Pork Market Shocks and Inflation Dynamics in China
Main Article Content
บทคัดย่อ
Background and Objectives: Pork occupies a central position in China’s consumption basket and plays a critical role in shaping inflation dynamics, particularly through its substantial weight in food prices and its salience in public inflation expectations. Fluctuations in pork prices have long been associated with the so-called “pig cycle,” traditionally viewed as a supply-driven phenomenon rooted in biological production lags. However, with the increasing complexity of China’s food system, market liberalization, and heightened uncertainty arising from disease outbreaks, policy interventions, and global shocks, pork prices are no longer determined solely by supply-side factors. Demand-side pressures and precautionary behavior have become increasingly important, potentially amplifying inflation volatility. Existing studies often rely on linear frameworks or event-based approaches, which may obscure the nonlinear and state-dependent nature of inflation responses to pork-market shocks. Against this backdrop, this study aims to examine how different types of pork-market structural shocks—supply, demand, and precautionary demand—affect inflation dynamics in China across distinct inflation volatility regimes. By explicitly incorporating regime dependence, the study seeks to provide a more nuanced understanding of pork-driven inflation transmission and its implications for macroeconomic stabilization.
Methodology: The analysis employs monthly data covering the period from January 2009 to November 2024. A two-stage empirical strategy is adopted. In the first stage, pork-market structural shocks are identified using a structural vector autoregression (SVAR) framework inspired by the commodity-market identification strategy proposed by Kilian. Pork supply shocks are proxied by changes in production, demand shocks by slaughter volume, and precautionary demand shocks by real pork prices. In the second stage, the transmission of these shocks to overall CPI inflation and food CPI inflation is examined using both a linear benchmark model and a nonlinear Markov-switching regression. The Markov-switching framework allows inflation dynamics to differ endogenously between low- and high-volatility regimes, capturing nonlinear pass-through mechanisms that cannot be identified in linear models. In addition, the study investigates the role of policy-specific economic policy uncertainty indices in driving regime transitions, thereby linking pork-market shocks to broader macroeconomic uncertainty.
Key Findings: The empirical results reveal strong state dependence in the inflationary effects of pork-market shocks. In low-volatility inflation regimes, supply shocks and precautionary demand shocks are the primary drivers of inflation, while demand shocks play a limited role. In contrast, during high-volatility regimes, demand shocks and precautionary behavior dominate inflation dynamics, indicating that consumption pressures and expectation-driven responses become more influential when inflation is unstable. Across both regimes, food CPI inflation responds more strongly to pork-market shocks than headline CPI inflation, underscoring pork’s role as a key amplifier of food-price pressures. The analysis further shows that supply shocks exhibit delayed effects on inflation, consistent with the long biological production cycle in pork markets, whereas demand and precautionary shocks exert more immediate impacts. Moreover, monetary policy uncertainty emerges as the most important factor triggering transitions between low- and high-volatility inflation regimes, highlighting the interaction between commodity-specific shocks and macroeconomic policy credibility.
Policy Implications: The findings point to the necessity of explicitly state-contingent inflation stabilization policies in China. In low-volatility environments, policy efforts should prioritize supply-side stabilization by enhancing production resilience, improving disease prevention, and strengthening the pork supply chain to mitigate delayed inflationary pressures. In high-volatility regimes, however, demand management and expectation anchoring become more critical, requiring timely policy communication, real-time price monitoring, and measures to curb precautionary and speculative behavior. Strengthening early-warning systems for pork prices and integrating information from futures markets and policy indicators can further improve inflation management. More broadly, the results suggest that effective inflation control in economies where staple food commodities play a central role requires adaptive policy frameworks that recognize nonlinear transmission mechanisms and regime-dependent dynamics.
Article Details

อนุญาตภายใต้เงื่อนไข Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The paper is published under CC BY-NC-ND, in which the article is freely downloaded and shared in its original form non-commercially and its citation details are identified.
เอกสารอ้างอิง
Abbas, S. K. (2022). Asymmetry in the regimes of inflation and business cycles: The New Keynesian Phillips curve. Applied Economics, 55(25), 2875–2888. https://doi.org/10.1080/00036846.2022.2107610
Abbas, S. K., & Lan, H. (2020). Commodity price pass-through and inflation regimes. Energy Economics, 92, 104977. https://doi.org/10.1016/j.eneco.2020.104977
Adjemian, M. K., Arita, S., Meyer, S., & Salin, D. (2024). Factors affecting recent food price inflation in the United States. Applied Economic Perspectives and Policy, 46(2), 648–676. https://doi.org/10.1002/aepp.13378
Baquedano, F. G., & Liefert, W. M. (2014). Market integration and price transmission in consumer markets of developing countries. Food Policy, 44, 103–114. https://doi.org/10.1016/j.foodpol.2013.11.001
Basher, S. A., Haug, A. A., & Sadorsky, P. (2018). The impact of oil-market shocks on stock returns in major oil-exporting countries. Journal of International Money and Finance, 86, 264–280. https://doi.org/10.1016/j.jimonfin.2018.05.003
Beirne, J., & Friedrich, C. (2017). Macroprudential policies, capital flows, and the structure of the banking sector. Journal of International Money and Finance, 75, 47–68. https://doi.org/10.1016/j.jimonfin.2017.04.004
Bloomberg. (2019). China insight: CPI basket decoded - food dominates, services key. https://www.bloombergchina.com/blog/china-insight-cpi-basket-decoded-food-dominates-services-key
Chatziantoniou, I., Degiannakis, S., Filis, G., & Lloyd, T. (2021). Oil price volatility is effective in predicting food price Volatility. Or is it? The Energy Journal, 42(6), 25–48. https://doi.org/10.5547/01956574.42.6.icha
Clemente, J., Montañés, A., & Reyes, M. (1998). Testing for a unit root in variables with a double change in the mean. Economics Letters, 59(2), 175–182. https://doi.org/10.1016/S0165-1765(98)00052-4
Coase, R. H., & Fowler, R. F. (1937). The pig-cycle in Great Britain: An explanation. Economica, 4(13), 55–82. https://doi.org/10.2307/2548787
Davidson, J., Halunga, A., Lloyd, T., McCorriston, S., & Morgan, W. (2016). World commodity prices and domestic retail food price inflation: Some insights from the UK. Journal of Agricultural Economics, 67(3), 566–583. https://doi.org/10.1111/1477-9552.12158
Deluna, R. S., Jr., Loanzon, J. I. V., & Tatlonghari, V. M. (2021). A nonlinear ARDL model of inflation dynamics in the Philippine economy. Journal of Asian Economics, 76, 101372. https://doi.org/10.1016/j.asieco.2021.101372
Furceri, D., Loungani, P., Simon, J., & Wachter, S. M. (2016). Global food prices and domestic inflation: Some cross-country evidence. Oxford Economic Papers, 68(3), 665–687. https://doi.org/10.1093/oep/gpw016
García-Germán, S., Bardají, I., & Garrido, A. (2016). Evaluating price transmission between global agricultural markets and consumer food price indices in the European Union. Agricultural Economics, 47(1), 59–70. https://doi.org/10.1111/agec.12209
Guo, F. (2013). What causes China’s high inflation? A threshold structural vector autoregression analysis. China & World Economy, 21(6), 100–120. https://doi.org/10.1111/j.1749-124X.2013.12048.x
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357–384. https://doi.org/10.2307/1912559
Hovhannisyan, V., & Gould, B. W. (2011). Quantifying the structure of food demand in China: An econometric approach. Agricultural Economics, 42(s1), 1–18. https://doi.org/10.1111/j.1574-0862.2011.00548.x
Hovhannisyan, V., & Gould, B. W. (2014). Structural change in urban Chinese food preferences. Agricultural Economics, 45(2), 159–166. https://doi.org/10.1111/agec.12038
Huang, H., & Xiong, T. (2025). Are Chinese live hog futures useful hedging tools? Applied Economics, 57(18), 2281–2298. https://doi.org/10.1080/00036846.2024.2323023
Huang, Y., & Luk, P. (2020). Measuring economic policy uncertainty in China. China Economic Review, 59, 101367. https://doi.org/10.1016/j.chieco.2019.101367
Hwang, I., & Zhu, X. (2024). State-dependent oil price shocks on inflation and the efficacy of inflation targeting regime. Journal of International Money and Finance, 144, 103077. https://doi.org/10.1016/j.jimonfin.2024.103077
Kashyap, P., Suter, J. F., & McKee, S. C. (2024). Measuring changes in pork demand, welfare effects, and the role of information sources in the event of an African swine fever outbreak in the United States. Food Policy, 126, 102672. https://doi.org/10.1016/j.foodpol.2024.102672
Kikuchi, J., & Nakazono, Y. (2023). The formation of inflation expectations: Microdata evidence from Japan. Journal of Money, Credit and Banking, 55(6), 1609–1632. https://doi.org/10.1111/jmcb.12944
Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3), 1053–1069. https://doi.org/10.1257/aer.99.3.1053
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
López-Villavicencio, A., & Pourroy, M. (2019). Inflation target and (a)symmetries in the oil price pass-through to inflation. Energy Economics, 80, 860–875. https://doi.org/10.1016/j.eneco.2019.01.025
Ma, M., Wang, H. H., Hua, Y., Qin, F., & Yang, J. (2021). African swine fever in China: Impacts, responses, and policy implications. Food Policy, 102, 102065. https://doi.org/10.1016/j.foodpol.2021.102065
Mishkin, F. S., & Schmidt-Hebbel, K. (2001). One decade of inflation targeting in the world: What do we know and what do we need to know? (NBER Working Paper No. 8397). National Bureau of Economic Research.
Peersman, G. (2022). International food commodity prices and missing (dis)inflation in the Euro area. Review of Economics and Statistics, 104(1), 85–100. https://doi.org/10.1162/rest_a_00939
Qiao, F., Huang, J., Wang, D., Liu, H., & Lohmar, B. (2016). China’s hog production: From backyard to large-scale. China Economic Review, 38, 199–208. https://doi.org/10.1016/j.chieco.2016.02.003
Taylor, J. B. (2000). Low inflation, pass-through, and the pricing power of firms. European Economic Review, 44(7), 1389–1408. https://doi.org/10.1016/S0014-2921(00)00037-4
Wang, J., Wang, X., & Yu, X. (2023). Shocks, cycles and adjustments: The case of China’s hog market under external shocks. Agribusiness, 39(3), 703–726. https://doi.org/10.1002/agr.21787
Wang, L., Chavas, J.-P., & Li, J. (2023). The dynamic impacts of disease outbreak on vertical and spatial markets: The case of African Swine Fever in China. Applied Economics, 55(18), 2005–2023. https://doi.org/10.1080/00036846.2022.2101605
Wang, Y., Wang, J., & Wang, X. (2020). COVID-19, supply chain disruption and China’s hog market: A dynamic analysis. China Agricultural Economic Review, 12(3), 427–443. https://doi.org/10.1108/CAER-04-2020-0053
Xin, X., & Wang, X. (2008). Was China’s inflation in 2004 led by an agricultural price rise? Canadian Journal of Agricultural Economics/Revue Canadienne d’Agroeconomie, 56(3), 353–364. https://doi.org/10.1111/j.1744-7976.2008.00133.x