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Modeling the time-varying effects of oil rent on manufacturing: implications from structural changes using Markov-switching model

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Abstract

Previous “oil curse” studies primarily estimate a single, linear effect of oil rents on income using time-invariant parameters over entire sample periods. This means the true effects of oil dependence cannot be captured if structural changes are taking place, or effects are non-linear. We introduce a two regime Markov-switching model into the resource effects literature to assess the time-varying effects of oil rent dependence on the Malaysian manufacturing sector. We also allow for non-linear threshold effects. We find the impact of oil rents is regime-dependent. Under a rarer “first regime” structure, there is no significant effect. Under a predominant “second regime,” there is an inverted U-shaped effect, with oil rents’ share of GDP up to 8% positively associated with manufacturing, and negatively associated beyond this. We find connections between regime changes and the 1997 Asian financial crisis and 2008 global financial crisis. Implications for effective diversification policies are discussed.

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Notes

  1. It checks the validity of the existence of a second regime via the statistical significance of the estimated transition matrix parameters between the regimes.

  2. We do not reproduce the results of the unit root tests to conserve space; however, the results are available upon request.

  3. The global recession in 1986 led to weakened international demand for electronics and commodities, which severely affected related production in Malaysia, particularly the semiconductor industry, and thus Malaysia’s GDP growth (Okposin and Ming, 2000).

  4. The RCM for a Markov Switching model is given by \(RCM=400\times \frac{1}{T}\sum_{t=1}^T{P}_t\left(1-{P}_t\right)\)

  5. Under the Markov-switching model, the expected durations of regime 1 (D1) and regime 2 (D2) are calculated as D1=1/(1-p11) and D2= 1/(1-p22).

  6. This option in Eviews creates a polynomial by taking sets of three adjacent points from a variable’s annual series and fitting a quadratic such that the average or sum of quarterly points matches the annual points actually observed. It thus generates a smoothed or averaged interpolation of quarterly data.

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Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Funding

This research was supported by Ministry of Higher Education Malaysia (MOHE), Fundamental Research Grant Scheme, (FRGS/1/2019/SS08/CURTIN/02/1).

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Authors and Affiliations

  1. Faculty of Art and Social Sciences, Nottingham University Business School, The University of Nottingham, Malaysia, Jln Broga, 43500, Semenyih, Selangor, Malaysia

    Ramez Abubakr Badeeb

  2. Faculty of Business, Curtin University Malaysia, Miri, Sarawak, Malaysia

    Ramez Abubakr Badeeb & Abey P. Philip

  3. Department of Economics and Finance, University of Canterbury, Christchurch, New Zealand

    Jeremy Clark

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Ramez Abubakr Badeeb: conceptualization; methodology; formal analysis; investigation; writing—review and editing; funding acquisition. Jeremy Clark: writing—review, editing; and supervision. Abey Philip: writing—original draft preparation

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Appendix

Appendix

See Tables 6 and 7

Table 6 Estimation of the long-term relationship with Markov shifts (quarterly data)
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Table 7 Markov transition probabilities and expected duration (quarterly data)
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Badeeb, R.A., Clark, J. & Philip, A.P. Modeling the time-varying effects of oil rent on manufacturing: implications from structural changes using Markov-switching model. Environ Sci Pollut Res 30, 39012–39028 (2023). https://doi.org/10.1007/s11356-022-25045-7

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