The Effect of GPM on Rice Price Volatility in Indonesia: Evidence from Provincial Data with Agricultural Controls

Authors

  • Nur Afini Department of Economics, Airlangga University, Indonesia
  • Tri Haryanto Department of Economics, Airlangga University, Indonesia

DOI:

https://doi.org/10.71094/vkj.v3i4.243

Keywords:

Rice price volatility, Agricultural factors, Generalized Method of Moments

Abstract

Rice price volatility remains a critical challenge for food security and economic stability in Indonesia. This study examines the effect of the Gerakan Pangan Murah (GPM) on rice price volatility using provincial-level panel data with monthly observations from 2023 to 2024. To obtain consistent and unbiased estimates, this study employs a dynamic panel data approach using the Generalized Method of Moments (GMM), specifically the System GMM estimator, which addresses potential endogeneity and captures the persistence of price movements. The model incorporates key agricultural control variables, including rice production, rainfall, and the farmer terms of trade (NTP), representing supply conditions and farmer welfare. The results indicate that rice price volatility exhibits strong persistence over time. After controlling for endogeneity, GPM does not have a statistically significant effect on price volatility, suggesting that the program is more responsive to market conditions rather than functioning as an effective stabilization instrument. In contrast, rice production and rainfall significantly increase price volatility, while NTP tends to reduce it, although with weaker statistical significance. These findings imply that structural factors play a more dominant role than short-term policy interventions in shaping rice price dynamics in Indonesia.

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References

Anshida, M., Murugan, P. P., Senthilkumar, M., Chandrakumar, M., Vanitha, G., Rani, A. J., & Arvind, G. (2026). Impact of climate change on food security: A systematic literature review and bibliometric analysis. Sustainable Futures, 11(December 2024), 101826. https://doi.org/10.1016/j.sftr.2026.101826

Atozou, B., Lawin, K. G., Valéa, A. B., & Aouini, S. (2019). Short and Long-term Asymmetric Farm-Retail Price Transmission Analysis in the Canadian Agri-food Industry: Evidence from Dairy and Pork Sectors with Threshold Cointegration Models. Journal of Field Robotics, 8(2), 66. https://doi.org/10.5539/JFR.V8N2P66

B. H. Baltagi. (2013). Econometric Analysis of Panel Data 5th ed., Wiley. https://doi.org/10.1007/978-3-030-53953-5

Barrett, C. B. (Ed.). (2013). Food Security and Sociopolitical Stability. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199679362.001.0001

Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. https://doi.org/https://doi.org/10.1016/S0304-4076(98)00009-8

Bonfiglioli, A., Crinò, R., & Gancia, G. (2022). Economic uncertainty and structural reforms: Evidence from stock market volatility. Quantitative Economics, 13(2), 467–504. https://doi.org/10.3982/qe1551

Dercon, S. (2004). Growth and shocks: evidence from rural Ethiopia. Journal of Development Economics, 74(2), 309–329. https://doi.org/https://doi.org/10.1016/j.jdeveco.2004.01.001

Dercon, S., & Christiaensen, L. (2011). Consumption risk, technology adoption and poverty traps: Evidence from Ethiopia. Journal of Development Economics, 96(2), 159–173. https://doi.org/https://doi.org/10.1016/j.jdeveco.2010.08.003

Egger, P., & Pfaffermayr, M. (2005). Estimating Long and Short Run Effects in Static Panel Models. Econometric Reviews, 23(3), 199–214. https://doi.org/10.1081/ETC-200028201

Greene, W. H. (2018). Econometric Analysis. Pearson. https://books.google.co.id/books?id=xGZRvgAACAAJ

Hasan, S. (2017). The distributional effect of a large rice price increase on welfare and poverty in Bangladesh. Australian Journal of Agricultural and Resource Economics, 61(1), 154–171. https://doi.org/10.1111/1467-8489.12141

Headey, D., & Fan, S. (2010). Reflections on the global food crisis How Did It Happen? How Has It Hurt? And How Can We Prevent the Next One?

Heino, M., Puma, M. J., Ward, P. J., Gerten, D., Heck, V., Siebert, S., & Kummu, M. (2018). Two-thirds of global cropland area impacted by climate oscillations. Nature Communications, 9(1), 1257. https://doi.org/10.1038/S41467-017-02071-5

Hertel, T. W. (2016). Food security under climate change. Nature Climate Change, 6(1), 10–13. https://doi.org/10.1038/nclimate2834

Indah, P. N. (2021). ANALYSIS OF CATTLE FARMER EXCHANGE RATE ( NTP-T ) AND THE FACTORS THAT INFLUENCE IT IN SIDOARJO REGENCY. 5(May), 1–8. https://doi.org/10.7821/ijetmr.v8.i5.2021.934

Li, J., Ding, H., Hu, Y., & Wan, G. (2021). Dealing with dynamic endogeneity in international business research. Journal of International Business Studies, 52(3), 1–24. https://doi.org/10.1057/S41267-020-00398-8

National Food Agency. (2024). Laporan Tahunan Badan Pangan Nasional Tahun 2023.

Packard, G. C., & Boardman, T. J. (2008). Model Selection and Logarithmic Transformation in Allometric Analysis. Physiological and Biochemical Zoology, 81(4), 496–507. https://doi.org/10.1086/589110

Pesaran, M. H., & Zhou, Q. (2018). Estimation of time-invariant effects in static panel data models. Econometric Reviews, 37(10), 1137–1171. https://doi.org/10.1080/07474938.2016.1222225

Pinstrup-Andersen, P. (2014). Food Price Policy in an Era of Market Instability. In UNU-WIDER STUDIES IN DEVELOPMENT ECONOMICS, Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198718574.001.0001

Putra, A. W., Putra, A. W., Supriatna, J., Koestoer, R. H., & Soesilo, T. E. B. (2021). Differences in Local Rice Price Volatility, Climate, and Macroeconomic Determinants in the Indonesian Market. Sustainability, 13(8), 4465. https://doi.org/10.3390/SU13084465

Ray, D. K., Gerber, J. S., MacDonald, G. K., & West, P. C. (2015). Climate variation explains a third of global crop yield variability. Nature Communications, 6(1), 5989. https://doi.org/10.1038/NCOMMS6989

Rizwan, M., Qing, P., Saboor, A., Iqbal, M., & Nazir, A. (2020). Production Risk and Competency among Categorized Rice Peasants: Cross-Sectional Evidence from an Emerging Country. Sustainability, 12(9), 3770. https://doi.org/10.3390/SU12093770

Roodman, D. (2009). How to do xtabond2 : An introduction to difference and system GMM in Stata. 1, 86–136.

Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences, 106(37), 15594–15598. https://doi.org/10.1073/pnas.0906865106

Secretariate General - Ministry of Agriculture Republic of Indonesia. (2023). Statistics of Food Consumption 2023. Kementan, 1–132. https://satudata.pertanian.go.id/assets/docs/publikasi/Buku_Statsitik_Konsumsi_Pangan_2023.pdf

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). In MIT Press. https://doi.org/10.2307/j.ctv5rdzwc.1

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Published

2026-05-29

How to Cite

Afini, N., & Haryanto, T. (2026). The Effect of GPM on Rice Price Volatility in Indonesia: Evidence from Provincial Data with Agricultural Controls. Varied Knowledge Journal, 3(4), 611–621. https://doi.org/10.71094/vkj.v3i4.243