IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-16-00355.html
   My bibliography  Save this article

Correlated Random Effects Quantile Estimation of the Tax-Price Elasticity of Charitable Donations

Author

Listed:
  • Nicky Lee Grant

    (University of Manchester)

Abstract

This paper provides quantile estimates of the tax-price elasticity of charitable donations controlling for unobserved heterogeneity. Utilising the Correlated Random Effects Quantile estimator of Bache, Dahl & Kristensen (2013) it is found that the size of the price elasticity is decreasing in the size of donation with very large donors being largely unresponsive to tax incentives for giving. We provide evidence that cross sectional quantiles estimates of the price elasticity not accounting for unobserved heterogeneity suffer a substantial downward bias for those with small to mid-level donations.

Suggested Citation

  • Nicky Lee Grant, 2016. "Correlated Random Effects Quantile Estimation of the Tax-Price Elasticity of Charitable Donations," Economics Bulletin, AccessEcon, vol. 36(3), pages 1729-1736.
  • Handle: RePEc:ebl:ecbull:eb-16-00355
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2016/Volume36/EB-16-V36-I3-P169.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabrielle Fack & Camille Landais, 2010. "Are Tax Incentives for Charitable Giving Efficient? Evidence from France," American Economic Journal: Economic Policy, American Economic Association, vol. 2(2), pages 117-141, May.
    2. Hsin-Yi Lin & Kuang-Ta Lo, 2012. "Tax Incentives and Charitable Contributions: the Evidence from Censored Quantile Regression," Pacific Economic Review, Wiley Blackwell, vol. 17(4), pages 535-558, October.
    3. Stefan Bache & Christian Dahl & Johannes Kristensen, 2013. "Headlights on tobacco road to low birthweight outcomes," Empirical Economics, Springer, vol. 44(3), pages 1593-1633, June.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Peter Backus & Nicky Grant, 2016. "Consistent Estimation of the Tax-Price Elasticity of Charitable Giving with Survey Data," Economics Discussion Paper Series 1606, Economics, The University of Manchester.
    6. Daniel Feenberg & Elisabeth Coutts, 1993. "An introduction to the TAXSIM model," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 12(1), pages 189-194.
    7. Abrevaya, Jason & Dahl, Christian M, 2008. "The Effects of Birth Inputs on Birthweight," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 379-397.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Samiul Haque, 2022. "US federal farm payments and farm size: Quantile estimation on panel data," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 139-154, February.

    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. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    2. Xavier Vollenweider, 2014. "A simple framework for the estimation of climate exposure," GRI Working Papers 158, Grantham Research Institute on Climate Change and the Environment.
    3. Geraci, Marco, 2019. "Modelling and estimation of nonlinear quantile regression with clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 30-46.
    4. Sinem Koçak & Özge Barış-Tüzemen, 2022. "Impact of the COVID-19 on foreign direct investment inflows in emerging economies: evidence from panel quantile regression," Future Business Journal, Springer, vol. 8(1), pages 1-12, December.
    5. Tilov, Ivan & Farsi, Mehdi & Volland, Benjamin, 2020. "From frugal Jane to wasteful John: A quantile regression analysis of Swiss households’ electricity demand," Energy Policy, Elsevier, vol. 138(C).
    6. Pourya Valizadeh & Shu Wen Ng, 2021. "Would A National Sugar‐Sweetened Beverage Tax in the United States Be Well Targeted?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 961-986, May.
    7. Mateut, Simona & Chevapatrakul, Thanaset, 2018. "Customer financing, bargaining power and trade credit uptake," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 147-162.
    8. Raffaele Miniaci & Paolo Panteghini, 2021. "On the Capital Structure of Foreign Subsidiaries: Evidence from a Panel Data Quantile Regression Model," CESifo Working Paper Series 9085, CESifo.
    9. Peter G. Backus & Nicky L. Grant, 2019. "How sensitive is the average taxpayer to changes in the tax-price of giving?," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(2), pages 317-356, April.
    10. Dariusz Wójcik & Eric Knight & Vladimír Pažitka, 2018. "What turns cities into international financial centres? Analysis of cross-border investment banking 2000–2014," Journal of Economic Geography, Oxford University Press, vol. 18(1), pages 1-33.
    11. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    12. Hope Corman & Dhaval Dave & Nancy E. Reichman, 2018. "Evolution of the Infant Health Production Function," Southern Economic Journal, John Wiley & Sons, vol. 85(1), pages 6-47, July.
    13. Nadine Levratto & Aziza Garsaa & Luc Tessier, 2013. "To what extent do exemptions from social security contributions affect firm growth? New evidence using quantile estimations on panel data," Working Papers hal-00833049, HAL.
    14. Graham, Bryan S. & Hahn, Jinyong & Poirier, Alexandre & Powell, James L., 2018. "A quantile correlated random coefficients panel data model," Journal of Econometrics, Elsevier, vol. 206(2), pages 305-335.
    15. Julia Cagé & Malka Guillot, 2021. "Is Charitable Giving Political? Evidence from Wealth and Income Tax Returns," Working Papers hal-03877993, HAL.
    16. Giovanni Dosi & Maria Enrica Virgillito & Xiaodan Yu, 2023. "Gains from trade or from catching-up? Value creation and distribution in the era of China’s WTO accession," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 119-166, March.
    17. Jonathan Meer & Benjamin A. Priday, 2020. "Tax Prices and Charitable Giving: Projected Changes in Donations under the 2017 Tax Cuts and Jobs Act," Tax Policy and the Economy, University of Chicago Press, vol. 34(1), pages 113-138.
    18. Lijuan Huo & Tae-Hwan Kim & Yunmi Kim, 2013. "Testing for Autocorrelation in Quantile Regression Models," Working papers 2013rwp-54, Yonsei University, Yonsei Economics Research Institute.
    19. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    20. Zheng Fang & Yoko Niimi, 2015. "Do Losses Bite More than Gains? Evidence from a Panel Quantile Regression Analysis of Subjective Well-being in Japan," Economic Growth Centre Working Paper Series 1507, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.

    More about this item

    Keywords

    Price Elasticity; Charitable Giving; Quantile Regression; Correlated Random Effects.;
    All these keywords.

    JEL classification:

    • H2 - Public Economics - - Taxation, Subsidies, and Revenue
    • H0 - Public Economics - - General

    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:ebl:ecbull:eb-16-00355. 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: John P. Conley (email available below). General contact details of provider: .

    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.