking. It was only in recent decades that some came to see the role as being more narrowly 

confined to the inflation control function. What we are now seeing is better appreciation of the 

broader original role". We extensively quoted Eday because he is an assistant governor of one of 

the central banks that pioneered the adoption of IT.  

Secondly, this shows that more targets may have been attributed to IT as a monetary policy 

framework, when in fact these targets could suitably be achieved using other complementary 

policy instruments. 

The review of the literature shows that the conflicting results about the effectiveness of IT may be 

due to differences in samples and periods across studies (see Thornton (2016) and Goncalves and 

Salles (2006). Secondly the differences in results may be due to methodology. Studies which use 



16 
 

propensity score matching methods (e.g., Mendonca and Souza (2011) and Lin and Ye (2009)) 

tend to find results in favour of IT. 

 

2.3. Methodology 
 

2.3.1. Defining the degree of inflation targeting 
 

How should we define inflation targeting? This is the question that was posed by Svensson (2002). 

In response, Svensson emphasizes three characteristics: a) the existence of a numerical target, b) 

the decision-making process of the central bank is inflation-forecast targeting, with the inflation 

forecast conditioned by the instrument setting that is consistent with the target, and c) there is a 

high degree of transparency and accountability by the central bank to achieve its target.  

In the earlier seminal contributions by Svensson (1997,1998, 1999), variations of inflation 

targeting are explained in terms of the weight that the central bank attaches to inflation stabilization 

relative to other stabilization objectives in its loss function. Thus, with a central bank that has a 

numerical inflation target to achieve, Svensson (1997) interprets inflation targeting as implying 

that the central bank chooses a sequence of current and future interest rates to minimize its loss 

function—the loss function having a relatively large weight on inflation stabilization. 

The variations of inflation targeting are strict and flexible inflation targeting. Strict inflation 

targeting is when the central bank is concerned only about inflation stabilization, whereas flexible 

inflation targeting includes concerns about other stabilization objectives—with the inflation 

objective carrying the greatest weight in the central bank’s objective function. To illustrate, 

Svensson (1997, 1998, 1999) derives an optimal interest rate rule from a closed-economy model 

that is augmented with the central bank’s loss function.  

 

 

 



17 
 

The model is composed of a Phillips curve to describe inflation dynamics and the IS curve to 

describe the dynamics of the output gap. The optimal interest rate rule closes the model by 

determining how the central bank adjusts the interest rate. Svensson’s setup can be described as 

follows: 

𝜋 = 𝜋 + 𝛼𝑦 ,      (1) 

 

𝑦 = 𝛽 𝑦 − 𝛽 𝑖 − 𝜋 |     (2) 

 

𝐿 = ∑ 𝜋 − 𝜋∗ + 𝜆𝑦 ,   (3) 

where 𝜋  is the inflation rate, 𝜋 |  is expected inflation at 𝑡 − 1, 𝑦  is the output gap, 𝑖  is the 

nominal interest rate, which in this set up is the instrument of monetary policy, 𝐿  is the central 

bank’s loss function, 𝛿 is the discount factor,  𝜋∗ is the inflation target and 𝜆 is the relative weight 

that the central bank places on the output stabilization objective. Based on this model, Svensson 

(1997, 1999) shows, through a dynamic programming procedure, that the optimal interest rate rule, 

which minimizes the loss function, takes the following form: 

𝑖 = 𝜋 + 𝑓 (𝜆)(𝜋 − 𝜋∗) + 𝑓 (𝜆)𝑦      (4) 

where: 

𝑓 (𝜆) =
1 − 𝑐(𝜆)

𝛼𝛽
, 𝑓 (𝜆) =

𝛽 + 1 − 𝑐(𝜆)
𝛽

, 𝑐(𝜆) =
𝜆

𝜆 + 𝛿𝛼 𝑘(𝜆) 

 

𝑘(𝜆) =
1
2

1 −
𝜆(1 − 𝛿)

𝛿𝛼
+ 1 +

𝜆(1 − 𝛿)
𝛿𝛼

+
4𝜆
𝛼

 

Note that as 𝜆 → 0 we have that 𝑘(𝜆) → 1 and this implies that 𝑐(𝜆) → 0. We now construct a 

ratio 𝐼𝑇 which is the ratio of the reaction of the central bank to inflation to the reaction of the 

central bank to the output gap as follows: 



18 
 

𝐼𝑇 = ( )

( )
.        (5) 

We observe that as 𝜆 → 0 we have that 𝐼𝑇 → , which is consistent with a shift towards strict 

inflation targeting. Therefore, the move towards strict inflation targeting leads to an increase in the 

reaction ratio. In other words, all else being the same, an increase in 𝐼𝑇 indicates an increase in 

inflation targeting while a decrease in 𝐼𝑇 indicates a decrease in inflation targeting.  

This allows us to develop an empirical measure of inflation targeting. To determine the degree of 

IT, we use the Taylor rule approach. We argue that the degree to which the central bank responds 

to inflation relative to other target variables is a suitable approximate measure of IT. As in Taylor 

(2001), Mohanty and Klau (2004) and Froyen and Guender (2018) among others, we assume an 

emerging market central bank that sets a target for the nominal interest rate taking into account 

inflation, economic activity and exchange rate variability. Accordingly, we propose the following 

Taylor rule: 

𝑖∗ = 𝑖 + 𝜙 , (𝜋 − 𝜋∗) + 𝜙 , 𝑦 + 𝜙 , Δ𝑞     (6) 

As derived by Svensson (1997) in the case of a closed economy, the parameters in eq. (6) are a 

combination of the central bank’s preferences and the structure of the economy. We assume that 

these parameters are time-varying. The central bank adjusts the nominal interest rate as follows: 

𝑖 = 𝑖 + 𝜙 , (𝑖∗ − 𝑖 )     (7) 

Taking eq. (6) into eq. (7) we obtain the following interest rate rule: 

𝑖 = 1 − 𝜙 , 𝑖 + 𝜙 , (𝑖 + 𝜙 , (𝜋 − 𝜋∗) + 𝜙 , 𝑦 + 𝜙 , Δ𝑞 )   (8) 

Based on eq. (6), we define the degree of IT to be the reaction ratio of the central bank to inflation, 

relative to total reaction to all variables. That is: 

𝐼𝑇∗ = ,

, , ,
       (9) 

 



19 
 

The increase in 𝜙 ,  relative to the other parameters implies that the central bank is increasing its 

degree of IT. The theoretical basis of this time-varying measure of IT is eq. (5), under the 

assumption that the model parameters are relatively stable over time. 

Alternatively, we can use eq. (8) to define the degree of IT to be: 

𝐼𝑇 = ,

, , , , ( , )
      (10) 

Existing literature, for example Goncalves and Salles (2006), follows Ball and Sheridan (2004) in 

using a difference-in-difference approach to investigate the macroeconomic effects of inflation 

targeting, which relies on the use of dummy variables. We use the measures of IT in eqs. (9) and 

(10) to test a relationship of the following form: 

Ω = 𝛼 + ∑ 𝜃 Ω + ∑ 𝛼 𝐼𝑇 + ∑ 𝛼 , 𝑍 + 𝜖   (11) 

Where Ω  is a macroeconomic variable (e.g., inflation, economic growth, the unemployment rate, 

or the long-term interest rate), 𝑍  is a vector of control variables that determine the macroeconomic 

variable and 𝐼𝑇  is the degree of inflation targeting. We use the examples of the inflation rate and 

the growth rate to illustrate the hypotheses to be tested. The hypothesis is that: inflation targeting 

reduces the inflation rate. Therefore, we expect that ∑ 𝛼 < 0 and is statistically significant. 

Alternatively, inflation targeting does not reduce the inflation rate.  

The second example is about the effects of inflation targeting on the growth rate. The hypothesis 

is that: inflation targeting increases the growth rate. Therefore, we expect ∑ 𝛼 > 0 and 

statistically significant. Alternatively, inflation targeting does not increase the growth rate. In 

which case may be negative or even if positive, may not be statistically significant.  

2.4. Empirical analysis 
 

2.4.1.  Data description 
We sourced data from the FRED database. The sample sizes depend on the availability of data for 

each country. The frequency of the data is quarterly. For the inflation rate we used the CPI to 

calculate the annualized inflation rate so that 𝜋 = 𝑝 − 𝑝 , where 𝑝  is the natural log of CPI. 

We also calculated in a similar way the annualized growth rate by using real GDP. The long-term 

interest rate is proxied by the 10-year government bond yield. The short-term interest rate is 



20 
 

measured using the treasury bill rate. As the starting point of our analysis, we graphically present 

our measure of IT, also illustrating how this measure behaves before and after the adoption of 

inflation targeting as a monetary policy framework. Figure 2.1 illustrates the IT measure: 

Advanced Economies 

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1975 1980 1985 1990 1995 2000 2005 2010 2015

AUSTRALIA - IT

before IT after IT

.25

.30

.35

.40

.45

.50

.55

.60

.65

1975 1980 1985 1990 1995 2000 2005 2010 2015

CANADA - IT

before IT after IT
0.0

0.2

0.4

0.6

0.8

1.0

98 00 02 04 06 08 10 12 14 16 18

EU - IT

 

0.0

0.2

0.4

0.6

0.8

1.0

98 00 02 04 06 08 10 12 14 16 18

JAPAN - IT

before IT after IT
.2

.3

.4

.5

.6

.7

.8

.9

90 92 94 96 98 00 02 04 06 08 10 12 14 16 18

NEW ZEALAND - IT

before IT after IT

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

88 90 92 94 96 98 00 02 04 06 08 10 12 14 16 18

UK - IT

before IT after IT

 



21 
 

.2

.3

.4

.5

.6

.7

.8

.9

1975 1980 1985 1990 1995 2000 2005 2010 2015

USA - IT

before IT after IT
0.0

0.2

0.4

0.6

0.8

1.0

1985 1990 1995 2000 2005 2010 2015

NORWAY - IT

before IT after IT

 

Figure 2.1 Evolution of IT Advanced economies 

Emerging Markets 

0.5

0.6

0.7

0.8

0.9

1.0

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19

BRAZIL - IT

after IT .0

.1

.2

.3

.4

.5

.6

.7

.8

.9

00 02 04 06 08 10 12 14 16 18

CHILE - IT

after IT
.0

.1

.2

.3

.4

.5

.6

.7

.8

96 98 00 02 04 06 08 10 12 14 16 18

MEXICO - IT

after ITbefore IT

.0

.1

.2

.3

.4

.5

.6

.7

.8

.9

1975 1980 1985 1990 1995 2000 2005 2010 2015

S.A - IT

before IT after IT
0.4

0.5

0.6

0.7

0.8

0.9

1.0

88 90 92 94 96 98 00 02 04 06 08 10 12 14 16 18

S.KOREA - IT

before IT after IT

 
.3

.4

.5

.6

.7

.8

98 00 02 04 06 08 10 12 14 16 18

POLAND - IT

bef ore IT after IT

 

.45

.50

.55

.60

.65

.70

.75

.80

2002 2004 2006 2008 2010 2012 2014 2016 2018

RUSSIA - IT

before IT after IT

 
.1

.2

.3

.4

.5

.6

.7

.8

00 02 04 06 08 10 12 14 16 18

CZECH - IT

after IT

 

Figure 2.2 Evolution of IT Emerging Market 



22 
 

We next conduct unit root tests on the variables of interest, using the Augmented Dickey Fuller 

test. Table 2.1 reports that the key variables through which we measure macroeconomic 

performance: the short rate, the bond yield, inflation rate and the unemployment rate, are largely 

non-stationary, while the growth rate is stationary. Our measure of IT is also not stationary in most 

of the economies in our sample. However, in economies such as Australia, New Zealand, Brazil, 

Chile and Russia, IT is stationary.  

Table 2.1 Augmented Dickey-Fuller unit root tests 

 Australia Canada Euro Japan N. Zealand UK US Norway 

Advanced          

Long rate -0.58 

(0.87) 

-0.94 

(0.77) 

-0.47 

(0.89) 

-0.63 

(0.86) 

-1.01 

(0.75) 

-1.53 

(0.88) 

-1.29 

(0.63) 

-1.48 

(0.54) 

Short rate -1.76 

(0.40) 

-1.72 

(0.42) 

-1.67 

(0.44) 

-1.85 

(0.36) 

-1.87 

(0.34) 

-1.17 

(0.69) 

-1.72 

(0.42) 

-1.19 

(0.68) 

Inflation rate -2.10 

(0.24) 

-2.26 

(0.19) 

-3.18* 

(0.02) 

-1.66 

(0.45) 

-1.15 

(0.70) 

-1.32 

(0.62) 

-1.99 

(0.29) 

-1.55 

(0.51) 

Output gap -6.62* 

(0.00) 

-5.87* 

(0.00) 

-4.29* 

(0.00) 

-4.24* 

(0.00) 

-4.54* 

(0.00) 

-4.34* 

(0.00) 

-6.47* 

(0.00) 

-4.95* 

(0.00) 

Growth rate -3.50* 

(0.01) 

-3.09* 

(0.03) 

-4.39* 

(0.00) 

-3.36* 

(0.01) 

-2.75** 

(0.07) 

-4.18* 

(0.00) 

-3.56* 

(0.01) 

-3.69* 

(0.01) 

Unemp. rate -2.21 

(0.20) 

-3.34* 

(0.01) 

-2.00* 

(0.04) 

-1.58 

(0.49) 

-2.02 

(0.28) 

-2.39 

(0.15) 

-3.37* 

(0.01) 

-2.25 

(0.19) 

Real deprec. -4.80* 

(0.00) 

-3.35* 

(0.01) 

-4.02* 

(0.00) 

-3.37* 

(0.01) 

-4.23* 

(0.00) 

-3.36* 

(0.01) 

-3.16* 

(0.02) 

-3.69* 

(0.01) 

IT -2.83** 

(0.06) 

-2.22 

(0.20) 

-1.82 

(0.37) 

-1.82 

(0.37) 

-4.95* 

(0.00) 

-2.41 

(0.14) 

-1.79 

(0.38) 

-2.03 

(0.27) 

Emerging Brazil Chile Czech Mexico S. Africa S. Korea Poland Russia 

Long rate -3.99* -0.47 -1.80 -2.21 -1.73 -1.48 -1.77 -8.59* 



23 
 

(0.01) (0.89) (0.38) (0.21) (0.41) (0.54) (0.39) (0.00) 

Short rate -2.82** 

(0.06) 

-2.12 

(0.24) 

-2.06 

(0.26) 

-3.01* 

(0.04) 

-2.83** 

(0.06) 

  -1.19 

(0.68) 

  -2.81** 

(0.06) 

 -3.03* 

 (0.03) 

Inflation rate -2.97* 

(0.04) 

-4.16* 

(0.00) 

-4.59* 

(0.00) 

-1.75* 

(0.00) 

-1.40 

(0.58) 

-1.55 

(0.51) 

-5.06* 

(0.00) 

 -5.71* 

 (0.00) 

Output gap -4.25* 

(0.01) 

-4.24* 

(0.00) 

-4.02* 

(0.01) 

-5.01* 

(0.00) 

-4.87* 

(0.00) 

-4.65* 

(0.00) 

-5.17* 

(0.00) 

 -3.90* 

 (0.00) 

Growth rate -2.58** 

(0.10) 

-3.32* 

(0.01) 

-4.28* 

(0.00) 

-5.02* 

(0.00) 

-3.36** 

(0.01) 

-3.69** 

(0.01) 

-4.72* 

(0.00) 

 -2.61** 

 (0.09) 

Unemp. rate -1.35 

(0.61) 

-2.44 

(0.13) 

-1.71 

(0.42) 

-2.56 

(0.10) 

-1.70 

(0.43) 

-2.24 

(0.19) 

-1.87 

(0.35) 

 -2.89* 

 (0.05) 

Real deprec. -3.68* 

(0.00) 

-2.86* 

(0.05) 

-3.38* 

(0.01) 

-5.59* 

(0.01) 

-4.33* 

(0.00) 

-4.01* 

(0.00) 

-3.35* 

(0.01) 

 -7.21* 

 (0.00) 

IT -3.27* 

(0.02) 

-3.87* 

(0.00) 

-2.30 

(0.17) 

-3.04* 

(0.03) 

-2.30 

(0.17) 

-1.63 

(0.46) 

-0.03 

(0.95) 

 -5.00* 

 (0.00) 

Note: Probability in parentheses, * significant at 5%, ** significant at 10% 

Given that some variables may be non-stationary while others may be stationary within the same 

equation, the ARDL-type specifications in eq. (11) are appropriate.  

2.4.2. Effect of IT on bond yields 
The first test of the effect of IT on macroeconomic performance is on bond yields. We expect that 

IT will lead to a low inflation environment, which will anchor inflation expectations at a low and 

stable level. This in turn will deliver a low long-term interest rate environment. Our specification 

assumes that bond yields are persistent, and they are also determined by the inflation rate and the 

short rate. We tested the effect of the output gap on bond yields and found it to be largely 

insignificant across economies, and so we dropped the output gap from the specification. We use 

the following specification in our regressions:   

𝑅 = 𝛼 + ∑ 𝜃 𝑅 + ∑ 𝛼 , 𝑖 + ∑ 𝛼 , 𝜋 + ∑ 𝛼 𝐼𝑇 + 𝜖 , (12) 

 



24 
 

Our hypothesis is that ∑ 𝛼 < 0, so that IT has a negative effect on bond yields. Table 2.2 

reports the results for eq. (12). In relation to advanced economies, we find that IT tends to 

significantly reduce bond yields in five of the eight economies. In the case of Japan, IT does not 

have a significant effect on bond yields. For Canada and New Zealand, we find that IT has a 

positive effect on bond yields. Therefore, while for most of the advanced economies IT improves 

the performance of the bond market by reducing bond yields, there are some economies where 

such benefit does not exist. 

Table 2.2 Effect of inflation targeting on bond yields 

 Australia Canada Euro Japan N. Zealand UK US Norway 

Advanced          

Constant 0.003 

(0.002) 

-0.000 

(0.002) 

0.002 

(0.001) 

-0.001* 

(0.000) 

-0.002 

(0.003) 

-0.001 

(0.003) 

0.005** 

(0.003) 

0.003** 

(0.002) 

𝑅  0.906* 

(0.029) 

1.079* 

(0.058) 

1.329* 

(0.087) 

0.951* 

(0.025) 

1.112* 

(0.090) 

1.124* 

(0.080) 

1.000* 

(0.061) 

1.117* 

(0.093) 

𝑅  - 

- 

-0.147* 

(0.053) 

0.437* 

(0.090) 

- 

- 

-0.154* 

(0.087) 

-0.185* 

(0.080) 

-0.111** 

(0.058) 

-0.169** 

(0.082) 

𝑖  0.305* 

(0.035) 

0.299* 

(0.043) 

- 

- 

0.565* 

(0.134) 

0.340* 

(0.067) 

0.242* 

(0.049) 

0.400* 

(0.040) 

0.210* 

(0.038) 

𝑖  -0.250* 

(0.040) 

-0.232* 

(0.047) 

-0.090 

(0.082) 

-0.999* 

(0.224) 

-0.303* 

(0.064) 

-0.198* 

(0.052) 

-0.330* 

(0.046) 

-0.190* 

(0.048) 

𝑖  - 

- 

- 

- 

0.182* 

(0.067) 

0.422* 

(0.157) 

- 

- 

- 

- 

- 

- 

- 

- 

𝜋  0.038* 

(0.010) 

- 

- 

0.315* 

(0.067) 

0.000 

(0.010) 

- 

- 

-0.009 

(0.026) 

0.036* 

(0.017) 

- 

- 

𝜋  - 

- 

0.029* 

(0.011) 

0.307* 

(0.067) 

- 

- 

-0.188* 

(0.064) 

- 

- 

- 

- 

-0.050 

(0.039) 



25 
 

𝜋  - 

- 

  - 

- 

- 

- 

- 

- 

0.152* 

(0.064) 

- 

- 

- 

- 

0.085* 

(0.038) 

𝐼𝑇  -0.024* 

(0.009) 

0.018* 

(0.007) 

0.006** 

(0.003) 

0.001 

(0.000) 

- 

- 

- 

- 

- 

- 

- 

- 

𝐼𝑇  0.020* 

(0.010) 

0.016** 

(0.009) 

-0.010** 

(0.006) 

- 

- 

0.025* 

(0.009) 

-0.014* 

(0.006) 

-0.006** 

(0.004) 

-0.003* 

(0.001) 

𝐼𝑇  - 

- 

- 

- 

-0.005 

(0.003) 

- 

- 

-0.021* 

(0.007) 

-0.017* 

(0.006) 

- 

- 

- 

- 

𝛼  
-0.004* 

 

0.034 -0.009 0.001* 

 

0.004* -0.031* -0.006** 

 

-0.003* 

𝑅  0.90 0.85 0.77 0.83 0.88 0.90 0.92 0.93 

Emerging Brazil Chile Czech Mexico S. Africa S. Korea Poland Russia 

Constant 0.002* 

(0.001) 

0.008 

(0.005) 

-0.004 

(0.005) 

0.036* 

(0.012) 

0.003* 

(0.001) 

0.003 

(0.006) 

0.009* 

(0.004) 

0.004** 

(0.002) 

𝑅  0.934* 

(0.028) 

0.966* 

(0.042) 

1.140* 

(0.083) 

0.646* 

(0.085) 

0.918* 

(0.023) 

0.848* 

(0.046) 

0.764* 

(0.078) 

0.117* 

(0.093) 

𝑅  - 

- 

- 

- 

-0.303* 

(0.088) 

- 

- 

- 

- 

- 

- 

- 

- 

-0.169** 

(0.087) 

𝑖  - 

- 

0.271* 

(0.065) 

- 

- 

0.531* 

(0.091) 

0.344* 

(0.053) 

0.460* 

(0.145) 

0.376* 

(0.071) 

0.210* 

(0.038) 

𝑖  -0.004 

(0.019) 

-0.541* 

(0.098) 

-0.173** 

(0.101) 

-0.389* 

(0.080) 

-0.308* 

(0.049) 

-0.625* 

(0.173) 

-0.270* 

(0.075) 

-0.190* 

(0.048) 

𝑖  - 

- 

0.292* 

(0.076) 

0.392* 

(0.121) 

- 

- 

- 

- 

-0.217* 

(0.083) 

- 

- 

- 

- 

𝜋  0.206* 0.137* - 0.020 0.063** 0.127** 0.230* - 



26 
 

(0.035) (0.053) - (0.077) (0.063) (0.068) (0.083) - 

𝜋  - 

- 

-0.222* 

(0.061) 

0.149* 

(0.043) 

0.121 

(0.099) 

-0.135* 

(0.062) 

-0.625* 

(0.173) 

-0.192* 

(0.081) 

-0.050 

(0.039) 

𝜋  - 

- 

0.106* 

(0.040) 

-0.152* 

(0.039) 

-0.221* 

(0.107) 

0.095* 

(0.039) 

0.217* 

(0.007) 

- 

- 

0.085* 

(0.038) 

𝐼𝑇     0.211* 

(0.069) 

  0.093 

(0.062) 

 

𝐼𝑇  -0.010* 

(0.002) 

-0.051* 

(0.013) 

0.054* 

(0.018) 

-0.188* 

(0.065) 

0.014* 

(0.007) 

0.021* 

(0.009) 

-0.200** 

(0.108) 

- 

- 

𝐼𝑇  0.007* 

(0.003) 

0.037* 

(0.011) 

-0.047* 

(0.015) 

0.140* 

(0.051) 

-0.011** 

(0.006) 

-0.023* 

(0.007) 

0.102** 

(0.054) 

-0.003* 

(0.001) 

𝛼  
-0.003* -0.014* 0.004* -0.048* 0.003* -0.002* -0.005** -0.003* 

𝑅  0.74 0.85 0.72 0.30 0.77 0.85 0.94 0.71 

Note: HAC std. errors in parentheses, * significant at 5%, ** significant at 10%. 

The results for emerging markets also show that in most cases IT has beneficial effects on the bond 

market. This finding is consistent with the one by O’Sullivan and Tomljanovich (2012), although 

these authors focused on bond market volatility. Two economies exhibit opposite results—South 

Africa and the Czech Republic show that IT has the effect of increasing bond yields. Based on the 

above results, we conclude that IT tends to improve the performance of the bond market.  

 

 

 

 

 

 



27 
 

 

 

2.4.3. Effect of IT on bond yields POST IT 
We also test the effect of IT on macroeconomic performance on bond yields, post IT adoption. 

The idea is that after the adoption of IT, bond markets should perform better in the sense that 

bond yields should decrease. The economies under consideration depend on the availability of 

data in the sample of the economies under study. 

 Table 2.3 provides a summary of the results.  

Table 2.3 Effect of inflation targeting on bond yields 

Advanced  Emerging 

 Australia Canada Japan N. Zealand UK US Norway  S. Africa 

Constant 0.023* 

(0.006) 

0.007 

(0.005) 

-0.001 

(0.000) 

-0.003 

(0.003) 

0.002 

(0.003) 

0.043* 

(0.008) 

0.024** 

(0.014) 

 -0.001 

(0.004) 

𝑅  1.141* 

(0.095) 

0.940* 

(0.024) 

0.940** 

(0.026) 

1.084* 

(0.088) 

1.206* 

(0.066) 

0.771* 

(0.103) 

0.874* 

(0.044) 

 0.087* 

(0.026) 

𝑅  -0.400* 

(0.096) 

- 

- 

- 

- 

-0157** 

(0.086) 

-0.247* 

(0.063) 

-0.146 

(0.151) 

- 

- 

 - 

- 

𝑖  0.707* 

(0.101) 

0.433* 

(0.161) 

0.573* 

(0.132) 

0.414* 

(0.100) 

0.281* 

(0.050) 

1.632* 

(0.265) 

0.432* 

(0.088) 

 0.341* 

(0.142) 

𝑖  -1.089* 

(0.159) 

0.694* 

(0.273) 

-0.085* 

(0.222) 

-0.606* 

(0.166) 

-0.578* 

(0.106) 

-1.447* 

(0.261) 

-0.687* 

(0.139) 

 -0.471* 

(0.425) 

𝑖  0.472* 

(0.104) 

0.297* 

(0.134) 

0.399* 

(0.157) 

0.243* 

(0.086) 

0.309* 

(0.079) 

- 

- 

0.303* 

(0.073) 

 0.519* 

(0.153) 

𝜋  0.128* 

(0.056) 

-0.062** 

(0.037) 

-0.002 

(0.011) 

0.101** 

(0.055) 

-0.001 

(0.026) 

- 

- 

- 

- 

 0.121* 

(0.060) 



28 
 

𝜋  -0.128** 

(0.071) 

- 

- 

- 

- 

-0.205* 

(0.074) 

- 

- 

-0.299* 

(0.074) 

-0.053 

(0.048) 

 -0.1428 

(0.059) 

𝜋  0.099** 

(0.052) 

 - 

- 

- 

- 

0.010** 

(0.057) 

- 

- 

- 

- 

0.085* 

(0.043) 

 - 

- 

𝐼𝑇  -0.035* 

(0.009) 

- 

- 

0.002* 

(0.001) 

0.006** 

(0.003) 

0.068* 

(0.034) 

0.162* 

(0.076) 

0.023** 

(0.014) 

 0.020* 

(0.005) 

𝐼𝑇  - 

- 

0.121** 

(0.065) 

- 

- 

- 

- 

-0.070** 

(0.036) 

-0.740* 

(0.184) 

- 

- 

 - 

- 

𝐼𝑇  - 

- 

0.113** 

(0.064) 

- 

- 

- 

- 

- 

- 

0.532* 

(0.131) 

- 

- 

 - 

- 

𝛼  
-0.035* 

(0.009) 

0.234* 

- 

0.002* 

(0.001) 

0.006** 

(0.003) 

-0.002* 

- 

-0.046* 

- 

0.023** 

(0.014) 

 0.020* 

(0.005) 

𝑅  0.97 0.98 0.96 0.99 0.99 0.85 0.98  0.94 

Note: HAC std. errors in parentheses, * significant at 5%, ** significant at 10%. 

For some of the advanced economies, e.g., Canada, Japan, New Zealand, and Norway, we find 

that IT does not improve bond market performance, post IT adoption. IT leads to higher bond 

yields in these economies. However, for Australia, UK and the USA, IT leads to lower bond yields, 

thus better bond market performance. For South Africa, an emerging market, we find that IT did 

not improve bond market performance. 

2.4.4. Effect of IT on economic growth 
We now test the effect of IT on economic growth. The channel of the effect of IT on economic 

growth is for example, through financial markets: IT is supposed to keep inflation low and stable, 

leading to stability of expectations of future short rates, which in turn stabilizes bond yields at low 

levels. Low levels of bond yields encourage investment and consumption by decreasing the cost 

of investment funding and increasing asset prices. Based on this channel, our null hypothesis is 

that IT will lead to higher economic growth.  



29 
 

Our specification assumes that economic growth is persistent and is also determined by the 

inflation rate and the short rate. We may think of this specification as the unrestricted IS curve 

expressed in growth terms, like the one found in Fair (2004: 66). We specify the growth rate as 

follows: 

𝐺𝑅 = 𝛼 + ∑ 𝜃 𝐺𝑅 + ∑ 𝛼 , 𝑖 + ∑ 𝛼 , 𝜋 + ∑ 𝛼 𝐼𝑇 + 𝜖 ,   (13) 

where 𝐺𝑅  denotes the growth rate of output and the rest of the variables are as defined in eq. (12). 

Table 2.4 reports the results from estimation of eq. (14). For advanced economies, the results are 

mixed. IT tends to reduce economic growth in Canada, UK, US and Norway. However, it raises 

economic growth in the other countries in the sample. For emerging markets, we find that IT tends 

to reduce economic growth in six of the eight economies in our sample. Russia and the Czech 

Republic are the only economies where IT tends to increase economic growth. 

Table 2.4 Effect of inflation targeting on growth rate 

 Australia Canada Euro Japan N. Zealand UK US Norway 

Advanced          

Constant 0.004 

(0.029) 

0.026 

(0.027) 

0.007 

(0.053) 

0.113** 

(0.043) 

0.175* 

(0.052) 

0.049 

(0.040) 

0.009* 

(0.004) 

0.573* 

(0.257) 

𝐺𝑅  0.646* 

(0.052) 

1.006* 

(0.067) 

1.045* 

(0.054) 

0.614* 

(0.100) 

0.552* 

(0.073) 

1.145* 

(0.088) 

1.096* 

(0.081) 

0.256* 

(0.116) 

𝐺𝑅  - 

- 

-0.295* 

(0.057) 

-0.337* 

(0.049) 

0.162** 

(0.097) 

- 

- 

-0.431* 

(0.095) 

 

-0.402* 

(0.079) 

- 

- 

- 

𝑖  0.024 

(0.037) 

- 

- 

1.133* 

(0.188) 

- 

- 

0.466* 

(0.216) 

0.370* 

(0.128) 

0.427* 

(0.090) 

- 

- 

𝑖  - -0.032 

(0.027) 

-1.038* 

(0.190) 

-0.092 

(0.386) 

-0.549* 

(0.207) 

-0.312* 

(0.115) 

-0.400* 

(0.091) 

-0.251* 

(0.126) 

𝑖  - - - - - - - - 



30 
 

- - - - - - - 

𝜋  -0.030 

(0.044) 

- 

- 

-0.284* 

(0.056) 

- 

- 

-0.611* 

(0.148) 

-0.170* 

(0.048) 

- 

- 

 

𝜋  - -0.001 

(0.035) 

- 

- 

-0.629* 

(0.166) 

0.542* 

(0.164) 

- 

- 

-0.230* 

(0.081) 

-0.613* 

(0.177) 

𝜋  - 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

0.181** 

(0.105) 

 

𝑞  0.037 

(0.023) 

- 

- 

- 

- 

- 

- 

- 

- 

-0.005 

(0.008) 

- 

- 

-0.08** 

(0.047) 

𝑞  -0.038** 

(0.022) 

0.040** 

(0.024) 

0.049** 

(0.025) 

-0.022* 

(0.008) 

0.053* 

(0.029) 

- 

- 

- 

- 

- 

- 

𝑞  - 

- 

-0.042** 

(0.023) 

-0.049** 

(0.025) 

- 

- 

-0.088* 

(0.031) 

- 

- 

- 

- 

- 

- 

𝐼𝑇  - 

- 

- 

- 

0.005** 

(0.003) 

- 

- 

0.190* 

(0.094) 

-0.028* 

(0.011) 

-0.006 

(0.009) 

-0.165* 

(0.069) 

𝐼𝑇  0.034* 

(0.017) 

-0.023* 

(0.009) 

- 

- 

-0.035* 

(0.017) 

-0.187* 

(0.083) 

- 

- 

0.012** 

(0.007) 

- 

- 

𝐼𝑇  -0.026** 

(0.016) 

- 

- 

- 

- 

0.044* 

(0.016) 

- 

- 

- 

- 

-0.012* 

(0.005) 

- 

- 

𝛼  
0.008* -0.023* 0.005 0.009* 0.003* -0.028* -0.006* -0.165* 

𝑅  0.72 0.88 0.96 0.82 0.74 0.93 0.85 0.48 

Emerging Brazil Chile Czech Mexico S. Africa S. Korea Poland Russia 

Constant 0.003 0.759* 0.118* 0.031* -0.040 0.114** -0.043 0.034 



31 
 

(0.008) (0.193) (0.052) (0.011) (0.025) (0.059) (0.089) (0.033) 

𝐺𝑅  1.225* 

(0.087) 

0.333* 

(0.089) 

1.262* 

(0.094) 

0.797* 

(0.085) 

1.130* 

(0.064) 

0.877* 

(0.058) 

0.439* 

(0.086) 

1.648* 

(0.101) 

𝐺𝑅  -0.316* 

(0.086) 

- 

- 

-0.443* 

(0.088) 

-0.429* 

(0.072) 

-0.420* 

(0.062) 

- 

- 

- 

- 

-0.771* 

(0.090) 

𝑖  - 

- 

0.272* 

(0.130) 

0.890* 

(0.245) 

- 

- 

0.092 

(0.070) 

- 

- 

- 

- 

- 

- 

𝑖  -0.385* 

(0.153) 

- 

- 

-0.772* 

(0.198) 

-0.167* 

(0.033) 

-0.165* 

(0.069) 

-0.453* 

(0.089) 

-0.155* 

(0.057) 

-0.101* 

(0.050) 

𝑖  0.448* 

(0.130) 

- 

- 

- 

- 

- 

- 

- 

- 

0.471* 

(0.100) 

- 

- 

- 

- 

𝜋  -0.054 

(0.092) 

-0.118 

(0.121) 

-0.221* 

(0.077) 

- 

- 

-0.207* 

(0.058) 

-0.963* 

(0.380) 

0.545* 

(0.171) 

- 

- 

𝜋  - 

- 

- 

- 

- 

- 

0.163* 

(0.049) 

0.386* 

(0.091) 

0.537** 

(0.321) 

-0.376* 

(0.165) 

0.053* 

(0.023) 

𝜋  - 

- 

- 

- 

- 

- 

- 

- 

-0.200* 

(0.060) 

- 

- 

- 

- 

- 

- 

𝑞   -0.141* 

(0.042) 

 0.036* 

(0.012) 

-0.025** 

(0.015) 

-0.015 

(0.011) 

-0.056** 

(0.029) 

 

 

𝑞    0.061** 

(0.034) 

 0.038* 

(0.015) 

 0.073* 

(0.030) 

0.034* 

(0.011) 

𝑞    -0.089* 

(0.034) 

    -0.040* 

(0.011) 

𝐼𝑇  - - - -0.052* -0.013* -0.032* 0.054* 0.004 



32 
 

- - - (0.020) (0.006) (0.015) (0.023) (0.008) 

𝐼𝑇  -0.034** 

(0.018) 

0.119* 

(0.047) 

0.012 

(0.008) 

- 

- 

- 

- 

- 

- 

-0.082* 

(0.027) 

- 

- 

𝐼𝑇  0.028 

(0.018) 

-0.243* 

(0.045) 

0.016* 

(0.008) 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

𝛼  
-0.006* -0.124* 0.028* -0.052* -0.013* -0.032* -0.028* 0.004 

 

𝑅  0.90 0.73 0.95 0.89 0.86 0.87 0.67 0.98 

Note: HAC std. errors in parentheses, * significant at 5%, ** significant at 10%. 

From the results in Table 2.4, we can conclude that for advanced economies the impact of IT on 

economic growth is broadly mixed. In emerging economies, we find that IT tends to have a 

negative effect on economic growth. These results are not consistent with the ones by Goncalves 

and Salles (2006) and Souza et al. (2016) among others, who find beneficial effects of IT on 

economic growth. 

2.4.5. Effect of IT on economic growth: POST IT 
 

We are also interested in ascertaining whether the effect of IT on economic growth changed 

because of the adoption of IT. Table 2.5 shows the results on the effect of IT on economic growth 

after its adoption. 

Table 2.5 Effect of inflation targeting on growth rate 

 Australia Canada Japan N. Zealand UK US Norway 

Advanced         

Constant 0.028 

(0.033) 

0.044 

(0.029) 

0.286** 

(0.149) 

0.108** 

(0.062) 

-0.023 

(0.026) 

-0.022 

(0.022

) 

0.711* 

(0.202) 

𝐺𝑅  0.364* 1.092* 1.021* 0.429* 1.162* 0.300 0.045 



33 
 

(0.140) (0.078) (0.282) (0.063) (0.095) (0.196

) 

(0.111) 

𝐺𝑅  - 

- 

-0.387* 

(0.072) 

-0.707* 

(0.226) 

- 

- 

-0.539* 

(0.076) 

0.089 

(0.131

) 

- 

- 

𝑖  0.208* 

(0.090) 

- 

- 

- 

- 

0.503* 

(0.140) 

0.355* 

(0.101) 

0.761 

(0.786

) 

- 

- 

𝑖  - 

- 

-0.069 

(0.047) 

-9.273* 

(2.725) 

-0.547* 

(0.125) 

-0.348* 

(0.099) 

-0.758 

(0.633

) 

-0.216* 

(0.107) 

𝑖  - 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

𝜋  -0.314* 

(0.074) 

- 

- 

- 

- 

-0.532* 

(0.126) 

- 

- 

- 

- 

- 

- 

𝜋  - 

- 

-0.064 

(0.045) 

-0.715* 

(0.303) 

0.399* 

(0.184) 

-0.075 

(0.062) 

-0.078 

(0.155

) 

-0.730* 

(0.164) 

𝜋  - 

- 

- 

- 

- 

- 

- 

- 

- 

- 

0.330*

* 

(0.182

) 

- 

- 

𝑞  0.038* 

(0.016) 

- 

- 

- 

- 

- 

- 

0.014* 

(0.006) 

- 

- 

-0.110* 

(0.041) 

𝑞  -0.041* 

(0.018) 

0.044* 

(0.022) 

-0.059** 

(0.033) 

0.101* 

(0.031) 

- 

- 

- 

- 

- 

- 



34 
 

𝑞  - 

- 

-0.049* 

(0.024) 

- 

- 

-0.123* 

(0.032) 

- 

- 

- 

- 

- 

- 

𝐼𝑇  - 

- 

- 

- 

- 

- 

0.444* 

(0.052) 

-0.046* 

(0.011) 

1.40* 

(0.452

) 

-0.178* 

(0.050) 

𝐼𝑇  0.076 

(0.186) 

-0.039* 

(0.015) 

-0.212** 

(0.117) 

-0.429* 

(0.049) 

- 

- 

-

2.484* 

(0.800

) 

- 

- 

𝐼𝑇  -0.076 

(0.176) 

- 

- 

0.244* 

(0.004) 

- 

- 

- 

- 

1.118* 

(0.451

) 

- 

- 

𝛼  
0.000 

- 

-0.039* 

(0.015) 

0.032** 

- 

0.015* 

- 

-0.046* 

(0.011) 

0.034 

- 

-0.178* 

(0.050) 

𝑅  0.69 0.92 0.82 0.77 0.94 0.73 0.46 

Emerging Mexico S.Africa S.Korea Poland    

Constant 0.030* 

(0.015) 

-0.035 

(0.040) 

0.391* 

(0.127) 

0.069 

(0.062) 

   

GR(-1) 0.956* 

(0.085) 

0.926* 

(0.095) 

0.751* 

(0.069) 

0.429* 

(0.110) 

   

GR(-2) -0.524* 

(0.088) 

-0.258* 

(0.087) 

- 

- 

- 

- 

   

     SR - 

- 

0.490* 

(0.114) 

- 

- 

- 

- 

   

SR(-1) -0.053 -0.555* -0.223* -0.131**    



35 
 

(0.064) (0.101) (0.099) (0.074) 

SR(-2) - 

- 

- 

- 

0.318* 

(0.107) 

- 

- 

   

𝜋  - 

- 

- 

- 

-0.592* 

(0.224) 

0.123 

(0.114) 

   

𝜋  -0.046 

(0.090) 

0.020 

(0.035) 

- 

- 

- 

- 

   

𝜋  - 

- 

- 

- 

- 

- 

- 

- 

   

LREER - 

- 

0.015** 

(0.008) 

-0.054* 

(0.020) 

-0.007 

(0.014) 

   

LREER(-1) 0.064* 

(0.022) 

- 

- 

- 

- 

- 

- 

   

LREER(-2) -0.064* 

(0.025) 

- 

- 

- 

- 

- 

- 

   

𝐼𝑇  - 

- 

-0.056* 

(0.013) 

-0.135* 

(0.043) 

0.341* 

(0.089) 

   

𝐼𝑇  -0.048** 

(0.026) 

- 

- 

- 

- 

-0.373* 

(0.086) 

   

𝐼𝑇  - 

- 

- 

- 

- 

- 

- 

- 

   

𝛼  
-0.048** 

(0.026) 

-0.056* 

(0.013) 

-0.135* 

(0.043) 

-0.032* 

- 

   

𝑅  0.86 0.94 0.89 0.68    

Note: HAC std. errors in parentheses, * significant at 5%, ** significant at 10%. 



36 
 

In relation to advanced economies, we find that IT tends to have negative or neutral effect on 

economic growth, post IT adoption. It is only in three out of eight economies where IT results in 

higher economic growth.  As for emerging countries, there is no conclusive result. Two of the 

countries exhibit positive effect, while for South Africa and Poland IT resulted in the negative 

effect on economic growth. 

2.4.6. Effect of IT on inflation  
We now test the effect of IT on inflation rate. We expect that IT will lead to lower inflation rate. 

Our specification assumes that inflation rate is persistent, and it depends on the output gap and real 

exchange rate depreciation and IT. We thus estimate a Phillips curve, augmented with the IT 

variable as follows: 

𝜋 = 𝛼 + ∑ 𝜃 𝜋 + ∑ 𝛼 , 𝑦 + ∑ 𝛼 , ∆𝑞 + ∑ 𝛼 𝐼𝑇 + 𝜖   (14) 

 

where 𝑦  is the output gap which is measured using the Hodrick-Prescott filter, ∆𝑞  is the rate of 

real exchange rate depreciation. Our hypothesis is that ∑ 𝛼 < 0 so that IT has a negative effect 

on inflation rate. Table 2.6 reports the results. 

Table 2.6 Effect of inflation targeting on inflation 

 Australia Canada Euro Japan N. Zealand UK US Norway 

Advanced          

Constant 0.001 

(0.002) 

0.011* 

(0.005) 

0.003* 

(0.001) 

0.005* 

(0.001) 

-0.002 

(0.004) 

-0.004** 

(0.002) 

-0.000 

(0.002) 

0.003 

(0.002) 

𝜋  1.110* 

(0.098) 

1.103* 

(0.053) 

1.009* 

(0.116) 

0.780* 

(0.094) 

1.031* 

(0.064) 

1.242* 

(0.087) 

1.279* 

(0.063) 

0.969* 

(0.079) 

𝜋  -0.041 

(0.106) 

-0.170** 

(0.089) 

-0.144 

(0.132) 

-0.066 

(0.113) 

-0.045 

(0.090) 

-0.295* 

(0.129) 

-0.411* 

(0.076) 

0.008 

(0.087) 

𝜋  0.089 

(0.085) 

0.126 

(0.081) 

0.040 

(0.095) 

0.112 

(0.105) 

-0.055 

(0.111) 

0.044 

(0.112) 

0.273* 

(0.082) 

0.020 

(0.105) 



37 
 

𝜋  -0.423* 

(0.100) 

-0.462* 

(0.115) 

-0.342* 

(0.143) 

-0.450* 

(0.124) 

-0.334* 

(0.138) 

-0.239* 

(0.104) 

-0.391* 

(0.142) 

-0.468* 

(0.160) 

𝜋  0.255* 

(0.075) 

0.352* 

(0.081) 

0.300* 

(0.109) 

0.256* 

(0.102) 

0.247* 

(0.089) 

0.200* 

(0.080) 

0.232* 

(0.084) 

0.398* 

(0.115) 

𝑦  - 

- 

0.136* 

(0.034) 

0.152* 

(0.045) 

- 

- 

- 

- 

0.082* 

(0.038) 

0.160* 

(0.029) 

- 

- 

𝑦  0.101* 

(0.049) 

- 

- 

- 

- 

0.084** 

(0.044) 

0.042 

(0.034) 

- 

- 

- 

- 

0.093* 

(0.044) 

∆𝑞  - 

- 

- 

- 

-0.010 

(0.006) 

- 

- 

-0.001 

(0.006) 

- 

- 

- 

- 

0.024** 

(0.013) 

∆𝑞  -0.005 

(0.006) 

-0.008 

(0.007) 

- 

- 

-0.012** 

(0.006) 

- 

- 

-0.008* 

(0.004) 

-0.016* 

(0.007) 

- 

- 

𝐼𝑇  - 

- 

- 

- 

-0.001 

(0.001) 

-0.029* 

(0.008) 

0.035* 

(0.017) 

0.007* 

(0.003) 

0.002 

(0.003) 

0.017* 

(0.007) 

𝐼𝑇  -0.025* 

(0.009) 

-0.021* 

(0.010) 

- 

- 

0.021* 

(0.008) 

-0.027** 

(0.016) 

- 

- 

- 

- 

-0.018* 

(0.008) 

𝐼𝑇  0.024* 

(0.010) 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

𝛼  
-0.001* -0.021* -0.001 -0.008* 0.008** 0.007* 0.002 -0.001* 

R 0.971 0.963 0.889 0.874 0.905 0.964 0.971 0.961 

Emerging 

 

Brazil Chile Czech Mexico S. Africa S. Korea Poland Russia 

Constant 0.006 -0.005 0.007 0.011 0.003 0.005 0.008* -0.013** 



38 
 

(0.004) (0.005) (0.006) (0.008) (0.003) (0.005) (0.004) (0.007) 

𝜋  1.542* 

(0.160) 

1.377* 

(0.078) 

0.879* 

(0.096) 

1.310* 

(0.056) 

1.225* 

(0.065) 

0.962* 

(0.054) 

1.020* 

(0.094) 

1.213* 

(0.075) 

𝜋  -0.894* 

(0.294) 

-0.504* 

(0.081) 

-0.108 

(0.155) 

-0.477* 

(0.083) 

-0.327* 

(0.083) 

0.048 

(0.078) 

-0.161 

(0.148) 

-0.356* 

(0.108) 

𝜋  0.381** 

(0.221) 

- 

- 

-0.087 

(0.139) 

0.381* 

(0.093) 

0.241* 

(0.096) 

-0.077 

(0.061) 

0.133 

(0.140) 

0.192** 

(0.115) 

𝜋  -0.314* 

(0.124) 

- 

- 

-0.484* 

(0.120) 

-0.404* 

(0.132) 

-0.648* 

(0.116) 

-0.248* 

(0.118) 

-

0.266*

* 

(0.140) 

-0.540* 

(0.180) 

𝜋  0.217* 

(0.069) 

- 

- 

0.363* 

(0.069) 

0.186* 

(0.089) 

0.504* 

(0.070) 

0.200* 

(0.082) 

0.196* 

(0.072) 

0.468* 

(0.140) 

𝑦  0.128* 

(0.046) 

- 

- 

0.143* 

(0.056) 

-0.814* 

(0.139) 

- 

- 

0.100* 

(0.039) 

0.247* 

(0.065) 

0.161* 

(0.055) 

𝑦  - 

- 

0.052 

(0.066) 

- 

- 

0.747* 

(0.123) 

0.152* 

(0.050) 

- 

- 

0.152* 

(0.065) 

- 

- 

∆𝑞  -0.019* 

(0.007) 

- 

- 

- 

- 

-0.056* 

(0.012) 

-0.031* 

(0.008) 

- 

- 

- 

- 

-0.030* 

(0.013) 

∆𝑞  - 

- 

-0.004 

(0.010) 

-0.017 

(0.014) 

- 

- 

- 

- 

0.001 

(0.003) 

-

0.015* 

(0.008) 

- 

- 

𝐼𝑇  0.023** 

(0.013) 

- 

- 

- 

- 

-0.109* 

(0.022) 

- 

- 

- 

- 

- 

- 

- 

- 

𝐼𝑇  -0.026* 0.002 -0.004 0.086* 0.003 -0.018* -

0.011* 

0.021** 



39 
 

(0.011) (0.009) (0.008) (0.017) (0.011) (0.009) (0.005) (0.011) 

𝐼𝑇  - 

- 

0.016* 

(0.005) 

- 

- 

- 

- 

-0.007 

(0.010) 

0.015* 

(0.007) 

- 

- 

- 

- 

𝛼  
-0.003** 0.018* -0.004 -0.023* -0.004 -0.003* -

0.011* 

0.021* 

R 0.93 0.91 0.91 0.99 0.95 0.92 0.99 0.96 

Note: HAC std. errors in parentheses, * significant at 5%, ** significant at 10%. 

In relation to advanced economies, we find that IT tends to significantly reduce inflation rate in 

four of the eight economies. For the remaining economies IT tends to increase inflation rate while 

it is not significant in one economy. It is thus true that, while for some of the advanced economies 

IT improves inflation performance, there are also some economies where this advantage is non-

existent, or it is not significant. Similar results obtain for emerging markets. IT decreases the 

inflation rate in some of these economies, while in others IT does not improve inflation 

performance.   

2.4.7. Effect of IT on inflation: Post IT 
We now test the effect of IT on inflation rate, post IT adoption. We expect that the benefits of IT 

will be preserved over time, leading to low inflation rates. 

Table 2.7 provides results for the effect of IT on the inflation rate after the adoption of IT.  

Table 2.7 Effect of inflation targeting on inflation: Post IT 

 Australia Canada Japan N. Zealand UK US Norway 

Advanced         

Constant 0.008 

(0.007) 

0.001 

(0.004) 

0.001 

(0.001) 

-0.004 

(0.004) 

-0.007** 

(0.004) 

-0.009 

(0.009) 

-0.016 

(0.018) 

𝜋  1.002* 

(0.122) 

1.148* 

(0.111) 

-0.254* 

(0.089) 

1.037* 

(0.069) 

1.225* 

(0.147) 

0.800* 

(0.175) 

0.729* 

(0.128) 

𝜋  -0.019 -0.342* 0.154** -0.090 -0.344** -0.349** 0.100 



40 
 

(0.149) (0.129) (0.081) (0.100) (0.191) (0.203) (0.155) 

𝜋  -0.133 

(0.114) 

-0.034 

(0.114) 

0.011 

(0.087) 

0.057 

(0.113) 

0.067 

(0.147) 

0.201 

(0.237) 

0.142 

(0.131) 

𝜋  -0.323** 

(0.180) 

-0.196 

(0.164) 

-0.260** 

(0.135) 

-0.509* 

(0.146) 

-0.305* 

(0.111) 

-0.027 

(0.280) 

-0.742* 

(0.098) 

𝜋  -0.256* 

(0.124) 

-0.267* 

(0.107) 

0.117 

(0.087) 

0.342* 

(0.094) 

0.247* 

(0.100) 

-0.006 

(0.166) 

0.300* 

(0.110) 

𝑦  -0.163 

(0.223) 

- 

- 

- 

- 

- 

- 

0.036 

(0.038) 

-0.485* 

(0.164) 

- 

- 

𝑦  -0.170 

(0.218) 

-0.106 

(0.104) 

0.232* 

(0.098) 

0.035 

(0.039) 

- 

- 

- 

- 

0.150* 

(0.075) 

𝑦  0.218 

(0.142) 

0.267* 

(0.115) 

- 

- 

- 

- 

- 

- 

- 

- 

0.018 

(0.067) 

∆𝑞  0.012 

(0.013) 

0.056* 

(0.020) 

0.019* 

(0.005) 

-0.001 

(0.005) 

- 

- 

- 

- 

0.069* 

(0.027) 

∆𝑞  -0.018 

(0.015) 

-0.085* 

(0.034) 

- 

- 

- 

- 

-0.009** 

(0.005) 

-0.035** 

(0.018) 

- 

- 

∆𝑞  0.014 

(0.013) 

0.058* 

(0.022) 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

𝐼𝑇  -0.735* 

(0.352 

- 

- 

-0.208* 

(0.022) 

0.011** 

(0.006) 

0.013* 

(0.006) 

0.024** 

(0.013) 

- 

- 

𝐼𝑇  1.306* 

(0.643) 

-0.310** 

(0.160) 

0.210* 

(0.022) 

- 

- 

- 

- 

- 

- 

0.273** 

(0.143) 

𝐼𝑇  -0.577** 0.314** - - - - -0.245** 



41 
 

(0.317) (0.161) - - - - (0.137) 

𝛼  
-0.006* 

- 

0.004** 

- 

0.002* 

- 

0.011** 

(0.006) 

0.013* 

(0.006) 

0.024** 

(0.013) 

0.028* 

- 

R 0.811 0.848 0.979 0.875 0.863 0.807 0.716 

Emerging S. Africa S. Korea Poland     

Constant 0.012* 

(0.004) 

0.014* 

(0.007) 

0.009* 

(0.004) 

    

𝜋  1.480* 

(0.122) 

0.953* 

(0.055) 

1.114* 

(0.095) 

    

𝜋  -0.704* 

(0.172) 

0.093 

(0.094) 

-0.379* 

(0.115) 

    

𝜋  0.296* 

(0.099) 

0.001 

(0.084) 

-0.305* 

(0.142) 

    

𝜋  -0.665* 

(0.146) 

-0.704* 

(0.232) 

-0.505* 

(0.175) 

    

𝜋  0.526* 

(0.106) 

0.519* 

(0.165) 

0.302* 

(0.094) 

    

𝑦  - 

- 

0.170* 

(0.034) 

0.294* 

(0.100) 

    

𝑦  0.272* 

(0.100) 

- 

- 

0.061 

(0.087) 

    

𝑦  - 

- 

- 

- 

- 

- 

    

∆𝑞  -0.010 0.002 -0.009     



42 
 

(0.014) (0.002) (0.008) 

∆𝑞  -0.021* 

(0.010) 

- 

- 

- 

- 

    

𝐼𝑇  -0.021** 

(0.012) 

- 

- 

- 

- 

    

𝐼𝑇  - 

- 

-0.013** 

(0.007) 

-0.010** 

(0.006) 

    

𝐼𝑇  - 

- 

- 

- 

- 

- 

    

𝛼  
-0.021** 

(0.012) 

0.013** 

(0.007) 

-0.010** 

(0.006) 

    

R 0.957 

- 

0.904 0.948     

Note: HAC std. errors in parentheses, * significant at 5%, ** significant at 10%. 

In relation to advanced economies, post IT adoption, we find that IT tends to significantly increase 

inflation rate in these economies. It is only in Australia where IT reduces the inflation rate. 

Therefore, for most of these countries, IT is not beneficial as far as decreasing the inflation rate. 

The results for emerging markets also show that in two of the three economies for which there is 

data, IT tends to reduce inflation after the adoption of IT. It is only South Korea which exhibits a 

positive effect of IT on inflation post IT adoption. 

2.5.  Conclusion  
This paper investigates the effect of IT on macroeconomic performance. Specifically, the paper 

focuses on the effect of IT on bond yields, the growth rate and the inflation rate. To conduct the 

study, we developed a measure of the degree of IT that is based on the relative strength of reaction 

of the central bank to inflation. Building on the theoretical models of IT, especially the seminal 

papers by Svensson (1997, 1999), we measure the degree of IT by the ratio of the reaction 

coefficient to inflation to the sum of the reaction coefficients in the Taylor rule. 



43 
 

 

The higher this ratio, the higher the degree of inflation targeting. Using recursive estimates, we 

managed to obtain time-variation in the Taylor rule coefficients, which then allows us to derive 

the degree of IT. 

Our findings are as follows. In terms of its effects on bond yields, IT tends to decrease bond yields, 

and therefore IT improves bond market performance. This finding is in line with O’Sullivan and 

Tomljanovich (2012), who found that IT leads to a drop in conditional volatility in bond markets.  

The implications are that IT tends to improve the performance of financial markets by decreasing 

the discount factor in asset pricing and thereby increasing asset prices. This is also in line with the 

finding by Dridi and Boughrara (2023) that IT helps contain stock market volatility. In terms of 

economic growth, IT produces mixed results for advanced economies. In some economies IT 

positively affects economic growth while in others it tends to reduce economic growth. For 

emerging markets, IT tends to reduce economic growth. In terms of inflation performance, there 

are also mixed results. 

The implications of these findings suggest that IT is good for the performance of financial markets. 

However, the financial market benefits of IT do not get systematically transmitted to real economic 

performance. For instance, by anchoring inflation expectations at a low level, we expected that the 

resultant decline in bond yields would increase economic growth. We expected that the resultant 

low inflation expectations are supposed to decrease the inflation rate, so that IT has a negative 

effect on inflation as well.  

In the light of the absence of these transmissions, we postulate that there could be some disconnect 

between financial markets and real economic activity in some the economies that are under 

consideration. This disconnect could be due to the dominant role played by retained earnings in 

financing real investment. 

Secondly, the disconnect could be due to limited financial market access by many households who 

account for a large fraction of aggregate consumption. Blinder (1997), Nelson (2001) and more 

recently Haque and Magnusson (2023) among others, noted that the real interest rate has limited 

effect of aggregate expenditure, one of the key factors that may explain the disconnect.   



44 
 

 

On IT and economic growth, we find varied outcomes. There are generally different outcomes for 

advanced and emerging economies. In some of the advanced economies lT reduces economic 

growth, while in others it increases economic growth. This may be explained by the structure of 

their economies. However, for emerging markets, in most cases, IT tends to reduce economic 

growth.  

On IT and inflation in advanced economies, we find that, while for some of the advanced economies 

IT improves inflation performance, there are also some economies where this advantage is non-

existent, or it is not significant. Similar results obtain for emerging markets. IT decreases the 

inflation rate in some of these economies, while in others IT does not improve inflation 

performance. 

3. Are Inflation Targets Optimal? 
3.1. Introduction 
Inflation targeting (IT) central banks believe that high inflation discourages savings because it 

erodes the value of money. They also believe that high inflation distorts the information which 

prices give to economic agents, and it therefore leads to sub-optimal allocation of resources. 

However, in a two-asset economy in which agents have a choice to hold either capital or money, 

Tobin (1965) shows that inflation leads to a portfolio shift towards capital, thereby increasing 

capital accumulation. Other authors, e.g., Pattanaik and Nadhanael (2013), are of the view that 

inflation does not affect the level of investment, but only negatively affects the efficiency of 

investment.  

The study by Bruno and Easterly (1995) reports that the problems that are associated with inflation 

become significant at high rates of inflation. In the light of these studies, it begs a question as to 

why central banks should make low inflation targets their overriding objective. This question is 

relevant because relationship between inflation and economic growth, and between inflation and 

unemployment in the long run remain an issue that divides economists, despite claims that there 

exists a consensus that low inflation is good for growth. 

 



45 
 

As pointed out by Debelle (1997), IT central banks accept the natural rate hypothesis first put 

forward by Friedman (1968), according to which there is only a short-run relationship between 

inflation and unemployment, however in the long run such a relationship does not exist.  

 

Subsequent studies have questioned this hypothesis. Tobin (1980) argued that the natural rate of 

unemployment floats together with the actual rate, the natural rate does not exist. This was further 

confirmed by Blanchard and Summers (1986), who found that there is hysteresis in 

unemployment—the equilibrium unemployment rate depends on the history of the actual 

unemployment rate. 

Fair (2000) also found that the inflation process in the US does not support the NAIRU hypothesis. 

Akerlof et al. (2000) found that the long run Phillips curve exhibits a negative relationship between 

inflation and unemployment. Snower (2002), Karanassou et al. (2005) and Vaona and Snower 

(2008) also find a positive relationship between inflation and economic activity in the long run.  

However, Russell and Banerjee (2008) identified a slightly positive relationship between inflation 

and the unemployment rate in the long run. This is consistent with Ascari et al. (2022), who find 

that for the US, inflation negatively affects the output gap once it exceeds a threshold of 4%.  

The above evidence, although conflicting, suggests that there is a level of inflation above which 

inflation negatively affects the performance of the economy and below which it does not. The 

question that this paper seeks to answer is: are existing inflation targets consistent with optimal 

economic performance? It is not clear how central banks around the world have set their inflation 

targets. Debelle (1997) suggests that the inflation targets should be set at low levels below which 

there would be no significant benefits from further lowering inflation. 

However, there is no evidence that many IT central banks have conducted studies to determine 

these threshold levels. This point is underscored by Horvath and Matějů (2011), who found that 

some IT central banks simply state the inflation target and do not explain how they arrived at it. 

Some studies e.g., Debelle (1997) and Adam and Weber (2023) explain why inflation targets are 

positive, citing shifts in relative prices, errors in the calculation of price indices and downward 

nominal wage and price rigidity. However, these studies do not explain how the numerical targets 

have been arrived at. 



46 
 

The aim of this paper is to determine if the current inflation targets are consist