inflation and economic growth research paper

Inflation and Growth

Models of inflation and growth in the sixties emphasized the portfolio substitution mechanism by which higher inflation made capital more attractive to hold relative to money, leading to higher capital intensity, and in the transition period to higher growth.The empirical evidence, however, is that growth and inflation are negatively correlated. Reasons for this negative correlation are investigated, and then embodied in a simple monetary maximizing model. Higher inflation is associated with lower growth because lower real balances reduce the efficiency of factors of production, and because there may be a link between government purchases and the use of the inflation tax. Comparative steady states and comparative dynamics is analyzed and the generally negative association between inflation and growth, both in steady states and in transition processes, is demonstrated.

• Acknowledgements and Disclosures

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Money Growth, Money Velocity and Inflation in the US, 1948–2021

• RESEARCH ARTICLE
• Published: 11 November 2023

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• Juan E. Castañeda   ORCID: orcid.org/0000-0002-4852-9646 1 &
• José Luis Cendejas   ORCID: orcid.org/0000-0001-8417-9455 2  

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Leading central banks did not anticipate the surge in inflation in 2021 and 2022. In our paper we assess whether changes in the velocity of money and monetary growth (broadly defined) explain long term inflation patterns in the US. We use a hundred-year sample to study the long term and the cyclical behaviour of money velocity. We find that  changes  in the velocity of money are short lived and revert to its mean. We also characterise the periods where changes in money velocity have kept closer to its mean as those of monetary equilibrium. We use a regime switching model to test for the impact of changes in the amount of money (broadly defined) and in money velocity in inflation over the medium and long term. Our model explains both the non-inflationary outcome of the Global Financial Crisis and the surge in inflation in the aftermath of the Covid-19 pandemic.

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Data Availability

Data used and sources detailed in footnote 2.

The relationship between money growth, inflation and money velocity has been modelled for the period 1948–2021 due to the availability of comparable data series since 1948. In Sect.  3 we cover a broader time period, from 1919 to 2021, as it is available for money growth and nominal GDP (on the contrary, for CPI inflation we have data since 1948).

By broad money, we mean a monetary aggregate that includes not just cash in circulation but the majority of bank deposits in the US. In this regard, we have made use of the monetary aggregate M3 calculated by Shadow Government Statistics ( http://www.shadowstats.com ), for 2006-2021, following the same method and data which the US Federal Reserve used to publish M3 (1959-2006), and broad money from Gordon ( 1986 ) for 1919-1958. For nominal GDP: Gross Domestic Product, Billions of Dollars, Quarterly ( https://fred.stlouisfed.org ) after 1959 and Gordon ( 1986 ) for 1919-1958.

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Acknowledgements

We want to thank Guillermo Sagnier for his excellent research assistance. Financial support of Universidad Francisco de Vitoria to this research is also acknowledged. We thank the referees for their comments, as well those from the attendees and fellow contributors to the ‘2022 Institute of International Monetary Research Conference’—where an earlier version of the paper was presented.

Universidad Francisco de Vitoria.

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Castañeda, J.E., Cendejas, J.L. Money Growth, Money Velocity and Inflation in the US, 1948–2021. Open Econ Rev (2023). https://doi.org/10.1007/s11079-023-09739-0

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DOI : https://doi.org/10.1007/s11079-023-09739-0

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Inflation targeting and economic performance over the crisis: evidence from emerging market economies

Asian Journal of Economics and Banking

ISSN : 2615-9821

Article publication date: 9 November 2021

Issue publication date: 1 November 2022

Inflation targeting has increasingly become a popular monetary framework since its first introduction in New Zealand at the beginning of 1990. However, the causality effects of this policy on economic performance, particularly in periods of economic turmoil remain controversial. Thus, this paper re-examines the treatment effect of inflation targeting on two important macro indicators which are inflation rate and output growth with the focus on emerging market economies. The global financial crisis, which is known as the great recession since the last decade, is investigated as an exogenous shock to test for the effectiveness of this popular regime.

Design/methodology/approach

The difference-in-difference approach in the fixed-model is employed for this investigation using a balanced panel data of 54 countries with 15 inflation-targeting countries for the period 2002 to 2010.

The examination finds that there is no significant difference in terms of the inflation rate and gross domestic product growth over the whole research period between the treatment and control groups. However, the outcome suggests that emerging economies can control the increase in inflation rate when the economy has to cope with the exogenous uncertainties.

Research limitations/implications

This finding indicates important policy implications for central banks in many countries.

Originality/value

Inflation targeting can help emerging countries to reduce an increase in inflation rate in the crisis period without many trade-offs in the growth of output.

• Inflation targeting
• Global financial crisis
• Emerging market countries
• Difference-in-difference approach
• Fixed model

Duong, T.H. (2022), "Inflation targeting and economic performance over the crisis: evidence from emerging market economies", Asian Journal of Economics and Banking , Vol. 6 No. 3, pp. 337-352. https://doi.org/10.1108/AJEB-05-2021-0054

Emerald Publishing Limited

Copyright © 2021, Thuy Hang Duong

Published in Asian Journal of Economics and Banking . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode .

1. Introduction

Many may continue to remember the global financial crisis which originated from the collapse of the US housing market in 2007, ruining several financial markets globally. This crisis is known as the Great Recession. Estimates from the International Monetary Fund ( IMF, 2010 ) show that real gross domestic product (GDP) growth in emerging and developing economies fell dramatically from 8.3% in 2007 to 6.1% in 2008, then to 2.4% in 2009. Inflation doubled in many countries during this period. While the consequences are on a global level, the magnitude of effects differed by country ( Claessens et al. , 2010 ).

As such shocks are risks in the context of the global economic integration, choosing a sound monetary framework to mitigate or even eliminate the consequences of economic disturbance is an important mandate for any central bank. In 1990, inflation targeting was first introduced by the Reserve Bank of New Zealand; gradually, it has been adopted by an increasing number of central banks as a strategy for monetary policy implementation. Under the inflation-targeting framework, the central bank officially announces a unique numerical target in the level or a range for annual inflation. Thus, this regime is expected to act as a guide for inflation expectations, assure a low and stable inflation rate and improve the central bank's credibility. However, to date, the effectiveness of this monetary framework remains controversial among policymakers and macroeconomists ( Ball and Sheridan, 2004 ; Vega and Wikelried, 2005 ; Stiglitz, 2008 ).

Numerous studies have been conducted to address whether inflation targeting matters ( Ball and Sheridan, 2004 ; Gonçalves et al. , 2006 ; Batini and Laxton, 2007 ; Lin and Ye, 2007 , 2009 ; Samarina et al. , 2014 ); nevertheless, significantly less analysis has been undertaken on evaluating this regime in the existing global economic downturn. On the empirical front, the work of Neumann and Von Hagen (2002) is one of few papers considering the impacts of inflation targeting implementation in periods of economic crisis. They compare the performance of inflation targeting and non-inflation targeting central banks under two exogenous shocks; namely, the oil price hikes of 1978 and 1998. Using the difference-in-difference approach with a sample of nine countries (six of which have adopted inflation targeting), they provide optimistic evidence about the performance of this policy in terms of lower levels of inflation, less volatile inflation and lower interest rates. Similarly, Miskin and Schimidt-Hebbel (2007) examine the macroeconomic and monetary policy performance of the inflation targeting policy before and after the oil price shock of 1997–1998 but use a more diverse sample of 34 industrial and emerging countries. The results from their panel vector autoregressive (panel VAR) models show that this monetary framework helps the targeters reduce the domestic inflation response to an oil price shock relative to periods before the adoption of inflation targeting and to the non-targeting countries. However, in terms of macroeconomic performance, Miskin and Schimidt-Hebbel (2007) find that emerging countries experienced major reductions in output as a trade-off for stable inflation.

Some recent studies focus on the global financial crisis of 2007–2008 to evaluate the treatment effects of inflation targeting. To the best of the author's knowledge, the first to publish on this theme is de Carvalho Filho (2010) , who used the fixed effects model for a sample of 84 advanced and emerging countries from January 2006 to August 2009 to compare inflation “targeters” and “non-targeters” in terms of the monetary policy responses and economic activity outcomes. His findings highlight the role of inflation targeting in smoothing this shock more effectively than the outcomes observed in their non-inflation targeting peers. Targeting countries enjoyed lower nominal policy rates, lower sovereign default risk and higher GDP growth rates in comparison to their counterparts. This conclusion regarding the positive impact of the inflation targeting policy on GDP growth is not supported by Armand (2012) . Considering the same control group and same break-date of the financial crisis of 2007 used by de Carvalho Filho (2010) , using the difference-in-difference models, Armand (2012) reveals an insignificant impact of this framework on GDP growth and inflation rate for both groups of countries.

Considered together, while these papers provide good evidence for treatment effects of inflation targeting policy on inflation control in the periods of economic disturbance, conclusions for the role of this policy in promoting GDP growth have limited consensus. Moreover, there are certain drawbacks of the empirical procedures in previous studies making their evaluation regarding the effectiveness of inflation targeting ambiguous. First , on the matter of the data, Blinder et al. (2008) suggest that the selection of the control group plays an important role in this line of research. Unfortunately, not all previous studies consider this issue carefully. Neumann and Von Hagen (2002) use a very small sample of three countries in the control group. Thus, it is unreasonable to generalize the treatment effect of the inflation targeting regime. In Miskin and Schimidt-Hebbel (2007) and de Carvalho Filho (2010) , the number of observations is larger, but no particular explanation of selected countries is provided. Second , the possibility of endogeneity is ignored in the difference-in-difference models of Neumann and Von Hagen (2002) . The adoption of inflation targeting is probably an endogenous choice considering different periods and unobserved macroeconomic situations. Hence, research that does not control for these initial conditions or for the time and country fixed effects tends to produce biased results. Following Ball and Sheridan's approach ( 2004 ), Armand (2012) controls the initial output when modeling the treatment effect of inflation targeting in an effort to deal with the endogenous issues. However, the satisfaction of the underlying assumption of a parallel trend in Armand's difference-in-difference approach ( 2012 ) appears to be ignored which could be against his conclusions considering a violated assumption.

Considering the shortcomings, this paper thus aims to re-evaluate the effectiveness of this popular monetary framework on the economy over the periods covering the global financial crisis. The important research questions whether a country that sets the inflation targeting as its primary monetary framework ( IT countries for short) can do better in terms of reducing the increase in inflation rate and accelerating the output growth during the crisis period in comparison to the countries that do not apply the inflation targeting framework ( non-IT countries for short) are addressed in this paper. This article contributes to the existing literature by providing a comprehensive analysis of inflation targeting impacts on the economy and dealing with the technical issues in previous studies. Specifically, panel data of 54 emerging countries (15 IT countries included) is exploited to simultaneously examine inflation and output outcomes from inflation targeting adoption. Instead of averaging the time-series observations, the author controls for time and country-specific dimensions to improve the inference on the causal effect of this framework. It is worth noting that both de Carvalho Filho (2010) and Armand (2012) evaluate the treatment effect of inflation targeting for periods before and during the global financial crisis in 2008–2009. Nevertheless, as the impact of this recession varied significantly across countries, it is not easy to distinguish if any estimated treatment impact is merely a function of treatment or includes the crisis. In this article, the author undertakes analyzes for the period 2002 to 2010 covering either pre- or post-crisis to avoid these confounding effects.

In contrast to some previous studies, this paper solely focuses on emerging economies as their similar characteristics could lead to a heterogeneous efficiency of inflation targeting regimes while coping with macroeconomics disturbances. Moreover, while many emerging markets experienced high volatility of the inflation and output before adopting inflation targeting, it is a fact that most advanced countries began this regime with relatively low and stable inflation ( Schaechter et al. , 2000 ). Hence, the inflation targeting adoption may not deliver important gains for the economy when considering advanced countries as observations. Furthermore, the main argument in favor of the inflation-targeting framework is that an improvement in credibility can be gained by central banks when they target a specific level or range of inflation. As central banks in emerging countries are likely to have significantly lower initial credibility than advanced peers ( Lin and Ye, 2009 ), it is reasonable to expect that the adoption of inflation targeting regime can substantially improve the credibility of such central banks, which leads to better policy effectiveness in developing countries.

The difference-in-difference fixed-effect model is employed to estimate the treatment effect of inflation targeting policy over the crisis period on two important indicators of the economy, namely inflation rate and output growth. For emerging economies, the study offers some evidence to suggest that inflation targeting is an effective policy that helps IT countries to avoid high inflation during economic uncertainties and maintain a similar performance in terms of output growth when compared to non-IT countries.

The rest of the article proceeds as follows. In Section 2 , the general background of the inflation-targeting framework is provided. Sections 3 and 4 discuss data and methodological issues, respectively. The empirical results for the treatment effects of inflation targeting regime on economic performance are reported in Section 5 . Sections 6 and 7 provide tests for the heterogeneity and robustness of estimates, respectively. Section 8 summarizes the main findings of the study and concludes.

2. Inflation targeting

2.1 what is inflation targeting.

The popularity of inflation targeting as an operational framework for monetary policy has been increasing since its introduction three decades ago. While this framework was restricted to a selected number of industrial countries for a certain period, in recent years, increasingly more emerging markets have been joining them in adopting this monetary policy regime. According to the International Monetary Fund's classification ( IMF, 2019 ), the number of IT countries significantly increased to 41 in 2019, and three-quarters of them were emerging countries ( Appendix Table A2 ). Although the inflation-targeting approach has been adopted in various ways, two main characteristics distinguish this nominal anchor from other monetary policy strategies as followed:

First, under an inflation-targeting framework, the central bank publicly announces a numerical level or range for annual inflation ( Gemayel et al. , 2011 ; Bernanke and Miskin, 1997 ). As targeting inflation directly requires the monetary authorities to forecast the likely path of prices, it is sometimes referred to as “inflation forecast targeting” ( Svensson, 1997 ). A variety of indicators needs to be closely monitored while considering this forward-looking assessment of inflation.

Second, in most cases, the primary focus of inflation considerably reduces the role of formal intermediate targets, such as exchange rate stability, output growth or unemployment rate ( Bernanke and Miskin, 1997 ; Batini and Laxton, 2007 ). Initial announcements of inflation indicators gradually transit from the current level of inflation to the desired inflation level, then to a steady-state level of price. To the extent that controlling inflation is inconsistent with intermediate goals, where the nominal exchange rate is unlikely to remain stable if the central bank reduces a high level of inflation by adjusting the policy interest rate or selling foreign currency from the national foreign-exchange reserve into the market. Thus, a clear mandate for the monetary authorities under the inflation targeting framework is attaining price stability rather than pursuing a multiplicity of monetary objectives.

2.2 Why can inflation targeting be expected to cope better than other regimes over periods of economic and financial downturns?

The existing literature offers some explanations of why inflation targeting can be a sound policy that can help countries mitigate risks from exogenous shocks. First , the inflation-targeting framework plays an important role not only in reducing high rates and volatility of inflation but also in preventing those targeting from persistent deflation which is a significant concern for monetary authorities in the post-crisis ( de Carvalho Filho, 2010 ; Ball and Sheridan, 2004 ; Vega and Wikelried, 2005 ; Lin and Ye, 2009 ). Second , the increasing credibility of policymakers in IT countries helps central banks face economic shocks with a significantly less contractionary monetary policy ( Lin and Ye, 2009 ; Neumann and Von Hagen, 2002 ). Third , the inflation targeting regime is generally accompanied by a flexible exchange rate regime ( de Carvalho Filho, 2010 ). The flexibility of the exchange rate is an essential factor that absorbs external shocks ( Bjørnland, 2004 ; Edwards et al. , 2005 ). Besides, in terms of fiscal policy performance, Lucotte (2012) and Tapsoba (2010) find that the treatment effects of inflation targeting on government revenues are positive and significant. Lucotte (2012) explains that this target encourages governments to strengthen the collection of domestic tax revenue as the monetary policy becomes tighter. An improved fiscal discipline acts as a beneficial condition for economic recovery in the post-crisis periods.

For each regression, this paper uses balanced panel data of annual series for the period 2002 to 2010, covering years before and after the global financial crisis. The sample countries comprise of 54 emerging countries, including 15 countries that adopted inflation targeting by the end of 2006 ( Table 1 ). Most of the data are collected from the World Bank and International Monetary Fund (IMF)'s International Financial Statistics.

The data set is divided into three sub-periods to evaluate the treatment effects of inflation targeting policy in smoothing the economic shocks. As the global financial crisis originated in mid-2007 following the collapse of the US housing market, the pre-crisis period consists of the 5 years preceding this crisis, from 2002 to 2006. As pointed in Armand (2012) , the consequences of this great downturn were notable until 2009. Thus, this paper examines the crisis period covering three consecutive years from 2007 to 2009. The year 2010 is also added to the baseline model as the post-crisis period to control the confounding effects of crisis consequences.

3.1 Dependent variables

The outcome variables considered in this study are the annual inflation rate and annual output growth which represent the economic performance in each country. In this paper, the annual inflation rate is defined as the percentage of changes in the annual Consumer Price Index (CPI), while output growth is measured by the percentage of changes in the annual real GDP.

Figures 1 and 2 represent a comparison of the average inflation rates between pre- and post-adoption of inflation targeting in IT countries from 1985 to 2002 and that between IT countries and non-IT countries during the study period 2002 to 2010, respectively. Similar comparisons of the average output growth are shown in Figures 3 and 4 . In Figures 1 and 3 , year T is the first year when the inflation targeting policy in each IT country started, and (…, T  − 2, T  − 1, T  + 1, T  + 2, …) are one or two years before and after the year  T (i.e. if an IT country starts the inflation targeting policy in 2000, then T  = 2000, T  + 1 = 2001, T  − 1 = 1999). The adoption years are when countries officially set inflation targeting as the nominal anchor of monetary policy based on IMF classification. The inflation rate and output growth in these two figures are the mean values of the inflation rate and GDP growth in countries that have the same year T . In Figures 2 and 4 , the inflation rate and output growth are the mean values of the CPI growth and the GDP growth in the treatment group (IT countries) and the control group (non-IT countries) over different periods.

The preliminary results emerging from these figures suggest some noticeable points. First , IT countries experienced periods of high and volatile inflation before implementing the inflation targeting policy, but this rate reduced remarkably and was controlled at low and stable rates since the monetary framework came into effect. Meanwhile, the differences in the average GDP growth in pre- and post- IT adoption in IT countries were not apparent. Second , on average, IT countries had significantly lower inflation rates than non-IT countries during the 11-year study period, particularly in the crisis period. The average inflation in treatment countries was only approximately 7% in 2008, while that in the control group was 17% in that year. Generally, there was no significant difference in the GDP growth between these two groups of countries in Figure 4 (except for 2009). These plots seem to indicate that IT countries tend to control inflation rates better than non-IT countries in periods of economic turmoil, while the tradeoffs of GDP growth are not too severe.

3.2 Control variables and sample countries

The control variables used in the analysis include variables that could explain the probability that an emerging country chooses specific inflation as a monetary target: the fixed exchange rate dummy variable, the growth of money supply aggregate (M2), trade openness and government debt. All these, except for the exchange rate variable, are described in the percentage of output (GDP). The summary statistic of these control variables and dependent variables is represented in Appendix Table A1 .

The treatment group includes 15 emerging market countries that adopted inflation targeting by the end of 2006. As explained in the previous sub-section, the policy adoption years are based on IMF classification. These years are mentioned as “conservative starting years” in Rose (2007) and Lin and Ye (2009) . Considering the control group, non-IT countries, this paper follows the selection criterion used in Lin and Ye (2009) to form two groups of countries that can be compared. In specific, the control group includes only non-targeting developing countries that simultaneously satisfy conditions of a real GDP per capita at least as large as the poorest targeting country, and that of population size at least as large as that of the smallest targeting country. According to these criteria, the control group for this subsample consists of 39 countries. Table 1 lists a total of 54 targeting countries and non-targeting developing countries, and the years in which those targeting officially adopted the inflation targeting regime.

In comparison to the sample countries in Lin and Ye (2009) , this paper includes three IT countries in the treatment group, namely Indonesia, Guatemala and Romania, as these countries adopted inflation targeting since 2005 and satisfied the criteria mentioned. Israel is removed from the sample as it was promoted from an advanced emerging market to a developed country on 19 September 2008 according to the classification of FTSE Russell ( FTSE, 2020 ). Thus, Israel has exceeded this paper's content in evaluating the treatment effects of inflation targeting among emerging countries. Another noteworthy point is that Poland became an advanced country in 2018. However, the year 2018 is not examined in this study; thus, Poland is suitable to be treated in the treatment group.

4. Methodology

The main objective of this study is to test the (null) hypothesis that the inflation targeting framework improves the economic performance in terms of the inflation rate and output growth in the IT countries during periods of economic uncertainty when the global financial crisis is taken into account. The difference-in-difference (DID for short) approach is used to provide the evaluation of inflation targeting impacts on these dependent variables. The DID model can compare treatment and control groups in terms of outcome changes over time. Thus, it is suitable to be employed in this case. Moreover, in comparison to some other quasi-experimental methods, the DID estimator is recognized to avoid selection bias by allowing for unobserved heterogeneity ( Khandker et al. , 2010 ). The DID assumes this unobserved heterogeneity is time-invariant. As such, the bias dies out by differencing. As the adoption of inflation targeting may be an endogenous choice depending on the economic situation over different periods ( Ball and Sheridan, 2004 ), the DID approach is estimated in the fixed-effect model to remove unobserved time-invariant confounders.

The DID specification in the panel fixed-effects is estimated by Equation (1) as follows: (1) Y i t = α 1 + α 2   T i t + α 3   I i t + α 4   Z i t ′ + θ t + w i + ε i t

In Equation (1) , subscripts i and t represent individual countries and year, respectively; Y is the dependent variables which represent inflation rate and output growth; T is a dummy variable denoting inflation targeting implementation in the crisis period, which takes on the value of one for years from 2007 to 2009 in IT countries, else zero; I is a dummy variable denoting the adoption of inflation targeting for all the study period, which equals one for all years after the adoption, else zero; Z' includes covariate variables, detailed below; θ are time fixed effects; w are country fixed effects, which control for time-invariant impacts on economic performance across countries; ε is the error term; and α 1 , α 2 , α 3 and α 4 are the parameters to be estimated. The examination focuses on α 2 and α 3 which represent the estimated treatment effect of the inflation targeting policy during the crisis period and since the adoption in IT countries, respectively.

Covariate variables include the dummy variable for the fixed exchange rate regime, the growth of money supply aggregate (M2), trade openness and government debt.

Granger-type causality test : The first and second leads of the treatment dummy variable are added to Equation (1) to examine the possibility that future treatment exposures are anticipated by current outcomes. Under the key assumption, future policy changes are expected to not be associated with current outcomes. Thus, the estimated coefficients of these leads should be statistically insignificant.

Group-specific linear time trend : Each group effect interacts with the linear time index, then this interaction term is added to Equation (1) . The common trend suggests that these group-specific linear time trends should be jointly insignificant. An F -test of the compound null is used to check for insignificance.

Covariate balance test: The DID validity assumes that differences between the two groups are stable over time; thus, changes in the distribution of covariates do not affect treatment exposure. In this approach, the DID validity is examined by estimating covariate balance regressions. In Equation (1) , the outcome variable is replaced with covariates. Then, each covariate is regressed against the treatment variable and group, and year fixed effects. The estimated coefficients attached to the treatment variable should be insignificant in all or almost all regressions for a valid assumption.

5. Empirical results and interpretations

Equation (1) is estimated for both the inflation rate and output growth from 2002 to 2010. As mentioned in the previous section, estimation for the DID model is applied in the fixed effects approach. Standard errors are clustered at the country level to eliminate the serial correlation over time within groups. All regressions include constant, covariate variables as specified in the text, fixed time effects and fixed country effects. The outcomes from the DID fixed effect approach are reported in Table 2 .

As per Table 2 , the estimated coefficient associated with inflation targeting implementation (IT implementation) is statistically insignificant as the inflation rate is the dependent variable. This output suggests that the inflation targeting framework has no apparent impact on the inflation rate in IT countries after the adoption, all else remaining unchanged. However, during the crisis period, the impact of inflation targeting on the inflation rate is statistically significant at 5% level. The specific results show that by applying inflation as the priority target, emerging economies can reduce the annual inflation rate by 2.2% relative to the non-IT group when the economy faces uncertain events.

The estimated treatment effect of the inflation targeting policy on output growth is represented in the two last columns in Table 2 . As the GDP growth is the dependent variable, the estimates for the inflation targeting implementation in emerging economies are statistically insignificant in either the crisis period or the stable period, all else remaining the same. Thus, in comparison to countries that pursue other primary monetary frameworks, rates of GDP growth in IT countries do not increase or decrease significantly via the inflation targeting policy over different periods.

These outcomes indicate that the inflation targeting policy helps emerging economies lower their inflation rate without placing a heavy burden on the output growth during an economic disturbance. This finding is consistent with the preliminary analysis in Section 3 which plots the remarkably lower rate of inflation in IT countries relative to the control group during the crisis period. The significant impacts of inflation targeting regime on lowering inflation rate covering the periods of economic shocks also fit with the extant literature, such as Neumann and Von Hagen (2002) , Miskin and Schimidt-Hebbel (2007) and de Carvalho Filho (2010) . In terms of output growth, the treatment effects of this monetary framework are insignificant which are supported by the conclusions in Armand (2012) . However, Miskin and Schimidt-Hebbel (2007) and de Carvalho Filho (2010) have contrast results to this paper in terms of the impacts on GDP growth. As mentioned in Section 1 of this paper, this difference could stem from the limitations of prior works in choosing improper control groups as well as dealing with selection bias issues.

The outcomes of the parallel trend tests based on Wing et al. 's (2018) approach are represented in Appendix Table A3 . Only the results of the main interests are shown in the table. As the table shows, two out of three tests support DID validity. The tests for the Granger-type causality procedure are implemented on estimates regarding the impact of inflation targeting policy on both dependent variables. Neither the first nor the second lead of the dummy variable of inflation targeting implementation have significant impacts on the inflation rate and output growth. The initial outcomes are not affected by anticipated effects; thus, the key assumption in the DID model appears to be valid in this approach.

This assumption is also satisfied by the covariate balance test. The specific results show that there is no estimated coefficient of the dummy variable for IT implementation which is statistically significant at 1 and 5% levels. This outcome indicates that the estimated treatment effect is not associated with changes in the distribution of covariates; hence, the estimates are stable over time. Only the test for group-specific linear time trend suggests that the parallel trend assumption should be problematic as the null hypothesis for the joint test of insignificant time-trend interaction terms is rejected. However, the assumption violation in this group-specific linear time trend test means that the core results should be considered more credible ( Wing et al. , 2018 ) rather than concluding them to be false. This paper proceeds with procedures for the heterogeneity and robustness of estimates in the DID models and analysis to ensure that the initial outcomes in the baseline models are robust and stable.

6. Heterogeneity analysis

In order to control for the heterogeneity within the sample, this paper runs regressions of the treatment effect of inflation targeting regimes across two sub-groups based on the level of trade openness. In this paper, trade openness is defined as the sum of exports and imports of goods and services measured as a share of GDP. The average percentage of trade openness for sample countries for the study periods from 2002 to 2010 is approximately 60%. Thus, this rate is used to divide countries into two sub-groups. Table 3 represents the outcomes of the heterogeneity test. Similar to previous tables, only the results of the main interests are provided. As per this table, the inflation targeting implementation has a negative and significant impact on the inflation rate during the crisis period in both sub-groups, while the impacts of this policy on output growth are not apparent as the economy copes with the exogenous shock. These results suggest that there is no heterogeneity treatment effect across the groups of trade openness levels. Thus, the estimated treatment effect of inflation targeting in initial results is stable across the sub-groups.

7. Robustness check

The Granger-type causality test in Section 5 suggests that estimates for the treatment variable do not change significantly as these leads are included in the model; thus, the initial outcomes in the baseline models are robust. In this section, the paper considers the year 2008 rather than 2007–2009 as the crisis period to further check the robustness of the estimated treatment effect. The dependent variable is now the changes in inflation and output growth between the crisis period 2008 and years before and after this event for the period 2002–2010. Table 4 reports the outcomes of this estimation. As observed in the table, the estimated coefficients associated with the IT dummy variables are not significantly different in comparison to the initial results. The estimated treatment effects of the inflation targeting on inflation rate are still insignificant in comparison to the pre- and post-policy adoptions during stable periods. However, inflation targeting works well in controlling the inflation rate during economic turmoil periods in IT countries relative to non-IT countries. When the output growth acts as the dependent variable, there is no significant difference between IT and non-IT countries in terms of the performance of this indicator during different periods covering the crisis. This outcome confirms that driven findings in this paper are robust.

8. Summary and conclusions

The increasing popularity of inflation targeting as a monetary framework over the last three decades partially suggests the effectiveness of this policy; however, the empirical evidence on its impact on economic performance shares limited consensus. This paper contributes to the existing research by conducting a comprehensive analysis of the treatment effect of inflation targeting two macro indicators, namely inflation rate and output growth, over the period of the global financial crisis which was considered a great recession starting in 2007. In doing so, the effectiveness of this popular monetary framework in mitigating consequences from such exogenous economic uncertainties is carefully examined.

The preferable methodological approach in this line of research – the DID model-is employed in this paper. The endogeneity of the IT regimes stemming from time-invariant factors is controlled in a fixed-effect model for the period 2002 to 2010. The sample countries consist of 54 emerging economies with 15 IT countries. When examining the case of emerging countries, the empirical results from this study indicate that inflation targeting reduces the inflation rate during the crisis period, even though the impact of this policy on the inflation indicator is insignificant during the stable periods. In terms of output growth, the inflation targeting regime does not make significant changes within the IT countries, as well as between the IT and non-IT countries over the periods covering the crisis period. This finding has proved to be robust and stable over sub-groups.

The evidence of this study does not conclude that inflation targeting is the best monetary framework and all countries must adopt it. The outcomes indicate that inflation targeting matters and works well in controlling the inflation rate when emerging countries face an exogenous shock as the global financial crisis in 2007 without significant trade-off performance of output growth. Thus, countries can consider this policy when aiming to stabilize prices during a such crisis.

This study can be extended by examining the impact of inflation targeting on other macro factors of economic performance, such as volatility of inflation rate and output growth, unemployment rate or industrial production performance. While doing this, emerging countries should have a more general view of the effectiveness of this monetary framework on the economy.

Average inflation rate in IT countries

Average inflation rate from 2002 to 2010 in IT and non-IT countries

Average GDP growth in IT countries

Average GDP growth from 2002 to 2010 in IT and non-IT countries

Sample countries

Emerging market and industrial countries that have adopted inflation targeting

Note(s): All regressions include constant, covariate variables as specified in the text, fixed time effects, and fixed country effects. Robust standard errors are in parentheses which are clustered at the country level. ** and * indicate statistical significance at the 5 and 10% level, respectively

Source(s): Author's calculation

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Acknowledgements

The author is grateful to Professor Blane D. Lewis and Mr. Phan Le for the guides and comments that helped significantly improve the quality of this study. Any errors and shortcomings, if any, remain the author's responsibility.

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Inflation, inflation uncertainty and the economic growth nexus: An impact study of South Africa

Inflation and inflation uncertainty are instrumental in the determination of financial stability, and ultimately, economic growth. We investigated the impact of inflation and inflation uncertainty on growth in South Africa by applying the autoregressive distributed lag (ARDL) estimation techniques on quarterly data covering the period 1961Q1 to 2019Q4. Unlike previous studies on South Africa, we investigated the joint impact of inflation and inflation uncertainty in South Africa, and also, pioneered in comparing the impact of both variables on growth before, and after, inflation targeting. This provided an opportunity to assess the effectiveness of inflation targeting while also investigating any changes in the behavior of the variables. We found that inflation negatively harms growth in both the short and long run, while inflation uncertainty is a short-run phenomenon in South Africa with no bearing in the long run. To promote growth, policymakers should continue to pursue policies that ensure price stability.

• The paper investigated the impact of inflation and inflation uncertainty on economic growth in South Africa covering the period 1961Q1 to 2019Q4.

• Using the autoregressive distributed lag estimation techniques, the paper found that inflation harms economic growth in both the short- and long-run in South Africa while inflation uncertainty is a short-run phenomenon as it affects economic growth only in the short run,

• Interestingly, after adoption of inflation targeting, inflation uncertainty lost it relevance as a factor determining economic growth in South Africa.

Graphical abstract

Specification table

Introduction

The impact of inflation on economic growth has been the subject of macroeconomic research and debates for quite a long time. The bone of contention, particularly, hovers on the debate on whether inflation impacts economic growth negatively or positively. One of the questions often raised is whether it is inflation that affects growth, or if it is uncertainty about inflation that upsets or motivates investment decisions and in turn affects growth [28] . Judson and Orphanides [43] caution that the debate surrounding inflation and growth is seldom settled because of estimation errors due to the omission of inflation uncertainty, which is a relevant variable in the determination of growth. Although literature is rich with studies concerning the relationship between inflation and economic growth, it is however, interesting to note that most scholars turn a blind eye to the impact of inflation uncertainty on economic growth. The pioneering work on inflation uncertainty was spearheaded by Tobin [69] who reasoned that inflation uncertainty induces households to hold more real capital assets, thereby stimulating capital productivity and economic growth. Tobin [69] , in line with Mundell [51] , were of the opinion that price instabilities provide room for growth.

The stagflation of the 1970s, stemming mainly from increases in oil prices, debunked the ideas and casted doubts on the existence of a positive relationship between inflation and economic growth. Theoretical studies took a different trajectory, with studies from Okun [60] , Friedman [28] , Stockman [67] and Ball [9] , explaining that price instability inhibits economic growth. The debate spills over to empirical studies which have contrasting conclusions. For instance, among others, Judson and Orphanides [43] , Grier and Perry [31] , Grier, Henry, Olekalns and Shields [30] , Apergis [5] as well as Iyke and Ho [38] documented a negative relationship between either inflation, or inflation uncertainty or both, and economic growth. In contrast, Coulson and Robins [20] , Jansen [39] and Fountas [25] reported a positive relationship among the variables. The debate also extends to empirical results between and within industrialised countries and emerging economies.

South Africa's significance in Africa as the financial capital and its influential role as a member of the Common Monetary Area (CMA) under the Southern African Customs Union (SACU) motivated the choice of South Africa in the paper. The history of price instabilities in South Africa dates from as far back as the 1960s. Several policies have been pursued to tame inflation, but with little success, until the adoption of inflation targeting in 2000. It was the adoption of inflation targeting which triggered the fierce debate over the relationship between price stability and economic growth in South Africa. At one end, Hodge [36] with the concurrent support from pressure groups such as the Congress of South African Trade Unions (COSATU) and the Economic Freedom Fighters (EFF) (see [58] and [21] ) pose that South Africa can make use of higher rates of inflation to accommodate growth. At the other end, Nell [57] , Niyimbanira [59] , Munyeka [52] and Kumo [45] find that high inflation harms economic growth. It is against this background that this paper endeavours to enrich literature by examining inflation, inflation uncertainty and the economic growth nexus in South Africa during the period 1961Q1 to 2019Q4. Although the autoregressive distributed lag (ARDL) bounds testing procedure is a widely used estimation technique in academic circles, this paper will be the first to make use of it in estimating inflation, inflation uncertainty and the growth nexus in South Africa. The ARDL bounds testing procedure suits the purpose of the study since it separates the long-run impact from the short-run impact, necessitating the paper to quantify the long-run and short-run impacts of the variables separately. Furthermore, according to the best of our knowledge, none of the studies on South Africa estimated the joint impact of both inflation and inflation uncertainty, which may have led to estimation errors from omitting a relevant variable, and accordingly, this paper pioneers in estimating the joint effect of the variables on economic growth in South Africa. Also, this paper has estimated the joint impact of inflation and inflation uncertainty before and after inflation targeting, thereby assessing the effectiveness of inflation targeting while also investigating any changes in behavior of the variables.

The paper is organised as follows. Section 2 presents the dynamics of inflation, inflation uncertainty and economic growth in South Africa between 1960 and 2019. Section 3 reviews the theoretical and empirical literature on inflation, inflation uncertainty and the economic growth nexus. Section 4 presents the data and the empirical methodology while Section 5 discusses the empirical results and Section 6 concludes the paper.

The dynamics of inflation, inflation uncertainty and economic growth in South Africa (1960 to 2019)

Sluggish economic growth, which is characterised as not pro-poor and having a very low employment coefficient, has become one of the befitting hallmarks of the South African economy [73] . Despite efforts to stimulate economic growth using different policies (see Table 1 ), economic growth remained sluggish and falling to average rates closer to 0 – with 1.0935% being the average rate of growth between 2013 and 2019 as shown in Table 1 . In terms of inflation, the South African Reserve Bank (SARB) has largely been successful in its fight for price stability [49] . Table 1 shows the rates of inflation and economic growth under different government and monetary policies between 1960 and 2019. The trends show that the rate of inflation has been high before the adoption of inflation targeting, with an average of 9.415%, while under inflation targeting, the rate of inflation averaged 5.307% as shown in Table 1 . However, economic growth was slightly higher before inflation targeting, as compared to the inflation targeting era.

Rates of inflation and economic growth under different government and monetary policy regimes between 1960 and 2019.

Source: Authors’ compilations based on data from SARB (2020) and IFS (2020).

The data in the table shows that the average rate of inflation dropped by 77%, from an average of 9.415% before the adoption of inflation targeting to 5.307% under inflation targeting, which can be attributed to the inflation targeting policy adopted by SARB in 2000. During the same period, the rate of economic growth decelerated by nearly 18%, from 3.107% before the adoption of inflation targeting to 2.641% after the adoption of inflation targeting.

It is also of significant importance to introduce the trends in inflation uncertainty during this period. Fig. I introduces inflation uncertainty trends from 1960 to 2019. The trends show that inflation uncertainty in South Africa moves in tandem with the rate of inflation; for instance, when inflation was high in the early 1980s, inflation uncertainty was also high; and when inflation was low after 2010, inflation uncertainty was also low. Particularly important to note from the trends is that inflation uncertainty has been on a downward spiral since the year 2000 when SARB adopted the inflation targeting policy.

Inflation, inflation uncertainty and economic growth trends (1960 to 2019).

Inflation, inflation uncertainty and economic growth: a theoretical and empirical review of literature

Literature shows that the debate on the relationship between inflation and growth dates back from the classical school of thought through to the new classical school of thought. The classical school reasons that competition for labor by capitalists increases the cost of labor as well as the costs of production which exert pressure on prices in the economy. The increase in costs of production erodes the capitalists’ profits, discouraging them from production. Accordingly, this implies a negative relationship between inflation and economic growth in both the short and the long run (see [68] and [35] ). In contrast, the Keynesians argue that demand for labor reduces unemployment while increasing economic growth, and it results in higher nominal wages and inflation as its by-products, hence a positive relationship in the long run [1] . Monetarists offer a distinguished view: They deduce that workers suffer from money illusion temporarily in that any increase in nominal wages (and inflation) induces workers to increase their supply of labor (and economic growth) temporarily before reverting to the original supply of labor – hence, inflation has no relationship with economic growth in the long run, but a positive one in the short run [47] . The new classicals, whose theory rests on the tenet of rational expectations, stress that inflation is not related to economic growth in both the short and the long run [48] . The bone of contention extends to different scholars and bodies of theory. The debate surrounding the theory is still ongoing and inconclusive. Although these economic schools of thought explain the relationship between inflation and economic growth, they do not explain the role of inflation uncertainty in the determination of economic growth.

It is interesting to note that the role of inflation uncertainty on economic growth was only introduced as late as 1965 in a study by Tobin [69] . It is only since then that scholars gained interest in the impact of inflation uncertainty on economic growth. Tobin [69] proposes that an increase in inflation uncertainty leads to a decline in accumulated wealth, prompting households to hold less non-interest-bearing assets but more real capital assets. As a result, these actions by households stimulate capital productivity and result in an increase in economic growth. Tobin [69] further outlines that under inflationary conditions, savings, investment spending and government spending increase which stimulate economic growth.

In the same vein, Ungar and Zilberfarb [70] theoretically argue that high inflation and its uncertainty induce economic agents to invest more in generating accurate forecasts on future values. This lessens inflation uncertainty over time as economic agents generate accurate predictions of future inflation. These accurate predictions, in a way, help in making informed investment decisions, which may promote investment spending, culminating in an increase in economic growth. In support, studies by Aghion and Saint-Paul [2] and Blackburn [11] demonstrate that inflation and inflation uncertainty lower the opportunity cost of investing in capital or labor resources in technological improvements, which stimulates investment spending, and ultimately economic growth. Induced by the stagflations of the 1970s, Friedman [28] queried the positive relationship between economic growth and inflation; and informally argued that inflation weakens the price mechanism, thereby harming economic growth.

Earlier on, Okun [60] had also revisited the relationship and suggested that inflation uncertainty exposes wealth and incomes to a greater risk since individuals forgo the purchase of goods to cushion them against the possible decrease in their real incomes, thereby harming economic growth. Citing the explanations given by Okun [60] as loosely structured while attempting to formalize the hypothesis given by Friedman [28] , Ball [9] explained that high rates of inflation generate inflation uncertainty and uncertainty about future monetary policy. The public casts doubt on the monetary policy authorities during periods of high inflation, which negatively affect the credibility of monetary policy authorities. In the same vein, an increase in inflation uncertainty inhibits decision making by the public, thereby negatively affecting economic growth.

The argument also extends to empirical findings. The existing literature suggests that inflation and inflation uncertainty could hurt or enhance economic growth. First, there are studies that focused on the impact of inflation on economic growth without controlling for the role of inflation uncertainty. These studies derived two conclusions – either a negative relationship or the existence of the threshold effects. For example, De Gregorio [22] , Gylfason and Herbertsson [34] , Gillman, Harris and Mátyás [29] , Barro [10] and Niyimbanira [59] found that inflation harms economic growth, while studies such as Sarel [64] , Bruno and Easterly [16] , Khan and Senhadji [44] , Yilmazkuday [75] , Ndoricimpa [55] and Phiri [63] challenged the notion of a monotonic relationship between inflation and economic growth and noted the presence of a threshold level of inflation, below which inflation enhances growth while hurting growth above that level.

Second, there are studies that mainly focus on the impact of inflation uncertainty on economic growth without focusing on the role of inflation. These studies obtained either a positive or a negative relationship between inflation uncertainty and economic growth. A positive relationship is documented by studies such as Coulson and Robins [20] , Jansen [39] as well as Bredin, Elder and Fountas [15] while, in contrast, Grier and Perry [31] , Grier, Henry, Olekalns and Shields [30] , Apergis [5] and Baharumshah, Hamzah and Sabri [7] , among others, found a negative relationship.

Finally, there are studies that included both inflation and inflation uncertainty in their analysis, and they arrived at mixed conclusions. A certain quarter of studies found that both variables negatively inhibit growth (see [43] ; Rother, 2004; [72] ; as well as [38] , among others). In contrast, Fountas [25] argued that inflation uncertainty positively impacts economic growth. Adding to the pool of inconclusive results, Grier and Tullock [32] concluded that under the condition of low inflation uncertainty, high inflation has no effect on economic growth while Clark [19] disputed the existence of a relationship between either inflation or inflation uncertainty and economic growth. Moreover, Fountas, Ioannidis and Karansos [26] obtained different results from different countries, hence inconclusive results. Baharumshah, Slesman and Wohar [8] obtained a negative relationship between inflation and economic growth, but a positive relationship between inflation uncertainty and economic growth. This leaves this category with no definite answers but with contrasting answers on the relationship.

It is interesting to note that although literature on the inflation-growth nexus on the South African economy is rich, none of the studies focused on the joint impact of inflation and inflation uncertainty on economic growth. Studies that investigated the impact of inflation on economic growth found that inflation harms growth efforts in South Africa (see [57 , 59] and [52] ), while those that focused on inflation uncertainty also found that inflation uncertainty harms economic growth (see [56] and [45] ). Some studies, for example Phiri [63] , obtained a non-linear relationship between inflation and economic growth while Hodge [36] documented that inflation positively affects growth in the short run, but negatively in the long run. This paper pioneers in investigating the joint impact of inflation and inflation uncertainty in South Africa.

Data and methodology

Data sources.

This study uses quarterly time-series data, covering the period 1961Q1 to 2019Q4, obtained from the SARB (2020) and International Financial Statistics [ [37] (2020]. The timespan of the data is limited to 1961Q1, and not periods before, due to the availability of data. The data ends at 2019Q4 and do not include 2020 due to the economic lockdowns from the prevalence of Covid-19 in 2020, which disturbed production and economic growth. However, although the dataset we used also includes the 2007/8 global financial crisis, a study by Armand [6] found evidence that the financial crisis did not impose any significant differences with regards to the inflation rates and GDP growth for inflation targeting economies. Nevertheless, after reporting on the main empirical results, we proceeded with robustness tests where we included a dummy variable to accommodate the financial crisis.

Definitions and justifications of variables

Economic growth

Economic growth is the dependent variable in the study. [66] (n.d.) measures economic growth using two different approaches: firstly, the quarterly growth rate of real gross domestic product (GDP) at a seasonally adjusted and annualized rate; and secondly, unadjusted year-on-year quarterly growth of real GDP. Although seasonally adjusted quarterly growth at an annualized rate is used as the official growth rate, irregular occurrences in specific quarters may render the data volatile. To circumvent this weakness, this paper measures economic growth using the unadjusted year-on-year quarterly growth of real GDP since it eliminates the impact of seasonal variations.

The consumer price index (CPI) is the standard index used to calculate the rate of inflation in South Africa [65] . Different measures of inflation are used, such as month on same month of previous year, month on previous month at an annual rate, quarterly average on previous quarterly average at an annual rate and quarter on quarter of previous year [50] . This paper uses the quarter on quarter of previous year measure. This method is chosen for its alignment and consistency with the method used for calculating economic growth in this paper. The inflation rate is expressed as a percentage.

Inflation uncertainty

Inflation uncertainty, defined by Grier and Perry (1998) as unpredictable volatility in the general prices, is an unobserved variable. Inflation uncertainty can be measured ex-ante, that is, before the period of inflation has passed; or ex-post, which is measured after the inflation period has occurred. This paper uses ex-post inflation uncertainty. Sample standard deviations of the inflation rate expressed as a percentage are used as the proxy for inflation uncertainty, in line with empirical work by different scholars such as Foster [24] ; Çekin and Valcarcel [17] , Barro [10] , as well as Iyke and Ho [38] .

Interest rates

The control variable in the paper is interest rates. The interest rates on 91-day treasury bills are used as the proxy for nominal interest rates in the paper. The treasury bill rate is chosen instead of the official repo rate due to its reasonable variation over time. The treasury bill rates are commonly used as the proxy for the official repo rate, for example, in Boinet and Martin [12] , Naraidoo and Raputsoane [53] and Lee and Werner [46] . Botha [14] also stated that treasury bills serve as a reference rate for the determination of interest rates on other money-market instruments. The inclusion of nominal interest rates is informed by literature from different studies such as Amusa, Gupta, Karolia and Simo-Kengne [3] as well as Bonga-Bonga and Simo-Kengne [13] , which proxied 91-day treasury bills for nominal interest rates as a control variable to investigate inflation and output growth dynamics.

Autoregressive distributed lag bounds testing procedure for co-integration

To investigate inflation, inflation uncertainty and the economic growth nexus in South Africa, the study uses the ARDL bounds testing procedure introduced by Pesaran and Shin [61] and later modified by Pesaran, Shin and Smith [62] . The choice of the ARDL bounds testing approach is justified by its several favourable properties. First, the modeling framework can derive a cointegrating relationship even when variables are integrated of either order one, or order zero; or even if it is a mixture of both [62] . Second, the ARDL bounds test comprises lags of both dependent and independent variables, making it a powerful tool for estimating both short- and long-run cointegrating relationships [61] . Third, ARDL is not sensitive to sample sizes and produces robust results even if the sample size is small. Fourth, the ARDL model captures the data generating process in general to specific modeling frameworks due to its ability to accommodate a sufficient number of lags [62] . Finally, even if there is endogeneity in the explanatory variables, ARDL provides unbiased estimates of the long-run model, with valid t-statistics [62] . The ARDL bounds testing procedure used in this paper uses the following equation:

Where Y is economic growth, INF denotes the inflation rate, VOL represents a measure of inflation uncertainty and R is the nominal interest rates. The parameters β and δ are, respectively, the short-run multipliers (elasticities) and the long-run multipliers (elasticities) of the model. The white noise residual term is denoted by ε t and is assumed to be independent and identically distributed. ∆ is the first difference operator, t denotes the time period and n is the maximum number of lags in the model which is based on the Schwarz Information Criterion (SIC). The SIC criterion eliminates the uncertainty problem in model selection [74] . Vrieze [71] also emphasises that SIC is consistent in selecting the true model, and the probability of efficacy approaches one as the sample size grows.

The ARDL bounds testing for cointegration is applied in the paper by following the upcoming procedures. The first procedure involves setting the following null hypothesis, which disputes the existence of a cointegration relationship:

which is tested against the alternative hypothesis, which supports the existence of a cointegration relationship:

Evidence of cointegration from Eq. (1) is found if at least one of the long-run multipliers is significantly different from zero. Failure to reject the null hypothesis will be sufficient proof for lack of evidence of cointegration between economic growth and its explanatory variables in the study. The second procedure is testing the existence of level relationships by comparing the F-statistic to the two sets of critical values constructed by Pesaran, Shin and Smith [62] . The first of the critical values, known as the lower critical bound (LCB), assumes that the variables are integrated of order zero, I(0); while the second set of critical values, known as the upper critical bound (UCB) assumes that the variables are integrated of order one, I(1).

A rejection of the null hypothesis of no long-run relationships implies that there is a long-run stable relationship between the set of explanatory variables and economic growth in the study. The next step will be estimating the error correction model (ECM). The ECM can be formulated as follows:

In Eq. (2) , δ is the coefficient of the error correction term - E C M t − 1 , which measures the short-run speed of adjustment towards the long-run equilibrium path of the estimated ARDL model. The coefficient of the error correction term is expected to be a negative sign.

Empirical results

Descriptive statistics.

Descriptive statistics, which gives a hindsight of the historical background and behavior of the data used in this paper is shown in Table 2 . Various measures of central tendency, as well as measures of dispersion, among other measures, are presented in the table. The data show that the average economic growth was 2.949%, while inflation averaged 8.022%. The rate of inflation reached a maximum of 19.25%, while economic growth also reached a maximum of 10.133%. Furthermore, the inflation and inflation uncertainty were positively skewed, as evidenced by the positive coefficients of skewness, which points out that both inflation and inflation uncertainty have been, in most cases, lower than their respective averages.

Descriptive statistics.

Source: Authors’ compilation based on data from SARB (2020) and IFS (2020).

Stationarity test results

The stationarity properties of the variables are first examined before investigating inflation, inflation uncertainty and the economic growth nexus in South Africa. The paper employed two-unit root tests, namely, the Augmented Dickey-Fuller (ADF) test and the Dickey-Fuller Generalised Least Squares test. Table 3 below presents the results of unit root tests of the variables in levels and at the first differences. According to Pesaran and Shin [61] , Pesaran, Shin and Smith [62] and Nayaran [54] , the ARDL model can be performed whether variables included in the model are I(0) or I(1). Accordingly, from the results presented in Table 3 , all the variables are integrated of order 0 or order 1, which allows the study to proceed with testing the long-run impact of inflation and inflation uncertainty on economic growth in South Africa.

Unit root tests.

Notes: *, ** and *** denote significance at 10%, 5% and 1% respectively; - denotes not applicable and all values are expressed in their absolute values.

Source: Authors’ compilation.

Empirical analysis using autoregressive distributed lag bounds testing procedure

The F-statistic for the period under study obtained from the ARDL bound test for cointegration shows evidence of cointegration since it is higher than the critical values proposed by Pesaran et al. [62] , as shown in Table 4 .

ARDL bounds test results for co-integration.

Note: *** denotes significance at 1%.

Since the equations used in the study show evidence of cointegration among the variables, the study proceeds with estimations of the model using the ARDL bounds testing approach. The Schwarz Information Criterion (SIC) was used to select the optimal lag length in the study. The optimal model selected for the period under study is ARDL(4,0,0,1). The long- and short-run results of the selected model are presented in Table 5 for the period 1961Q1 to 2019Q4.

The long- and short-run results for the full sample period (1961Q1 to 2019Q4).

Notes : *, ** and *** denote significance at 10%, 5% and 1% respectively; Δ is the first difference operator. S denotes stable.

Source : Authors’ compilations.

The empirical results from the data show that economic growth decreased by 0.149% for every 1% change in inflation in the long run. The results of a long-run negative relationship between economic growth and inflation are well documented, both theoretically and empirically. Theoretically, these results are consistent with studies such as, among others, Okun [60] , Friedman [28] , Stockman [67] , Ball [9] and De Gregorio [22] , while empirically in line with studies by Judson and Orphanides [43] , Grier and Grier [33] , Barro [10] as well as Munyeka [52] . However, inflation uncertainty shows an insignificant long-run relationship with economic growth in the long run. Interest rates show a significant long-run relationship with economic growth, implying that high interest rates harm economic growth efforts in the long run.

In the short run, data proves that inflation yields a short-run relationship with economic growth in South Africa. The short-run inflation coefficient for the period under study is −0.1744. This implies that 1% increase in the rate of inflation attracts a decrease in economic growth of 0.1744%. Although inflation uncertainty shows an insignificant long-run impact on economic growth, it nurtures a significant negative relationship in the short run. This suggests that inflation uncertainty is a short-run phenomenon. The adaptive expectations theory by Friedman [27] can be employed to justify this phenomenon on the basis that uncertainties in decision making by economic agents decrease over time. This implies that, in the long run, inflation uncertainty may lose relevance and significance as an economic variable. For every 1% increase in inflation uncertainty, data shows contraction in economic growth of 0.0025% in the short run. Unlike in the long run where high interest rates harm economic growth, in South Africa, interest rates are positively related to economic growth in the short run. This can be attributed to the attractiveness of South African financial assets when interest rates are high. The error correction term (ECM) which measures the speed of adjustment towards the long-run equilibrium shows that 1% deviation from the equilibrium path in each quarter was corrected in the successive quarter at a rate of −0.36%.

Panel C in Table 5 shows the results of post-estimation diagnostic and stability tests conducted on the model. The model passed all the diagnostic tests, as well as the stability tests, which provides evidence that the results are reliable.

Empirical results from the study show that inflation harms economic growth in both the short and the long run. Inflation uncertainty is insignificant in the long run but yields a significant negative short-run impact on economic growth in South Africa.

Is there any chance that the adoption of inflation targeting in 2000 may have changed the behavior and relationship among these variables but remained concealed in the set of data used in the paper? Although Antonakakis, Christou, Gil-Alana and Gupta [4] found that in a sample of 24 countries that adopted inflation targeting, 22 of them has experienced a reduction in inflation volatility, it is interesting to probe and investigate, using a different data set and estimation technique if the same outcome can be derived. In the same vein, this will also help in testing if there are any changes in the behavior and relationship among the variables within our model, despite the policy changes. Accordingly, the paper goes ahead and separates the period under study into two periods; that is, the period before the adoption of inflation targeting policy (1961Q1 to 1999Q4); and the period under inflation targeting policy (2000Q1 to 2019Q4). Table 6 presents the long- and short-run results for the pre-inflation targeting period (1961Q1 to 1999Q4).

The long- and short-run results for the pre-inflation targeting period (1961Q1 to 1999Q4).

The preferred optimal model for the pre-inflation targeting period (1961Q1 to 1999Q4) is ARDL(4,0,0,1). Panel C of Table 6 points out that the coefficients are structurally stable and free from autocorrelation, heteroskedasticity and incorrect functional forms. The calculated F-statistic shows evidence in favor of cointegration in the model. Inflation maintains a significant negative relationship with economic growth in both the short- and the long-run. Interest rates negatively affect growth in the long run, but positively in the short run. Inflation uncertainty maintained an insignificant relationship with economic growth in the long run, while nurturing a significant negative relationship in the short run, further ascertaining the notion that inflation uncertainty is a short-run phenomenon in South Africa. The results are therefore very similar to those obtained under the full sample period, that is, 1961Q1 to 2019Q4. The only exception is that the coefficients under the pre-inflation targeting period point out that economic growth was more responsive under the pre-inflation targeting period, compared to the full sample period. The focus now turns to the period under the inflation targeting regime. Table 7 presents the estimation results for the inflation targeting period (2000Q1 to 2019Q4).

The long- and short-run results for the inflation targeting period (2000Q1 to 2019Q4).

Notes : *, ** and *** denote significance at 10%, 5% and 1% respectively; Δ is the first difference operator.

The optimal model selected for the inflation targeting period is ARDL(1,0,0,1), and the results show that the model is free from autocorrelation and heteroskedasticity and there is no evidence of incorrect functional form. The parameters also experience structural stability. Furthermore, there is evidence of cointegration in the model, as shown by the statistically significant F-statistic. The impact of inflation on economic growth remained consistently negative in both the short and long run during all three periods in the paper. However, economic growth became more responsive to changes in inflation rate in the long run after the adoption of inflation targeting, as evidenced by an increase (in absolute terms) of the inflation coefficient from -0.1491 to −0.4143. Therefore, inflation became an increasingly important determinant for economic growth in the long run. Interestingly, inflation uncertainty did not only remain insignificant in the long run, but also became an insignificant variable in explaining economic growth after the adoption of inflation targeting. This implies that inflation uncertainty was controlled under inflation targeting such that it became an irrelevant and insignificant factor in explaining economic growth, even in the short run. This can be attributed to the success of inflation targeting in tying down inflationary uncertainties. Interest rates became insignificant in the long run but maintained a significant positive relationship with economic growth in the short run.

Robustness checks

The above results also need to stand the test of, (i) addition of a different control variable, in this case a dummy variable representing the 2007/8 global financial crisis, and (ii) an alternative maximum lag. To this end, we introduced a financial crisis dummy, which equates to one if the economy is in a recession, and zero otherwise. The optimal model based on the SIC is ARDL(4,0,0,1,0). Table 8 presents the results.

The long- and short-run results for the full-sample period including a dummy variable (1961Q1 to 2019Q4).

The results in Panel D of Table 8 clearly shows that the model is structurally stable and in correct functional specification while free from heteroskedasticity and autocorrelation. Furthermore, the model shows proof of cointegration, and the estimated error correction term indicates cointegration and convergence. The results, therefore, pass the test of reliability. Inflation rate has a significant negative impact on economic growth in both the short and the long-run, which is consistent with the main results. Moreover, the proof that inflation uncertainty is a short-run phenomenon is also maintained. Interest rates also yield a negative relationship with economic growth in the long run, but a positive one in the short run, which is in line with the main results.

Our data has a mixture of variables that are integrated at level (I(0), and inflation rate that is integrated of order 1 (I(1). This has, in turn, limited the choice of estimation methods since most methods require that either all variables are integrated of order 1 or at the same level, otherwise would risk spurious regression, for example, the vector error correction model, Engle and Granger [23] , the Full-Maximum Likelihood test of Johansen [41 , 42] and Johansen and Juselius [40] . However, the ARDL is immune from such restrictions. Nevertheless, in a quest for robustness, it would be interesting to check if the results will still remain the same should the lag length differ. Choosing an inappropriate lag length can lead to biased results that are not acceptable for policy analysis [18] . We therefore refrained from manually choosing the optimal lag length, but resorted to the AIC method, proposed by Pesaran et al., [62] and Narayan [54] . We also relaxed the number of maximum lags to 6. Table 9 displays the estimation results. The optimal model, according to the AIC model is ARDL(5,0,0,4,0).

The long- and short-run results for the full-sample period including a dummy variable and lag length relaxed (1961Q1 to 2019Q4).

The model is free from heteroskedasticity and autocorrelation while also structurally stable and in correct functional form as shown in Panel D of Table 9 . The error correction term is negative and significant, proving convergence and cointegration in the model. Furthermore, the F-statistic confirms the existence of cointegration in the model. The model is therefore reliable. Synthesising the results obtained in the previous models, the relationship between inflation and economic growth remains negative in both the short and the long run. Inflation uncertainty remains insignificant in the long-run while posing a significant negative impact in the short run. Interest rates maintain the negative relationship in the long run, and a positive one in the short run. Therefore, the results obtained in this paper are reliable and robust.

This paper investigated inflation, inflation uncertainty and the economic growth nexus in South Africa using the ARDL bounds testing procedure for the period 1961Q1 to 2019Q4. The results we derived from the full sample (1961Q1 to 2019Q4) show that inflation harms economic growth in both the short and the long run while inflation uncertainty is insignificant in the long run but have a significant detrimental impact to economic growth in the short run. This confirmed an interesting finding that inflation uncertainty is a short-run phenomenon in South Africa without any long-run bearing, corroborating with the monetarists’ adaptive expectations theory that uncertainties decrease over time [27] . We then split the datasets into two – one for the pre-inflation targeting period (1961Q1 to 1999Q4); and the other one for the inflation targeting era (2000Q1 to 2019Q4) – to investigate if there was any change in the behavior of the variables due to the adoption of inflation targeting. Interestingly, inflation remained a significant negative factor to economic growth, but inflation uncertainty further became an insignificant variable in both the short and the long run after the adoption of inflation targeting. This implies that inflation targeting tied down inflation uncertainty to an extent that it became an irrelevant and insignificant factor in explaining economic growth, even in the short run. In light of these findings, we recommend that policymakers should pursue policies that ensure price stability to create a conducive environment for both short- and long-run growth. Price stability is a necessary condition for economic growth; however, it is not a sufficient factor for determining economic growth. Therefore, we further recommend that policymakers should pursue policies that stimulate economic growth, while allowing the SARB to commit to fighting inflation and inflation uncertainty.

Declaration of Competing Interest

The authors of this paper certify that there is no financial or personal interest that influenced the presentation of the paper.

Inflation and the gap between economic performance and economic perceptions

Subscribe to governance weekly, william a. galston william a. galston ezra k. zilkha chair and senior fellow - governance studies.

March 25, 2024

• There is modest but inconclusive evidence supporting a link between poor evaluations of the current economy and variables such as economic inequality, the rise of conspiracy theories, economic insecurity, and negative expectations about the economic future.
• The apparent gap between economic conditions and public attitudes disappears when voters’ perceptions of what is most important about the economy are taken into account.
• History suggests that economic perceptions lag well behind changes in economic conditions.

Many Biden administration officials and sympathetic analysts are baffled by what they see as a huge gap between negative public sentiment about the economy and its actual performance. They do not understand why supermajorities rate the condition of the economy as “only fair” or “poor” and trust former President Trump more than President Biden to steward the economy over the next four years. After all, Biden supporters rightly insist, GDP growth averaged a robust 3.4% annually during Biden’s first three years, compared to 2.7% for Trump’s. Between January 2021 and January 2024, employment grew by more than 11 million, unemployment fell by four million, and the unemployment rate plunged from 6.3% to 3.7%. In fact, unemployment has remained below four percent for two full years, the longest in history. Biden’s first three years witnessed the creation of 791,000 manufacturing jobs, almost twice the number during Trump’s first three years, and Black unemployment hit a historic low of 4.8%.

Faced with the disconnect between these figures and public opinion, economists and political scientists have explored several hypotheses to explain the gap. There is modest but inconclusive evidence supporting a link between poor evaluations of the current economy and variables such as economic inequality, the rise of conspiracy theories, economic insecurity, and negative expectations about the economic future.

Two other potential explanations for the gap are more promising. First, Brookings scholars Ben Harris and Aaron Sojourner have documented a rise of negative news coverage of the economy , corrected for underlying conditions, since 2018, and they cite a growing body of literature finding a link between the tone of news coverage and measures of consumer sentiment. They acknowledge, however, that the direction of causation is not entirely clear: “Are consumers more negative about the economy because of the news,” they ask, “or is the news reporting more negative stories to match consumers’ beliefs?” Further complicating the picture, they note that the gap between news reports and economic conditions has closed in recent months. While it seems likely that news coverage would have some effect on economic sentiments, the size of this effect is difficult to measure without additional research.

A second line of explanation seems more promising. In an article by a distinguished team of political scientists, David Brady, John Ferejohn, and Brett Parker explore the influence of partisanship on voters’ evaluations of the economy. To no one’s surprise, they find that partisan affiliations do influence economic perceptions. More significantly, they find that the impact of partisanship on economic attitudes has doubled since 2001, consistent with the intensification of partisan polarization during this period. They also find “no evidence” that Republicans are more responsible than Democrats for the growing gap in economic perceptions (or vice versa).

This does not mean that economic sentiments are driven entirely by political affiliations. Although the impact of affiliation has grown substantially, diminishing the accuracy of economic models of voting behavior, models using economic as well as political variables are better predictors than those that take only politics into account.

This returns us to the initial question—the apparent gap between economic conditions and public attitudes. I want to offer a dissenting hypothesis: The gap disappears when voters’ perceptions of what is most important about the economy are taken into account. Numerous surveys have shown that voters regard inflation as the single most important indicator of how the economy is doing—and that they are more likely to define inflation as the level of prices (high or low) rather than the pace of price increases (fast or slow). Prices have risen by 18% during Biden’s first three years in office, compared to 6.2% during Trump’s first three years . Voters notice the difference, and it matters to them.

Why it matters becomes clear when we look at specific goods and services . Since January of 2021, rents have risen by 19.5%; used cars, trucks, and meat by 20%; restaurants and groceries by 21%; airfares by 23.5%; electricity by 28%; gas by 34.6%; eggs by 37.4%; and auto insurance by 44%.

Is it irrational for voters to give much more weight to inflation than to unemployment in assessing economic conditions? Not necessarily, for several reasons. First, inflation leads to higher interest rates, raising the cost of home mortgages, auto loans, and credit card debt. An important new paper finds that including interest rates in the cost of living dramatically reduces the gap between consumer sentiment and economic conditions.

Second, inflation dilutes—and can negate—the impact of rising nominal wages. During Biden’s first three years, average wages for non-supervisory workers rose by 15.4%, but the 18% increase in prices led to a reduction of 2.6% in purchasing power. By contrast, wages rose by 9.3% during Trump’s first three years, yielding a 3.1% increase in purchasing power. Similarly, median household income, corrected for inflation, rose by 10.5% during Trump’s first three years. (The increase for Hispanic households was even larger—11.7%—which may help explain the apparent shift of these households toward Trump.) Although the Census Bureau has not yet reported on household income for 2023, the reports for Biden’s first two years in office —2021 and 2022—show a decline of 2.3%.

Third, less tangibly but not necessarily less significantly, there is evidence from previous periods in the United States and elsewhere that inflation has a symbolic meaning, a broader sense of loss of control. In an Economist/YouGov poll released in early February, only 17% of respondents felt that things in the country these days are “under control,” compared to 66% who said that they were “out of control.”

Finally, unlike unemployment, inflation directly affects everyone. It erodes not only wages and incomes but also the value of savings and retirement funds. It hits lower-income and working-class households, who live close to the margin and must devote a higher share of their income to the basics—rent, food, electricity, and transportation—especially hard.

Defenders of the Biden administration’s economic record note—correctly—that the period of price increases outrunning wages ended in the spring of 2023 and that the past year has witnessed substantial increases in real incomes. But history suggests that economic perceptions lag well behind changes in economic conditions. For example, the recession that began in July 1990 during the presidency of George H. W. Bush officially ended in the spring 1991, but Bill Clinton nevertheless ran successfully against Bush’s economic record 18 months later, in the fall of 1992. Even if the rate of inflation continues to decline while wages and incomes increase, President Biden is in a race against time for voters’ economic sentiments to shift in his favor by Election Day. More favorable news coverage may help, but deep partisan divisions may mute the electoral impact of economic improvements, as they have increasingly since the beginning of the 21st century.

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Who is most affected by inflation? Consider the source

Key takeaways.

• The impact of inflation depends on what’s causing it.
• Inflationary oil supply shocks tend to hurt the least affluent by more than the most affluent.
• Inflationary monetary shocks do the opposite: They hurt the most affluent more than the least affluent.
• This discrepancy is largely driven by the different response of asset prices: Monetary policy raises home and stock prices, which hurts those buying houses, while oil shocks do the opposite.

For the first time in years, inflation has surged across the world. In the U.S. and Europe, consumer prices grew in 2022 by almost 9 percent after years of inflation rates around 2 percent or less. [1] This surge has reignited interest in a longstanding question: Who is most hurt by increasing prices?

There are two reasons why this seemingly simple question may be difficult to answer.

First, inflation can arise from different sources. For instance, conventional wisdom says the inflation spike during the 1970s was caused by a rapid increase in the price of oil—an aggregate supply contraction. The subsequent disinflation during the 1980s is often attributed to hawkish monetary policy and specifically Paul Volcker’s decision to rapidly raise nominal interest rates—an aggregate demand shock. Inflationary episodes driven by aggregate supply and aggregate demand shocks need not produce the same winners and losers. A monetary expansion may be extremely different from an oil supply contraction, for instance.

The second challenge is that the drivers of inflation affect more than just prices of goods and services. Both oil supply and monetary policy also affect unemployment, wages, and asset prices, which likely affect different households quite differently. It may be important, therefore, to consider movements in both prices and income when assessing whether inflation differentially affects the most or least affluent.

Why might inflation affect different households differently?

The classic view in macroeconomics is that inflation transfers resources from so-called net nominal savers to net nominal borrowers. To understand this, suppose that Alice lent Bob $100 at an interest rate of 5 percent. Bob then needs to pay Alice back $105 the next year. Bob is a nominal borrower and Alice is a nominal saver. If prices do not rise over the course of this year, then Bob’s repayment is worth 5 percent more goods and services than the amount he borrowed. However, if prices rise by 10 percent over this year, then Bob’s repayment buys fewer goods and services than his original $100 debt allowed him to purchase. In this sense, the inflation has made Bob richer and Alice poorer.

Doepke and Schneider (2006) studied the scale of this redistribution and found that the main losers from inflation are old, rich households—the major bondholders in the economy. In contrast, young, middle-class households are the largest winners from inflation in the U.S., because the real value of their substantial fixed-rate mortgage debt is eroded by inflation. [2] Focusing solely on this channel, inflation has often been considered to be a progressive force—it transfers resources from the wealthiest to borrowers.

This logic also holds for governments, firms, and foreigners. Since the U.S. government issues nominal bonds (i.e., borrows) to finance deficit spending, inflation reduces the real value of what they owe. Much of U.S. government debt is held by foreigners and rich Americans, so this is a force for inflation to redistribute real resources from foreigners and rich Americans to the U.S. government.

Of course, this is just one way in which inflation affects households. Most directly, inflation affects the price of goods and services that households purchase. If inflation tends to disproportionately affect the prices of goods that the least well-off households consume, then inflation could in principle be regressive. As a concrete example: If aggregate inflationary periods are accompanied by spikes in the prices of gasoline, households that spend a larger share of their income on fuel— which are largely less affluent households—will be more affected by inflation.

Finally, household income may also respond to inflation. Wages are often annually adjusted to keep up with inflation, while Social Security benefits are usually indexed to inflation. These movements in income will naturally offset whatever pain you suffer from paying higher prices. As an extreme example, if prices rise by 2 percent and household income also rises by 2 percent, the inflation will have no real effect, as households will be able to afford exactly as many goods and services as they could without the inflation.

Who is most affected by inflationary shocks?

To properly assess the winners and losers from inflation, one needs to consider all of these effects —on prices, income, and wealth—on one scale. This is the goal of a recent paper I wrote with colleagues (del Canto et al., 2023). We write down a simple economic theory of a household choosing consumption of a variety of goods, supplying labor, and investing in a number of assets.

We then consider how the household’s well-being changes when some shock affects the prices the household faces. This exercise shows that the impact of this shock on household well-being is summarized by a simple statistic: Did the price of the goods that the household consumes go up by more than its income in present value terms?

Assessing the effect of inflationary shocks on household well-being therefore requires two inputs. First, one needs estimates of the effect of the shock on the prices of a variety of goods and assets, as well as its effects on income. Fortunately, there is a large existing toolbox of techniques precisely designed to estimate such effects.

Second, one needs to measure consumption of a variety of goods by households, the assets they invest in, and the salaries they receive. Such measures can be produced from a variety of cross-sectional surveys, such as the Consumer Expenditure Survey, the Survey of Consumer Finances, and the Current Population Survey.

Our paper studies the distributional effects of two major drivers of short-run inflation fluctuations—oil supply and monetary policy shocks. We estimate the impact of these shocks using a standard Structural Vector Autoregression (SVAR) approach, where we isolate surprises in oil supply (monetary policy) using high-frequency movements in oil prices (treasury yields) around OPEC supply announcements (FOMC meetings).

Figure 1 shows the total losses to various households arising from inflations tied to a 10 percent increase in the price of oil (Panel A) and a 25 basis point cut in nominal interest rates (Panel B). The horizontal axis plots the age of the household head, while the three lines indicate three education groups—high school or less (dark blue), some college (light blue), or at least a bachelor’s degree (red).

Figure 1 – Welfare Losses from Inflationary Shocks

Figure 1 shows that inflationary monetary and oil supply shocks have vastly different distributional consequences. This is despite both shocks being scaled so that they generate the same aggregate inflation. The figure shows that when inflation is driven by the Fed unexpectedly cutting interest rates, young and middle-aged college-educated households lose the most, while older and less-educated households are largely unaffected or even benefit.

This is in sharp contrast with what happens when inflation is driven by a jump in oil prices. Such inflations lead to welfare losses, which are largest for younger, less-educated households and for retirement-age college-educated households. Meanwhile, young and middle-aged college-educated households actually benefit from the inflationary oil shock. Quantitatively, a 10 percent jump in oil prices means less-educated households must be paid around 0.5 percent of their quarterly consumption to afford their no-shock choices, while college-educated households would be willing to pay up to 0.25 percent of quarterly consumption to have the shock.

Figure 2 – Channels of Welfare Losses

What drives these patterns? Figure 2 decomposes the welfare losses into components coming from consumption prices, movements in labor income, movements from asset prices and dividends, and shifts in government transfer income such as Social Security benefits. Panel A presents the effects arising from an inflationary monetary policy shock. The top left figure shows that all households are about equally hurt from rising prices of consumption after a monetary shock. This is ultimately because differences in consumption bundles across households are small: Even though gas prices do rise more than the price of, say, clothes after a monetary inflation, motor fuel occupies a similar share—around 4 to 6 percent—of consumption for all household groups.

Paychecks, likewise, are not the primary driver of the distributional consequences of monetary inflation—all three education groups see similar increases in pay (and thus negative losses) from monetary expansions, though the gains are perhaps slightly larger for the least educated. Labor income increases nearly exactly offset the losses households see from increased expenditures. The exception is for older non-working households, which do not see changes in earnings. These households, however, see increases in their transfer income—specifically, Social Security benefits— which offset the price increases.

Monetary inflations therefore principally have distributional effects because of differences in households’ asset portfolio decisions. This can be seen by the fact that the portfolio channel in the bottom left mirrors the total welfare effect from Figure 1. Monetary policy has a couple of key effects on asset prices.

A cut in interest rates pushes up the stock market and house prices. These higher prices benefit households that are selling stocks and houses, but hurt those buying stocks and houses. Younger college-educated households are precisely those that buy houses and equities.

Meanwhile, older households that may be selling assets from their retirement accounts or downsizing their home benefit from these high asset prices. These effects are partially offset by a decline in mortgage rates and the fact that dividends on the S&P500 fall, which hurts older households holding a lot of equities. However, these dividend and mortgage rate effects turn out to be smaller than the effect from asset accumulation. [3]

Compared to monetary shocks, inflationary oil shocks have an almost opposite impact.  First, oil price spikes are slightly more regressive on the consumption price side, mostly because oil price spikes have big impacts on the price of motor fuel and utilities, both of which occupy a larger share of less-educated households’ consumption. Labor income falls after a contractionary oil shock, as unemployment rates rise, but transfer income—which is largely indexed to inflation—still rises for older households.

But the major difference between monetary and oil shocks arises because of their different effects on asset prices. Oil shocks tend to hurt the stock market and have limited effects on mortgage rates or housing markets. This generates a portfolio effect on households that is the opposite to that of a monetary inflation. This is the major reason inflationary oil shocks are regressive while monetary inflations are progressive.

Policy considerations

Inflations driven by oil supply and monetary shocks have historically had opposite distributional consequences—oil supply shocks most hurt the least affluent while inflationary monetary policy most hurts the most affluent. An implication of this is that disinflationary monetary shocks—an increase in interest rates—likely have the same distributional consequences as inflationary oil shocks. This could present a challenge for policymakers: If the Fed responds to inflation driven by reductions in global oil supply by raising interest rates, that could exacerbate the regressivity of the initial oil shock. It should be noted that this conclusion requires some more research since we are only able to estimate the effects of a surprise monetary shock rather than an anticipated policy rule. Regardless, there is no simple answer to the question: “Is inflation regressive?”

About the Author

John Grigsby is a Visiting Fellow at SIEPR. He is an Assistant Professor in the Department of Economics and School of Public and International Affairs at Princeton University. He is an empirical macroeconomist studying a broad set of questions related to wage and unemployment dynamics, the drivers of historical innovation, the functioning of mortgage markets and the distributional consequences of inflation.

[1] Consumer Price Index for All Urban Consumers: All Items in U.S. City Average and Inflation, consumer prices for the Euro Area

[2] This conclusion would be different in countries where most mortgage debt is subject to an adjustable interest rate, as is the case in many European countries and the UK.

[3] Note that all monetary effects are estimated based on surprises to the policy rate and should not be interpreted as the effects of changing the policy rule .

[1] del Canto, Felipe, John Grigsby, Eric Qian, and Conor Walsh. “Are Inflationary Shocks Regressive? A Feasible Set Approach.” NBER Working Paper #31124 (2023).

[2] Doepke, Matthias, and Martin Schneider. “Inflation and the Redistribution of Nominal Wealth.” Journal of Political Economy (2006). 114(6).

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Improved business outlooks, faster job growth boost Texas outlook

March 28, 2024

A majority of Texas Business Outlook Surveys (TBOS) participants expect increasing demand over the next six months, signaling an improving business outlook, even as inflation and wage growth in Texas remain elevated. Separately, the pace of payroll employment growth also picked up in February from January levels.

The positive sentiment outlook follows otherwise mixed indicators from Dallas Fed TBOS surveys, including only modest growth in the Texas service sector and contraction in manufacturing.

Service, manufacturing surveys offer mixed signals

Texas service sector activity expanded at a modest pace in March, as indicated by the Texas Service Sector Outlook Survey revenue index. Meanwhile, Texas factory activity weakened in March, the Texas Manufacturing Outlook Survey showed ( Chart 1 ). The surveys’ diffusion indexes indicate growth when positive and contraction when negative. Three-month moving averages that smooth out volatility are consistent with economic activity converging to long-run average growth.

Downloadable chart | Chart data

Texas payroll employment growth in February accelerated notably from January levels. The number of Texas jobs rose 4.3 percent in February (month-over-month, annualized rate), up from a downwardly revised January reading of 1.9 percent (initially 2.0 percent). Employment gains were broad based and led by manufacturing, leisure and hospitality, professional and business services, and financial activities. Employment declined only in the trade, transportation and utilities sector.

The Texas unemployment rate remained at 3.9 percent, while the labor force grew at a 2.1 percent annualized rate.

Wage growth remains elevated; inflation eases slightly

Wage growth remains high, reflecting still-strong demand for labor, elevated inflation and a low unemployment rate. The Texas Employment Cost Index for wages and salaries rose from 3.9 percent growth year over year in third quarter 2023 to 4.3 percent in the fourth quarter. It had trended lower for more than a year.

TBOS wage indexes plateaued during the first three months of 2024 after having receded from highs in mid-2022. Survey respondents in March expected wages to increase 3.6 percent over the next 12 months.

Inflation in Texas, however, appeared to slightly ease at the start of 2024. The 12-month change in the Texas consumer price index (CPI) was 4.8 percent in January, down from 5.0 percent in December. This reflects a break from second half 2023, when the Texas CPI accelerated even as the comparable U.S. measure slowed ( Chart 2 ).

The TBOS selling price index, a weighted average of manufacturing and service sector price indexes, indicated slight moderation of price pressures in January and February. However, the index doesn’t show the level of inflation, only the direction of change.

Demand expectations improve

Texas firms’ demand expectations are more bullish so far this year than they were at year-end 2023. More than half of companies surveyed expect demand to increase over the next six months, up from 38 percent in November 2023 ( Chart 3 ).

Among manufacturing firms, those in chemicals, food and fabricated metals were most optimistic, while computer and electronics and transportation equipment companies’ expectations receded from November levels.

In the service sector, expectations increased most among health care, financial activities and transportation services firms, while lagging in retail and leisure and hospitality.

Additionally, the Bureau of Labor Statistics Job Openings and Labor Turnover Survey (JOLTS), a proxy for demand-driven activity, showed job vacancies still elevated in Texas at 781,000 in January, though the total reflected the second consecutive monthly decline. The January JOLTS job openings rate of 5.3 percent significantly exceeded its prepandemic average of 4.0 percent.

Texas firms rely on domestic, international migrants for labor needs

Labor shortages have receded among Texas firms. Labor force growth—including from domestic and international migration—has played a significant role. About 30 percent of Texas firms have relied on hiring workers who moved to Texas from a different U.S. state, while 15 percent did so with workers who relocated from another country over the past year, as indicated in the February TBOS special questions.

Large firms (more than 500 employees) tended to rely more on migrants than small firms . About 42 percent of large firms depended on domestic migrants compared with 28 percent of small firms; 26 percent of large firms relied on international migrants compared with 14 percent of small firms. Over the past year, service sector firms have drawn more on domestic migration, while manufacturing companies have turned to workers from outside the U.S. ( Chart 4 ).

Outlook points to above-trend growth in 2024

The Texas economy was forecast to slow this year to its trend job growth of about 2 percent. However, the most recent data have surprised to the upside, and the forecast lifted to 2.5 percent in March.

Overall, Texas economic growth remains healthy and is expected to outpace the nation, as it typically does. The risks to the outlook are mixed. High inflation at the national level may keep interest rates high, curbing growth. Conversely, a more positive outlook among our survey contacts may portend greater-than-expected expansion.

About the authors

Jesus Cañas is a senior business economist in the Research Department at the Federal Reserve Bank of Dallas.

Diego Morales-Burnett is a research analyst in the Research Department at the Federal Reserve Bank of Dallas.

The views expressed are those of the authors and should not be attributed to the Federal Reserve Bank of Dallas or the Federal Reserve System.

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