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Title: Using Meta-Analysis to Estimate World Oil Demand Elasticity

Abstract

Oil demand elasticities are important behavioral parameters for assessing the effectiveness of many energy policy proposals and the economic impact of oil market shocks. Most of the literature on petroleum demand computes elasticities for one particular petroleum product in a country or group of countries. There are fewer published values for world oil demand elasticity because the reduced-form single equation approach used in much of the petroleum product demand literature leads to endogeneity bias when applied to global demand. This study uses meta-analysis techniques to compute world oil demand elasticity. Metaregressions are first conducted to identify sources of variation in elasticity estimates for transportation and non-transportation sectors collected from 75 studies published from 2000 to 2015. The estimated coefficients from the metaregressions are then summarized into elasticity baseline values for several combinations of moderator variable values. Finally, a global oil demand elasticity is computed as the consumption-weighted average of the two sectoral elasticities. Resulting mean valuesfor the short-run oil demand elasticities with respect to price range from -0.7 to -0.14 depending on the attributes selected for the price variable. The long-run range of mean values is much wider (-0.26, -0.83).

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2];  [1]
  1. ORNL
  2. Econotech
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1491306
Report Number(s):
ORNL/TM-2018/1070
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Uria Martinez, Rocio, Leiby, Paul Newsome, Oladosu, Gbadebo A., Bowman, David, and Johnson, Megan. Using Meta-Analysis to Estimate World Oil Demand Elasticity. United States: N. p., 2018. Web. doi:10.2172/1491306.
Uria Martinez, Rocio, Leiby, Paul Newsome, Oladosu, Gbadebo A., Bowman, David, & Johnson, Megan. Using Meta-Analysis to Estimate World Oil Demand Elasticity. United States. doi:10.2172/1491306.
Uria Martinez, Rocio, Leiby, Paul Newsome, Oladosu, Gbadebo A., Bowman, David, and Johnson, Megan. Sat . "Using Meta-Analysis to Estimate World Oil Demand Elasticity". United States. doi:10.2172/1491306. https://www.osti.gov/servlets/purl/1491306.
@article{osti_1491306,
title = {Using Meta-Analysis to Estimate World Oil Demand Elasticity},
author = {Uria Martinez, Rocio and Leiby, Paul Newsome and Oladosu, Gbadebo A. and Bowman, David and Johnson, Megan},
abstractNote = {Oil demand elasticities are important behavioral parameters for assessing the effectiveness of many energy policy proposals and the economic impact of oil market shocks. Most of the literature on petroleum demand computes elasticities for one particular petroleum product in a country or group of countries. There are fewer published values for world oil demand elasticity because the reduced-form single equation approach used in much of the petroleum product demand literature leads to endogeneity bias when applied to global demand. This study uses meta-analysis techniques to compute world oil demand elasticity. Metaregressions are first conducted to identify sources of variation in elasticity estimates for transportation and non-transportation sectors collected from 75 studies published from 2000 to 2015. The estimated coefficients from the metaregressions are then summarized into elasticity baseline values for several combinations of moderator variable values. Finally, a global oil demand elasticity is computed as the consumption-weighted average of the two sectoral elasticities. Resulting mean valuesfor the short-run oil demand elasticities with respect to price range from -0.7 to -0.14 depending on the attributes selected for the price variable. The long-run range of mean values is much wider (-0.26, -0.83).},
doi = {10.2172/1491306},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {12}
}

Technical Report:

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