IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/57302.html
   My bibliography  Save this paper

Disentangling income and price effects in the demand for time online

Author

Listed:
  • González Chapela, Jorge

Abstract

The large negative impact of income on time spent online has been attributed to a negative own-price effect created by variation in the opportunity cost of time across internet users. Nonetheless, the coefficient on income could also be capturing a negative income effect: High-income users could reduce time spent online to consume, for example, leisure activities of higher quality. This paper estimates a demand function for time online using a time-use survey containing information on household income and individual labor earnings. In accordance with the negative income effect hypothesis, income still exerts a large negative impact after earnings are controlled for, whereas the response to earnings is negative only in certain ranges of the earnings distribution.

Suggested Citation

  • González Chapela, Jorge, 2014. "Disentangling income and price effects in the demand for time online," MPRA Paper 57302, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:57302
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/57302/1/MPRA_paper_57302.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    2. Smaranda Pantea, 2014. "Has the digital divide been reversed? – Evidence from five EU countries," electronic International Journal of Time Use Research, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)) and The International Association for Time Use Research (IATUR), vol. 11(1), pages 13-42, December.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    4. Stewart, Jay, 2013. "Tobit or not Tobit?," Journal of Economic and Social Measurement, IOS Press, issue 3, pages 263-290.
    5. Austan Goolsbee & Peter J. Klenow, 2006. "Valuing Consumer Products by the Time Spent Using Them: An Application to the Internet," American Economic Review, American Economic Association, vol. 96(2), pages 108-113, May.
    6. repec:ipt:wpaper:2013-06 is not listed on IDEAS
    7. Mark Aguiar & Erik Hurst, 2007. "Life-Cycle Prices and Production," American Economic Review, American Economic Association, vol. 97(5), pages 1533-1559, December.
    8. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
    9. Phaneuf, Daniel J., 2011. "Can consumption of convenience products reveal the opportunity cost of time?," Economics Letters, Elsevier, vol. 113(1), pages 92-95, October.
    10. Orviska, Marta & Hudson, John, 2009. "Dividing or uniting Europe? Internet usage in the EU," Information Economics and Policy, Elsevier, vol. 21(4), pages 279-290, November.
    11. Feather, Peter M & Shaw, W Douglas, 2000. "The Demand for Leisure Time in the Presence of Constrained Work Hours," Economic Inquiry, Western Economic Association International, vol. 38(4), pages 651-661, October.
    12. Walid Hadhri & Mohamed Ayadi & Adel Ben Youssef, 2012. "Difference between Adoption and Access Frequency to Internet and Consumer Surplus," Post-Print halshs-00937177, HAL.
    13. Goldfarb, Avi & Prince, Jeff, 2008. "Internet adoption and usage patterns are different: Implications for the digital divide," Information Economics and Policy, Elsevier, vol. 20(1), pages 2-15, March.
    14. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762.
    15. Biddle, Jeff E & Hamermesh, Daniel S, 1990. "Sleep and the Allocation of Time," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 922-943, October.
    16. Fernando Lera-Lopez & Margarita Billon & Maria Gil, 2011. "Determinants of Internet use in Spain," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 20(2), pages 127-152.
    17. Almudena Sevilla & Jose Gimenez-Nadal & Jonathan Gershuny, 2012. "Leisure Inequality in the United States: 1965–2003," Demography, Springer;Population Association of America (PAA), vol. 49(3), pages 939-964, August.
    18. Gerpott, Torsten J. & Thomas, Sandra, 2014. "Empirical research on mobile Internet usage: A meta-analysis of the literature," Telecommunications Policy, Elsevier, vol. 38(3), pages 291-310.
    19. Kenneth A. Small & Clifford Winston & Jia Yan, 2005. "Uncovering the Distribution of Motorists' Preferences for Travel Time and Reliability," Econometrica, Econometric Society, vol. 73(4), pages 1367-1382, July.
    20. Rajeev Goel & Edward Hsieh & Michael Nelson & Rati Ram, 2006. "Demand elasticities for Internet services," Applied Economics, Taylor & Francis Journals, vol. 38(9), pages 975-980.
    21. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-694, July.
    22. Kolko, Jed, 2010. "How broadband changes online and offline behaviors," Information Economics and Policy, Elsevier, vol. 22(2), pages 144-152, May.
    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. Maria Rosa Battaggion & Alessandro Vaglio, 2020. "TV watching in the new millennium: insights from Europe," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(4), pages 645-661, December.

    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. Jorge Gonz�lez, 2015. "Estimating income elasticities of leisure activities using cross-sectional categorized data," Documentos de Trabajo dt2015-02, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    2. Jorge González Chapela, 2015. "Split or straight? Evidence of the effects of work schedules on workers’ well-being, time use, and productivity," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 6(2), pages 153-177, June.
    3. Srinuan, Chalita & Bohlin, Erik, 2013. "Analysis of fixed broadband access and use in Thailand: Drivers and barriers," Telecommunications Policy, Elsevier, vol. 37(8), pages 615-625.
    4. Pretnar, Nick, 2022. "Measuring Inequality with Consumption Time," MPRA Paper 118168, University Library of Munich, Germany.
    5. González Chapela, Jorge, 2014. "Some estimates for income elasticities of leisure activities in the United States," MPRA Paper 57303, University Library of Munich, Germany.
    6. Mark C. Senia & Helen H. Jensen & Oleksandr Zhylyevskyy, 2017. "Time in eating and food preparation among single adults," Review of Economics of the Household, Springer, vol. 15(2), pages 399-432, June.
    7. González Chapela, Jorge, 2014. "Split or straight? Some evidence on the effect of the work shift on Spanish workers' well-being and time use," MPRA Paper 57301, University Library of Munich, Germany.
    8. Smaranda Pantea & Bertin Martens, 2014. "The Value of the Internet for Consumers," JRC Working Papers on Digital Economy 2014-08, Joint Research Centre.
    9. M. R. Vicente, 2015. "Determinants of C2C e-commerce: an empirical analysis of the use of online auction websites among Europeans," Applied Economics Letters, Taylor & Francis Journals, vol. 22(12), pages 978-981, August.
    10. Billari, Francesco C. & Giuntella, Osea & Stella, Luca, 2018. "Broadband internet, digital temptations, and sleep," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 58-76.
    11. Hirte, Georg & Tscharaktschiew, Stefan, 2018. "The impact of anti-congestion policies and the role of labor-supply margins," CEPIE Working Papers 04/18, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    12. Zhiguo Xiao & Jun Shao & Mari Palta, 2010. "GMM in linear regression for longitudinal data with multiple covariates measured with error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 791-805.
    13. Granguillhome Ochoa, Rogelio & Lach, Samantha & Masaki, Takaaki & Rodríguez-Castelán, Carlos, 2022. "Mobile internet adoption in West Africa," Technology in Society, Elsevier, vol. 68(C).
    14. Xiangdan Piao, 2021. "Marriage Stability and Private Versus Shared Expenditures Within Families: Evidence from Japanese Families," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 533-559, January.
    15. Erich Battistin & Raffaele Miniaci & Guglielmo Weber, 2003. "What Do We Learn from Recall Consumption Data?," Journal of Human Resources, University of Wisconsin Press, vol. 38(2).
    16. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2018. "Nonparametric estimation in case of endogenous selection," Journal of Econometrics, Elsevier, vol. 202(2), pages 268-285.
    17. Jara-Díaz, Sergio & Rosales-Salas, Jorge, 2017. "Beyond transport time: A review of time use modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 97(C), pages 209-230.
    18. Lachos, Victor H. & Prates, Marcos O. & Dey, Dipak K., 2021. "Heckman selection-t model: Parameter estimation via the EM-algorithm," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    19. Cockx, Bart & Ghirelli, Corinna, 2016. "Scars of recessions in a rigid labor market," Labour Economics, Elsevier, vol. 41(C), pages 162-176.
    20. Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," JRFM, MDPI, vol. 13(11), pages 1-24, November.

    More about this item

    Keywords

    Internet usage; Shadow value of time; Spanish Time Use Survey; Type II Tobit model.;
    All these keywords.

    JEL classification:

    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

    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:pra:mprapa:57302. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.