Input costs’ pass-through to output prices: a cross-country empirical analysis

ABSTRACT

This article decodes the pass-through behaviour from input prices to output prices, which is a key component in assessing second-order effects of cost-push pressures. Using panel GMM model, this paper has tried to empirically test the degree of pass-through of Purchasing Managers Index (PMI) input prices to output prices in a cross-country framework for 28 countries for the period 2011–2023. Empirical results support modest pass through with the sensitivity of output prices to input prices being lower for advanced nations and inflation targeting countries and higher for manufacturing sector with signs of non-linearity.

KEYWORDS:

• PMI
• GMM
• Consumer Price Index
• monetary policy

JEL:

• E31
• C2
• E5

Acknowledgement

The authors are thankful to Shri Muneesh Kapur, Executive Director at RBI for his guidance and encouragement.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Another implication is that as countries have become more open over time because of globalization, one would expect their inflation rates to have become more sensitive to cyclical global factors as well (Forbes 2019). Also, both headline consumer and producer prices inflation were witnessed to move closely together between those Asian economies that trade more with one another due to cross-border price spill overs. through interlinked production networks (Auer and Mehrotra 2014).

2. ECB (2017), ‘What can recent developments in producer prices tells us about pipeline pressures?’, Economic Bulletin, Box, Issue 3.

3. A diffusion index is a feasible system used to convert survey responses into a single figure reading. Diffusion indexes are typically used when surveys ask respondents to report whether a variable has risen, remained unchanged or fallen during the survey period in question.

4. The surveys ask respondents to report the change in each variable compared to the prior month, noting whether each has risen/improved, fallen/deteriorated, or remained unchanged. These objective questions are accompanied by one subjective ‘sentiment’ question asking companies whether they forecast their output to be higher, the same or lower in a year’s time. These indexes therefore vary between 0 and 100 with levels of 50.0 signalling no change on the previous month. Readings above (below) 50.0 signal an improvement (deterioration) or increase (decrease) on the previous month. The greater the divergence from 50.0 the greater the rate of change signalled.

5. The services PMI was introduced in 1996 by S&P Global economists (known as NTC Research) to accompany the manufacturing PMI.

6. It may be noted that in this monthly panel data, standard output gap variables cannot be utilized which are generally quarterly or annual and vary across nations. Hence, to harmonize frequency and nature of the variable across nations, PMI output indicator has been used.

7. Evidence from panel estimation for IT economies and a control group of economies with high industrial growth that do not target inflation revealed that IT helps countries to achieve lower inflation in the long run, strengthen their monetary policy independence, improves policy efficiency, and obtain inflation outcomes that are closer to the target levels. The performance attained by industrial economies with IT setup dominates over the performance of emerging economies with IT setup.

8. Quantile estimation is done for three models with Covid-19, country-specific and IT adoption framework dummies, respectively.

9. D1 (D1 = 1, COVID Period; D1 = 0, Non-COVID Period), D2 (D2 = 1, AEs; D1 = 0, EMEs) and D3 (D3 = 1, Countries adopting IT framework; D3 = 0, Countries adopting non-IT framework) are used as three dummies in the model.

Additional information

Notes on contributors

Aastha

Aastha is currently working as Manager in the Monetary Policy Department of the Reserve Bank of India and holds a master’s degree in Economics from JNU. Her research interests focus on monetary policy, inflation targeting frameworks, and natural language processing (NLP) tools.

Sangita Misra

Sangita Misra is Director in the Monetary Policy Department of the Reserve Bank of India. She has been working with the Reserve Bank for over two decades and has wide experience in the areas of monetary policy making, fiscal policy, international diplomacy, external sector and real sector. She is a PhD from IIT Mumbai and has done her Masters from Delhi School of Economics. She has published several research articles in these areas in domestic and international journals and has represented the Bank at various domestic and global policy forum/seminars.