IDEAS home Printed from https://ideas.repec.org/p/ucr/wpaper/202003.html
   My bibliography  Save this paper

Per-Cluster Instrumental Variables Estimation: Uncovering the Price Elasticity of the Demand for Gasoline

Author

Listed:
  • Michael Bates

    (Department of Economics, University of California Riverside)

  • Seolah Kim

    (UCR)

Abstract

We propose a per-cluster instrumental variables estimator (PCIV) for estimating population average effects under correlated random coefficient models in the presence of endogeneity. We demonstrate consistency, showing robustness over standard estimators, and provide analytic standard errors for robust inference. We compare PCIV, fixed-effects instrumental variables, and pooled 2-stage least squares estimators using Monte Carlo simulation verifying that PCIV performs relatively well. We also apply the approaches, examining the monthly responsiveness of gasoline consumption to prices as instrumented by state fuel taxes. We find that US consumers are on average more elastic in their demand for gasoline than previous estimates imply.

Suggested Citation

  • Michael Bates & Seolah Kim, 2019. "Per-Cluster Instrumental Variables Estimation: Uncovering the Price Elasticity of the Demand for Gasoline," Working Papers 202003, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:202003
    as

    Download full text from publisher

    File URL: https://economics.ucr.edu/repec/ucr/wpaper/202003.pdf
    File Function: First version, 2019
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Espey, Molly, 1998. "Gasoline demand revisited: an international meta-analysis of elasticities," Energy Economics, Elsevier, vol. 20(3), pages 273-295, June.
    2. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    3. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    4. James Heckman & Edward Vytlacil, 1998. "Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return is Correlated with Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 974-987.
    5. John Coglianese & Lucas W. Davis & Lutz Kilian & James H. Stock, 2017. "Anticipation, Tax Avoidance, and the Price Elasticity of Gasoline Demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 1-15, January.
    6. Gary Solon & Steven J. Haider & Jeffrey M. Wooldridge, 2015. "What Are We Weighting For?," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 301-316.
    7. Laurence Levin & Matthew S. Lewis & Frank A. Wolak, 2017. "High Frequency Evidence on the Demand for Gasoline," American Economic Journal: Economic Policy, American Economic Association, vol. 9(3), pages 314-347, August.
    8. Jonathan E. Hughes & Christopher R. Knittel & Daniel Sperling, 2008. "Evidence of a Shift in the Short-Run Price Elasticity of Gasoline Demand," The Energy Journal, International Association for Energy Economics, vol. 29(1), pages 113-134.
    9. Marshall Burke & Solomon M. Hsiang & Edward Miguel, 2015. "Global non-linear effect of temperature on economic production," Nature, Nature, vol. 527(7577), pages 235-239, November.
    10. Lucas W. Davis & Lutz Kilian, 2011. "Estimating the effect of a gasoline tax on carbon emissions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(7), pages 1187-1214, November.
    11. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    12. Anthony C. Fisher & W. Michael Hanemann & Michael J. Roberts & Wolfram Schlenker, 2012. "The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Comment," American Economic Review, American Economic Association, vol. 102(7), pages 3749-3760, December.
    13. Shanjun Li & Joshua Linn & Erich Muehlegger, 2014. "Gasoline Taxes and Consumer Behavior," American Economic Journal: Economic Policy, American Economic Association, vol. 6(4), pages 302-342, November.
    14. Baranzini, Andrea & Weber, Sylvain, 2013. "Elasticities of gasoline demand in Switzerland," Energy Policy, Elsevier, vol. 63(C), pages 674-680.
    15. Richard Tol, 2002. "Estimates of the Damage Costs of Climate Change. Part 1: Benchmark Estimates," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 21(1), pages 47-73, January.
    16. Roth, Alvin E., 1986. "Laboratory Experimentation in Economics," Economics and Philosophy, Cambridge University Press, vol. 2(2), pages 245-273, October.
    17. Cragg, John G. & Donald, Stephen G., 1993. "Testing Identifiability and Specification in Instrumental Variable Models," Econometric Theory, Cambridge University Press, vol. 9(2), pages 222-240, April.
    18. Murtazashvili, Irina & Wooldridge, Jeffrey M., 2016. "A control function approach to estimating switching regression models with endogenous explanatory variables and endogenous switching," Journal of Econometrics, Elsevier, vol. 190(2), pages 252-266.
    19. Scott, K. Rebecca, 2012. "Rational habits in gasoline demand," Energy Economics, Elsevier, vol. 34(5), pages 1713-1723.
    20. Dahl, Carol & Sterner, Thomas, 1991. "Analysing gasoline demand elasticities: a survey," Energy Economics, Elsevier, vol. 13(3), pages 203-210, July.
    21. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    22. Raj, Baldev & Srivastava, V K & Ullah, Aman, 1980. "Generalized Two Stage Least Squares Estimators for a Structural Equation with Both Fixed and Random Coefficients," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(1), pages 171-183, February.
    23. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    24. Jeffrey M. Wooldridge, 2005. "Fixed-Effects and Related Estimators for Correlated Random-Coefficient and Treatment-Effect Panel Data Models," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 385-390, May.
    25. Murtazashvili, Irina & Wooldridge, Jeffrey M., 2008. "Fixed effects instrumental variables estimation in correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 142(1), pages 539-552, January.
    26. Olivier Deschenes & Michael Greenstone & Jonathan Guryan, 2009. "Climate Change and Birth Weight," American Economic Review, American Economic Association, vol. 99(2), pages 211-217, May.
    27. Bryan S. Graham & James L. Powell, 2012. "Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models," Econometrica, Econometric Society, vol. 80(5), pages 2105-2152, September.
    28. Michael David Bates & Katherine E. Castellano & Sophia Rabe-Hesketh & Anders Skrondal, 2014. "Handling Correlations Between Covariates and Random Slopes in Multilevel Models," Journal of Educational and Behavioral Statistics, , vol. 39(6), pages 524-549, December.
    29. Chamberlain, Gary, 1992. "Efficiency Bounds for Semiparametric Regression," Econometrica, Econometric Society, vol. 60(3), pages 567-596, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Bates & Seolah Kim, 2019. "Estimating the Price Elasticity of Gasoline Demand in Correlated Random Coefficient Models with Endogeneity," Working Papers 202304, University of California at Riverside, Department of Economics, revised Aug 2023.
    2. Tilov, Ivan & Weber, Sylvain, 2023. "Heterogeneity in price elasticity of vehicle kilometers traveled: Evidence from micro-level panel data," Energy Economics, Elsevier, vol. 127(PA).
    3. Gillingham, Kenneth & Munk-Nielsen, Anders, 2019. "A tale of two tails: Commuting and the fuel price response in driving," Journal of Urban Economics, Elsevier, vol. 109(C), pages 27-40.
    4. Shanjun Li & Joshua Linn & Erich Muehlegger, 2014. "Gasoline Taxes and Consumer Behavior," American Economic Journal: Economic Policy, American Economic Association, vol. 6(4), pages 302-342, November.
    5. Mohammad Vesal & Amir Hossein Tavakoli & Mohammad H. Rahmati, 2022. "What do one hundred million transactions tell us about demand elasticity of gasoline?," Empirical Economics, Springer, vol. 62(6), pages 2693-2711, June.
    6. Pier Basaglia & Sophie Behr & Moritz A. Drupp, 2023. "De-Fueling Externalities: How Tax Salience and Fuel Substitution Mediate Climate and Health Benefits," Discussion Papers of DIW Berlin 2041, DIW Berlin, German Institute for Economic Research.
    7. Ghoddusi, Hamed & Morovati, Mohammad & Rafizadeh, Nima, 2019. "Foreign Exchange Shocks and Gasoline Consumption," Energy Economics, Elsevier, vol. 84(C).
    8. Levin, Laurence & Lewis, Matthew S. & Wolak, Frank A., 2022. "Reference dependence in the demand for gasoline," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 561-578.
    9. Pier Basaglia & Sophie M. Behr & Moritz A. Drupp, 2023. "De-Fueling Externalities: Causal Effects of Fuel Taxation and Mediating Mechanisms for Reducing Climate and Pollution Costs," CESifo Working Paper Series 10508, CESifo.
    10. Julian Dieler, 2016. "Effectiveness of Climate Policies: Empirical Methods and Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 68.
    11. Słoczyński, Tymon, 2012. "New Evidence on Linear Regression and Treatment Effect Heterogeneity," MPRA Paper 39524, University Library of Munich, Germany.
    12. M. Adam & O. Bonnet & E. Fize & T. Loisel & M. Rault & L. Wilner, 2023. "How does fuel demand respond to price changes? Quasi-experimental evidence based on high-frequency data," Documents de Travail de l'Insee - INSEE Working Papers 2023-17, Institut National de la Statistique et des Etudes Economiques.
    13. Scott, K. Rebecca, 2015. "Demand and price uncertainty: Rational habits in international gasoline demand," Energy, Elsevier, vol. 79(C), pages 40-49.
    14. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, Institute of Labor Economics (IZA).
    15. Juan Carlos Escanciano, 2020. "Irregular Identification of Structural Models with Nonparametric Unobserved Heterogeneity," Papers 2005.08611, arXiv.org.
    16. Sen, Suphi & Vollebergh, Herman, 2018. "The effectiveness of taxing the carbon content of energy consumption," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 74-99.
    17. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90059, University Library of Munich, Germany.
    18. Tobias Klein, 2013. "College education and wages in the U.K.: estimating conditional average structural functions in nonadditive models with binary endogenous variables," Empirical Economics, Springer, vol. 44(1), pages 135-161, February.
    19. Silvia Tiezzi & Stefano F. Verde, 2017. "The signaling effect of gasoline taxes and its distributional implications," RSCAS Working Papers 2017/06, European University Institute.
    20. Laurence Levin & Matthew S. Lewis & Frank A. Wolak, 2017. "High Frequency Evidence on the Demand for Gasoline," American Economic Journal: Economic Policy, American Economic Association, vol. 9(3), pages 314-347, August.

    More about this item

    Keywords

    population average effects; climate policy; gasoline taxation;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ucr:wpaper:202003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kelvin Mac (email available below). General contact details of provider: https://edirc.repec.org/data/deucrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.