Global Inflation After COVID-19 and Monetary-Policy Responses in Developing and Developed Countries

Authors

  • Muhammad Irfan Faculty of Management Sciences, Sarhad University of Science and Information Technology, Peshawar, Pakistan
  • Sundas Iftikhar School of Economics, Pakistan Institute of Development Economics (PIDE), Pakistan
  • Waas Khan Faculty of Management Sciences, Sarhad University of Science and IT, Peshawar, Pakistan
  • Imran Ul Haq Senior Subject Specialist in Economics, GHSS Jolozai, Nowshera, Pakistan

DOI:

https://doi.org/10.63544/ijss.v5i5.338

Keywords:

Global Inflation, COVID-19, Monetary Policy, Developed Countries, Developing Countries, Interest Rates, Exchange-Rate Depreciation, Post-Pandemic Recovery

Abstract

This study examines the drivers of global inflation after COVID-19 and compares the timing, instruments, and effectiveness of monetary-policy responses in developed and developing countries. It adopts a quantitative, explanatory, and comparative panel-data design covering 2019–2025, with 2019 serving as the pre-pandemic baseline. Secondary data on consumer-price inflation, policy interest rates, exchange rates, economic growth, fiscal support, energy and food prices, external conditions, and governance are drawn from the International Monetary Fund, World Bank, Bank for International Settlements, and other official sources. The analysis combines descriptive trend comparisons with pooled ordinary least squares, fixed-effects, and random-effects models, including an interaction term to test whether monetary-policy transmission differs by development status. The findings indicate that post-pandemic inflation resulted from the interaction of supply-chain disruptions, reopening demand, expansionary fiscal and monetary policies, commodity-price shocks, and exchange-rate depreciation. Developed economies initially relied on very low interest rates, quantitative easing, and extensive fiscal support, then shifted to rapid rate increases and balance-sheet tightening. Developing economies often tightened earlier because of imported inflation, currency depreciation, capital outflows, and external-debt pressures. Although global tightening and easing supply constraints reduced inflation after its 2022 peak, disinflation was generally faster in developed economies. Developing countries experienced more persistent pressures because of import dependence, weaker currencies, limited fiscal space, and less effective policy transmission. The study concludes that monetary tightening is necessary but insufficient and should be complemented by fiscal discipline, credible institutions, supply-side reforms, food and energy security, reserve management, and targeted protection for vulnerable households.

References

Aguilar, A., Cantú, C., & Guerra, R. (2023). Fiscal and monetary policy in emerging market economies: What are the risks and policy trade-offs? BIS Bulletin, 71. Bank for International Settlements. https://www.bis.org/publ/bisbull71.htm

Akash, M. M. R. (n.d.). Cardiotoxicity prediction models in cancer patients using artificial intelligence and genomics. International Journal of Drug Delivery Technology, 16, 60–73.

Akram, M., Khan, W., Rasheed, M. D., Imran, M., Iqbal, M. W., Delshadi, A., & Sultana, M. (2026). Cybersecurity risk assessment model for Internet of Medical Things (IoMT) devices in healthcare systems. Spectrum of Engineering Sciences, 4(3), 312–326.

Ali, N. M., Ahmed, M. E., Fakhrul Islam, M., Gomes, C. A., & Islam, M. S. (2026). Toward a circular US economy: Green and artificial intelligence innovation, renewable energy, and domestic material consumption. Energy Sources, Part B: Economics, Planning, and Policy, 21(1), Article 2685043.

Al Kium, A., Sarker, S., Shikha, S. A., Kamal, M. A. T., Jabed, M. I. K., Munifa, N. K., ... John, D. B. (2023). Health equity and digital disparities in cancer screening and cardiovascular care across socioeconomic and ethnic groups: A systematic review. Vascular and Endovascular Review, 6(2), 35–44.

Ari, A., Garcia-Macia, D., & Mishra, S. (2023). Has the Phillips curve become steeper? (IMF Working Paper No. 2023/100). International Monetary Fund. https://doi.org/10.5089/9798400242915.001

Ari, A., Mulas-Granados, C., Mylonas, V., Ratnovski, L., & Zhao, W. (2023). One hundred inflation shocks: Seven stylized facts (IMF Working Paper No. 2023/190). International Monetary Fund. https://doi.org/10.5089/9798400254369.001

Bank for International Settlements. (2024). Annual economic report 2024. https://www.bis.org/publ/arpdf/ar2024e.htm

Bhowmik, P. K., Chowdhury, F. R., Sumsuzzaman, M., Ray, R. K., Khan, M. M., Gomes, C. A. H., ... Gomes, C. A. (2025). AI-driven sentiment analysis for Bitcoin market trends: A predictive approach to crypto volatility. Journal of Ecohumanism, 4(4), 266–288.

Deb, P., Estefania-Flores, J., Firat, M., Furceri, D., & Kothari, S. (2023). Monetary policy transmission heterogeneity: Cross-country evidence (IMF Working Paper No. 2023/204). International Monetary Fund. https://doi.org/10.5089/9798400257322.001

Gopinath, G. (2023). Crisis and monetary policy. Finance & Development, 60(1). International Monetary Fund. https://www.imf.org/en/publications/fandd/issues/2023/03/crisis-and-monetary-policy-gita-gopinath

Harding, M., Lindé, J., & Trabandt, M. (2023). Understanding post-COVID inflation dynamics (IMF Working Paper No. 2023/010). International Monetary Fund. https://doi.org/10.5089/9798400231162.001

Hasan, M. R., Rahman, M. A., Gomes, C. A. H., Nitu, F. N., Gomes, C. A., Islam, M. R., & Shawon, R. E. R. (2025). Building robust AI and machine learning models for supplier risk management: A data-driven strategy for enhancing supply chain resilience in the USA. Advances in Consumer Research, 2(4), 1152–1171.

Imam, P. A., & Poghosyan, T. (2026). One global shock, many inflation paths: Explaining post-COVID inflation divergence (IMF Working Paper No. 2026/103). International Monetary Fund. https://doi.org/10.5089/9798229045148.001

International Monetary Fund. (2024b). World economic outlook, October 2024: Policy pivot, rising threats. https://www.imf.org/en/Publications/WEO/Issues/2024/10/22/world-economic-outlook-october-2024

International Monetary Fund. (2026). Monetary and financial statistics: Interest rates. IMF Data. https://data.imf.org/en/datasets/IMF.STA:MFS_IR

Jabed, M. I. K. (2024). Stock market price prediction using machine learning techniques. American International Journal of Sciences and Engineering Research, 7(1), 1–6.

Jabed, M. I. K., Sirazy, M. R. M., Mandal, S., Akter, S. A., Hassan, A., & Esa, H. (2026a). Developing AI-based financial forecasting and cybersecurity systems for the US digital economy. Frontiers in Computer Science and Artificial Intelligence, 5(5), 30–38.

Jabed, M. I. K., Imran, M., Khan, A. A., Mehedi, M., Islam, A., & Pervez, R. (2026b). Explainable machine learning framework for early heart disease detection using SMOTE and SHAP. Vascular and Endovascular Review, 9(1), 316–324.

Jabed, M. I. K., Ahmed, M. P., Tofa, F. M., Islam, M. F., Gomes, C. A., & Sirazy, R. M. (2026c). Federated intrusion detection for Internet of Medical Things networks: Differential privacy, non-IID robustness, and cross-device generalization. Journal of Computer Science and Technology Studies, 8(8), 303–315.

Jabed, M. I. K., Manzoor, M. A., Tofa, F. M., & Khan, M. H. (2026d). Interpretable ensemble learning approach for breast cancer diagnosis using SHAP-based explainable AI. Journal of Computer Science and Technology Studies, 8(8), 244–255.

Jabed, M. I. K., Imran, M., Gomes, C. A., Ponduru, P. S., Mandal, S., & Hassan, S. (2025a). Deep learning and explainable AI framework for predicting lung cancer severity. Journal of Computer Science and Technology Studies, 7(12), 573–598.

Jabed, M. I. K., Imran, M., Gomes, C. A., & Ponduru, P. S. (2025b). Machine learning-based prediction of poor self-rated health among U.S. adults using behavioral and socioeconomic factors. World Journal of Advanced Research and Reviews, 27(1), 2817–2829. https://doi.org/10.30574/wjarr.2025.27.1.2649

Ponduru, P. S. (2023). Road accident prediction using LSTM GRU neural networks [Doctoral dissertation, California State University, Northridge].

Ponduru, P. S. (2024). Decision intelligence for AI and emerging technologies: The AEGIS-DM framework for trustworthy, cost-aware, and low-latency decision making.

Ponduru, P. S., Nandanavanam, P. P. V., & Ponduru, S. K. K. (2026a). Ecological proportionality in generative AI: The ethics of marginal capability and environmental sufficiency.

Ponduru, P. S., Nandanavanam, P. P. V., & Ponduru, S. K. K. (2026b). SAFE-HealCloud: Safety-aware, agentic self-healing for cloud infrastructure. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 12(4), 163–188.

Rahman, M. A., Devnath, R. K., Niloy, S. B., Mehedi, C. M., Chowdhury, T. H., & Jabed, M. I. K. (2025, October). A stacking ensemble framework for predicting employee turnover: Explainable AI with SHAP. In 2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS) (pp. 1–6). IEEE.

Rahman, M. A., Jabed, M. I. K., Devnath, R. K., Mehedi, C. M., Begum, M., & Mahmud, T. (2026, May). MSRFF: An interpretable CNN-Vision Transformer framework for diabetic retinopathy detection. In 2026 2nd International Conference on Computational Intelligence Approaches and Applications (ICCIAA) (pp. 1–7). IEEE.

Rasheed, M. D., Akram, M., Imran, M., Ahmed, R. H., & Rauf, A. (2025). Towards human-centric smart manufacturing: A digital twin enabled affective ergonomic framework for adaptive human-robot collaboration. International Journal of Advances in Signal and Image Sciences, 1544–1555.

World Bank. (2025a). Inflation, consumer prices (annual %): Metadata glossary. World Development Indicators. https://databank.worldbank.org/metadataglossary/world-development-indicators/series/FP.CPI.TOTL.ZG

World Bank. (2025b). Worldwide governance indicators: Documentation. https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation

Author Biographies

Muhammad Irfan, Faculty of Management Sciences, Sarhad University of Science and Information Technology, Peshawar, Pakistan

Sundas Iftikhar , School of Economics, Pakistan Institute of Development Economics (PIDE), Pakistan

Waas Khan , Faculty of Management Sciences, Sarhad University of Science and IT, Peshawar, Pakistan

Imran Ul Haq , Senior Subject Specialist in Economics, GHSS Jolozai, Nowshera, Pakistan

Downloads

Published

24-09-2026

How to Cite

Irfan, M., Iftikhar , S., Khan , W., & Haq , I. U. (2026). Global Inflation After COVID-19 and Monetary-Policy Responses in Developing and Developed Countries. Inverge Journal of Social Sciences, 5(5), 86–102. https://doi.org/10.63544/ijss.v5i5.338

Similar Articles

<< < 18 19 20 21 22 23 24 25 26 > >> 

You may also start an advanced similarity search for this article.