literature review of inflation

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Research Article

Inflation targeting: A time-frequency causal investigation

Roles Conceptualization, Formal analysis, Methodology, Software, Supervision, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Economics, National University of Sciences and Technology, Islamabad, Pakistan

Roles Data curation, Formal analysis, Investigation, Software, Writing – original draft

• Tanweer Ul Islam, 
• Dajeeha Ahmed

• Published: December 11, 2023
• https://doi.org/10.1371/journal.pone.0295453
• Reader Comments

The enduring discourse regarding the effectiveness of interest rate policy in mitigating inflation within developing economies is characterized by the interplay of structural and supply-side determinants. Moreover, extant academic literature fails to resolve the direction of causality between inflation and interest rates. Nevertheless, the prevalent adoption of interest rate-based monetary policies in numerous developing economies raises a fundamental inquiry: What motivates central banks in these nations to consistently espouse this strategy? To address this inquiry, our study leverages wavelet transformation to dissect interest rate and inflation data across a spectrum of frequency scales. This innovative methodology paves the way for a meticulous exploration of the intricate causal interplay between these pivotal macroeconomic variables for twenty-two developing economies using monthly data from 1992 to 2022. Traditional literature on causality tends to focus on short- and long-run timescales, yet our study posits that numerous uncharted time and frequency scales exist between these extremes. These intermediate scales may wield substantial influence over the causal relationship and its direction. Our research thus extends the boundaries of existing causality literature and presents fresh insights into the complexities of monetary policy in developing economies. Traditional wisdom suggests that central banks should raise interest rates to combat inflation. However, our study uncovers a contrasting reality in developing economies. It demonstrates a positive causal link between the policy rate and inflation, where an increase in the central bank’s interest rates leads to an upsurge in price levels. Paradoxically, in response to escalating prices, the central bank continues to heighten the policy rate, thereby perpetuating this cyclical pattern. Given this observed positive causal relationship in developing economies, central banks must explore structural and supply-side factors to break this cycle and regain control over inflation.

Citation: Islam TU, Ahmed D (2023) Inflation targeting: A time-frequency causal investigation. PLoS ONE 18(12): e0295453. https://doi.org/10.1371/journal.pone.0295453

Editor: Kittisak Jermsittiparsert, University of City Island, CYPRUS

Received: September 5, 2023; Accepted: November 22, 2023; Published: December 11, 2023

Copyright: © 2023 Islam, Ahmed. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data relevant to this paper are available from GitHub at https://github.com/Tanweerulislam/Data-Inf-PR/blob/main/Inf-PR%20data.xlsx .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

There is a long-standing debate among economists about the effectiveness of interest rates as a monetary policy tool for managing inflation in developing economies [ 1 ]. While some argue for its universal effectiveness, others suggest it may be less potent in developing economies [ 2 ]. Proponents of universal effectiveness cite its successful application in numerous developed economies to combat inflation [ 2 , 3 ]. They posit that the basic principles of economics are universally applicable, indicating that interest rate policy should perform as effectively in developing economies as it does in their more advanced counterparts.

Those economists who contend that interest rate policy is less effective in developing economies emphasize several factors that can impede its implementation and effectiveness in these countries [ 2 , 4 ]. For example, the prevalence of sizable informal sectors in developing economies, which often operate with distinct financial regulations compared to the formal sector. This circumstance can pose a challenge for central banks in effectively transmitting monetary policy signals to the entire economy [ 5 ]. The relatively weaker financial systems in developing economies can also impede the effective implementation of monetary policy [ 2 ]. Moreover, developing economies often grapple with substantial external debt, exposing them to fluctuations in global financial markets. This susceptibility can create difficulties for central banks as they endeavor to oversee and stabilize domestic inflation rates [ 6 , 7 ]. Furthermore, in developing economies, reduced central bank credibility leads expectations to be anchored in historical data, thereby increasing their reliance on past information. Consequently, this can lead to higher inflation by diminishing the efficacy of inflation targets as the primary influencers of inflation expectations [ 8 ].

Supply shocks can trigger substantial and enduring fluctuations in headline inflation. This can add complexity to the trade-off between economic output and inflation [ 9 ], posing challenges for monetary authorities and rendering monetary policy less effective in developing economies. In a high inflation scenario, understanding the implications of interest rates becomes a complex task. Assessing the true level of the real interest rate is particularly challenging when inflation expectations are in constant flux [ 10 ]. Further, in the presence of supply shocks, applying monetary tightening has a marked and positive influence on inflation in both advanced and emerging economies [ 11 ]. Rodrik and Velasco [ 12 ] argue that the sensitivity of investment and consumption to interest rates in developing economies can be low, limiting the effectiveness of interest rate policies in controlling inflation.

Developing economies face challenges in achieving consistent economic growth and have increasingly focused on using interest rates to control inflation, aiming for price stability in monetary policy. However, the relationship between inflation and interest rates remains unclear, with studies reporting varying causality directions. Some studies find unidirectional causality from interest rates to inflation [ 13 , 14 ], and inflation to interest rate [ 15 – 17 ], while others identify bidirectional relationships [ 18 , 19 ].

The debate about the effectiveness of interest rate policy to control inflation in developing economies is likely to endure. The evidence indicates that the effectiveness of such policies can vary due to structural and supply-side factors. Additionally, the literature does not provide a clear consensus on the causal relationship between inflation and interest rates. Nevertheless, it is important to recognize that interest rate policies remain a prominent strategy in many developing economies. This leads to the question: Why do central banks in these economies continue to favor interest rate-based monetary policy? To address this, our study utilizes wavelet transformation to dissect interest rate and inflation data across different frequency scales. One of the key advantages of wavelets lies in their ability to unveil concealed cyclic trends, patterns, and non-stationarity prevalent in economic time series data that may not be apparent with traditional time series analysis [ 20 ]. This innovative approach provides an opportunity for a meticulous exploration of the intricate causal interplay between these two pivotal macroeconomic variables. Conventional literature on causality often confines itself to the delineation of short- and long-run timescales. In contrast, our study advances the argument that numerous unexplored time and frequency scales exist between these two extremes [ 21 ], potentially exerting a profound impact on the causal relationship and its directional flow. It expands the horizons of the existing literature on causality and opens new avenues for understanding the nuances of monetary policy in developing economies.

2. Literature review

2.1. theoretical framework.

The theoretical framework of using interest rates as a monetary policy tool to control inflation is primarily rooted in the Quantity Theory of Money (QTM) and the Phillips Curve. The Quantity Theory of Money posits that a change in the rate of money supply growth leads to a corresponding change in the rate of growth in nominal income and in inflation [ 22 ]. Raising interest rates results in higher borrowing costs, prompting consumers and businesses to typically cut back on their spending and borrowing. This decrease in spending leads to a reduction in the demand for money which theoretically leads to lower inflation.

The Phillips Curve delineates an inverse correlation between inflation and unemployment [ 23 ], thereby signifying a trade-off for central banks. When central banks opt to increase interest rates, their objective is to mitigate inflation within an overheated economy. However, such a decision may entail a temporary elevation in unemployment levels. Conversely, the reduction of interest rates can catalyse economic activity but concurrently has the potential to exacerbate inflation. Consequently, central banks employ interest rates as a mechanism to attain equilibrium between inflation and employment.

2.2. Empirical debate

Monetary policy stands as a pivotal instrument for fostering economic growth in developing nations. The discourse surrounding this subject is long-lasting, with a multitude of studies elucidating its influence and efficacy. It holds the potential to achieve price stability, stimulate investments, and bolster economic vitality. Yet, the effectiveness of monetary policy in these contexts remains intricate and subject to debate. In this literature review, we amalgamate insights from pertinent research to illuminate the intricate connection between monetary policy and inflation in developing countries.

Inflation targeting is a monetary policy framework wherein the central bank defines a precise inflation target and deploys its policy tools to achieve this goal. Literature has demonstrated the efficacy of inflation targeting in reducing inflation rates and stimulating economic growth in developed countries. Notably, Masson et al. [ 2 ] observed that developing economies implementing inflation targeting experienced lower inflation compared to countries without such a policy framework. Nonetheless, arriving at a definitive conclusion is not as straightforward when considering developing economies [ 4 ] as the preconditions for adopting such a framework are not yet present in these economies [ 2 ]. In these contexts, the implementation and effectiveness of interest rate-based monetary policy can be hindered by structural and supply-side factors.

Alberola & Urrutia [ 5 ] argue that monetary policy actions are sacrificed due to the presence of an informal sector in developing economies. Their results indicate that informality can dampen inflation volatility in response to various shocks but, at the same time, diminish the efficacy of monetary policy. The monetary policy impact takes a longer time in relatively weaker financial systems in developing economies [ 24 ] as compared to developed financial systems. Further, when people have less trust in the central bank, they are more likely to base their expectations about future inflation on past inflation rates. Inflation targets by the central banks become less effective in impacting future inflation expectations [ 8 ].

According to Coletti et. al. [ 9 ], supply shocks can cause large and long-lasting fluctuations in inflation, which can make it difficult for central banks to manage inflation and economic output. This is especially true in developing economies, where monetary policy may be less effective. Further, in the presence of supply shocks, applying monetary tightening has a marked and positive influence on inflation in both advanced and emerging economies [ 11 ]. Under high inflation, it can be difficult to understand the effects of interest rates as the real interest rates can change quickly when inflation expectations are unstable [ 10 ]. Thus, the effectiveness of interest rate policies in controlling inflation may be limited in developing economies, because investment and consumption may be less sensitive to interest rates [ 12 ].

Thus, developing economies have been struggling to achieve steady economic growth [ 25 ]. Controlling inflation with the help of interest rates has become the only goal of many Central Banks during the previous few decades [ 26 ] because inflation targeting is a complete monetary framework that ensures price stability along with other monetary objectives [ 27 – 29 ]. However, inflationary pressures can lead to contractionary monetary policy that impacts economic growth, poverty, and inequality [ 30 – 33 ]. Thus, the direction of causality between inflation and interest rate is the prime concern because it is related to the potency of the monetary policy framework.

2.3. Causality debate

The literature does not provide any clarity on the direction of causality between the interest rate and inflation. Mehregan et al., [ 13 ] examine the causal relationship between inflation and interest rate by utilizing panel data from 24 developing countries. A unidirectional relationship from interest rate to inflation is established for 23 countries. Ahmadi et al.[ 14 ] rely on Hsiao’s causality test to establish the causal relationship from interest rate to inflation only for Qatar and Djibouti using quarterly data for sixteen Middle East-North Africa (MENA) countries for the period 1997–2008. No causality is found for the rest of the member countries. The Toda-Yamamoto causality test shows a unidirectional causal relationship from interest rate to inflation for the United Kingdom and Switzerland while for Germany the causality runs two-way [ 18 ]. Another study conducted on Turkey using monthly data shows a unidirectional causal relationship from inflation to interest rate between 2005(04)-2006(05) and from interest rate to inflation for 2015–2016 [ 34 ]. Asgharpur et al., [ 35 ] employ a panel causality approach to 40 Islamic countries to explore the causal nexus between interest rate and inflation. The study establishes the unidirectional relationship from interest rate to inflation. Mirza & Rashidi [ 36 ] consider two scenarios for assessing the causal relationship between the variables: lending rate & inflation and real interest rate and inflation. The study utilizes panel data ranging from 2006 to 2013 for South Asian Association for Regional Cooperation (SAARC) countries and finds no causal relationship for the first scenario and a bidirectional causal relationship for the second scenario.

Fatima & Sahibzada [ 19 ] determine the nature of the relationship between inflation, interest rate, and money supply for the economy of Pakistan from 1980–2010. The Granger causality analysis indicates the existence of a bidirectional relationship between inflation and interest rate which corroborates with the findings in [ 33 ]. Nezhad & Zarea [ 37 ] examine the causality relationship between inflation and interest rate in Iran. Toda and Yamamoto’s Granger causality analysis shows one-way causality from the interest rate to the inflation rate. Amaefula [ 38 ] finds strong evidence of unidirectional Granger causality from interest rate to inflation rate for Nigeria using monthly data from 1995 to 2014 whereas Alimi & Ofonyelu [ 17 ] employs Toda-Yamamoto test to establish a unidirectional relationship from inflation to nominal interest rate for Nigeria using data for the period 1970–2011 which is in line with the findings for Kenya [ 39 ], Jorden [ 40 ], Pakistan [ 15 ], and Bangladesh [ 16 ].

Based on the synopsis of previous studies above, there is a lack of consensus among researchers concerning the efficacy of interest rate policies in managing inflation within developing economies due to the structural and supply-side factors. Furthermore, the direction of causality between interest rates and inflation in developing nations remains a subject of debate. The sample size, econometric methods, frequency, and time scales could be the possible reasons behind the conflicting causality results. Literature covers the first two reasons, sample size and econometric methods well [ 41 – 44 ] however, the frequency and time scale impact has not been considered. Literature on causality considers only the short- and long-run as time scales while this study argues that there exist more time and frequency scales in between the short- and long-run that may impact the causal relationship and its direction. Therefore, we employ the wavelet transformation to decompose the interest rate and inflation series into different frequency scales. This will help us to study the causal dynamics between the two important macroeconomic variables in detail.

3. Data & methodology

This study utilizes international financial statistics (IFS) and central banks’ repositories to collect the data on Consumer Price Index (CPI) based inflation and Policy Rate (PR) of twenty-two developing countries for varying periods (see Table 2 , Col 1, 2). This study aims to investigate the causal relationship between inflation and interest rates through a time-frequency analysis, necessitating the use of high-frequency data. The selection of the countries in our sample is contingent on the accessibility of monthly data concerning these macroeconomic variables. Time series analysis of macroeconomic indicators is generally grounded in the time domain and ignores the frequency domain. Spectrum analysis allows us to decompose the series into a spectrum of cycles of varying length that helps to extract the main oscillatory components of the series [ 45 , 46 ] including low frequency (trend), medium frequency (cycles), and high frequency (noise) components. There are different tools available to transform the time series into these components including the Fourier and Wavelet transformations. The Fourier transformation requires the stationarity of the data [ 47 ] whereas the macroeconomic variables are less likely to be integrated of order zero (i.e., stationary). To explore the scale-dependent causal linkages between inflation and policy rate, we employ Wavelet transformation to decompose the series into various frequency scales. Wavelet transformation has an edge over the Fourier transformation as its window size adjusts optimally both for low and high frequencies [ 46 ]. Further, its good frequency and time resolutions enable it to capture movements both at low and high frequencies which allows it to cater for outliers, regime changes, and shocks [ 48 ]. For wavelet decomposition, we need to select a suitable filter like Haar and Daubechies. The Haar filter is simple but neither continuous nor differentiable however the Daubechies is an orthogonal, smooth, symmetric, and localized filter [ 48 ]. Therefore, this study uses the Daubechies’s Daub4 wavelet filter while decomposing the series.

After decomposing the inflation and policy rate series for all the selected countries, we estimate correlation coefficients for inflation and policy rate at different scales to understand the direction and strength of the relationship between the variables. The t-test is used to establish the statistical significance of the correlation coefficients. To explore the direction of causality, we employ the Granger Causality test to the pair of variables at various scales. The direction of the relationship and causality together would help us answer the question of whether the policy rate is an appropriate tool for controlling inflation.

3.1. Wavelet decomposition

This study employs Daubechies’s wavelet to decompose inflation and policy rate and considers the length of the business cycle to sixty-four months [ 46 ]. The series is decomposed into different frequency components of length between (2 k , 2 k +1 ) months where k = 1,2,3,…,5 keeping in view the monthly frequency of our data [ 49 , 50 ]. Both series are decomposed into five orthogonal frequency components, trends, and business cycles ( Table 1 ). To better understand the decomposition, following Hanif et al., [ 48 ] & Tiwari et al., [ 51 ], the components are categorized as noise (D1), cycles (D2-D5), business cycle, and trend (Col 3, Table 1 ).

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https://doi.org/10.1371/journal.pone.0295453.t001

These frequency scales depict varying cyclical fluctuations which are usually ignored while conducting the conventional causal analysis. The implication of ignoring these cyclic fluctuations is that we could not study the relationship between the variables at different scales that exist between the short- and long-run. Fig 1 indicates the phenomenon: cyclical fluctuations recede as the aggregation increases over the months.

https://doi.org/10.1371/journal.pone.0295453.g001

4. Results & discussion

Table 2 below provides descriptive statistics on the inflation and policy rates of the selected twenty-two developing countries. Theoretically speaking, the higher the interest rate, the lower the inflation rate like in the case of Congo, the average policy rate is at 19.18 and the average inflation rate is at 2.80. Whereas the mean inflation and policy rates for other countries like Morocco, Ghana, Nigeria, Uzbekistan, etc. do not follow the inverse relationship as per the theory. On balance, Fig 2 shows a positive relationship between the two rates i.e., the higher the policy rate, the higher the inflation. These initial findings raise a valid question on the appropriateness of the policy rate as a monetary tool and the disagreement among the researchers regarding the direction of causality motivates us to explore the more time and frequency scales that exist between the short- and long-run.

https://doi.org/10.1371/journal.pone.0295453.g002

https://doi.org/10.1371/journal.pone.0295453.t002

This study utilizes correlation and causality analysis to test the appropriateness of the policy rate as a monetary tool. Theoretically, we should expect a negative and significant correlation between the policy rate and inflation with the direction of causality running from policy rate to inflation. Correlation and causality results are furnished in Tables 3 and 4 respectively.

https://doi.org/10.1371/journal.pone.0295453.t003

https://doi.org/10.1371/journal.pone.0295453.t004

In Table 3 , we observed a negative and significant correlation between the policy rate and inflation, at levels, for four countries including Algeria, Morocco, Mongolia, and Sri Lanka which is in line with the theory. However, interestingly, no causal relationship is observed for these countries [ 14 ] except for Sri Lanka ( Table 4 ). Policy rate causes inflation in Sri Lanka with a negative and significant relationship [ 13 ] which contradicts the finding in [ 36 ] of bidirectional causality for the SAARC countries. At levels, no causality in case of India and the bidirectional causality in the case of Pakistan [ 19 ] might lead [ 36 ] to conclude the bidirectional causality for the panel of SAARC countries. Positive and significant correlation is observed for the remaining eighteen countries with mixed results on the direction of causality ( Table 3 ) which is not in line with the theory. Unidirectional causality runs from policy rate to inflation for Pakistan, Sri Lanka, and South Africa whereas causality runs from inflation to policy rate for Cambodia and Zambia. Two-way relationship is established for Egypt, Ghana, Kenya, and Nigeria.

Considering the noise series (D1), a negative and significant correlation is recorded for five countries at level whereas lagged policy rate has a significant and positive association with inflation ( Table 3 ). Three out of these five countries (Pakistan, Congo, Mauritania) show no causal relationship at level for the noise series and the remaining two countries (Uganda & Zambia) show a two-way causal relationship ( Table 4 ). The direction of causality runs from lagged policy rate to inflation in the case of Bangladesh, Sri Lanka, and Uzbekistan with a positive correlation whereas inflation causes the lagged policy rate in the case of Cambodia and Ghana.

For D2 (4–8 months) component at level, negative and significant correlation is observed for only Congo and Mauritania where inflation causes the policy rate. For the remaining countries, positive and significant correlation contradicts the theory while the causality runs from policy rate to inflation for five countries, from inflation to policy rate for five countries, and a bidirectional relationship is established for nine countries. The lagged policy rate causes inflation only in the case of India but with a positive and significant correlation meaning that a positive shock to the policy rate would lead to higher inflation in the future. On balance, the findings for the D2 component reject the policy rate as an appropriate monetary tool.

For D3 (8–16 months), no causality is observed for Algeria, Morocco, Mongolia, Nepal, and Mauritania [ 14 ]. Congo is the only country that has a significant and negative correlation with bidirectional causality. Thirteen other countries are showing bidirectional causality but with positive and significant correlation which is not in line with our hypothesis: there is a negative relationship between the policy rate and inflation. Policy rate causes inflation in Nigeria [ 38 ] with the positive direction of the relationship between the variables.

While considering D4 (16–32 months), a lagged policy rate causes inflation in the case of Pakistan and Bangladesh with a positive and significant correlation between the variables of interest. On balance, the remaining countries exhibit a two-way causality while the negative and significant correlation is only observed for Algeria and Mauritania. It is pertinent to mention that no causal relationship is observed for Mauritania. No causality may be attributed to the existence of a parallel financial market in Mauritania [ 52 ].

For D5 (32–64 months), results indicate a bidirectional causal relationship between the policy rate and inflation for all the selected developing countries however negative correlation is found only for Algeria and Morocco. Positive and significant correlation for the rest of the economies is not in line with our theoretical hypothesis.

For the business cycle (4–64 months) series, policy rate causes inflation in the case of India, Kyrgyzstan, Bangladesh, Sri Lanka, Egypt, and Uganda however the correlation is positive and significant between the variables of interest. This means an increase in interest rate would further add fuel to inflation. While Congo, Cambodia, Ghana, Kenya, Nigeria, and Zambia are experiencing a reverse causality i.e., inflation is causing the policy rate with a significant positive correlation except for Congo meaning that an increase in inflation would push the interest rate in the upward direction. These results corroborate the findings in the literature for countries like Nigeria and Kenya [ 17 , 39 ].

Considering the trend (> 64 months), a negative and significant relationship is established only for Algeria, Morocco, Mongolia, and Sri Lanka while the rest of the countries show a positive and significant correlation between the policy rate and inflation. The direction of causality is unanimously bidirectional for all the selected developing countries.

To sum up, Algeria, Morocco, and Mauritania are the countries bearing a negative and significant relationship between the policy rate and inflation for most of the frequency scales. However, these countries exhibit either no causality or bidirectional causality for most of the scenarios. Further, Mauritania experiences reverse causality (from inflation to policy rate) for the D2 component. For the rest of the countries, the correct combination of correlation sign and direction of causality is not found. Hence, we reject the hypothesis that increasing the policy rate would control the inflation in developing countries under consideration.

5. Conclusion

This study examines the suitability of policy rates as instruments for managing inflation in 22 developing countries using wavelet decomposition. Theoretically, the policy rate and inflation are negatively correlated i.e., the inflation rate would decline in response to an increase in policy rate and the direction of causality runs from policy rate to inflation. The investigation reveals several noteworthy findings at different time scales.

At levels , on average, a positive relationship exists between policy rate and inflation across the countries studied ( Fig 2 ). The direction of causality is found to be both uni- and bi-directional with positive and significant correlations between these variables. The only exception is the Sri Lanka where the policy rate does Granger cause inflation with negative correlation which is in line with the theory.

The short run analysis is based on the two components of the series namely D1(2–4 months) and D2 (4–8 months). First component analysis reveals that the policy rate and inflation are negatively and significantly correlated with bidirectional causality for Uganda and Zambia only. Unidirectional causality runs from policy rate to inflation in the case of Bangladesh, Sri Lanka, and Uzbekistan with a positive and statistically significant correlation. In the context of second component the policy rate causes inflation in case of India, Philippines, and Mongolia with positive and statistically significant correlation. This suggests that an increase in the policy rate could potentially lead to a rise in inflation within these countries. While the causality direction remains ambiguous for the remaining countries in the study, it is important to note that there is a positive correlation between the policy rate and inflation.

The medium short run analysis utilizes the D3 (8–16 months), D4 (16–32 months), and D5 (32–64 months) components of the policy rate and inflation. For the first component (D3), Congo is the only country having a significant and negative correlation with bidirectional causality. Thirteen other countries are showing bidirectional causality but with positive and significant correlation. While considering the D4 & D5 components, on balance, a two-way causality with a positive correlation is found. In the long run (> 64 months), the direction of causality is unanimously bidirectional for all the selected developing countries with a positive and significant correlation between the policy rate and inflation.

Theory suggests that a negative causal relationship between the policy rate and inflation is instrumental for central banks to implement tight monetary policy as a measure to effectively control inflation. However, this exercise reveals that the developing economies bear a positive causal relationship between the policy rate and inflation. This indicates that as the central bank raises interest rates, it triggers an increase in price levels. Furthermore, in response to rising prices, the central bank continues to raise the policy rate, perpetuating this cycle. Given the observed positive causal relationship between the policy rate and inflation in developing economies, central banks should explore structural and supply-side elements to disrupt this cycle and successfully manage inflation.

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• 39. MMASI B., “An Investigation of the Relationship Between Interest Rate and Inflation in Kenya,” University of Nairobi, 2013.

Inflation expectations and consumer spending: the role of household balance sheets

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• Published: 09 March 2022
• Volume 63 , pages 2479–2512, ( 2022 )

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• Lenard Lieb   ORCID: orcid.org/0000-0002-2814-2022 1 &
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Research interest in the reaction of consumption to expected inflation has increased in recent years due to efforts by central banks to kick-start demand by steering inflation expectations. We contribute to this literature by analysing whether various components of households’ balance sheets determine how consumption reacts to expected inflation. Two channels in particular are conceivable: an increase in inflation expectations can raise consumption through direct increases in expected real wealth, e.g. for households with nominal financial liabilities. By affecting the real interest rate, expected inflation can interact with wealth if only those households can adapt their consumption to current real interest rates that are not budget-constrained or sufficiently liquid to shift funds between consumption and savings. We investigate these channels empirically using household-level information on balance sheets, durable consumption and inflation expectations from the Dutch Central Bank’s Household Survey. We find that investments in risky assets as well as net worth moderates the relation between expected inflation and durable spending decisions. The net worth effect is most pronounced for households with fixed interest rate mortgages.

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1 Introduction

Hypotheses on why inflation expectations can have an impact on consumption on the micro-level are based on two arguments. First, inflation expectations change the real interest rate and could therefore affect consumption through intertemporal substitution. Second, they affect expected real wealth and therefore consumption out of real wealth. In both cases the composition of a household’s balance sheet can alter the size and direction of the effect of inflation expectations on spending. Attempts to gauge this interaction in the literature have been incomplete. Several authors estimated the impact of inflation expectations on consumption. These studies have often exploited some sort of natural experiment such as the zero lower bound or value-added tax increases to identify a causal relationship using cross-sectional (Bachmann et al. 2015 ; Ichiue and Nishiguchi 2015 ; D’Acunto et al. 2016 ) or panel data (Burke and Ozdagli 2013 ; Crump et al. 2015 ; Duca et al. 2018 ) without reaching consensus on the sign or size of the effect. However, no analysis has properly accounted for the potential role of the balance sheet as a moderator of the effect of price expectations on spending. In this paper we investigate empirically whether different components of a household’s balance sheet interact with its inflation expectations in affecting realised consumer spending. To this end, we use panel data on household level balance sheets, inflation expectations and durable consumer spending from the Dutch Central Bank’s (DNB) Household Survey.

While the use of micro-level data to study the nexus between inflation expectations and consumer spending has allowed researchers to estimate cross-sectional effects, almost no attention has been paid to analyse the economic mechanisms behind these “general” effects. Changes in the real interest rate affect a household’s optimal allocation of consumption over time. Differences in inflation expectations can lead to differences in the perceived real interest rate both over time and across households. Depending on their balance sheets, households might or might not be able to shift funds from savings to current spending or vice versa. Additionally, access to and costs of credit financed consumption might differ between households depending on the available collateral. We characterise these two channels through which inflation expectations can affect spending as real interest rate dependent. Another channel that motivates the research question of this paper is a real wealth channel. Inflation expectations determine expected real wealth. In case of rising inflation expectations debtors expect increases in real wealth, while creditors expect falls in real wealth. The net nominal position of their balance sheet measures their exposure to price level changes. Empirical evidence suggests that consumption is sensitive to changes in wealth (Case et al. 2005 ; Mian et al. 2013 ). Consequently, inflation expectations and balance sheet positions might interact on the micro-level. This could have macroeconomic effects if debtors have a higher propensity to consume than creditors. Here we refer to the growing heterogeneous agent literature that emphasises the relevance of differential marginal propensities to consume of households with differing balance sheet compositions (Cloyne et al. 2020 ; Auclert 2019 ). Another reason is the inflation-hedging nature of certain assets: owners of real estate and stocks are relatively well protected against devaluation effects of inflation (Fama and Schwert 1977 ; Kim and In 2005 ) whereas financial liabilities are repaid in nominal terms. Accordingly, spending of net debtors is expected to be more sensitive to changes in expected inflation than for net owners of real estate and stocks. Footnote 1

Our approach departs from the literature in important ways. First, we try to identify specific economic channels that determine the effect of inflation expectations on spending. The granular information on households’ balance sheet in our data set allows us to test explicitly what role balance sheets play in moderating the effect of price expectations on durable spending. Second, we analyse realised spending, rather than planned spending or attitudes towards spending. These two latter measures, often used in the literature, will likely overestimate a positive effect of inflation expectations on spending since households might be willing to consume but liquidity constraints impede them from doing so. Third, observing households over time allows us to better capture the intertemporal dimension of consumption decisions, which is particularly important if agents are forward looking and expectations play a crucial role.

Sufficient and accurate control for confounders in analyses of large scale surveys poses problems. The DNB Household Survey contains a wide range of household characteristics. Including all characteristics that could potentially impact consumption behaviour is not feasible. Selecting controls only based on personal judgement or theory might lead to omission or unnecessary inclusion of some variables. Instead we apply a data-driven post-double variable selection procedure of the type introduced by Belloni et al. ( 2014a ). With penalised regression techniques, we only select those variables that impact the dependent variable and the independent variables of interest in the data. This limits the danger of omitted variable bias while ensuring a parsimonious specification. Moreover, the panel dimension of our data allows us to control for time-invariant confounders in general.

The results of our paper give support to channels we classified as real interest rate and real wealth dependent. Financial investments moderate the effect of inflation expectations on spending which can be explained by the real interest rate channel. We also find that the positive relation between expected inflation and the probability of positive durable expenditures is amplified for households with lower net worth. The effect is stronger among a subsample of households with fixed interest mortgages. We interpret this result as evidence for the real wealth channel which depends on the net nominal position of the balance sheet combined with heterogeneities arising from the composition of the balance sheet.

The rest of the paper is organised as follows. In Sect.  2 we review the related literature. We discuss possible economic mechanisms that link consumption decisions, inflation expectations and the balance sheet in Sect.  3 . The data are presented in Sect.  4 . In Sect.  5 we present our econometric framework. Results are discussed in Sect.  6 . Section  7 concludes.

2 Related literature

A number of influential contributions by Coibion and Gorodnichenko ( 2012 , 2015a ) and Coibion et al. ( 2017 ) have initiated a renewed discussion about the formation of inflation expectations and their macro- and microeconomic effects. They provide substantial evidence that inflation expectations by consumers, businesses and even professionals and central bankers do not satisfy the conditions for full information rational expectations. Thus, consumers make systematic forecasting errors that, according to Coibion and Gorodnichenko ( 2015b ), can help explain macro-puzzles, such as the missing disinflation in the US after 2009. In this paper we complement their work by investigating the channels through which consumers’ inflation expectations affect microeconomic choices.

More closely related to our research question are previous studies that have used micro-data to estimate the effects of inflation expectations on consumer spending. As stated above, no clear consensus has been reached on the direction or size of the effect. Bachmann et al. ( 2015 ) use repeated cross sections of the Michigan Survey of Consumers to investigate the effect of inflation expectations of households on their “readiness to spend”. The authors relate readiness to spend to a survey question on whether the current period is a good time to spend money on durable goods. They find that during the zero lower bound episode higher inflation expectations had slightly negative effects on the probability for households to have positive spending attitudes arguing that high inflation expectations might be correlated with increased economic uncertainty. The authors perform a number of regressions in search of heterogeneities in the relationship between inflation expectations and spending attitudes, for instance by including binary measures of home ownership and proxying an individual’s debtor status with age. They do not specifically analyse wealth channels that moderate the spending response to inflation expectations. Ichiue and Nishiguchi ( 2015 ) approach the problem similarly, but with Japanese data and find strong positive effects of inflation expectations on planned spending. They argue that, after a long period of zero nominal interest rates, Japanese consumers have understood how inflation affects the real interest rate and therefore react. The authors do not further investigate the role of balance sheets. In contrast to both of these studies, we construct a measure of realised spending and allow for a moderating role of balance sheet variables in the relation between expected inflation and spending.

A very different approach has been taken by D’Acunto et al. ( 2016 ). Their paper uses a value-added tax increase in January 2007 in Germany to estimate the effects of exogenous changes in inflation expectations. Compared to households in other European countries that did not experience the VAT increase, German households were substantially more likely to have positive attitudes towards spending in the months before the tax increase came into force. A limitation of this approach is that the price expectations of German households in November and December of 2006 contained considerably less uncertainty than those of households in other European countries. Households knew that a VAT increase will unambiguously increase prices of consumer products. They usually cannot form expectations with such certainty and precision. The effect of inflation expectations on consumption might differ substantially in times with less salient events or policy changes that nonetheless impact inflation.

The study most similar to ours is Burke and Ozdagli ( 2013 ). Using survey responses on expected inflation and realised spending on a wide range of products of a panel of American households between 2009 and 2012, they find much less clear results than the studies presented above. Households do not seem to increase their durable expenditures as a result of higher inflation expectations. In addition, they find evidence for effects on non-durable expenditures, driven by owners of real estate. Even though we analyse durable expenditures, this finding justifies our strategy of carefully investigating potential interactions of expected inflation with balance sheet variables. Burke and Ozdagli ( 2013 ) can only observe binary measures of balance sheet variables, such as home ownership. Crump et al. ( 2015 ) estimate the subjective elasticity of intertemporal substitution based on survey responses on expected inflation and planned consumer spending of a panel of American households in the Survey of Consumer Expectations. They find that the elasticity of planned consumption to changes in expected inflation is around 0.5. While planned spending is a better proxy for spending than “readiness to spend”, it isn’t a realised measure neither. Based on a large panel of Eurozone households, Duca et al. ( 2018 ) find small positive effects of increased inflation expectations on households’ “readiness to spend”. While they control for household wealth, they do not examine the balance sheet channels we suggest.

3 Mechanisms

Next we discuss different mechanisms through which balance sheets could affect households’ spending responses to changes in expected inflation. Potential candidates are real interest rate and real wealth changes that result from changed inflation expectations. In addition to balance sheet size and its net position, we also discuss how differences in its composition could moderate the spending response of inflation expectations.

3.1 Intertemporal Substitution

Consumers adapt their spending behaviour when relative prices change by substituting the more expensive for the cheaper good. Price changes over time also change the purchasing power of consumers’ income in different periods which may affect their selected intertemporal consumption bundle. These standard substitution and income effects of relative price changes can be illustrated by the following basic set-up. Consider the following intertemporal budget constraint for a household with nominal income \(y_t\) , nominal interest rate i and consumption good \(c_t\) with price \(p_t\) in periods 1 and 2:

By normalising \(p_1\) to 1 and defining \(\pi ^e = \frac{p_2 - p_1}{p_1}\) , we can rewrite the previous equation as

An increase in \(\pi ^ { e }\) raises the expected future price of the consumption good relative to its current price and lowers the real interest rate. This triggers the standard substitution effect: consumers want to increase current spending relative to future spending since the price of the good is lower in the current period. In contrast, the direction of the income effect depends on whether the consumer is borrower or saver. The lower real interest rate benefits the borrower: by transferring income from period 2 to period 1, one can increase total consumption compared to a situation with higher real interest rates. Savers lose: the income they transfer from period 1 to period 2 earns less real interest, therefore total consumption falls. Even this very basic set-up predicts differential consumption responses for households based on their balance sheet position: debtors will increase their current consumption by more than savers if their expectations about future prices rise. The qualitative conclusion does not change if future income is indexed to inflation, only the degree to which consumption is transferred to the current period would be lower.

However, not all households face the same perceived borrowing conditions. Analogous to the argument made by Bernanke ( 1993 ) for firms, households with higher net worth are generally seen as more credit-worthy by banks and might face better borrowing conditions. Thus, even under constant economy-wide nominal interest levels the perceived borrowing conditions for households do not only depend on their inflation expectations. The same change in inflation expectations can lead to different household-specific perceived borrowing conditions if the balance sheet quality differs. Applying this idea to the relationship between inflation expectations and consumption is not new: Ichiue and Nishiguchi ( 2015 ) make the same point in their analysis, but cannot convincingly test it.

3.2 Real Wealth

An increase in expected inflation leads to a reduction in expected real wealth since the expected price level of the future period is now higher than before while nominal wealth has remained constant. For debtors the opposite is true: higher inflation will reduce the expected real value of debt and thus increase their expected net worth. The observation that changes in wealth have effects on consumption has been widely documented in the past using both macro- and micro-data (Case et al. 2005 ; Mian et al. 2013 ). The most appropriate measure for the exposure of a household’s financial position to changes in the price level is its nominal net worth, i.e. assets minus liabilities.

3.3 Balance Sheet Composition

However, this view on the real wealth channel may be too simplistic. There are various reasons why differences in the composition of the balance sheet could lead to different consumption reactions of households with the same nominal net worth. First, there are differences in the sensitivity of various assets and liabilities to inflation. Real estate or financial investments can serve as a protection against inflation. Fama and Schwert ( 1977 ) have shown that returns on real estate protect fully against unanticipated as well as anticipated inflation. They regressed the expected nominal return of several assets on expected inflation. If the coefficient of expected inflation is equal to one, the nominal return compensates for losses in real returns on average. Thus, the expected real return does not change when inflation expectations change. More recent studies have confirmed the long-run inflation hedging nature of real estate and found mixed evidence for the short-run analysis conducted by Fama and Schwert ( 1977 ) (Anari and Kolari 2002 ; Hoesli et al. 2008 ). While Fama and Schwert ( 1977 ) cannot confirm the inflation hedging nature of stocks in the short term, later studies came to the conclusion that in the long-run stock investments have the same inflation hedging property as real estate (Schotman and Schweitzer 2000 ; Kim and In 2005 ). Households with a substantial part of their wealth invested in these asset classes might not regard higher future inflation as a threat to their future wealth since their investment strategy is designed to protect against such developments. Even if this protection is not perfect, it is superior to, say, for cash holdings. Households with cash holdings as their only assets have no way of protecting themselves against real losses due to inflation. Similarly, debt contracts usually specify a nominal amount that has to be repaid. Here, higher inflation expectations lead to an expected decrease in the real value of debt, i.e. increasing real wealth. To summarise, households who invested large parts of their wealth into real estate or financial investments are expected to exhibit less sensitivity to inflation expectations in their consumption decisions. Households with relatively large exposure to cash or debt may react more strongly since their expected real wealth necessarily changes in response to changing inflation expectations.

Composition effects could play a role on the liability side as well. While most liabilities are repaid in nominal terms, differences across liabilities arise with respect to the interest payment schemes. Specifics of mortgage contracts play an important role in the transmission of nominal interest rates to household behaviour, especially consumption: Di Maggio et al. ( 2017 ) show that holders of adjustable rate mortgages respond significantly stronger to nominal interest rate shocks than those with fixed rate mortgages and without mortgages. These results have been confirmed in different settings. Cloyne et al. ( 2020 ) show that household balance sheet composition in the US and the UK alters the spending response to changes in the nominal interest rate, suggesting differing marginal propensities to consume between home owners with mortgages (high) and outright owners (low). Cumming and Hubert ( 2019 ) show a positive relation between the share of financially constrained (adjustable rate) mortgage holders and aggregate consumption responses to monetary policy shocks. While in the US and the UK interest on mortgages is predominantly paid at adjustable rates, interest in the Netherlands is predominantly paid at rates fixed for more than one year (83% of the total volume (DNB 2020 )). We argue that households with these kind of mortgages are an interesting subsample to study the spending response to changes in inflation expectations on. The argument builds on a similar intuition as that applied by the authors cited above. Without nominal rigidities, changes in inflation expectations should not have real effects. The insensitivity of interest payments on fixed rate mortgages to nominal rates potentially increases the impact of changes in inflation expectations on real expected disposable income. Footnote 2 If the marginal propensity to consume for more constrained households is indeed higher, those fixed rate mortgage holders with lower net worth should exhibit a stronger response to changes in their inflation expectations. We test this hypothesis in Sect.  6.3 .

Any of the above channels imply that individuals with a different balance sheet composition (both concerning the relative sizes of assets and liabilities and the relative importance of specific classes of assets and liabilities), but identical changes in inflation expectations could exhibit differing spending responses. These considerations give rise to an econometric specification in which we allow for interactions between households’ expected inflation and its different balance sheet components. Section  5 outlines how we aim to test the different mechanisms and what effects they would imply for our empirical analysis. By accounting for this interaction we depart from the previous literature on the topic. All of the aforementioned authors have stressed in their papers that wealth might play a role in the relationship between expected inflation and (durable) consumption. Our key contribution consists of testing this channel in a novel way.

Our aim in this study is to explore the interaction between households’ inflation expectations and their balance sheets in determining spending decisions. Information on all variables needs to be at the household level and available for the same household over several years.

Contrary to previous studies, we set out to analyse realised consumer spending instead of attitudes to spending in general. However, specific survey answers on total (durable) expenditures might involve substantial measurement error. It is much easier to recall expenditures for specific durable goods since these items are seldom purchased and each individual purchase accounts for a substantial fraction of total spending of that period.

Additionally, our analysis requires balance sheet information on the household level. The literature on wealth effects on consumption concludes that different types of assets and liabilities might have different effects on consumer expenditures (Case et al. 2005 ). To provide a thorough account of the interaction we want to analyse individual balance sheet components as well as the net financial position of the households.

For the reasons mentioned above we make use of the DNB Household Survey (DHS) administered by CentERdata (Tilburg University, The Netherlands) and issued by the Dutch Central Bank (DNB). It includes households’ self-reported balance sheets and their expected one year ahead inflation rate. Part of the self-reported balance sheet consists of vehicles owned by the household. We use this information to construct a variable of household vehicle expenditures (more details below). The DHS is an unbalanced panel of 12.439 households with annual observations between 1993 and 2018. More than one household member can respond to the survey. Since the balance sheets are aggregated at the household level, we primarily use responses to household member specific questions from the first member of the household. If the first member has not answered a specific question we use the response of the second member. This results in 52.055 household-year observations from which we construct our variables of interest. The survey is typically completed by respondents between the 15th and 26th calendar week of a year with some exceptions in case respondents need to be reminded of completion.

We want to stress the unique fit of this data set for our purposes. To our knowledge, no previous study has made use of such extensive balance sheet information to analyse the effect of inflation expectations on realised consumer spending.

In the following, we give an overview of the different variables of interest and provide descriptive statistics.

4.1 Measuring durable consumption

In recent papers many authors concentrate on analysing the effects of inflation expectations on durable consumption (Burke and Ozdagli 2013 ; Bachmann et al. 2015 ; Ichiue and Nishiguchi 2015 ). We follow the literature in this respect. Durable consumption is the component of aggregate consumption most likely to be affected by variations in the real interest rate since it is more likely to be credit financed than expenditures on non-durable goods. Additionally, demand for non-durable consumption is less elastic to changes in macroeconomic conditions in general.

The DNB Household Survey does not include questions on expenditures on different classes of durable goods. However, households do report a large part of their assets. Among those are vehicles, such as cars, motorbikes and boats. For each of these items households report the purchasing price and additional details on the vehicle, such as its build year. We construct our expenditure variable by recording each time the purchasing price changes. If the purchasing price stays constant but the build year changes, a purchase is recorded as well. For the extensive margin, the consumption variable takes the value 0 in case we record no change in the vehicle and 1 in case there is a change. Footnote 3 The fraction of households that have purchased a vehicle in a specific year is shown in Fig.  1 a. Footnote 4 For those households that did buy a car we construct a variable capturing the intensive margin of the purchase, i.e. the amount a household spent on vehicles, i.e. the sum of changed purchasing prices. Figure  1 b shows the mean, the 10th and 90th percentile of this variable’s distribution over the sample period.

It is unclear whether we should expect the effects outlined above to materialise on the extensive or the intensive margin of a purchase. In theory, the mechanisms could play a role in both decisions a household has to make. When emphasising the extensive margin, we assume that households’ tastes regarding durable goods are relatively fixed over time and the element of the decision that is subject to variations in expected inflation is the timing of the purchase. In a year in which a household has higher inflation expectations it might be more likely to buy the durable item it had already planned to acquire for longer. This reasoning is consistent with some results that emerged from the literature analysing the “hot potato” effect of inflation. The “hot potato” effect refers to the observation that consumers spend their money faster in times of high inflation. In a search based monetary theory model, Liu et al. ( 2011 ) find that inflation affects especially the extensive margin of the purchasing decision.

On the extensive margin, we observe 12,620 vehicle purchases throughout the entire sample period. In 39,435 household-year observations, no purchase has taken place. Figure  1 c shows from how many household observations we can draw to construct the extensive margin variable. For roughly 30% of households we only observe the purchasing decision once. This means that these households participated in two consecutive waves of the survey, allowing us to evaluate whether the purchasing price of their vehicles changed. Figure  1 d depicts the fraction of households with a certain number of vehicle purchases. For a majority of households we do not observe any purchase. Roughly 45% of households we observe between one and five purchases.

However, the sample that enters our regression analysis shrinks considerably since not all households answer all survey questions. Due to limited overlap with the variables capturing expected inflation, the remaining balance sheet variables, current and expected income, we are left with 8663 observations from 3092 households. We use the full sample when applying a linear probability model. The application of the conditional logit model reduces our sample size further as it drops households for which the extensive margin variable does not change value, leaving us with 4790 observations from 909 households.

On the intensive margin we would be limited to a much lower number of observations. In our preferred specification we would have to rely on a sample of 2645 observations from 1396 households. In a fixed-effects framework an average number of 1.89 observations per panel unit would not allow us to draw any meaningful conclusions. Therefore, we do not proceed with analysing the intensive margin further.

How much can vehicle expenditures tell us about durable consumption? To answer this question, we take a look at the aggregate durable and vehicle expenditures in the Netherlands. Figure  1 e shows all subcategories of total durable consumption as defined by CBS, the Dutch statistical agency. Vehicle expenditures account for about 20% of total durable consumption in the Netherlands across the whole sample period. They are the second biggest component of durable consumption after textiles and clothing. Additionally, as Fig.  1 f shows, they are highly correlated with total durable expenditures (correlation coefficient of 0.95 between 1995 and 2015). We indeed find that, on the aggregate level, vehicle purchases in the Netherlands instrument overall durable consumption very well, as indicated in Fig.  1 f and as suggested by an effective (marginal) F-statistic of 183. Furthermore, vehicle purchases have frequently been used in the literature to gauge the dynamic behaviour of aggregate durable spending (see e.g. Mian et al. 2013 ; Berger and Vavra 2015 ; Ravn et al. 2020 ).

Descriptives

4.2 Inflation expectations

In the DHS households are asked the following question about their expectations for one year ahead inflation:

What is the most likely (consumer)prices increase over the next twelve months, do you think?

Since 2008 the possible answers are given between 1 and 10% in steps of one. Before, respondents were free to respond with any number they liked. To ensure consistency between the responses given before and after 2008, we enforce the same limitation in the answer range before 2008. Figure  1 g shows the development of this variable over time. There is a clear peak after the introduction of the Euro. After that the downward trend in average expectations continues until well after 2008 and has stabilised close to but above 2% after that.

Figure  1 h compares average expected inflation in the Netherlands with the realised CPI values. Expected inflation is structurally higher than realised inflation but trends are well anticipated by households. The latter observation is more relevant for our study since we are mainly interested in changes in inflation expectations. Secondly, this alleviates concerns that inflation expectations by (laymen) survey respondents are completely detached from actual inflation and instead measure expectations or perceptions of some other variables.

4.3 Balance sheet

Table  1 shows the individual balance sheet components that households report as well as the aggregation level at which we include them in our models (in bold). Grouping of assets is largely determined by the liquidity of the balance sheet item. Among illiquid assets we differentiate between real estate and other assets to acknowledge the special role housing wealth could play. We group liabilities according to maturity. Mortgages and other longer term debt (referred to as loans) are aggregated separately. The net worth variable is constructed by subtracting liabilities from assets.

Instead of having to interpret our results in units of currency, we prefer to analyse percentage changes. The usual log-transformation is not well suited for our variables since many households do not possess some of the balance sheet variables. Their observations would be lost in case of a log-transformation. In the case of the net worth variable all negative net worth observations would be dropped as well. Instead, we perform an inverse hyperbolic sine transformation (ihs). Footnote 5 Table  2 gives descriptive statistics for all balance sheet variables that enter our regressions in the empirical analysis.

5 Empirical approach

As pointed out in Sect.  3 , there are several arguments why inflation expectations could matter for spending decisions and how wealth could alter size and direction of this relation. In this section we motivate our econometric approach in light of the transmission channels we aim to investigate. To that end, we run fixed effects linear probability models (LPM) as well as conditional logit (CL) regressions with the binary purchasing variable as dependent variable.

5.1 Specification

Our analysis consists of two baseline specifications. We estimate a fixed effects linear probability model as well as a conditional logit. Below we outline these two specifications. For the linear probability model we run the following regression:

where \(\alpha _ { i }\) and \(\kappa _ { t }\) are household and year-fixed effects, \(E _ { it-1 }\left( \pi _ { t } \right) \) is household \(i's\) expectation at time \(t-1\) for the inflation rate at time t , \(W _ { it - 1 }\) is the value of a particular balance sheet variable in \(t-1\) , and \({\varvec{X}} _ { it-1 }\) is household i ’s set of other characteristics at time \(t-1\) .

In addition, we estimate the following conditional logit model:

where \(\lambda \) denotes the logistic function. The fixed effects logit model imposes the condition that \(T>\sum _{t=1}^{T} c _ {it} > 0\) , where T is the total number of periods that the household participated in the survey. This condition implies that only households whose expenditure variable takes on both possible values (0 and 1) are included in the estimation. We construct inference based on bootstrapped standard errors.

Next we discuss how to interpret the models in ( 1 ) and ( 2 ) in light of the mechanisms outlined in Sect.  3 . Two coefficients in the above regressions are of special interest: \(\sigma \) , the coefficient for expected inflation, and \(\delta \) , the coefficient of the interaction term. \(\delta \) measures in which direction and with what magnitude a specific balance sheet component scales the effect of inflation expectations on consumption. Conversely, when including a single balance sheet components, \(\sigma \) measures the effect of expected inflation on consumption if the household has no holdings of the balance sheet component. For instance, when including net worth as the balance sheet variable, \(\sigma \) measures the relation between inflation expenditures and spending if net worth would be zero. As we argued in Sect.  3 , the real interest rate channel would suggest a positive effect of the interaction between expected inflation and household wealth, implying negative effects for any interaction between liabilities and expected inflation. In contrast, the real wealth channel would suggest a negative interaction effect between household wealth and expected inflation. However, many assets serve as hedges against inflation. The real wealth channel on its own would thus predict no significant interaction effect when financial investments or real estate holdings are interacted separately with expected inflation. Any interaction between liabilities and expected inflation is thus expected to have positive effects on the spending variable. The mechanisms that we discussed in Sect.  3 suggest opposite effects of the interaction between wealth and expected inflation. The coefficient of the interaction term is the average magnitude of the real interest and the real wealth channel. That is, if \(\sigma \) is significantly different from zero, one of the two effects dominates. However, this would not necessarily prove the absence of the other effect.

Table  3 gives an overview of the coefficients we would expect for the variables of interest in our regression if the channels could be measured separately. Thus, if the coefficients in our models align with the signs or magnitudes of these coefficients we could claim that the respective channel dominates over the other.

5.1.1 Timing of households’ consumption decision

We only include households that are observed in at least two waves of the survey; otherwise, we cannot determine differences (or lack thereof) in their vehicles’ purchasing prices. Since we construct the expenditure variable by comparing purchasing prices of vehicles and do not use specific questions on the subject, we do not observe the exact date of the purchase. In our regressions we relate the vehicle purchase that occurred between period \(t-1\) and t to the balance sheet, inflation expectations and other characteristics observed in period \(t-1\) . Since households are asked about their expectations for the coming 12 months, we consider these 12 months as the current period in which the effect on spending should play out. Figure  2 shows which period’s observation of each of the previously introduced variables is used in our analysis.

Timing of the purchasing decision

5.1.2 Selection of controls

Consumers’ purchasing decisions are driven by many factors. We attempt to isolate the role of inflation expectations and various balance sheet items. However, if we do not control for other key predictors, estimation of the coefficients of interest may be biased. While it is plausible to assume that current and expected income are relevant covariates in this context, the survey provides us with detailed information on individual household characteristics (e.g. attitudes toward saving and risk-taking, financial literacy, health, financial situation and expectations, etc.) and, hence, contain other possibly relevant predictors.

In order to identify relevant covariates, we use the “post-double-selection” method proposed by Belloni et al. ( 2014b ). This involves a two-step LASSO regression, which in a first step selects covariates that predict the dependent variable, and in a second step selects variables predicting our independent variables of interest. The second step is necessary to control for the omitted variable bias. Note that selected controls may differ across regressions as we perform the “post-double-selection” for each regression separately. We always include current and expected income. Since we include individual fixed effects in all (LASSO) regressions we expect that most time-invariant household characteristics are controlled for, and only few (if any) additional controls are needed to estimate the impact of inflation expectations on car purchases. Footnote 6

For the exposition of the results of our analysis, we proceed in steps. First, we present the results from our baseline analysis in which we are mainly interested in the interaction terms between expected inflation and various balance sheet variables. We present estimates from fixed effects LPM and Logit regressions. In Table  4 results from the Logit regressions are marked as CL in the column title. Lastly, we analyse a subsample of households that have fixed interest rate mortgages.

As we argued in Sect.  3 , the different balance sheet components are not expected to moderate the effect of inflation expectations on spending in the same fashion. The main reason are differences in their inflation-hedging potential. Certain assets like stocks or real estate protect the investor better against inflation than cash, for example. Additionally, we expect a difference between assets and liabilities in general. Debt is usually repaid in nominal terms, which makes its expected real value sensitive to expectations about inflation.

6.1 Balance sheet components

Table  4 presents the baseline results. For the regressions results shown in columns one and two, we included all single balance sheet components and their interactions with expected inflation. Collinearity is not an issue since net worth is not included and therefore free to move. The results do not depend much on the specification used, both the linear probability model (LPM) and the conditional logit (CL) give similar results in terms of sign and magnitude of the estimator. All but one balance sheet component do not significantly alter the relationship between inflation expectations and the probability to purchase a vehicle. For the interaction term between financial investments and expected inflation both the LPM and CL estimates are positive, the LPM estimate marginally above the 10% significance threshold, the CL estimate marginally below. The relation between expected inflation and the spending decision seems to be marginally different for households with within-household deviations from their average financial investment holdings compared to those at their average value. Households with higher than average financial investments exhibit a stronger positive reaction of expected inflation on their probability to spend. To quantify this relation, consider a household with inflation expectations 2%-points Footnote 7 above their mean: a 10% increase in financial investments increases their predicted purchasing probability by around 3.8%-points. Compare that to a household that is 5%-points above their mean expected inflation: here, a 10% increase in financial investments increases the predicted purchasing probability by almost 10%-points.

Column 2 of Table  4 shows the results of the analogous conditional logit regression to the OLS regression in column 1. The results look qualitatively similar. The only balance sheet item that significantly alters the effect of inflation expectations on spending probabilities are financial investments. The estimated coefficient of 0.0226 corresponds to an odds ratio of roughly 1,023. An odds ratio larger than one means that as the value of the interaction term increases, the odds of having positive vehicle expenditures in a given year rise. Footnote 8

This result is in line with the real interest channel presented in Sect.  3 . A falling perceived real interest rate increases incentives to substitute future spending for current spending. Only households with either sufficient collateral or sufficient internal finance are able to act on their increased willingness to spend. However, as the predicted probability plot shows, this moderating effect does not seem to be large enough to affect the outcome in an economically meaningful way.

Plot of the predicted probability of positive vehicle expenditures for households for given percentiles of the net worth distribution (based on estimates of column (5), Table  4 )

6.2 Net worth

We continue our analysis by taking a different perspective on the role that individual balance sheet components play. The rationale for analysing components individually is that they differ in terms of their return or real value sensitivity to inflation. At the same time, no component on its own is an appropriate measure of household wealth. Therefore, we now analyse whether net household wealth modifies the relation between expected inflation and the probability to spend. Columns 3 and 4 of Table  4 provide the baseline results of this analysis. We apply the same strategy as above by interacting the expected inflation rate of each household with their net worth to explain the following period’s spending decision. The net worth variable is transformed from levels using the inverse hyperbolic sine transformation that accommodates zero and negative values while mimicking a log-linearisation (see Sect.  4 ).

In none of the three specifications in which we include net worth (columns 3, 4 and 5 in Table  4 , we find strong evidence in favour of a moderating effect of net worth on spending. However, all point estimates are negative and of similar magnitude. This means that for households with a net worth that is below their household specific mean, the predicted probability to spend increases.

A quantification of the fixed effects logit results in the same fashion as previously done for the linear probability model is not possible. Predicted probabilities can only be calculated by setting the fixed effects of all households to a uniform level and assuming different values for the explanatory variables. We want to stress that this is not an innocuous assumption. The reason why we chose to run fixed effects regressions is that we believe there are good reasons why time-invariant household heterogeneity should be controlled for in our analysis. By setting all fixed effects to zero we essentially assume this is not the case. The reason why we present our results in this way nonetheless is to illustrate how the estimated interaction effect would play out absent any other heterogeneity and to quantify our results in a meaningful way. The predicted probabilities are not to be interpreted as such literally. Including fixed effects would certainly alter them. Figure  3 shows the predicted probabilities of positive vehicle expenditures for different values of expected inflation and net worth. Footnote 9 Each panel displays the predicted probability of positive expenditures on the vertical axis and expected inflation on the horizontal axis for values of net worth corresponding to the 50th, 75th, 90th, 95th and 99th percentile of the distribution in 2018.

We can clearly see that at the lower end of the net worth distribution, i.e. households with negative net worth, there is a stark difference in the point estimates of the predicted probability of positive expenditure between low and high levels of expected inflation. The negative point estimates we found are driven by those households at the lower end of the net worth distribution. However, due to its insignificance and the imprecise estimation of the coefficients for expected inflation and net worth, we cannot make strong statements about the robustness of this result. As the figure shows, for households with high expected inflation and low net worth, the 90% confidence interval includes all possible probabilities. Note that unobserved, time-invariant household heterogeneity is not taken into account here. Additionally, the confidence interval becomes very wide for high levels of inflation expectations.

In Column 5 of Table  4 we present the results of a specification in which we include only the two balance sheet measures whose interactions with expected inflation we emphasised above: net worth and financial investments. This exercise supports the findings from above. Financial investments amplify the spending response to expected inflation while net worth has an (insignificant) dampening effect. This shows how the net nominal exposure to inflation (measured by net worth) and balance sheet composition (in this case, financial investments) can alter the spending response. While the former effect would support the relevance of a real wealth channel were it stronger, the latter is in line with the intertemporal substitution channel.

Predicted probability plot: fixed rate mortgages (based on estimates of column (5), Table  5 ). Percentiles refer to the net worth distribution

6.3 Fixed interest rate mortgage holders

For our research question fixed interest rate mortgage holders are an interesting case. An important part of their expenses is directly tied to the nominal interest rate. In our analysis thus far we have not been able to perfectly control for the nominal interest rate. Time fixed effects take out variation in spending decisions due to movements of the economy-wide nominal interest level. Controlling for net worth can also capture household specific movements in the nominal interest rate by acting as a measure of available collateral or the risk that a household will not be able to service its debt. Especially the latter is an imperfect measure though. The payment of interest on mortgages at fixed rates introduces an insensitivity of a large part of disposable income to business cycles. At the end of 2018 mortgages worth roughly 30 billion € were outstanding in the Netherlands, making up roughly 4% of GDP. Mortgages corresponding to about 83% of the total volume have interest rates that are fixed for more than one year (DNB 2020 ). With constant real income, changes in inflation expectations therefore have a direct effect on expected real disposable income. If less wealthy households have a higher propensity to consume, those households in our sample that are more financially constrained (i.e. those with a lower net worth) should exhibit a stronger spending response to expected inflation.

We apply this specification to the sub-sample of households with fixed interest rate mortgages. In our sample around 90% of households that report a mortgage as part of their balance sheet have a fixed interest rate mortgage. Unfortunately, the number of households with a variable interest rate mortgage is too low to perform the same analysis. We therefore resort to a sub-sample analysis instead of interacting all variables of interest with the mortgage’s interest rate policy. Table  5 shows the results of this exercise. Apart from interacting the household’s net worth with expected inflation we control for the household’s net income as well as its expected income for the following period. The Lasso post double variable selection procedure did not select any additional control variables. A comparison with the results to those in column 4 of Table  4 reveals that the observed behaviour from the full sample is much stronger in the sub-sample of households with fixed interest rate mortgages. The coefficients on expected inflation, net worth and their interaction are all larger in absolute value and have a p -value below 0.1. Figure  4 shows the predicted probabilities for different values of net worth under the assumption that the fixed effects are equal to zero. (We refer to the previous section for a critical discussion of this assumption.) Absent time-invariant household heterogeneity, the figure visualises the mechanics of the interaction between expected inflation and net worth. Low net worth households with fixed interest rate mortgages react more strongly to higher inflation expectations than those with a higher net worth. This result holds when including an interaction term between expected inflation and the amount of outstanding mortgages the household has in its balance sheet. This interaction term is insignificant and its inclusion barely changes the values of the other coefficients of interest. In column 5 of Table  5 we include net worth and financial investments as balance sheet variables, the two measures that turned out to significantly affect the spending response in the whole sample. Among fixed rate mortgage holders the coefficient on financial investments is roughly the same as before, but not significant anymore. These results show that while individual components of a household’s balance sheet, such as fixed interest mortgages, matter for their consumption decisions the net nominal position determines the strength of this relation. Footnote 10

How can these highly indebted households finance a vehicle purchase? Descriptive statistics can shed some light on this question. First, due to these households’ likely limited access to external finance, we should expect them to buy less expensive vehicles. This is indeed the case: for households with negative net worth, the average purchasing price is only half that of the rest of the sample. Additionally, even though these households are net debtors, over 90% of them have positive cash balances. This suggests that they do have internal finance available to make a car purchase. Another frequently applied method of payment for cars is to include the old car in the payment for the new one, in which case even less cash would be necessary.

As we pointed out in Sect.  4 , potential measurement error could be driven by the fact that households incorrectly remember purchasing prices of their cars and change their response to the purchasing price question from one year to the other without actually having purchased a new car. “Appendix A” therefore gives an overview of the results of the preceding sections using a dependent variable that is robust to such small changes in purchasing prices. All results presented above hold when utilising this modified version of the dependent variable.

7 Conclusion

In this paper we provide evidence of a balance sheet channel through which inflation expectations affect durable consumer spending. We use a household survey that contains uniquely detailed balance sheet information as well as a large range of other household characteristics including inflation expectations. We discuss different hypotheses why balance sheets could potentially mediate the spending response to expected inflation. Our results suggest a mediating role of the real wealth channel: the positive response of the probability to spend when inflation expectations increase is stronger for households with lower average net worth. This effect is stronger for households with fixed interest rate mortgages. We relate our findings to the growing literature on the consequences of heterogeneous agents for the transmission of monetary policy, in particular to Cloyne et al. ( 2020 ). They show that mortgage holders react particularly strongly to interest rate shocks in their spending choices. We show that a similar pattern is observable for changes in expected inflation.

We find differential effects of inflation expectations across the wealth distribution: households with high amounts of debt and substantially overestimated inflation expectations seem to commit costly mistakes if inflation does not live up to their expectations (which it did not throughout our sample). Here, our study connects well to Vellekoop and Wiederholt ( 2017 ). These authors show that households with higher inflation expectations have lower net worth and are less likely to own non-liquid assets, such as bonds, stocks or real estate. The remaining, inflation-sensitive balance sheet components have much higher relative importance than for households with lower inflation expectations. One conclusion for policy is therefore to improve the accuracy of households’ inflation expectations. Recent research has shown that this can be done in two ways. More financially literate individuals tend to be better at forecasting inflation (Bruine de Bruin et al. 2010 ). At the same time, central banks themselves can contribute to better formation of expectations. Coibion et al. ( 2019 ) show that providing survey respondents with details about FOMC meetings—be it only the decision or the entire minutes—substantially improves the accuracy of their inflation forecasts. Better central bank communication could thus play an important role in helping households avoid costly mistakes in their economic decision making.

Availability of data and material

The data are freely available from the sources mentioned in the manuscript.

Other channels that are not affected by wealth have also been put forward: Wiederholt ( 2014 ) suggests that high inflation expectations could be a sign of policy uncertainty and thus depress spending. Cavallo et al. ( 2017 ) show that the existence of a relationship between inflation expectations and consumption can be explained by rational inattention: when the benefits of forming accurate expectations outweigh their costs—such as in episodes of high inflation—household spending behaviour is more sensitive to inflation expectations.

This is the case under the assumption that real income stays constant.

In “Appendix A” we show that our results are confirmed when recording purchases only if the purchasing price differs by more than 1000€ between two years. We are therefore confident that our results are not driven by erroneous recollection of purchasing prices.

The peak in 2009 in the extensive margin is due to a car scrapping scheme implemented by the Dutch government as a response to the crisis of 2008. No corresponding peak is observed on the intensive margin. This means households did not buy more expensive cars due to the scrapping scheme, there were simply more households that bought a car in that year. We use year-fixed effects to account for such effects.

This transformation has been widely used in empirical work on household wealth (Burbidge et al. 1988 ; Pence 2006 ). For values close to zero, the transformation is approximately linear and resembles a logarithmic shape for larger absolute values: \( x^{ihs}= \log \left( x + \left( x ^ { 2 } + 1 \right) ^ { \frac{ 1 }{ 2 } } \right) \) .

Not including fixed effects results indeed in a number of additionally selected controls. Many of the selected covariates are often indeed time-invariant and make intuitive sense, for example “ Expected response to credit application ”, “ Financial literacy ”, or “ Car provided by employer ”.

This corresponds roughly to an increase in inflation expectations of two standard deviations.

One may argue that the significant interaction between financial investment and inflation expectations points to an endogeneity problem. If holders of risky assets would form more accurate expectations about future price level changes, the interpretation of the results above would be altered. To investigate that issue, we compare inflation expectations of households that are holding risky assets with those of households that are not. Year-by-year KS tests show that the distributions of inflation expectations rarely differ between holders of risky assets and the rest of the sample.

The predicted probabilities are obtained in the following way: for all combinations of a given grid of values for expected inflation (1 to 10 in intervals of 1) and the ihs-transformed net worth variable (fixed at the shown percentiles of the net worth distribution in 2018) the plot shows the average predicted probability across the sample (not the predicted probability at the mean of the remaining covariates). Net worth in the regression was measured using positive values only but re-transformed to negative numbers for negative net worth for better readability. Each observation is treated as if the given values in the grids were the observed values for expected inflation and net worth. Then each household’s predicted probability is computed based on the grid values and the remaining observed covariate values. The resulting probability in the graph is the average predicted probability for each combination across households. Additionally, the fixed effect for each household is set to 0.

A similar endogeneity problem as discussed in Sect.  6.1 may bias our results. However, we do not find evidence that fixed interest rate mortgage holders are any better in predicting inflation. Year-by-year KS-tests show that expected inflation does not differ significantly between fixed mortgages holders and the rest of the sample.

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The paper has profited greatly from discussions with Clemens Kool and Tom van Veen, for which we thank them. We thank CentEr Data for making the dataset available to us. We would also like to thank Fiorella De Fiore, Paul Hubert, Narayana Kocherlakota and Wilbert van der Klaauw as well as participants at the GSBE PhD-colloquium at Maastricht University, the Economic Research Seminar at the University of Graz, the 12th International Workshop of Methods in International Finance Network in Louvain-la-Neuve, the 12th International Conference on Computational and Financial Econometrics in Pisa, the Conference of the International Association of Applied Econometrics in Nicosia, the International Panel Data Conference in Vilnius, the Belgian Macroeconomics Workshop in Ghent and the Empirical Monetary Economics Workshop at Sciences Po Paris for helpful comments.

Appendix A: Measurement of vehicle purchases

We measure vehicle purchases by comparing the survey participants’ responses to questions on the price and build year of their vehicles to the same respondents’ answers from previous years. If the reported purchasing price changes from one year to the next, a purchase is recorded. This comparison could erroneously record a purchase if respondents remember the price incorrectly in one year, but not the next. Therefore, we report the results to all regressions in Sect.  6 replacing the baseline purchasing variable with an alternative variable that is robust to such reporting errors. We exclude any recorded purchases in which the purchasing price differs by 1000 € or less from the price of the previous car. As the observation count in the bottom of the tables show, this reduces the number of households for which we observe years with and without purchases only slightly. At the same time, all effects that we describe in the baseline results remain roughly constant or become somewhat stronger when using the purchasing measure that is robust to small changes.

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Lieb, L., Schuffels, J. Inflation expectations and consumer spending: the role of household balance sheets. Empir Econ 63 , 2479–2512 (2022). https://doi.org/10.1007/s00181-022-02222-8

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Marxian analyses of inflation tend to fall under three broad categories, those that emphasise primarily the role distributive conflicts, monopoly power, or state intervention on the dynamics of credit money. This article reviews these interpretations, and indicates how they can be integrated. The proposed approach, based on the ‘extra money’ view, departs from the circuit of capital and the endogeneity of credit money in order to explain inflation in inconvertible paper money systems.

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Driving the pulse of the economy or the dilution effect: Inflation impacting economic growth

Piumi atigala.

1 SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Tharaka Maduwanthi

Vishmi gunathilake, sanduni sathsarani, ruwan jayathilaka.

2 Head - Department of Information Management, SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Associated Data

All Data files are available from Central Bank of Sri Lanka ( https://www.cbsl.gov.lk ) and Department of Census and Statistics, Sri Lanka ( http://www.statistics.gov.lk ).

Economic growth becomes a critical component in the development of every country since it enhances living standards and other related concerns while eliminating poverty. As a developing country, Sri Lanka must place more emphasis to achieve sustainable economic growth. In addition, various factors have positive and negative impacts on economy’s growth. As such, the specific goals of any economy are to sustain long-term economic growth and low inflation. As a result, generally, high inflation is destructive for an economy and low inflation is beneficial. Therefore, it is worth investigating the impact of inflation on economic growth concerning a stable inflation level. This study examines the impact of inflation on economic growth in Sri Lanka by employing the Auto Regressive Distributed Lag model as the estimation technique. Furthermore, the findings illustrate a negative relationship between inflation and economic growth in the short run; when inflation increases by 1%, economic growth decreases by United States Dollar (USD) 3,427.94 million and long run economic growth declines by 107,263.8 million USD. Subsequently, with the current economic reality of Sri Lanka, the macroeconomic policies should be adaptable to maintain the stability of the inflation rate for a sustainable economy.

Introduction

Sri Lanka still remains as a developing nation although being self-sufficient in human and natural resources. However, the country’s prolonged low per capita income and high inflation are warning signs that Sri Lanka is not economically prosperous yet. According to Mensah, Allotey [ 1 ], the goal of basic macroeconomics is to achieve sustainable economic growth by maintaining a low inflation. In addition, exchange rate, government expenditure, money supply, oil prices and long-term interest rates are the main criteria for inflation in Sri Lanka. Additionally, creating a conducive environment for economic growth, encouraging investors, increasing employment opportunities and creating a better standard of living are all positive factors that contribute to a low inflationary environment. As Bhattarai [ 2 ] indicated, maintaining price stability is critical for the long-term growth and development of the economy. Furthermore, as rising inflation adversely affects the economy, identifying the factors that influence inflation is critical for the economy’s proactive decision-making.

According to Gregorio [ 3 ], inflation has a negative impact on the economic growth of American countries. Similarly, Smyth [ 4 ] indicated that when inflation rises to 10%, economic growth decelerates to 0.025% in Germany. Based on these common findings from past literature indicating both inverse and adverse impacts, the present study investigates the impact of inflation on economic growth in Sri Lanka using 21 years of quarterly time series data during 2000–2021. Additionally, this paper contributes to identifying Sri Lanka’s unstable inflation scenario, enabling economic policymakers to make the right economic decisions.

Problem statement

Sri Lanka has been a developing country for decades, even after gaining independence in 1948. The country’s economy was further impeded and impotent to achieve sustainable growth by the war between the Sri Lankan government and the Liberation Tigers of Tamil Eelam (LTTE) for nearly three decades, one of the longest running civil wars in Asia. The latter can be considered as one of the major incidents that had a massive adverse impact on Sri Lanka’s economic growth caused by the rise of inflation due to the collapse of the tourism industry (as due to the war, Sri Lanka was perceived as an unsafe destination), the decline in productivity due to the inefficiency of the manufacturing industry in Sri Lanka as well as the high cost of defense.

Since the end of the civil war in Sri Lanka after the period of 2008 and 2009, the highest inflation rate 12.1% was reported in December 2021. The reasons behind the rising inflation are the depreciation of the Sri Lankan Rupee (LKR), cost-push inflation due to rising oil prices, excessive printing of money by the Central Bank of Sri Lanka (CBSL) that caused rise in inflation (as the money supply plays a more important role in determining prices), increase in the price of goods and services in the economy, and the eroding purchasing power of money. Moreover, agriculture production capacity declined since farmers halted cultivation, partly due to not achieving the harvest as expected (due to the government ban of importing chemical fertiliser to encourage farmers to use local organic fertilizers causing resistance among farmers) and imposing sanctions on imports in the face of the global COVID-19 pandemic. In addition, this situation worsened due to levying one-third of imported goods by the government as well as the devaluation of LKR against the appreciation of United States Dollar (USD) which in turn lead to higher prices on imported goods (such goods becoming expensive), thus contributed to inflation growth.

According to the CBSL, in 2000 and 2021, Sri Lanka’s inflation rate remained at 6.18% and 5.92% respectively. This indicates that Sri Lanka’s inflation has been steadily fluctuating but with no significant decrease during this period. Furthermore, Sri Lanka’s economic growth rate of 6% in the year 2000 declined to -3.57% in the year 2021. Accordingly, it appears that Sri Lanka’s economic growth has fluctuated significantly reaching negative growth during the end of the above mentioned period. Thus, this study investigates the impact of inflation on the slowdown in economic growth in Sri Lanka.

This study aims to investigate the impact of inflation on economic growth in Sri Lanka. As a consequence, this study differs from the existing empirical studies and it contributes to the literature in three ways. Firstly, the present study used quarterly data for a long period of time and employed Auto Regressive Distributed Lag (ARDL) model to conduct. Using quarterly data rather than annual data provides the ability to identify and monitor the impact of inflation on economic growth from frequent points. In addition, enriching the existing literature, the present study will further be an immense benefit for any researcher or stakeholder in this subject area to gain more insights.

Secondly, considering the past literature, most studies have examined the relationship between inflation and economic growth. A few studies have investigated the impact of inflation on economic growth, whereas studies of this kind have not been recently conducted based on data considering two decades during 2000–2021 contributes to the empirical gap. Moreover, this study is timely due to the behaviour of inflation and economic growth in the country at present, specially due to the economic instability triggered by the COVID-19 pandemic.

Finally, the present study contributes to modifying policies to improve the existing economic situation while providing recommendations to the government and other economic decision makers and allows ascertaining the effectiveness of decisions and policies by policymakers so far on the behaviour of inflation and economic growth in Sri Lanka. Consequently, this study is significant and worthwhile to conduct.

Furthermore, the rest of the study consists of five main sections, each providng a detailed overview of this study. The second section discusses the literature review and underlying the significance of this study, the third section discusses the data and methodology and the empirical findings are evaluated in the fourth section. The fifth section consists of concluding remarks with policy implications and recommendations.

Literature review

In the preliminary stage of this study, researchers investigated research papers from various reliable sources while ensuring quality. As depicted in Fig 1 , research papers were selected by referring to Science Direct, Wiley Online, Emerald Insight, JSTOR, Springer, Sage Publications, Research Gate, and Google Scholar and the search terms used were: impact or relationship, inflation and economic growth, Gross Domestic Product (GDP), and ARDL. Further, 197 publications were initially identified through the above sources and excluded 32 publications in the screening process due to unsatisfactory/irrelevant or insufficient information and lack of relevance.

Source: Based on authors’ observations.

The remaining 165 research papers were subjected to a second screening procedure; 136 publications were selected based on the title and relevancy of the abstract. At this stage of screening, 116 publications were identified based on key terms and text to find the most relevant publications for this study. Thereafter, this study was limited to 80 publications as the remainder were not contextually relevant. As a result, it was realised through the process that the remaining 56 research papers were eligible for conducting; moreover, these can be referred to as quality publications to accomplish the purpose of the study regarding the impact of inflation on economic growth in Sri Lanka.

Inflation is a key macroeconomic factor in each and every economic reality. According to McConnell, Brue [ 5 ], the rise in the overall price level is referred to as inflation and in a situation where inflation is high, the purchasing power of goods and services is declining. Previous research has shown that inflation has a significant impact on the economy [ 6 ]. Moreover, the stability of a country depends on the behavior of inflation and by monitoring those fluctuations, government get the opportunity to maintain the inflation through fiscal and monetary policy. Cost-push inflation and demand-pull inflation are the two types of inflation identified by Mankiw [ 7 ]. Demand-pull inflation is generated by the increase in aggregate demand where cost-push inflation resulting because of increase of the price and decrease in output levels. According to McConnell, Brue [ 5 ], Sri Lanka is now experiencing cost-push inflation, which the CBSL interprets as temporary due to supply chain disruptions and rising global commodity prices.

Scholars have deliberated inflationary impacts from various perspectives. According to existing literature, these have been tested in developed and developing countries and factors that influence on inflation. Many studies discovered that the inflation as one of the macroeconomic factors that affect on economic stability [ 8 ]. The effect of persistent high inflation rate both in the long run and short run was investigated in similar studies by Faria and McAdam [ 9 ]. Lopez-Villavicencio and Mignon [ 10 ] and Kankpeyeng, Maham [ 11 ] examined the impact of inflation on economic growth in diverse groups of countries, including both developed and developing economies. In addition, Gregorio [ 12 ] observed a long-term relationship between inflation and economic growth using data from 12 Latin American countries over the 1951–1985 period.

Empirical studies unveiled some exciting and essential findings of inflation impacting economic growth. Furthermore, it is vital to be vigilant on concerns about the changes in inflation and economic growth in other countries. As Risso and Carrera [ 13 ] and Uddin [ 14 ] pursued, the 9% of inflation rate is higher than the expected level of the economic progress of Mexico. In such situations, one must analyse the reactions of inflation and adopt suitable policies to avoid such unintended consequences that cause massive impacts. Moreover, Marinakis [ 15 ] has carried out a study by using statistics from Latin America during 1988–1991 and proved that all successive attempts of stabilisation failed in Latin America because of the inflation which faced a crisis in 1980s. However, Van [ 16 ] has used economic theories such as econometric model, Fisher and Freidman theories to analyse inflation and its effects on the economic growth of Vietnam. In addition, Baharumshah and Soon [ 17 ] conducted a study in relation to the Malaysian economy and revealed that the upward inflation decreased uncertainty but on the contrary, economic instability decelerated the output growth in the economy.

Since inflation and economic growth deal with time series data, the analysis can be conducted based on figures on the financial year. Hwang and Wu [ 18 ] and Khieu [ 19 ] found that when inflation rises by 1%, economic growth slows by 0.61% and when inflation declines by 1%, the economy grows by 0.53%. This further means that a high inflation scenario can be more destructive than the benefits gained from a low inflation scenario. Economists agreed that significantly lower but positive inflation contributes to economic growth, which in turn enables macroeconomic policy to maintain price stability and achieve sustainable economic growth. In general, inflation was deliberated as a crucial issue in sustainable economic growth, because the latter is a key factor in affirmative economic health as pointed out by Girdzijauskas, Streimikiene [ 20 ].

For analysis in Taiwan and Japan, Lee and Wong [ 21 ] referred the regression model to investigate the link between the inflation threshold and economic growth. As presented by Hoang [ 22 ] and Ball [ 23 ], in the long run, an increase in the inflation rate has a greater impact than a decrease and severe inflation will inflict damage on economic activity. The empirical findings denoted that economic growth will enhance when inflation is lower in nature. In contrast, Faria and Carneiro [ 24 ] and Akinsola and Odhiamb [ 25 ] confirmed that inflation does not affect the real output in the long run and it does have a negative impact on real output in the short run. Further, Akinsola and Odhiamb [ 25 ] indicates that inflation affects economic growth over time due to changes in the charasteristics of developed and developing countries. In addition to these studies, the relationship between inflation and economic growth has been hypothesized based on unilateral factors. As stated by Sapic, Obradovic [ 26 ], there is an unblemished linkage between inflation and economic growth in Serbian economy.

Besides, a few similar studies were conducted based on the context in Sri Lanka. The negative relationship in long run and substantial correlation between inflation and economic growth in Sri Lanka was scrutinised by Madurapperuma [ 27 ] and Bandara [ 28 ].

Other than inflation, some other factors significantly affect economic growth. Khan [ 29 ] stated that apart from inflation, many factors affect economic growth, such as the monetary policy as a key economic factor and unemployment rate. Armantier, Kosar [ 30 ] has conducted a research to investigate how the New York Federal Reserve’s consumer expectations survey reported inflationary beliefs during the first six months of the COVID–19 outbreak. During this pandemic, consumer expectations and inflation uncertainty increased. The rise in inflation uncertainty has been attributed to the high rate of precautionary savings.

Feng [ 31 ] conducted a study to examine the relationship between China’s political events and macroeconomic performance. By reviewing the results, it can be stated that under previous political leaderships, the economic progress has been stable and the unemployment and inflation was low during the years the communist party has been in the power. According to this study, regardless of many variables that affect economic growth in Sri Lanka, the main concern should be inflation.

However, the analysis can be conduct based on many analytical tools and methodologies to find out the best outcome. In their study, Burdekin, Goodwin [ 32 ] referred a panel estimate of the impact of inflation on economic growth. Chaudhry, Ayyoub [ 33 ] employed the annual time series data during 1972–2010, researched using the Ordinary Least Square method and analysed whether inflation encourages the economy. These findings determined that the rise in inflation has been attributed to the appreciation of the currency and the deficit.

Besides, Oikawa and Ueda [ 34 ] established an endogenous growth model, which was referred to investigate the link between inflation and economic growth. The model calibration demonstrates that the optimal inflation rate is close to the growth-maximising inflation rate, and that its divergence has significant consequences. In addition, Huybens and Smith [ 35 ] developed a monetary growth model indicating that inflation is inversely associated with the financial market, influencing economic growth. According to Aydın, Esen [ 36 ], the panel data threshold analysis proved a nonlinear relationship between inflation and growth rate. Further, Khoza, Thebe [ 37 ] used the Smooth Transition Regression model to establish the monotonic link between inflation and economic growth in South Africa. Thus, the findings confirmed that the 5.3% of inflation threshold caused a negative impact on economic growth over this level.

In order to have a better understanding on the impact, it is vital to identify whether there is a significant relationship between inflation and the economic growth. In this sense, the study referred to many tests including the ARDL test to examine both long run and short run impacts of inflation on economic growthto evaluate the effect of a government debt ceiling on Africa’s economic growth. Using the ARDL model, Mandeya and Ho [ 38 ] and Nadabo and Maigari [ 39 ] explored that inflation has a negative impact on growth both in the short and long run, and the inflation uncertainty in South Africa is a short-term phenomenon with no long-term implications.

Additionally, a series of steps are to be followed in ARDL bounds test. The study based on bound testing approach developed by Pesaran, Shin [ 40 ] indicated that no long-run relationship exists in six countries except in one country. Here, researchers concluded that most countries have a short run relationship between inflation and economic growth. Furthermore, Manamperi [ 41 ], Datta and Mukhopadhyay [ 42 ] explored a significant short-run relationship but not a long-run relationship between inflation and economic growth.

According to Pinshi [ 43 ], the Error Correlation Method (ECM) estimates that the dependent variables return to equilibrium after the other variables change. ECM can be used to examine the short- and long-term effects of exchange rate changes on inflationary behaviours based on the p-value. Further, Phiri [ 44 ] has employed both thresholds ECM and Granger Causality analysis to ascertain the relationship between the economic growth and financial development. Autocorrelation is the similarity between a time series and a lagged version over a succession period of time. Durbin Watson (DW) has used to evaluate serial correlation errors as a test to detect autocorrelation. It is a complex test, but has demonstrated numerous regression analysis that are relevant in certain situations. As per Hajria, Khardani [ 45 ], the Breusch Godfrey test for the ARDL model evaluates the correlation and thereby concerns the quality of the compatibility. According to Mohseni and Jouzaryan [ 46 ] and Sapic, Obradovic [ 26 ], Cumulative Sum (CUSUM) test is referred to check the stability of the selected method. The structural changes can be assessed by monitoring statistics and graphical illustrations perceived through CUSUM.

Additionally, policy implications in the country can directly affect the economy when inflation exaggerates economic growth as a macroeconomic factor. According to Chowdhury [ 47 ], macroeconomic factors of Indonesia is crucial when declaring policies related to their economy. The findings implied that the policies will sustain social spending and relieve the government of debt, and economic recovery would not be stymied by tightening immature monetory policy. Moreover, numerous studies have revealed that the impact of inflation on economic growth led to significant changes in policies. As per Rhys and Barry [ 48 ], an increase of 1% in inflation is likely to cause 77% rise in Malaysia’s economic growth rate which caused changes in policies.

The findings of Mendonca [ 49 ] has illustrated that the inflationary process implemented in Brazil after following the inflation target is not related to the emergence of output-inflation trade off. As inflation rises, governments adopt a variety of financial restriction tactics to safeguard specific sectors of the economy. Accordingly, establishing maximum interest rates on deposits and loans, restricting credit supplied to certain economic activities, and taxing the earnings of financial intermediaries are some of financial restriction tactics employed [ 50 , 51 ]. An increase in inflation to a particular level reduces both the amount of savings and the number of savers, according to a theoretical research by Moore [ 52 ], Azariadis and Smith [ 53 ], Choi, Smith [ 54 ]. Thus, an increase in inflation reduces the amount of credit available in an economy.

Most empirical studies were based on developed countries, where relevancy of such findings in the context of developing countries is questionable. This paper is pioneer in examining the impact of inflation on Sri Lanka’s economic growth as a developing country.

Data and methodology

Objective of this study is to scrutinise the impact of inflation on economic growth in Sri Lanka. Data related to variables were collected and determined through time series data. This research is totally based on quantitative data gathered through the CBSL and the Department of Census and Statistics (DCS) from Q1 2000 to Q4 2021. The impact can be examined by analysing data related to inflation rate (INF) as the independent variable and GDP as the dependent variable on economic growth. STATA analysis tool was used to estimate the model and this study provides Data file for this analysis in S1 Appendix .

Methodology

Time series variables and its changes can be analysed by using an econometric model. Research approach for the present study is designed to inquire the correspondence between inflation on economic growth which investigates whether there is a positive or negative impact on economic growth through inflation of Sri Lanka; in addition, significance of the long run and short run impact is also ascertained. In this research, lags for the variables are considered by using exclusive sources and has not captured ethical issues.

In addition, data points are indexed based on time and the present research is to be conducted through the time series analysis, that also enhances reliability of the research. As the econometric model, ARDL model will evaluate the long-term as well as short-term impacts of inflation on economic growth. This model also allows investigating lags between variables. The general equation for the ARDL model can be expressed in ( 1 ):

Explanation:

Dependent variable–Y

Independent variable–X

Short-run coefficients– β i , δ i

ARDL long-run coefficient– φ 1 , φ 2

Disturbance (white noise) term— μ t

ARDL model examines the correlation between the variables and the stability of the referred variables must be investigated when conducting the research. According to Shahid [ 55 ], the consequences of inflation on growth of the Pakistan economy were examined through time series data and the same application was referred in this research. In future, based on the results generated through the selected methodology, the responsible authorities can endeavour to amend policies that support economic growth and for stability of macroeconomic factors such as inflation.

Results and discussion

The following findings indicate the extend of the impact of inflation on economic growth in Sri Lanka. Firstly, fluctuations in both inflation and economic growth for the selected time period should be evaluated. These are depicted through statistical tables and graphical illustrations.

The above graph shows the significant changes in both INF and GDP over the period of Q1 2000 and Q4 2021 in Sri Lanka. Fig 2 illustrates that INF rises rapidly 2.2% to 15.3% from Q1 2000 to Q4 2001, along with the marginal rise in GDP from 339,578 million USD to 368,461 million USD. Moreover, in the Q4 2021, INF rises to 9.9% and the GDP is likely to slow down from 2,497,489 million USD to 2,169,203 million USD. Through this analysis, a better understanding can be gained about how INF has obstructed GDP, and hence the impeding economic growth prospects. Therefore, when considering the impact of INF on GDP, this study can be expanded.

Source: Authors’ illustration based on CBSL [ 56 ] and DCS [ 57 ].

Stationary test

To examine the impact of INF on GDP in Sri Lanka, the overall regression results might be fabricated and testing for stationarity as the first step is significant. In case the data set is not stationary, it is impossible to generate results which are useful and can be used in usual econometric procedures.

According to Fig 3 , both (a) GDP and (c) INF can be identified as time series in which the situations and statistical properties change over time and it can be concluded that both variables are non-stationary. However, as (b) and (d) time series lines are drawn on the first difference, it can be inferred that GDP and INF are stationary with statistical properties and instances that change over time to some extent. Further verification of the stationarity of these two variables can be investigated on the basis of statistical evidence, by employing Augmented Dickey Fuller (ADF) test and Dickey Fuller for Generalized Least Square (DF-GLS) test.

The unit root test is used to assess the time series data’s stationarity properties, as the findings provided from regression models have the possibility to generate spurious results [ 42 ]. According to the ADF results of GDP and INF in Table 1 , the MacKinnon approximate p- value is higher than the 5% critical value. Therefore, GDP and INF can be considered as non-stationary variables and the unit root null hypothesis cannot be rejected at that level of significance. According to Hoang [ 22 ], the time series that regressive outcomes can be distorted when the estimated variables are non-stationary. Moreover, since the p- value of GDP and INF at first differences (ΔGDP and ΔINF) are 0.0000, nonstationary null hypothesis is rejected and concluded that the differences of the GDP and INF are stationary.

*, ** and *** indicate significance at 10%, 5% and 1% respectively; Δ is the first difference.

Source: Authors’ compilation based on CBSL [ 56 ] and DCS [ 57 ].

Furthermore, results of the ΔGDP and ΔINF of DF- GLS test, the values of tau t-statistic were attained as -11.18 and -4.88 respectively. Since the null hypothesis of unit root can be rejected at all significance levels, the time series is stationary. In accordance with studies conducted by Pesaran, Shin [ 40 ], the ARDL model can be employed since the variables are I(0) or I(1). Based on the above results, all the variables are integrated and stationary at I(1) sequence, which allow the study to examine the impact of inflation on economic growth in Sri Lanka.

ARDL bound test for co-integration

After determining the order of integration of the macroeconomic variables used in this study, ARDL bounds test is to be assessed. This is to evaluate the long-run co-integrating relationship among the variables and thereby, to identify whether the ECM should be run or not.

After determining the optimal lag length the Akaike Information Criterion was employed and it indicated that the maximum number of optimal lags for ARDL is 5,0 for GDP and INF, respectively. This implies that the ARDL model fits well into this dataset, and this provides the ability to identify the number of periods that impact economic growth and also enhances quality of the study.

As Pesaran, Shin [ 40 ] has presented, ARDL bound test for co-integration provides evidence for cointegration since the F statistic is greater than the critical values. As shown in Table 2 , the value of F statistic is 1.6 which is higher than the critical values of 5%, concludes there is a co-integration between GDP and INF. Since there is co-integration, the study proceeds with model estimations using the ARDL bound test and ECM approaches. As stated by Mandeya and Ho [ 38 ] and Manamperi [ 41 ], before analysing impact of INF on GDP, it is essential to examine the relationship between these two variables. According to ARDL bound test results, the t statistic is (-1.8) and the critical value of upper bound is less than that (-3.2) at significant level of 5% and the null hypothesis can be rejected. Furthermore, this indicates that there is a short run relationship between GDP and INF; it shows that when INF increases, GDP declines, but it does not remain the same for a long time. Similarly, it can be further explained that the relationship between GDP and INF diminishes over time and hence the relationship exists in the short run.

** indicates significance at 5%, I(0) indicates lower bound and I(1) indicates upper bound.

ECM directly assesses the sensitivity of the dependent variable after the other independent variables have changed [ 43 ]. According to ECM results provided in Table 2 , there is a negative significant coefficient and a long-term impact in the time series; the findings illustrate that 67% variation in the GDP have been explained by the INF. It further revealed that whenever the INF increases by 1%, the GDP of Sri Lanka falls by 107,263.82 million USD in the long run. Theoretically and empirically these findings are further linked to those discovered by Ball [ 23 ]. As such, these reconfirm that INF will have a significant impact on GDP in Sri Lanka in long run.

In the short run, when INF increases by 1%, GDP decline by 3427.94 million USD. This suggests that there is a negative impact of INF on GDP in Sri Lanka in the short run, and Faria and Carneiro [ 24 ] further justify this phenomenon. Thus, INF remains as a significant negative factor forGDP, and INF has become a critical variable in short and long term impeding GDP. This suggests that GDP has the potential to adjust to a long-term equilibrium after the short-term shocks created by INF. Further, it highlights that GDP is particularly affected by extraordinary extensive INF in Sri Lanka.

Normality and stability tests for autocorrelation

Based on the distributed sample size, this research directed to choose DW test to check after a regression analysis which test autocorrelation in the residuals from the statistical model.

According to the Table 3 , 1.6513 of DW d-statistic test refers, INF is having positive influence on GDP. As per Hendry, Mizon [ 58 ], there should not be any exogenous cause to independent variable INF. If there is a positive serial correlation of INF in the previous quarter, it is likely to cause positive correlation on GDP in the next quarter, which will vital to determine changes in GDP much proactively and be geared for necessary amendments in policies to avoid unrealistic changes.

**indicates significance at 5%; S indicates stability.

Secondly, Breusch-Godfrey LM test will estimate serial correlation based on the number of lags unlike in basic autocorrelation tests. Lags will determine through autocorrelation between residuals of ARDL. Since the p value of LM test is 0.4348 and it is less than 5%,the null hypothesis can be rejected and there is no serial correlation between residuals. However, Hajria, Khardani [ 45 ] stated the goodness-of-fit examining Breusch-Godfrey LM test in Autoregressive models. If the null hypothesis is rejected, there is autocorrelation in the residuals. Negative serial correlation can be noted and such variables should not be over differenced for the purpose of maintaining a quality analysis.

Cumulative sum for autocorrelation

The stability of the model can investigated through CUSUM test based on the residuals of the selected data points. The former tests were conducted systematic changes of INF and GDP. Many researchers took advantage of ARDL model to achieve their research objectives, and the CUSUM test proved the model’s stability in advance [ 46 ].

The stability of short and long run coefficients is analysed through CUSUM test [ 26 ]. In addition, CUSUM test ensures whether acceptable outcomes are being achieved and can be determined through stability of the ARDL model. Fig 4 demonstrates that the calculated model meets the stability criteria with no roots outside of the significant limit. Accordingly, the coefficient of the long run ARDL is consistent with the CUSUM squared test findings. The model parameters indicates that the model is stable, whereas the statistics lying within the boundary line means the outcomes of the ARDL test are accurate.

Overall, the ADF for unit root test demonstrated that series for ΔINF and ΔGDP is stationary. Further, ARDL bound test was referred to investigate the existence of the long run and short run relationship to determine the impact by employing ECM test. The short run relationship was concluded through ARDL bounds test and the significant impact in long and short run was determined in ECM test. Finally, the stability of the ARDL model assessed through CUSUM test where it reclined at 5% boundary. Therefore, all the test results revealed that there is an impact of Inflation on economic growth in Sri Lanka.

This research study explores how inflation impacts on economic growth in Sri Lanka over the time period from Q1 2000 to Q4 2021. By utilising the ARDL model, the full sample of this study inspected that there is a short run relationship between inflation and economic growth, respectively; further, analysis reveals that while inflation increases, GDP declines but this impact does not remain the same for a long time. Based on the findings of the ECM test, it can be concluded that inflation disrupts economic growth and there is a significant negative impact of inflation on economic growth in Sri Lanka in both short run and long run. This implies that inflation has become a critical factor in influencing economic growth. As demonstrated by the CUSUM test results, the residuals were typical, constant, and mostly stable. The findings implies that the economy should place more emphasis on the significance of inflation on economic growth and ensure price stability to create a conducive economic environment for short and long-term growth.

Further, available literature indicates mixed results and somewhat contrary in developing countries, with inflation having adverse impacts than in developed countries. In future, the scope of studies is worth expanding to cover the impact of economic shocks, recessions and booms and extraordinary events of high magnitude such as pandemics of this nature in both developed and developing countries. These findings can provide a big picture of the many potential scenarios that can occur due to many fluctuations in the economy—locally, regionally and globally and thus calls for adaptability in pursuit of economic resilience.

Policy implication and recommendations

This research study demonstrates that the rise of the price level or the inflation, has a negative impact on both in the long run and short run on the economic growth in Sri Lanka. Consequently, these findings have significant policy implications (fiscal and monetary policies) for both local policymakers and macro-economists, emphasising that the stability of the inflation rate is a prerequisite for enhancing the economic growth in Sri Lanka. Generally, this study seeks for a much clear explanation regarding the increase or decrease in the average price level has a significant impact on the country’s economic growth. Findings can be critical in investigating the impact of essential goods in Sri Lanka, which are mostly imports. Accordingly, it can be ensured that maintaining a low inflation rate in the long run is conducive to boosting the country’s economic growth, living standards and for a sustainable economy. Furthermore, this study identifies the depreciation of the LKR against the USD rising oil prices, increased tendency to print local money, higher government expenditure, lower agricultural production, and increases in the prices of essential goods and services as the main reasons for the rapid rise in inflation in Sri Lanka. This is due to the market’s demand for products and services grows rapidly relative to the existing supply in the country. Furthermore, the contentious policy decision of the Government of Sri Lanka to ban the use of chemical fertilisers for cultivation in 2021 has led to agriculture supply disruptions resulting in a surge in the prices of goods and services in the economy. Accordingly, as a response to this economic concern, local policymakers should focus more on the development of agricultural sector in the country through the development of agricultural infrastructure. Another essential concern is to develop the manufacturing sector, where most import substitutes can be produced locally, i.e. supply deficiencies will be much less, hence causing less inflationary pressures. As such, this study recommends that the policymakers should act proactively and in future take efforts to control the country’s current high level of inflation and implement policies that stimulate constant economic growth.

Supporting information

S1 appendix, acknowledgments.

The authors would like to thank Ms. Gayendri Karunarathne for proof-reading and editing this manuscript.

Funding Statement

The authors received no specific funding for this work.

Data Availability

• PLoS One. 2022; 17(8): e0273379.

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Driving the Pulse of the Economy or the Dilution Effect: Inflation Impacting Economic Growth

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RDP 9701: Inflation Regimes and Inflation Expectations 2. Literature Review

Joseph E. Gagnon

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2.1. Models of Inflation

The literature on models of inflation is too voluminous to review in depth here. For the purposes of this paper, we are less interested in the dynamic interactions of inflation and other variables over the business cycle and more interested in the determination of the rate of inflation in the long run. Driffill, Mizon and Ulph (1990) and Woodford (1990) provide surveys of the theoretical and empirical literature on the costs and benefits of inflation. Unfortunately, the only conclusion that comes close to achieving a consensus is that inflation variability per se is harmful and that central banks should stabilise the inflation rate to the extent that they can without inducing costly variability in other economic variables. No consensus exists on the optimal steady-state rate of inflation.

Fischer (1990) surveys the literature on the institutional framework of monetary policy and the determination of the long-run inflation rate. The treatment is purely theoretical and focuses on the issue of ‘rules versus discretion’. A basic conclusion is that a pure rule-based policy has not existed since the Gold Standard, and many would argue that even under the Gold Standard there was a substantial discretionary aspect to monetary policy. One drawback of discretionary policy setting is that no one has designed an institutional framework that indisputably avoids the potential inflationary bias created by the time inconsistency problem. [4]

More recently, attention has focused on the adoption of explicit inflation targets by a number of central banks. Walsh (1995) discusses the circumstances under which explicit inflation targets and enforcement clauses in the central bank governor's contract are optimal. For a brief review of the international policy debate, see International Monetary Fund (1996). At this stage it appears to be too soon to conclude much about the desirability and durability of inflation targeting.

Empirical analyses of the long-run properties of inflation rates have generally occurred in the context of the real interest rate literature. See, for example, Rose (1988) and Mishkin (1992). Using data from the entire postwar period, one cannot reject a unit root in inflation for most industrial countries using standard Augmented Dickey-Fuller tests. However, for many countries one can reject non-stationarity of the inflation rate in certain subsamples.

Hassler and Wolters (1995) and Baillie, Chung and Tieslau (1996) use the Phillips-Perron test and the KPSS test on postwar monthly inflation rates and reject both a unit root and stationarity for several countries. To reconcile these conflicting findings they turn to models with ‘fractional integration’ and find that they are strongly supported by the data. Fractional integration allows for slow mean reversion that does not decay as rapidly as the asymptotically exponential pattern associated with standard autoregressive-moving average models. This slow mean reversion is termed ‘long memory’.

Other researchers have sought to explain the apparent non-stationarity of inflation as the result of regime shifts in the mean and variability of the inflation rate. Chapman and Ogaki (1993), Bai and Perron (1995) and Hostland (1995) find significant evidence of regime shifts in US, UK, and Canadian inflation. Evans and Lewis (1995), Ricketts and Rose (1995) and Simon (1996) estimate Markov-switching models for inflation in the G7 countries and Australia. At least two regimes are significant in all countries except Germany.

Occasional shifts in the inflation regime are more economically interpretable than fractional integration. Moreover, if there are only a small number of regimes that cycle back and forth, or if the regime-generating process is stationary, inflation rates will appear to have long memory, which is consistent with the fractional integration literature.

2.2. Evidence from Bond Markets

Instead of modelling the inflation process, a more direct way to learn about long-run inflation expectations is to examine the inflation premia in long-term bond markets. Fuhrer (1996) shows that the pure expectations theory of the term structure fits better when one allows structural breaks in the Fed reaction function, especially the implicit inflation target. Gagnon (1996) shows that the inflation premium in long-term interest rates is more closely correlated with a long backward average of inflation than a short backward average, implying that there is long memory in long-run inflation expectations and/or the inflation risk premium.

Focusing directly on countries that have announced explicit inflation targets, Ammer and Freeman (1995) and Freeman and Willis (1995) provide evidence that announced inflation targets have not been fully credible in terms of lowering long-term inflation expectations implicit in bond yields down to the official target range for inflation.

The time inconsistency problem refers to the temptation for a central bank to induce extra output by creating more inflation than the public expects, even though it knows that this extra output cannot be sustained in the long run. [4]

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A literature review of Inflation Targeting

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This research paper presents a review of inflation targeting structures and the conditions a central bank should consider before adopting the regime. Inflation targeting has been broadly adopted in developed and developing market economies. The frameworks of inflation targeting are, in most cases, very identical across countries and a wide consensus has been promoted which supports flexible inflation targeting.

Economic Issues

Sunil sharma PGI18CS042

Raimundo Soto

Khalilillo Khamidov

This article focuses on inflation targeting (hereafter IT) as a superior monetary policy strategy for attaining price stability, and its theoretical framework, prerequisites to introduce. The article analyses benefits and costs of adoption of inflation targeting and also examines the IT experiences of some industrial and emerging markets. The growing body of empirical researches indicates that the adoption of IT is useful for countries that must enhance their credibility for the management of monetary policy. Personally, the authors suggest that Uzbekistan should also take IT into account seriously and further consider. In the long run, without prejudice to the goal of price stability countries can achieve other objective: high employment, economic growth, financial markets stability, interest rate stability, and stability in foreign exchange markets.

Klaus Schmidt-Hebbel

Inflation targeting (IT) was started in 1990 and spread subsequently to 35 other advanced and emerging/developing countries until now. Drawing from existing and new research, this paper takes stock of IT’s past performance and limitations, and discusses its main challenges to remain the monetary regime of choice in the future. Adopting and developing IT takes different forms but central banks gradually converge to a common policy framework – although the framework itself continues evolving over time. There is significant evidence on the success of IT – in particular for emerging economies and lower income countries – in improving central banks’ institutional set-up, conduct of monetary policy, and macroeconomic performance. The last decade presented the greatest challenges to IT, due to the commodity price shock of 2006-07 and then the Global Financial Crisis and its aftermath. The future of IT in general, and in developing countries in particular, will be determined by how well cen...

Aisha M Abubakar

Inflation targeting has been adopted as a monetary policy framework by many economies – particularly emerging and advanced countries. Although several researchers have argued that it is a recommended policy measure to curb inflation in a prospectively high inflation-saddled economy, other scholars think otherwise. A critical review of the arguments for and against inflation targeting as a tool of ensuring price stability in presented in this paper. Accordingly, the paper points out that the targeting framework has been established and proven effective in several countries, but it is arguably not satisfactory enough. The paper recommends that a realistically attainable percentage – usually a flexible target range and a time-lag for achieving the target should be set. This is because over-ambitiousness on the part of monetary authorities can invariably lower the economic prestige, credibility and monetary policy independence of a country particularly if the set-target becomes unattainable. Notwithstanding, developing countries need to create an enabling environment in terms of strong financial markets and commitment to price stability alongside establishing a well-developed forecasting framework in order to reach the desired effects of inflation targeting .

The purpose of this article is to analyse the possibility of the application of the experience of the several developed countries in inflation targeting. Objectives include finding relevant policy framework which helps to fight inflation efficiently as well as fits the current practise of Uzbekistan in terms of economic activity.

Istvan Abel

This paper surveys the changes triggered by the financial crisis and the theoretical and practical options for the renewal of the inflation targeting framework. While a comprehensive overview would be impossible to provide, it seeks to present the changes in the monetary strategy of Magyar Nemzeti Bank against this wider context. First, it describes in brief the inflation targeting framework, its key elements and the principles of how it operates in practice. The paper then explains criticisms of the inflation targeting framework in light of the financial crisis and the practical, strategic and theoretical innovations that these have led to. On this note, there follows a discussion of the options most widely adopted as solutions to the challenges and what proposals were made but never used. Finally, the paper provides an overview of the practical lessons learnt in recent years regarding these instruments in advanced and emerging economies. International experience demonstrates that ...

One decade of inflation targeting in the world offers lessons on the design and implementation of inflation targeting, the conduct of monetary policy, and country performance under inflation targeting. This paper reviews briefly the main design features of 19 inflation targeting experiences, analyzes statistically if countries under inflation targeting are structurally different from non-inflation targeting industrial countries, and reviews existing

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How Quickly Do Prices Respond to Monetary Policy?

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FRBSF Economic Letter 2024-10 | April 8, 2024

With inflation still above the Federal Reserve’s 2% objective, there is renewed interest in understanding how quickly federal funds rate hikes typically affect inflation. Beyond monetary policy’s well-known lagged effect on the economy overall, new analysis highlights that not all prices respond with the same strength or speed. Results suggest that inflation for the most responsive categories of goods and services has come down substantially from recent highs, likely due in part to more restrictive monetary policy. As a result, the contributions of these categories to overall inflation have fallen.

Monetary policy affects inflation with a lag. This means that, although interest rates react quickly when the Federal Reserve raises the federal funds rate, the effects on inflation are slower and indirect. Higher interest rates increase borrowing costs, slowing investment and overall demand, which ultimately eases the pressure on prices. Understanding the timing and strength of this mechanism is key for policymakers.

Many researchers have estimated the speed and strength of the economy’s response to monetary policy, notably Romer and Romer (2004). The focus is typically a broader measure of inflation, such as headline or core, which reflects an average across many goods and services. However, not all prices of the component goods and services react to monetary policy in the same way. For example, food and energy prices, which are excluded from core but included in headline inflation, often move more in response to global market fluctuations, such as changes in international oil prices, rather than to changes in domestic monetary policy.

In this Economic Letter , we estimate how prices of different goods and services respond to changes in the federal funds rate and use those estimates to build a monetary policy-responsive inflation index. We find substantial variation in how prices react to monetary policy, which suggests that understanding the makeup of overall inflation can provide insights into the transmission of monetary policy to inflation. The extent to which categories that are more responsive to the federal funds rate contribute to inflation affects how much slowing in economic activity is needed to reduce overall inflation. Our analysis indicates that recent ups and downs of inflation have been focused in categories that are most sensitive to monetary policy. Inflation rates for the most sensitive categories—and their contributions to headline inflation—rose from the first half of 2020 through mid-2022, reaching a higher peak than headline inflation, and then began to decline. The inflation rate for this most responsive group of goods and services categories is now close to its pre-2020 rate. Our findings suggest that the Fed’s rate hikes that began in March 2022 are exerting downward pressure on prices and will continue to do so in the near term. Our estimated lags are consistent with the view that the full effects of past policy tightening are still working their way through the economy.

Measuring how prices react to monetary policy

To understand which goods and services are most responsive to monetary policy, we need to determine how their prices react to changes in the federal funds rate, the Federal Reserve’s main policy rate. Because the Federal Reserve adjusts the federal funds rate target in response to macroeconomic developments, including inflation, we use a transformation of the federal funds rate in our estimation. This transformed series, developed by Romer and Romer (2004) and updated by Wieland and Yang (2020), captures the differences between Federal Reserve staff forecasts and the chosen target rate, leaving only policy shocks, or movements in the federal funds rate that are not driven by actual or anticipated changes in economic conditions. We use this series as a so-called instrument for the federal funds rate, such that our results can account for how the federal funds rate itself, rather than its transformation, affects inflation.

We use an approach developed by Jordà (2005) that compares two forecasts—with and without rate shocks—to estimate how the federal funds rate affects price movements over time. Specifically, we estimate the relationship between the federal funds rate and the cumulative percent change in prices, controlling for recent trends in the federal funds rate, inflation, and economic activity. Repeating this estimation over multiple horizons produces a forecast comparison, or impulse response function, that gives us an estimate of the expected percent change in prices following a rate increase. For example, applying this method to the headline personal consumption expenditures (PCE) price index indicates that four years after a 1 percentage point increase in the federal funds rate, overall prices are typically about 2.5% below what they would have been without the rate increase.

Creating a policy-responsive inflation index

We estimate impulse response functions separately for the 136 goods and services categories that collectively make up headline PCE inflation. Figure 1 shows examples of the largest cumulative percent price declines over a four-year period in response to a 1 percentage point increase in the federal funds rate. The goods and services categories selected as examples account for large shares of total expenditures in headline PCE inflation. We also include one example of the few categories where prices do not decline, higher education, shown as a small positive value.

Figure 1 Reaction to a policy rate increase: Selected PCE categories

The takeaway from Figure 1 is that headline PCE inflation is made up of categories that differ in their responsiveness to increases in the federal funds rate. Some respond more strongly, such as those with larger typical cumulative price declines, while others respond less strongly, such as those with smaller typical price declines. Focusing on the most responsive categories can shed light on how monetary policy has influenced the path of inflation over the post-pandemic period. We use our results to divide the categories into two groups of goods and services. The most responsive group (blue bars) contains goods and services whose largest cumulative percent price decline over a four-year window is in the top 50% of all such declines. The least responsive group (red bars) contains goods and services in the bottom 50%.

Following the methods in Shapiro (2022), we use these two groups, along with the share of total expenditures for each good or service, to create two new aggregate PCE inflation measures. Figure 2 shows their 12-month percent changes over time. The blue shading marks the period from mid-2019 until early 2020 when the Federal Reserve lowered the federal funds rate. The vertical yellow line marks the start of the most recent tightening cycle in March 2022. Inflation in the most responsive categories (blue line) is more volatile than overall headline PCE inflation (green line) from the Bureau of Economic Analysis (BEA), and inflation in the least responsive categories is less volatile (red line).

Figure 2 Most and least responsive inflation rates

After the start of the 2020 recession, inflation rates for both categories rose but have since come down from their recent peaks. This pattern is particularly pronounced for the most responsive inflation group, for which inflation peaked at 10.5% in mid-2022 and has fallen to 0.9% as of January 2024; this is just under its average of 1% from 2012, when the Federal Reserve officially adopted a numerical inflation objective, to 2019. Inflation in the least responsive group peaked later, in early 2023, and has fallen only slightly to 3.8% as of January 2024; it remains well above its 2012–2019 average of 1.8%.

How does policy-responsive inflation react to rate increases?

The inflation rates of categories in the most and least responsive groups can move for reasons beyond changes in the federal funds rate, such as global or national macroeconomic developments. To assess the specific role of policy rate increases, we use the methodology described earlier to estimate how the most and least responsive inflation groups tend to react to rate hikes.

The results in Figure 3 suggest that an increase in the federal funds rate typically starts exerting downward pressure on the most responsive prices after about 18 months, when the line showing the impulse response function falls below zero. Month-to-month price changes start falling after a little over a year, depicted when the slope drops below zero and stays negative. This is quicker than the response of overall headline prices from the BEA (not shown), which becomes negative after a little over 24 months and shows month-to-month declines after about 18 months.

Figure 3 Reaction of most and least responsive prices to rate hikes

Because we grouped inflation categories based on the size of their response, there is not necessarily a tie-in to the speed of each categories’ change. However, our results suggest that looking at the most responsive goods and services may also be a useful way of assessing how quickly monetary policy affects inflation.

Applying the typical impact timing of the most responsive group of goods and services to the most recent tightening cycle, shown by the federal funds rate line in Figure 4, leads to several conclusions. First, rate cuts from 2019 to early 2020 could have contributed upward price pressures starting in mid- to late 2020 and thus could explain some of the rise in inflation over this period. Second, the tightening cycle that began in March 2022 likely started putting downward pressure on prices in mid-2023 and will continue to do so in the near term. This is consistent with the view that the full effects of monetary policy tightening have yet to be felt. Finally, though inflation for the most responsive categories has been falling since mid-2022, the early part of this decline was likely to have been driven more by changes in prevailing economic conditions than by policy tightening, given estimated policy lags. Some research has considered whether policy lags have shortened (see, for example, Doh and Foerster 2021); however, because inflation began falling mere months after the first rate hike, the drop in inflation may have been too soon to be caused by policy action.

Figure 4 Headline inflation contributions and the federal funds rate

Our findings in this Letter are useful for broadening our understanding of how monetary policy affects inflation. For example, if inflation and the contributions to overall headline inflation are high in a set of categories that are more responsive to monetary policy, as was the case in early 2022, then rate hikes during the most recent tightening cycle are likely to continue to reduce inflation due to policy lags. On the other hand, though inflation in the least responsive categories may come down because of other economic forces, less inflation is currently coming from categories that are most responsive to monetary policy, perhaps limiting policy impacts going forward.

Doh, Taeyoung, and Andrew T. Foerster. 2022. “ Have Lags in Monetary Policy Transmission Shortened? ” FRB Kansas City Economic Bulletin (December 21).

Jordà, Òscar. 2005. “Estimation and Inference of Impulse Responses by Local Projections.” American Economic Review 95(1), pp. 161–182.

Romer, Christina, and David Romer. 2004. “A New Measure of Monetary Shocks: Derivation and Implications.” American Economic Review 94(4), pp. 1,055–1,084.

Shapiro, Adam. 2022. “ A Simple Framework to Monitor Inflation .” FRB San Francisco Working Paper 2020-29.

Wieland, Johannes, and Mu‐Jeung Yang. 2020. “Financial Dampening.” Journal of Money, Credit and Banking 52(1), pp. 79–113.

Opinions expressed in FRBSF Economic Letter do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco or of the Board of Governors of the Federal Reserve System. This publication is edited by Anita Todd and Karen Barnes. Permission to reprint portions of articles or whole articles must be obtained in writing. Please send editorial comments and requests for reprint permission to [email protected]

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Inflation and the response of public wages in the euro area

Prepared by Cristina Checherita-Westphal and Aurelian Vlad

Published as part of the  ECB Economic Bulletin, Issue 5/2023 .

In the context of elevated inflation in the euro area (despite recent declines), it is useful to look at the response of public wages to gauge further pressures on private sector wages and core inflation. While the public sector accounts for only about one-fifth of the economy-wide compensation of employees in the euro area, it can provide a relevant signal for wage negotiations in the private sector. Public wage growth can thus have a bearing on inflation through both a direct channel (aggregate demand) and an indirect channel (as a signal for changes in private sector compensation). This box provides an update on previous analyses of public wage projections for the euro area, including as regards the institutional features that govern public wage-setting across euro area countries. [ 1 ] It is based on the June 2023 Eurosystem staff projections and is underpinned by a questionnaire completed by members of the Working Group on Public Finance (WGPF) of the European System of Central Banks. [ 2 ]

After having generally grown at rates above inflation since the inception of the euro, public wages took a deep cut in real terms in 2022, but they are expected to partly recover over the projection horizon 2023-25 (Chart A). In the period 2001-21, euro area public wages and compensation per employee grew on average at an annual rate of 2.3%. This was slightly higher than private wage growth (2.0%) and above HICP inflation (1.7%), albeit with differences across periods, especially during crisis episodes. For instance, public wages grew faster than inflation and private wages before and during the global financial crisis and more slowly during the sovereign debt crisis. In 2020, when the coronavirus (COVID-19) crisis hit, public wages continued to grow steadily, reflecting, among other things, bonuses in the health sector. [ 3 ] In 2022 euro area public wage growth declined in real terms – by 4.3 percentage points – but a partial catch-up is projected over the period 2023-25, with an average nominal growth rate of 4.1%. Cumulatively over the period 2022-25, nominal public wage growth is still projected to lag inflation by about 2.5 percentage points (with the difference narrowing to below 1 percentage point for the period 2020-25, which includes the impact of the COVID-19 crisis).

Euro area public wage growth compared with inflation and other employee compensation indicators

(percentages per annum)

Sources: June 2023 Eurosystem staff macroeconomic projections database and ECB calculations. Notes: The data on wages and compensation shown in the chart represent annual growth rates. These three euro area aggregates are GDP-weighted averages of country‑specific data. Data on the wage per employee in the public sector (also referred to in this box as “average public wages”) are computed at country level by dividing expenditure recorded under “Wages and salaries” in the Government Finance Statistics database (also referred to in this box as the “public wage bill”) by the number of government employees. Employee compensation reported in government statistics usually includes employers’ social security contributions in addition to wages and salaries.

In terms of institutional arrangements, automatic price indexation of public wages remains relatively limited in the euro area (covering about one‑fifth of the total public wage bill), but there are indications that inflation is increasingly being used as a reference in wage-setting. Updated information based on the WGPF questionnaire indicates that full or partial price indexation continues to be reported in only five countries, representing 19% of the euro area public wage bill in 2022. In two of these countries (Belgium and Luxembourg, covering 5% of the euro area public wage bill), public wages are fully indexed to prices with a backward-looking index automatically linked to the cost of living. In Cyprus and Malta, similar but limited indexation is in place. In Italy, which accounts for the largest share in this group (13% of the euro area public wage bill in 2022), agreements are renewed on the basis of three-year negotiation rounds (with the latest ones being for the periods 2019-21 and 2022-24) and expected inflation excluding energy is used as the reference indicator in wage negotiations, with ex post inflation compensation applied to existing contracts. While in other euro area countries, inflation does not play a formal role in public wage-setting, there are indications that it is increasingly being used as a reference in the process, including in wage negotiations. [ 4 ]

In most cases, public wages are set via formal collective public wage agreements, which typically last between one and three years, and are updated in negotiation rounds. Five countries (France, Greece, Latvia, Portugal and Spain), representing close to 40% of the euro area wage bill in 2022, do not have horizontal statutory agreements that apply to the entire government or to specific government sub-sectors. In these cases, public wages are usually updated in the context of budget discussions. Where collective agreements exist, their statutory or customary length varies across countries: one year in Austria, Lithuania and Slovakia; slightly more than one year in Ireland and Slovenia; around two years in Estonia, Finland, Germany, Luxembourg and the Netherlands; two to three years in Cyprus; three years in Italy; and over three years in Croatia and Malta. [ 5 ]

Taking into account the above-mentioned wage-setting schemes and other relevant factors, public wages in the euro area as a whole and in most euro area countries are expected to grow at rates that are cumulatively higher than inflation over the projection horizon (Chart B). At the level of the euro area as a whole and in most euro area countries (16 countries, representing 60% of the euro area wage bill in 2022), average public wages (per employee) are projected to grow at rates above inflation (well above that level in some countries) cumulatively over the period 2023-25. This reflects backward-looking indexation schemes or partial compensation for the cut in real wages in 2022. [ 6 ] The euro area’s total public wage bill, which also reflects developments in the number of public employees, is projected to rise at somewhat higher rates (14.3% cumulatively over the period 2023-25, compared with 12.4% for average public wages), albeit somewhat below the nominal GDP growth rate (Chart B, panel a).

Public wage projections reflect substantial heterogeneity at country level, mainly mirroring inflation differentials, but also other factors, such as fiscal positions. [ 7 ] In terms of the annual profile, the euro area aggregate hides differences across countries, with several smaller economies with high inflation (for example, the Baltic countries) and those with automatic indexation recording above-average wage growth in 2023. In general, public wage growth is expected to decline in the last year of the projection horizon (2025), reflecting cooling inflation and the fading impact of temporary bonuses in some countries. In a few cases, including where agreements are renewed with a significant lag, as in Italy, wage growth in 2025 is projected to be more substantial than in the preceding years. A simple correlation exercise at country level shows that average public wage growth over the projection period tends to be stronger in countries with higher inflation in 2022 relative to other euro area countries, reflecting backward-looking (partial) inflation compensation (Chart B, panel b, first sub-panel). In terms of grouping by indexation type, projected public wage growth is actually higher on average in countries without automatic price indexation (Chart B, panel b, second sub-panel). This may indicate (i) that countries in this category (particularly the Baltic countries and other more open economies) were more exposed to the inflation shock in 2022 and (ii) that, at high levels, inflation is increasingly being reflected in nominal wage‑setting, even in institutional arrangements where this is not a formal requirement. In terms of grouping by fiscal fundamentals, countries with high levels of government debt seem to be more restrained in granting public wage increases (Chart B, panel b, third sub-panel). [ 8 ] Lastly, looking at the fiscal support measures implemented by euro area governments to compensate for the high energy prices and inflation over the period 2021-23, projected public wage growth for the period 2023‑25 is somewhat more restrained in countries with higher levels of such support (Chart B, panel b, fourth sub-panel).

Projections of public wages in the euro area for the period 2023-25

a) Growth rates of public wage indicators for the euro area over the period 2023-25

(percentages)

b) Average growth rates of public wages for groups of euro area countries over the period 2023-25

Sources: June 2023 Eurosystem staff macroeconomic projections database and ECB calculations. Notes: In panel a, the category “Average wages” is the same as that used in Chart A and Chart B, panel b. It includes agreed wages and the wage drift. Agreed wages refer mostly to public wage increases resulting from negotiated wage agreements, where such agreements exist, or similar basic wage increase frameworks. The wage drift is usually calculated as a residual, which conceptually should reflect factors such as: (i) employee career development/promotion scales; (ii) the impact of structural changes and part-time work; and (iii) wages and bonuses outside negotiated wage agreements. Data are shown at the euro area aggregate level (GDP‑weighted averages of country-specific data). In panel b, the data represent simple averages (not weighted by GDP) across countries in the respective groups. In the “Inflation” sub-panel, the “Above EA” (“Below EA”) group comprises countries with an HICP inflation rate that was higher (lower) in 2022 than the euro area (EA) aggregate. In the “Indexation type” sub-panel, Belgium, Cyprus, Italy, Luxembourg and Malta are in the automatic (full or partial) indexation group, with all other euro area countries in the “No formal role for inflation” group. In the “Public debt” sub-panel, countries with government debt above 90% of GDP in 2022 (namely, Belgium, France, Greece, Italy, Portugal and Spain) are in the high-debt group, with the remainder of the euro area countries in the low-debt group. The “Energy/inflation support” sub-panel shows countries in which the cumulative gross fiscal costs of such measures over the period 2021-23, as estimated by the Eurosystem, are above/below the simple euro area average across countries.

Looking ahead, public wages, while not expected to lead to significant second‑round effects, should continue to be monitored closely. At the euro area aggregate level, wage growth in the public sector is projected to remain below that of the private sector over the period 2023-24, but stand somewhat above it in 2025 (for which projections are also surrounded by a higher degree of uncertainty). At the country level, due attention should also be paid over the medium term to the fiscal consequences of increases in public wages by properly balancing macroeconomic stabilisation and fiscal sustainability objectives, especially in countries with high levels of debt and high ageing-related costs.

See, for instance, Checherita-Westphal, C. (ed.), “ Public wage and pension indexation in the euro area: an overview ”, Occasional Paper Series , No 299, ECB, August 2022. That paper also contains an analysis of the role of public wages in driving private wage dynamics. Based on a review of empirical literature, it concludes that panel studies focusing on euro area countries generally find evidence of a positive relationship between public and private wages, including bi-directional causation. The evidence at individual country level, in particular with regard to causality, is less clear-cut across various studies. Based on various samples of euro area countries, the paper finds a close positive relationship between public and private wages, with the causation going in both directions. Notably, panel regression results point to an increase in average private wage growth of between 0.3 and 0.5 percentage points for a 1 percentage point increase in public wage growth, while controlling for other determinants of private wages.

See the June 2023 Eurosystem staff projections for more details on developments in economy-wide compensation per employee and inflation.

It should be noted that the decline in economy-wide wages in 2020 and their partial rebound in 2021 are distorted by the impact of job retention schemes, which were financed by governments to safeguard private sector employment and do not allow for an accurate comparison with public wage growth.

For instance, in Germany the new wage agreement concluded in April 2023 for the federal and municipal levels of government includes temporary inflation compensation premia of €3,000 per employee, payable from June 2023 to February 2024 (exempt from wage tax and social contributions). The end of these premia in 2024 leads to somewhat lower gross wage increases in 2025. Overall, the agreement provides for pay increases of above 7% in 2023 and almost 5% in 2024. For federal civil servants, it results in pay increases of around 5.5% in 2023 and 4% in 2024.

In the Netherlands, the duration varies across different collective agreements, but is usually between one and two years. In Estonia, the collective wage agreement covers about 15% of public sector employees, as it only applies to the health sector. In Finland, there is no “statutory” length, i.e. the legislation leaves the length of collective agreements open. In practice, agreements often last two years, although there are exceptions. In Malta, collective wage agreements within central government have a much longer duration than in other countries (averaging 5.3 years, with the current civil service agreement running from 2017 to 2024 and covering about 7.5 years). Other entities within Malta’s general government sector have separate collective agreements, which typically cover about three years.

Looking at the remaining countries, the average public wage is projected to grow broadly in line with inflation in Ireland and France, and somewhat below inflation in Greece (as public wages are still frozen in 2023) and Italy (reflecting, among other things, the benchmark indicator used as a reference, namely inflation net of energy).

The Eurosystem’s fiscal projections at country level are, as a general rule, confidential and are therefore not shown in this box.

This correlation holds when the five countries with automatic public wage indexation are excluded from the sample (with the difference between the low-debt group and the high-debt group even increasing). A similar picture (with a somewhat lower correlation coefficient) can be seen when the threshold variable used is the ratio of the budget balance to GDP in 2022.

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Inflation Expectations are Mixed; Consumers Express Concerns about Retaining and Finding Jobs

NEW YORK—The Federal Reserve Bank of New York’s Center for Microeconomic Data today released the March 2024 Survey of Consumer Expectations , which shows inflation expectations remained unchanged at the short-term horizon, increased at the medium-term horizon, and decreased at the longer-term horizon. Labor market expectations were also mixed. While expectations about earnings growth and an increase in the unemployment rate were unchanged, respondents were more pessimistic about losing their job and finding a new job. Finally, although household finance perceptions and expectations were largely unchanged, the perceived probability of missing a minimum debt payment rose to its highest level since the onset of the COVID-19 pandemic.

The main findings from the March 2024 survey are:

• For the third consecutive month, median one-year ahead inflation expectations remained unchanged at 3.0% in March. In contrast, the median three-year ahead inflation expectations increased to 2.9% from 2.7%, whereas the median five-year ahead decreased to 2.6% from 2.9%. The survey’s measure of disagreement across respondents (the difference between the 75th and 25th percentile of inflation expectations) rose at the one- and three-year ahead horizons and remained unchanged at the five-year ahead horizon.
• Median inflation uncertainty—or the uncertainty expressed regarding future inflation outcomes—was unchanged at the one-year ahead horizon, rose slightly at the three-year ahead horizon, and declined marginally at the five-year ahead horizon.
• Median home price growth expectations were unchanged for the sixth consecutive month at 3.0%. The series has remained within a narrow range of 2.8% to 3.1% since June 2023.
• Median year-ahead expected price changes increased for all goods in the survey, by 0.2 percentage point for gas and food to 4.5% and 5.1%, respectively; 1.3 percentage points for the cost of medical care to 8.1%; 2.6 percentage points for rent to 8.7%; and 0.7 percentage point for the cost of a college education to 6.5%.
• Median one-year ahead expected earnings growth was unchanged at 2.8% for the second consecutive month, matching the series 12-month trailing average.
• Mean unemployment expectations—or the mean probability that the U.S. unemployment rate will be higher one year from now—remained essentially unchanged at 36.2%, below the series 12-month trailing average of 38.6%.
• The mean perceived probability of losing one’s job in the next 12 months increased by 1.2 percentage point to 15.7%. This is above pre-pandemic levels and the highest reading since September 2020. The mean probability of leaving one’s job voluntarily in the next 12 months also increased (by 1.1 percentage points) to 20.6%, above the series 12-month trailing average of 18.9%.
• The mean perceived probability of finding a job (if one’s current job was lost) decreased for the third consecutive month to 51.2% in March from 52.5% in February, the lowest reading in almost three years, and well below its February 2020 pre-pandemic level of 58.7%.

Household Finance

• Median expected growth in household income was unchanged at 3.1% in March. The series has been moving within a narrow range of 2.9% to 3.3% since January 2023 and remains above the February 2020 pre-pandemic level of 2.7%.
• Median household spending growth expectations declined by 0.2 percentage point to 5.0%. The series has remained stable between 5.0% and 5.3% since August 2023, but remains well above its February 2020 pre-pandemic level of 3.1%.     
• Perceptions of credit access compared to a year ago improved slightly with a larger share of respondents reporting that it was easier to obtain credit than 12 months ago, and a slightly smaller share of respondents reporting that it was harder to obtain credit. In contrast, consumers were slightly more pessimistic about future credit access with a larger share of respondents expecting tighter credit conditions a year from now, and a smaller share of respondents expecting looser credit conditions.
• The average perceived probability of missing a minimum debt payment over the next three months rose by 1.5 percentage points to 12.9%. This is the highest reading in four years since the onset of the COVID-19 pandemic. The increase is most pronounced among respondents between the ages of 40 and 60, and those with income below $50,000.
• The median expected year-ahead change in taxes (at current income level) increased by 0.3 percentage point to 4.2%, marginally above the series 12-month trailing average of 4.1%.
• Median year-ahead expected growth in government debt increased to 9.6% from 9.3% in February.
• The mean perceived probability that the average interest rate on saving accounts will be higher in 12 months decreased by 1.9 percentage points to 24.2%, a new series low since the inception of the survey in June 2013.
• Perceptions about households’ current financial situations improved slightly with more respondents reporting being better off than a year ago, and fewer respondents reporting being worse off. Year-ahead expectations were mostly unchanged.
• The mean perceived probability that U.S. stock prices will be higher 12 months from now rose by 0.3 percentage points to 39.2%.

About the Survey of Consumer Expectations (SCE)

The SCE contains information about how consumers expect overall inflation and prices for food, gas, housing, and education to behave. It also provides insight into Americans’ views about job prospects and earnings growth and their expectations about future spending and access to credit. The SCE also provides measures of uncertainty regarding consumers’ outlooks. Expectations are also available by age, geography, income, education, and numeracy. 

The SCE is a nationally representative, internet-based survey of a rotating panel of approximately 1,300 household heads. Respondents participate in the panel for up to 12 months, with a roughly equal number rotating in and out of the panel each month. Unlike comparable surveys based on repeated cross-sections with a different set of respondents in each wave, this panel allows us to observe the changes in expectations and behavior of the same individuals over time. For further information on the SCE, please refer to an overview of the survey methodology here , the interactive chart guide , and the survey questionnaire .

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What the latest CPI data tells us

Wait, what is inflation, are we still in a period of high inflation.

• How the Federal Reserve’s rate hikes relate to inflation
• What does inflation mean for you? 

Inflation Jumps to 3.5%, Jeopardizing Hopes for Predicted Rate Cuts

The steady creep of inflation continues to defy expectations. Will the Fed backtrack on cutting interest rates this year?

Dashia Milden

Dashia is a staff editor for CNET Money who covers all angles of personal finance, including credit cards and banking. From reviews to news coverage, she aims to help readers make more informed decisions about their money. Dashia was previously a staff writer at NextAdvisor, where she covered credit cards, taxes, banking B2B payments. She has also written about safety, home automation, technology and fintech.

Tiffany Connors

Tiffany Wendeln Connors is a senior editor for CNET Money with a focus on credit cards. Previously, she covered personal finance topics as a writer and editor at The Penny Hoarder. She is passionate about helping people make the best money decisions for themselves and their families. She graduated from Bowling Green State University with a bachelor's degree in journalism and has been a writer and editor for publications including the New York Post, Women's Running magazine and Soap Opera Digest. When she isn't working, you can find her enjoying life in St. Petersburg, Florida, with her husband, daughter and a very needy dog.

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Key takeaways

• The Consumer Price Index rose 3.5% year over year, and the core index has risen 0.4% for three straight months.
• Inflation rates have steadily inched upward since the beginning of 2024, which could indicate that inflation is harder to tame than previously estimated.
• The upward trend in inflation reduces the chances the Federal Reserve will follow through on its predicted three rate cuts this year.

Inflation took an upward swing in March, with prices rising 3.5% in the last 12 months, up from 3.2% in February, according to the latest release of the Consumer Price Index . Inflation’s continued resilience threatens the chances the Federal Reserve will make its predicted three rate cuts this year.

Core prices, which exclude volatile items like food and energy, increased 0.4% in March, as it has for the past three months.

With inflation sticking stubbornly above its 2% target and unemployment remaining low , the Fed is unlikely to lower rates until the second half of 2024, if at all, so don’t expect a respite from high interest rates on credit card debt or loans anytime soon.

“I don’t see how you can justify cutting rates with a job market that looks like this, an economy that is growing at a pretty good clip, and inflation is still running almost 2% above their target,” said Gregory Heym, chief economist at real estate service company Brown Harris Stevens.

Following its March Federal Open Market Committee meeting, Chair Jerome Powell said the Fed still expects to make rate cuts “at some point this year.” But Powell emphasized that the decision to cut interest rates depended on inflation and that the Fed remains committed to bringing inflation back to its 2% target.

The Fed indicated at its final meeting in 2023 that it anticipated making multiple interest rate cuts in 2024. The Federal Open Market Committee voted at its first two meetings in 2024 to continue holding the benchmark interest rate steady at a target range of 5.25% to 5.5%. The Fed will vote on rates at its upcoming meetings scheduled for April 30 to May 1 and June 11 to 12. 

Inflation isn’t something that can be tackled overnight, and it’s still taking a toll on US households and consumers. Here’s a quick primer on the state of inflation and steps you can take to prepare for what’s ahead.

At 3.5%, the inflation rate is still lower than rates we saw last year -- it was at 5% in March 2023. However, even though prices aren’t increasing by as much as they were a year ago, they’re still higher than they were before the pandemic. 

Since the beginning of the year, the index’s inflation data has bucked expectations and continued inching upward, with core index increasing 0.4% in January, February and March.  And overall inflation hasn’t been this high since September. 

The Bureau of Labor Statistics’ CPI is one of the most closely watched gauges of US inflation, tracking data on 80,000 products, including food, education, energy, medical care and fuel. 

Inflation means your dollar bill doesn’t stretch as far as before, whether at the grocery store or a used car lot. 

Inflationary pressures happen over time and require historical context to understand. For example, in 1993, the average cost of a movie ticket was $4.15. Today, watching a film in the theater will easily cost you $13 for the ticket alone, never mind the popcorn, candy or soda. A $20 bill 30 years ago would buy someone more than double what it buys today. And while wages have also risen over the past few decades, they haven’t kept up with inflation. Consumers have less purchasing power. 

Inflation affects everyone differently, and it isn’t determined by observation. It’s backed by a consensus of experts who rely on market indexes and research. 

Along with the CPI increase, the Personal Consumption Expenditures price index, prepared by the Bureau of Economic Analysis, also increased in March: Core inflation, excluding volatile energy and food, was up 2.8% year over year and 0.3% from the previous month. The PCE index includes all goods and services and is the Federal Reserve’s preferred inflation gauge.

The BLS also puts together a Producer Price Index , which tracks inflation from the perspective of the producers of consumer goods, measuring changes in seller prices in industries like manufacturing, agriculture, construction, natural gas and electricity. 

The current inflationary period started back in April 2021, when consumer prices jumped at the fastest pace in over a decade. Inflation was originally thought to be temporary while economies bounced back from COVID-19. 

But as months progressed, supply chain bottlenecks persisted and prices skyrocketed. The US was then hammered by unanticipated shocks to the economy, including subsequent COVID variants, lockdowns in China and Russia’s invasion of Ukraine, leading to a choked supply chain and soaring energy and food prices. 

How the Federal Reserve’s rate hikes relate to inflation

The Fed moderates inflation and employment rates by managing the money supply and setting interest rates. Part of its mission is to keep average inflation at a steady 2% rate. 

When the Fed increases the federal funds rate -- the interest rate banks charge each other for borrowing and lending -- it restricts how much money is available to borrow and spend, which has an impact on economic growth. Banks pass on rate hikes to consumers, meaning everything from credit card APRs to interest rates on personal loans tick up. Consequently, this can drive consumers, investors and businesses to pause their investments, leading to a rebalance in the supply-and-demand scales. 

In general, when interest rates are low, the economy and inflation grow. And when interest rates are high, the economy and inflation slow.

When the inflation rate hit 8.5% in March 2022, the Fed set off an aggressive sequence of interest rate hikes in an attempt to slow the economy and curb prices by reducing consumer borrowing. After 11 rate hikes, the Federal Reserve paused interest rates at a target range of 5.25% to 5.5% in July 2023. 

The Federal Reserve’s next meeting to vote on interest rates is slated for April 30 – May 1.

What does inflation mean for you? 

Periods of high inflation make it harder to afford everyday essentials. Interest rate hikes mean it costs more for businesses and consumers to take out loans, so buying a car or home gets more expensive. As interest rates increase, liquidity in securities and cryptocurrency markets decreases, causing those markets to dip. Credit card debt and other forms of high-interest debt become more expensive. 

Though inflation has been easing for the past few months, it’s still unpredictable. Fed officials stated after their December meeting that inflation had “eased,” voting nearly unanimously that the policy rate will be lower by the end of 2024 but tempered expectations of rate cuts at its first meeting this year.

In the short term, experts recommend avoiding taking on new debt unless absolutely necessary. 

“When interest rates are high, the goal is to put off borrowing money as long as possible,” said Summer Red, AFC and Education Manager for the Association for Financial Counseling & Planning Education. “If you have to borrow money at high interest rates, be sure to shop around for the best interest rate and make sure that your loans do not have a prepayment penalty, so that you can pay off the loan early.”

One option is applying for a debt consolidation loan that could combine any high-interest variable debt into a lower-interest, fixed-rate loan and establishing a payoff plan. Getting a balance transfer card can also help you avoid high interest for a period of time. If the economy continues to be volatile, it’s also important for households to build up a financial cushion. 

While inflation has been stubborn, there’s one financial advantage to increased rates: Many CDs , high-yield savings accounts , money market accounts and treasury bonds are offering annual percentage yields, or APYs, at around 4% and 5% -- the highest savings rates seen since the 1990s. Experts recommend taking advantage of putting your funds in one of these accounts to get a bigger return on your balance before the Feds lower interest rates. The interest you earn can help you reach your emergency fund or sinking fund goal faster.

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If Trump wins, he plans to free Wall Street from 'burdensome regulations'

• Medium Text

THINK TANK TRANSITION

Potential personnel.

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Reporting by Lawrence Delevingne and Douglas Gillison. Additional reporting from Steve Holland, Gram Slattery and Nathan Layne. Editing by Tom Lasseter and Claudia Parsons.

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Delevingne works primarily on enterprise stories related to finance. He joined Reuters in 2015 and previously reported for CNBC.com and Absolute Return. Delevingne is a graduate of Columbia’s Graduate School of Journalism and Georgetown’s School of Foreign Service.

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Stocks tumble, dollar firms amid geopolitical risk, mixed central bank views

U.S. stocks sold off sharply on Friday while the dollar jumped as investors grappled with rising geopolitical tensions and persistent inflation that could lead to diverging monetary policy between the U.S. and Europe.

If Trump Wins, He Plans to Free Wall Street From 'Burdensome Regulations'

If Trump Wins, He Plans to Free Wall Street From 'Burdensome Regulations'

FILE PHOTO: U.S. President Donald Trump cuts a red tape while speaking about deregulation at the White House in Washington, U.S., December 14, 2017. REUTERS/Kevin Lamarque/File Photo

By Lawrence Delevingne and Douglas Gillison

WASHINGTON (Reuters) - A second Trump White House would seek to sharply reduce the power of U.S. financial regulators, according to a review of public documents and interviews with people allied with the former president.

In the wake of the worst economic crisis since the Great Depression, Congress dramatically expanded the U.S. government's oversight of the financial industry to prevent a repeat of the 2008 global banking meltdown.

Donald Trump would likely renew his efforts to scale back those reforms, if elected, as well as pare protections for small-scale investors and borrowers, and allow companies to raise money with less scrutiny, according to the interviews and proposals from groups positioned to influence a new conservative administration. Reuters spoke with, among others, about a dozen people who have provided advice or been consulted by Trump or his allies.

The Republican Party’s presumptive nominee has not announced a formal policy staff or released detailed positions on how he would regulate Wall Street, aside from short videos and snippets in campaign appearances.

But, the sources told Reuters, a constellation of experts and Trump allies are pitching regulatory rewrites, identifying potential staff and floating ideas on TV, in op-eds and directly to Trump at his Mar-a-Lago Club in Palm Beach, Florida.

Some of the ideas in Trump’s current policy orbit have long circulated in conservative economic conversation. They include curtailing the Dodd-Frank Act, a set of post-2008 financial crisis rules intended to reduce systemic risk. Another idea is to make it easier for private companies to raise capital – in turn opening access to less transparent and more difficult-to-trade private funds and securities.

More recent policy ideas include attacking environmental, social and governance (ESG) investments and disclosures, which help screen businesses based on socially conscious factors, or potential dramatic cuts to staff at regulators through a mechanism known as Schedule F, which would reclassify up to 50,000 civil servants across the government as easily-replaceable political appointees.

Karoline Leavitt, national press secretary for the Trump campaign, said Trump had success in peeling back regulations during his administration.

"President Trump's pro-growth, deregulatory agenda ignited the greatest economy in history,” Leavitt said in an email to Reuters.

The Trump administration, with mixed success, worked to reverse a range of Obama-era rules, such as those that eased regulations for Wall Street banks or “fiduciary” rules for brokers.

Excluding the immediate effects of the coronavirus pandemic, official data show unemployment at its lowest since the 1960s under both Trump and Biden. Though pandemic and other distortions can make comparisons difficult, in inflation-adjusted terms the U.S. economy grew more slowly in Trump’s first three years in office (8.1%) than under Biden (10.6%), according to Commerce Department data.

Michael Faulkender, a former Trump Treasury official, has called publicly for scrapping bank stress testing under the 2010 Dodd-Frank Act in favor of stronger capital requirements, saying that requiring banks to pass the same set of evaluations leaves the system open to collapse if they all run into the same problems at once.

He is now chief economist at the America First Policy Institute (AFPI), which was founded by former Trump officials. Asked about his policy positions, Faulkender pointed to his previous writing about ESG investing.

“As the academic literature has documented, ESG is too much in the eye of the beholder,” he told Reuters. “Therefore, it can and has been used to deviate from the fiduciary duty that money managers have to their clients, and it has distracted financial supervisors from the safety and soundness criteria that should be used in ensuring the ongoing strength of the U.S. financial system.”

TARGETING CLIMATE CHANGE RULES

Robert Bowes, a former Trump appointee who has worked with the conservative Heritage Foundation, has called for the abolition of the Consumer Financial Protection Bureau – created by the Dodd-Frank Act to police the lending industry at the federal level – and referred to the Securities and Exchange Commission as an “unaccountable meddling shakedown agency” that “uses its regulation to target political enemies, to ram through woke and radical green agenda.”

In an email, Bowes told Reuters he was “very concerned about the disastrous bank regulation and economic policies by the Biden administration.”

Asked about that characterization and others about burdensome regulations, a Biden White House spokesperson said congressional Republicans have pushed to continue Trump-era policies by “gutting life-saving regulations and legalizing predatory business practices,” thereby increasing risks to the financial system and the economy.

It’s unclear what ideas Trump will take up, and what can become settled policy. But taken together, the ideas being promoted in conservative circles would overturn key aspects of current financial regulation.

The changes would reverse reforms ranging from investor protections to risk management by the biggest banks, Brian D. Feinstein, an expert on financial regulation at the University of Pennsylvania’s Wharton School, said of the policy proposals being floated for a second Trump administration.

“It would upend the U.S.'s entire system of financial regulation,” he said.

Campaign spokeswoman Leavitt characterized Biden’s administration as engaging in a "massive push to increase burdensome regulations, especially on our energy and auto industries."

The Biden administration has pushed regulations to spur the use of electric vehicles and renewable energy sources, in addition to seeking fair lending requirements, increased investor disclosures and bank capital hikes.

Trump has repeatedly said he wants much less regulation than now exists. A person who regularly speaks with him on economic matters said Trump would be “sure” to “go after all of this climate change stuff,” likely a nod to new corporate climate risk disclosure rules and ESG investments.

Feinstein, the Wharton professor, said that some of the proposed policies from Trump’s allies would need to go through Congress, such as limiting the Dodd-Frank Act, making their fortunes uncertain. That will depend on the outcome of November’s elections in the U.S. Senate and House of Representatives. Currently, Democrats control the Senate and Republicans have a narrow House majority.

But agencies like the Securities and Exchange Commission, whose five-person bipartisan commission is appointed by the White House (usually one each year) and approved by the Senate, would have power to push through other proposals, such as those related to environmental reporting, Feinstein said.

And bureaucratic changes such as expanding the definition of political appointees through Schedule F could have a major effect on financial regulators by removing job protections for many career professionals, compelling them to pursue the president’s preferences rather than their own independent judgment, he added. The Biden administration has maneuvered to slow such a move by Trump should he return to office.

Even if Trump loses the election, the judicial appointments from his 2017-2021 presidency could change the legal landscape for the Consumer Financial Protection Bureau and the Securities and Exchange Commission, with the Supreme Court considering challenges to the power of those agencies to issue regulations.

THINK TANK TRANSITION

The Heritage Foundation, the influential Washington-based conservative think tank, has positioned itself as central to getting the agenda through regardless.

Heritage’s preparations, dubbed “Project 2025,” include a more-than-900-page book of policy ideas and an expansive database of pre-screened personnel. The group has compiled policy recommendations since the Reagan era, but the latest edition includes more detail on financial regulation than in 2016.

Among Heritage’s policy authors is Stephen Moore, a conservative economist and longtime advisor to Trump who recently pitched him at Mar-a-Lago on candidates to lead the Federal Reserve. Moore proposes a transformation of the U.S. Department of the Treasury that would slash the Internal Revenue Service’s budget and terminate employees who have participated in diversity initiatives, among other things.

Moore told Reuters he’d like to see “less of the regulators sticking their fingers in all these financial transactions, especially in areas like banking regulation,” singling out bank capital requirements in particular.

A spokesperson at Heritage declined to comment.

The America First Policy Institute, the nascent think tank led by Trump White House strategist Brooke Rollins, is also angling for influence. The group is home to more than 50 former Trump administration officials and staff, including Larry Kudlow, the FOX Business Network host and former White House economic adviser who remains close to Trump; Faulkender, who led the Covid-era Paycheck Protection Program at Treasury; and Robert Lighthizer, the former U.S. Trade Representative.

The group has also written a high-level policy agenda and is “crafting action-oriented plans for each federal department and agency” as part of the “America First Transition Project.”

A spokesman for AFPI said in an email that its Transition Project "is focused on unleashing American prosperity by implementing the America First Agenda."

Lighthizer did not respond to a request for comment.

POTENTIAL PERSONNEL

Steven Cheung, the Trump campaign’s communications director, said in an emailed statement that there has been “no discussion” of potential personnel.

But during a January campaign speech, Trump floated billionaire investor and donor John Paulson as a potential Treasury Secretary. Paulson has said he supports the “reduction of unnecessary regulation”; on Saturday, he hosted other major donors and Trump at his Palm Beach home, raising $50.5 million, according to the campaign.

Trump wants Paulson to lead Treasury, and if not him, Scott Bessent, another investor and campaign contributor, according to a source familiar with internal conversations among Trump and his advisers.

Former SEC Chair Jay Clayton is among other potential candidates for Trump’s Treasury team, according to two sources familiar with the situation, but considered a long shot.

The consideration of Paulson, Bessent and Clayton was previously reported by Bloomberg and The Wall Street Journal.

Clayton, in an email to Reuters, said only that he expected the financial team in a new Trump administration would be similar to the first, which he said was “focused on lifting real wages, facilitating growth through domestic investment, and providing strong long term returns for retirees.”

Paulson, in a statement to Reuters, said, "It’s too early to discuss any positions in President Trump’s administration." Bessent did not respond to a request for comment.

(Reporting by Lawrence Delevingne and Douglas Gillison. Additional reporting from Steve Holland, Gram Slattery and Nathan Layne. Editing by Tom Lasseter and Claudia Parsons.)

Copyright 2024 Thomson Reuters .

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