Estimation of the price elasticity of petroleum products' consumption in Ukraine

• Authors: Leonid Galchynskyi

Abstracts

EN

Research background: The analysts of the petroleum product markets of industrial countries believe that the elasticity of demand varies at different periods, which gave rise to the hypothesis that behavioral and structural factors have changed the consumers? reaction during the last few decades, with a change in prices of petroleum products. Purpose of the article: The purpose of this article is to study the elasticity of demand and prices in order to identify changes in consumer behavior in the oil market after significant socio-economic shocks and to establish a correlation between changes in elasticity and price volatility, with the Ukrainian petroleum products market as an illustrative example. Methods: Based on the time series of the petroleum product market of Ukraine, static and dynamic models for assessing the demand elasticity were constructed. It was found that the time series of demand for petroleum products is non-stationary but then the time series of the first differences is stationary according to the extended Dickey-Fuller test; further, the fact of co-integration between time series of consumption, income, and prices was established by the Johansson test. This made it possible to construct co-integration dependence, allowing, in turn, the development of models for assessing the elasticity of demand for petroleum products, on the basis of which objective assessments of changes in consumer behavior were established. Analysis of the monthly calculation of petroleum products? price volatility during the period 2008 to 2018 has showed that the values of volatility increased abnormally in the period between the beginning of 2014 and the middle of 2015. The estimates of price and demand elasticities obtained for the two periods up to the beginning of 2014 and the second half of 2015 differ significantly from the values of the corresponding elasticities between the beginning of 2014 and the middle of 2015. Findings & Value added: Assessments of income elasticities and price elasticities for petroleum products in the Ukrainian market were obtained by three co-integration models, both short and long term, for each of the three previously defined time intervals. In one of them, characterized by a high level of price volatility conditionally referred to as a crisis, the value of elasticities differed markedly from the corresponding values in the other two periods, in particular, -0.383 for price elasticity and 1.068 for a long-term bond. In the other two periods, these were, respectively, 0.543 for price elasticity and 0.274 for long-term pre-crisis elasticity, and -0.470 for price elasticity and 0.235 for long-term post-crisis elasticity. Appropriate elasticity estimates were obtained for both the short-run and the dynamic model, for the same defined intervals. A comparison of these estimates showed the closeness of the values of elasticities for the pre-crisis and post-crisis intervals and a marked difference from the estimates of the elasticities in the crisis interval. Thus, it was found that a significant change in elasticities is accompanied by an increase in price volatility.

Keywords

EN

petroleum product market; elasticity; volatility; model; co-integration

• Publisher: Instytut Badań Gospodarczych
• Journal: Equilibrium. Quarterly Journal of Economics and Economic Policy
• Year: 2020
• Volume: 15
• Issue: 2
• Pages: 315-339

Dates

published

2020

Contributors

author

Leonid Galchynskyi

• National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

• https://orcid.org/0000000238051474

References

• empirical analysis using co-integration techniques. Energy Economics, 30(6). doi: 10.1016/j.eneco.2008.05.002.
• Alves, D. C. O., & Bueno R. D. (2003). Short-run, long-run and cross elasticities of gasoline demand in Brazil. Energy Economics, 25(2). doi: 10.1016/S0140-9883(02)00108-1.
• Archibald, R., & Gillingham, R. (1980). An analysis of the short-run consumer demand for gasoline using household survey data. Review of Economics and Statistics, 62.
• Baranzini, A., & Weber, S. (2013). Elasticities of gasoline demand in Switzerland. Energy Policy, 63. doi: 10.1016/j.enpol.2013.08.084.
• Brons, M., Nijkamp, P., Pels, E., & Rietveld, P. (2008). A meta-analysis of the price elasticity of gasoline demand. A SUR approach. Energy Economics, 30(5). doi: 10.1016/j.eneco.2007.08.004.
• Dahl, C. A. (2012). Measuring global gasoline and diesel price and income elasticities. Energy Policy, 41(0). doi: 10.1016/j.enpol.2010.11.055.
• Dahl, C., & Sterner, T. (1991). Analysing gasoline demand elasticities: a survey. Energy Economics, 13(3). doi: 10.1016/0140-9883(91)90021-Q.
• Dickey, D. A., & Fuller W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366). doi: 10.2307/2286348.
• Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica, 55(2). doi:10.2307/1913236.
• Erdogdu, E. (2014). Motor fuel prices in Turkey. Energy Policy, 69. doi: 10.1016/ j.enpol.2013.10.075.
• Espey, M. (1998). Gasoline demand revisited: an international meta-analysis of elasticities. Energy Economics, 20(3). doi: 10.1016/S0140-9883(97)00013-3.
• Fuel consumption (2014). State Statistics Service of Ukraine. Retrieved from http://www.ukrstat.gov.ua/operativ/menu/menu_u/spr.htm (11.12.2018).
• Galczynski, L. (2013). The short-time forecasts of gasoline proces in Ukraine. Zeszyty naukowe. Uniwersytet Ekonomiczny w Poznaniu, 242.
• Ghoddusi, H., Rafizadeh, N., & Rahmati, M. H. (2018). Price elasticity of gasoline smuggling: a semi-structural estimation approach. Energy Economics, 71. doi: 10.1016/j.eneco.2018.02.008.
• Goodwin, P., Dargay, J., & Hanley, M. (2004). Elasticities of road traffic and fuel consumption with respect to price and income: a review. Transport Review, 24(3). doi: 10.1080/0144164042000181725.
• Hausman, J. A., & Newey, W. K. (1995). Nonparametric estimation of exact consumers surplus and deadweight loss. Econometrica, 63(6). doi: 10.2307/217 1777.
• Havranek, T., Irsova, Z., & Janda, K. (2012). Demand for gasoline is more price-inelastic than commonly thought. Energy Economics, 34(1). doi:10.1016/j.ene co.2011.09.003.
• Johansen, J. (1991). Estimation and hypothesis testing of cointegrating vectors in Gaussian vector autoregressive models. Econometrica, 59. doi: 10.2307/2938 278.
• Johansen, J. (1995). Likelihood-based inference in cointegrating vector autoregressive models. Oxford University Press.
• Kayser, H. (2000). Gasoline demand and car choice: estimating gasoline demand using household information. Energy Economics, 22. doi: 10.1016/S0140-9883(99)00043-2.
• Labandeira, X., Labeaga, J. M., & López-Otero, X. (2017). A meta-analysis on the price elasticity of energy demand. Energy Policy, 102. doi: 10.1016/j.enpol. 2017.01.002.
• Leszkiewicz-Kędzior, K. (2011). Modelling fuel prices. An I (1) analysis. Central European Journal of Economic Modelling and Econometrics, 3(2).
• Lin, C. Y. C., & Prince, L. (2013). Gasoline price volatility and the elasticity of demand for gasoline. Energy Economics, 38. doi: 10.1016/j.eneco.2013.03.001.
• Lin, C. Y. C., & Zeng, J. J. (2013). The elasticity of demand for gasoline in China. Energy Policy, 59. doi: 10.1016/j.enpol.2013.03.020.
• Liu, W. (2014). Modeling gasoline demand in the United States: a flexible semiparametric approach. Energy Economics, 45. doi: 10.1016/j.eneco.2014.07.004.
• Nelson, J. P., & Kennedy, P. E. (2009). The use (and abuse) of meta-analysis in environmental and natural resource economics: an assessment. Environmental and Resource Economics, 42(3). doi: 10.1007/s10640-008-9253-5.
• Nicol, C. J. (2003). Elasticities of demand for gasoline in Canada and the United States. Energy Economics, 25(2). doi: 10.1016/S0140-9883(03)00002-1.
• Prices of petroleum products in Ukraine (2014). Service PsycheaFuel. Retrieved, from https://psychea.com.ua/psyfuel (10.01.2018).
• Santos, G. F. (2013). Fuel demand in Brazil in a dynamic panel data approach. Energy Economics, 36. doi: 10.1016/j.eneco.2012.08.012.
• Sita, B. B., Marrouch, W., & Abosedra, S. (2012). Short-run price and income elasticity of gasoline demand: evidence from Lebanon. Energy Policy, 46. doi: 10.1016/j.enpol.2012.03.041.
• Sterner, T. (2006). Survey of transport fuel demand elasticities. Naturvårdsverket.
• Svy`denko, A. V. (2015). Ocinka elasty`chnostej popy`tu v rozdribnomu segmenti ry`nku avtomobil`nogo paly`va Ukrayiny`. Global`ni ta nacional`ni problemy` ekonomiky, 76.
• Valadkhani, A. (2010). Modelling the price of unleaded petrol in Australia’s capital cities. Australasian Accounting, Business and Finance Journal, 4(2).
• Wadud, Z., Noland, R. B., & Graham, D. J. (2010). A semiparametric model of household gasoline demand. Energy Economics, 32(1). doi: 10.1016/j.eneco. 2009.06.009.
• Wages and salaries, social and labour relationship (2013). State Statistics Service of Ukraine. Retrieved from http://www.ukrstat.gov.ua/operativ/menu/ menu_u/zp.htm (10.10.2018).

Identifiers

DOI

10.24136/eq.2020.015

Biblioteka Nauki

22444419