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Assessing the Impact of Oil Price Volatility on Food Prices in Saudi Arabia: Insights From Nonlinear Autoregressive Distributed Lags (NARDL) Analysis

Author

Listed:
  • Darwez Faten

    (Quantitative Method, College of Business Administration, King Faisal University, Al Ahsa 31982, Saudi Arabia)

  • Alharbi Farea

    (Economic and finance department, university of Taif, Saudi Arabia)

  • Ifa Adel

    (The University of Sousse, Higher Institute of Finance and Taxation, Sousse, Tunisia)

  • Bayomei Samah

    (Business Administration Department, King Faisal University, Kingdom of Saudi Arabia)

  • Mostfa Engy

    (Business Administration Department, King Khalid University, Kingdom of Saudi Arabia)

  • Lutfi Abdalwali

    (Business Administration Department, King Faisal University, Kingdom of Saudi Arabia)

  • haya Mohammed Abu

    (Business Administration Department, King Faisal University, Kingdom of Saudi Arabia)

  • Alrawad Mahmaod

    (College of Business Administration and Economics, Al-Hussein Bin Talal University, Ma’an 71111, Jordan)

Abstract

This research investigates the impact of oil price fluctuations on food prices in Saudi Arabia between 1979 and 2020 using Nonlinear Autoregressive Distributed Lags (NARDL) methodology. The study employs Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests to determine the integration order of the variables and Bounds testing to confirm the existence of long run cointegration relationships between each variable. The results indicate that oil shocks influence food prices through several mechanisms. Firstly, the cost of energy increases due to the rise in oil prices, affecting the agricultural commodity market, including farm equipment, food processing, packaging, and distribution costs. Secondly, the demand for agricultural commodities to produce biofuels affects food availability and thus food prices. Thirdly, as an oil-based economy, the oil price shock affects food prices through its impact on government spending, which affects aggregate demand and liquidity. Therefore, Saudi Arabia should adopt appropriate policies to mitigate the impact of oil price shocks on food prices, including investing in renewable energy sources, diversifying its economy, and improving food production and supply chain efficiency. Additionally, implementing appropriate fiscal policies to ensure sufficient budget allocation for food support programs is crucial. However, Investing in groundwater exploration can contribute significantly to the development of Saudi agriculture and to gradually achieving food self-sufficiency.

Suggested Citation

  • Darwez Faten & Alharbi Farea & Ifa Adel & Bayomei Samah & Mostfa Engy & Lutfi Abdalwali & haya Mohammed Abu & Alrawad Mahmaod, 2023. "Assessing the Impact of Oil Price Volatility on Food Prices in Saudi Arabia: Insights From Nonlinear Autoregressive Distributed Lags (NARDL) Analysis," Economics, Sciendo, vol. 11(2), pages 5-23, December.
    • Handle: RePEc:vrs:econom:v:11:y:2023:i:2:p:5-23:n:4
    DOI: 10.2478/eoik-2023-0056
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    References listed on IDEAS

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    1. Cecchetti, Stephen G, 2000. "Making Monetary Policy: Objectives and Rules," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 16(4), pages 43-59, Winter.
    2. Nazlioglu, Saban & Soytas, Ugur, 2011. "World oil prices and agricultural commodity prices: Evidence from an emerging market," Energy Economics, Elsevier, vol. 33(3), pages 488-496, May.
    3. Esmaeili, Abdoulkarim & Shokoohi, Zainab, 2011. "Assessing the effect of oil price on world food prices: Application of principal component analysis," Energy Policy, Elsevier, vol. 39(2), pages 1022-1025, February.
    4. Robert J. Gordon, 1981. "Inflation, Flexible Exchange Rates, and the Natural Rate of Unemployment," NBER Working Papers 0708, National Bureau of Economic Research, Inc.
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    More about this item

    Keywords

    Food prices; Oil prices; Real GDP; Nonlinear ARDL; Saudi Arabia;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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