Hostname: page-component-6bf8c574d5-m789k Total loading time: 0 Render date: 2025-03-10T18:21:49.872Z Has data issue: false hasContentIssue false
  • English
  • Français

Consumer preferences and inflation diffusion

Published online by Cambridge University Press:  10 March 2025

Christian Glocker  and
Philipp Piribauer
Christian Glocker*
Affiliation:
Austrian Institute of Economic Research, Vienna, Austria
Philipp Piribauer
Affiliation:
Austrian Institute of Economic Research, Vienna, Austria
*
Corresponding author: Christian Glocker; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We study how consumer preferences affect the transmission of microeconomic price shocks to consumer price index (CPI) inflation. These preferences give rise to complementarities and substitutions between goods, generating demand-driven cross-price dependencies that either amplify or mitigate the impact of price shocks. Our results demonstrate that while both effects are present, positive spillovers due to complementarities dominate. The magnitude of these cross-price effects is significant, demonstrating their importance in shaping CPI inflation dynamics. Most importantly, demand-driven price linkages decisively shape the impact of producer prices on CPI inflation. These findings underscore the need to take into account demand-driven price dependencies when assessing the impact of price shocks on CPI inflation, rather than relying solely on supply-related ones.

Keywords

inflationconsumer behaviorcross price dependenciesnetwork analysisC11C53D85E31
Type
Articles
Information
Macroeconomic Dynamics , Volume 29 , 2025 , e80
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdallah, C. and Kpodar, K.. (2023) How large and persistent is the response of inflation to changes in retail energy prices? Journal of International Money and Finance 132, 102806.CrossRefGoogle Scholar
Acemoglu, D., Carvalho, V. M., Ozdaglar, A. and Tahbaz-Salehi, A.. (2012) The network origins of aggregate fluctuations. Econometrica 80(5), 1977–2016.Google Scholar
Afonso, O. and Magalhães, M.. (2020) How powerful are network effects? A skill-biased technological change approach. Macroeconomic Dynamics 24(4), 882–919.CrossRefGoogle Scholar
Aguiar, A. and Martins, M.. (2005) Testing the significance and the non-linearity of the Phillips trade-off in the Euro area. Empirical Economics 30(3), 665–691.CrossRefGoogle Scholar
Ahmad, Y. S. and Staveley-O’Carroll, O. M.. (2017) Exploring international differences in inflation dynamics. Journal of International Money and Finance 79, 115–135.CrossRefGoogle Scholar
Auer, J. and Papies, D.. (2020) Cross-price elasticities and their determinants: a meta-analysis and new empirical generalizations. Journal of the Academy of Marketing Science 48(3), 584–605.CrossRefGoogle Scholar
Auer, R. A. and Mehrotra, A.. (2014) Trade linkages and the globalisation of inflation in Asia and the Pacific. Journal of International Money and Finance 49, 129–151.CrossRefGoogle Scholar
Balcilar, M., Elsayed, A. H. and Hammoudeh, S.. (2023) Financial connectedness and risk transmission among MENA countries: evidence from connectedness network and clustering analysis. Journal of International Financial Markets, Institutions and Money 82, 101656.CrossRefGoogle Scholar
Baumeister, C., and Hamilton, J. D.. (2019) Structural interpretation of vector autoregressions with incomplete identification: revisiting the role of oil supply and demand shocks. American Economic Review 109(5), 1873–1910.CrossRefGoogle Scholar
Baumeister, C. and Peersman, G.. (2013) Time-varying effects of oil supply shocks on the US economy. American Economic Journal: Macroeconomics 5(4), 1–28.Google Scholar
Baumeister, C. and Hamilton, J. D.. (2015) Sign restrictions, structural vector autoregressions, and useful prior information. Econometrica 83(5), 1963–1999.CrossRefGoogle Scholar
Bilgin, N. M. (2022) Inflation diffusion through production networks. SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.3740856.CrossRefGoogle Scholar
Bilgin, N. M. and Yılmaz, K.. (2018) Producer price inflation connectedness and input-output networks. Koç University-TUSIAD Economic Research Forum Working Papers 1813, Koç University-TUSIAD Economic Research Forum.CrossRefGoogle Scholar
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. and Hwang, D.-U.. (2006) Complex networks: structure and dynamics. Physics Reports 424(4), 175–308.CrossRefGoogle Scholar
Cai, M. and Vandyck, T.. (2020) Bridging between economy-wide activity and household-level consumption data: matrices for European countries. Data in Brief 30, 1–4.CrossRefGoogle ScholarPubMed
Canova, F. and De Nicolo, G.. (2002) Monetary disturbances matter for business fluctuations in the G-7. Journal of Monetary Economics 49(6), 1131–1159.CrossRefGoogle Scholar
Choi, S., Furceri, D., Loungani, P., Mishra, S. and Poplawski-Ribeiro, M.. (2018) Oil prices and inflation dynamics: evidence from advanced and developing economies. Journal of International Money and Finance 82, 71–96.CrossRefGoogle Scholar
Chow, G. C. and Lin, A.-L.. (1971) Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. The Review of Economics and Statistics 53(4), 372–375.CrossRefGoogle Scholar
Colizza, V., Flammini, A., Serrano, M. A. and Vespignani, A.. (2006) Detecting rich-club ordering in complex networks. Nature Physics 2(2), 110–115.CrossRefGoogle Scholar
Crespo Cuaresma, J. and Glocker, C.. (2023) Production structure, tradability and fiscal spending multipliers. Journal of International Money and Finance 138, 102921.CrossRefGoogle Scholar
Das, S. R., Kalimipalli, M. and Nayak, S.. (2022) Banking networks, systemic risk, and the credit cycle in emerging markets. Journal of International Financial Markets, Institutions and Money 80, 101633.CrossRefGoogle Scholar
Diewert, W. E. (2009) Cost of living indexes and exact index numbers. In: Diewert, W. E. (ed.), Quantifying Consumer Preferences, pp. 207–245. Slottje (London: Emerald Group Publishing Limited, Bingley) chapter 8CrossRefGoogle Scholar
Fadinger, H., Ghiglino, C. and Teteryatnikova, M.. (2022) Income differences, productivity, and input-output networks. American Economic Journal: Macroeconomics 14(2), 367–415.Google Scholar
Fan, T., Lü, L., Shi, D. and Zhou, T.. (2021) Characterizing cycle structure in complex networks. Communications Physics 4(272), 1–9.CrossRefGoogle Scholar
Frank, O. and Harary, F.. (1982) Cluster inference by using transitivity indices in empirical graphs. Journal of the American Statistical Association 77(380), 835–840.CrossRefGoogle Scholar
Friesenbichler, K. S. and Glocker, C.. (2019) Tradability and productivity growth differentials across EU member states. Structural Change and Economic Dynamics 50, 1–13.CrossRefGoogle Scholar
Fu, C. and Wang, B.. (2023) Income tax reductions in production networks. Macroeconomic Dynamics 28(6), 1–28.Google Scholar
Gandy, A. and Veraart, L. A. M.. (2019) Adjustable network reconstruction with applications to CDS exposures. Journal of Multivariate Analysis 172, 193–209.CrossRefGoogle Scholar
Gelos, G. and Ustyugova, Y.. (2017) Inflation responses to commodity price shocks – How and why do countries differ? Journal of International Money and Finance 72, 28–47.CrossRefGoogle Scholar
Glasserman, P. and Young, H. P.. (2016) Contagion in financial networks. Journal of Economic Literature 54(3), 779–831.CrossRefGoogle Scholar
Glocker, C. and Piribauer, P.. (2021) Digitalization, retail trade and monetary policy. Journal of International Money and Finance 112, 102340.CrossRefGoogle Scholar
Glocker, C. and Towbin, P.. (2015) Reserve requirements as a macroprudential instrument–Empirical evidence from Brazil. Journal of Macroeconomics 44, 158–176.CrossRefGoogle Scholar
Glocker, C. and Wegmüller, P.. (2024) Energy price surges and inflation: fiscal policy to the rescue? Journal of International Money and Finance 149, 103201.CrossRefGoogle Scholar
Gutman, I. (1978). The energy of a graph. In 10. Steiermärkisches Mathematisches Symposium (Stift Rein Graz, 1978), vol. 103 of Ber. Math.-Statist. Sekt. Forsch. Graz, Austria, pp. 1–22.Google Scholar
Hahn, P. R. and Carvalho, C. M.. (2015) Decoupling shrinkage and selection in bayesian linear models: a posterior summary perspective. Journal of the American Statistical Association 110(509), 435–448.CrossRefGoogle Scholar
Hall, S. G., Tavlas, G. S. and Wang, Y.. (2023) Drivers and spillover effects of inflation: The United States, the Euro area, and The United Kingdom. Journal of International Money and Finance 131, 102776.CrossRefGoogle Scholar
Hazell, J., Herreño, J., Nakamura, E. and Steinsson, Jón. (2022) The slope of the Phillips curve: evidence from U.S. States. Quarterly Journal of Economics 137(3), 1299–1344.CrossRefGoogle Scholar
Holland, P. W., Laskey, K. B. and Leinhardt, S.. (1983) Stochastic block models: first steps. Social Networks 5(2), 109–137.CrossRefGoogle Scholar
Holme, P. and Saramäki, J.. (2012) Temporal networks. Physics Reports 519(3), 97–125.CrossRefGoogle Scholar
Horvath, S. (2011). Weighted network analysis: applications in genomics and systems biology. Bücher (Springer New York): SpringerLink.CrossRefGoogle Scholar
IMF. (2022a) Calculating consumer price indices in practice, In Consumer Price Index Manual, Washington, USA: International Monetary Fund IMF, pp. 153–177. chapter 9,Google Scholar
IMF. (2022b) The economic approach to index number theory: the many-household case, In ‘Consumer Price Index Manual’ (Washington, USA: International Monetary Fund, IMF) chapter 18, pp. 243-269.Google Scholar
Jehle, G. A. and Reny, P. J.. (2011) Advanced microeconomic theory. 3rd edn. (Harlow: Pearson Education).Google Scholar
Karrer, B. and Newman, M. E. J.. (2011) Stochastic block models and community structure in networks. Physical Review E 83(1), 016107.CrossRefGoogle Scholar
Kim, H.-J. and Kim, J. M.. (2005) Cyclic topology in complex networks. Physical Review E 72(3), 14–23.CrossRefGoogle ScholarPubMed
Lucas, R. E. (1977) Understanding business cycles. Carnegie-Rochester Conference Series on Public Policy 5(1), 7–29.CrossRefGoogle Scholar
Mas-Colell, A., Whinston, M. and Green, J.. (1995. Microeconomic Theory. (Oxford University Press).Google Scholar
Mehta, N. and Ma, Y.. (2012) A multicategory model of consumers purchase incidence, quantity, and brand choice decisions: methodological issues and implications on promotional decisions. Journal of Marketing Research 49(4), 435–451.CrossRefGoogle Scholar
Mulhern, F. J. and Leone, R. P.. (1991) Implicit price bundling of retail products: a multiproduct approach to maximizing store profitability. Journal of Marketing 55(4), 63–76.CrossRefGoogle Scholar
Neely, C. J. and Rapach, D. E.. (2011) International comovements in inflation rates and country characteristics. Journal of International Money and Finance 30(7), 1471–1490.CrossRefGoogle Scholar
Newman, M. E. J. (2010) Networks: an introduction. (Oxford; New York: Oxford University Press).CrossRefGoogle Scholar
Peña, I. and Rada, J.. (2008) Energy of digraphs. Linear and Multilinear Algebra 56(5), 565–579.CrossRefGoogle Scholar
Piribauer, P., Glocker, C. and Krisztin, T.. (2023) Beyond distance: the spatial relationships of European regional economic growth. Journal of Economic Dynamics and Control 155, 104735.CrossRefGoogle Scholar
Ravn, M. and Uhlig, H.. (2002) On adjusting the Hodrick-prescott filter for the frequency of observations. The Review of Economics and Statistics 84(2), 371–375.CrossRefGoogle Scholar
Regmi, A. and Seale, J. L.. (2010) Cross-price elasticities of demand across 114 countries, ’ USDA-ERS Technical Bulletin No. 1925, US Department of Agriculture Economic Research Service.CrossRefGoogle Scholar
Rubio-Ramírez, J. F., Waggoner, D. F. and Zha, T.. (2010) Structural vector autoregressions: theory of identification and algorithms for inference. The Review of Economic Studies 77(2), 665–696.CrossRefGoogle Scholar
Sethuraman, R., Srinivasan, V. and Kim, D.. (1999) Asymmetric and neighborhood cross-price effects: some empirical generalizations. Marketing Science 18(1), 23–41.CrossRefGoogle Scholar
Teteryatnikova, M. (2014) Systemic risk in banking networks: advantages of “tiered” banking systems. Journal of Economic Dynamics and Control 47, 186–210.CrossRefGoogle Scholar
Uhlig, H. (2005) What are the effects of monetary policy on output? Results from an agnostic identification procedure. Journal of Monetary Economics 52(2), 381–419.CrossRefGoogle Scholar
van Oest, R. (2005) Which brands gain share from which brands? Inference from store-level scanner data. Quantitative Marketing and Economics 3(1), 281–304.CrossRefGoogle Scholar
Varian, H. R. (1992) Microeconomic Analysis. 3rd edn. (New York: Norton).Google Scholar
Werner, D. (2009) Funktionalanalysis, Einführung in die höhere Analysis. (Berlin, Heidelberg: Springer), pp. 1–88.CrossRefGoogle Scholar
Wickens, M. (2012) Macroeconomic Theory: A Dynamic General Equilibrium Approach Second Edition number 9743, In Economics Books, 2nd edn. (Princeton University Press).Google Scholar
Zhang, W., Li, W. and Deng, W.. (2021) The characteristics of cycle-nodes-ratio and its application to network classification. Communications in Nonlinear Science and Numerical Simulation 99, 1–11.CrossRefGoogle Scholar
Supplementary material: File

Glocker and Piribauer supplementary material

Glocker and Piribauer supplementary material
Download Glocker and Piribauer supplementary material(File)
File 479.7 KB