IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v64y2021ics0160791x20312859.html
   My bibliography  Save this article

Geographical variation in mobile phone ownership and SMS literacy among women (age 15–49) in India: A cross-sectional analysis based on National Family Health Survey-4

Author

Listed:
  • Venkataramanan, R.
  • Kumar, Abhishek
  • Mantena, Sreekar
  • Subramanian, S.V.

Abstract

In India, studies on mobile phone ownership and digital literacy have focused on the gender dimension, perspectives of women and men towards mobile phone use, and socio-economic determinants of ownership. There are no studies with a large sample size that present the variation in mobile phone ownership and ability to read short message services (SMS) message among women across different geographic levels. We use multilevel models to estimate the contribution of geographic levels in explaining the variation in mobile phone ownership and SMS literacy. The data from the fourth round of National Family Health Survey (NFHS) which collected information on health and nutrition indicators was used for our analysis. 122,351 women were interviewed about mobile phone ownership and SMS literacy. Information on mobile phone ownership and ability to read text messages were used to create the dependent variables. The independent variables included women's education, age, religion, social class, wealth quintile and place of residence. Two and four level variants of a multilevel model, with individuals (level-1) nested within primary sampling unit (level 2), districts (level 3) and States (level 4), were used to estimate the probability of mobile phone ownership and ability to read text message. The results from multilevel model and variance partition analysis indicate that contribution of State and primary sampling unit towards explaining variation in mobile phone ownership and ability to read text message is greater than the districts. The variance estimates are sensitive to the number of levels included in the multilevel model, so relying on estimates obtained for a particular level could lead to a bias. Our findings suggest that understanding the magnitude of inequalities at different geographic levels should be warranted more attention before tackling the socio-economic factors. State estimates should be supplemented with information available for lower geographic levels. It is important to identify the geographical clusters with high and low coverage of mobile ownership and ability to read text messages before implementing mhealth interventions.

Suggested Citation

  • Venkataramanan, R. & Kumar, Abhishek & Mantena, Sreekar & Subramanian, S.V., 2021. "Geographical variation in mobile phone ownership and SMS literacy among women (age 15–49) in India: A cross-sectional analysis based on National Family Health Survey-4," Technology in Society, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:teinso:v:64:y:2021:i:c:s0160791x20312859
    DOI: 10.1016/j.techsoc.2020.101482
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X20312859
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2020.101482?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ministry of Finance, Government of India,, 2016. "Economic Survey 2015-16," OUP Catalogue, Oxford University Press, number 9780199469284.
    2. Tewathia, Nidhi & Kamath, Anant & Ilavarasan, P. Vigneswara, 2020. "Social inequalities, fundamental inequities, and recurring of the digital divide: Insights from India," Technology in Society, Elsevier, vol. 61(C).
    3. Kim, Rockli & Mohanty, Sanjay K. & Subramanian, S.V., 2016. "Multilevel Geographies of Poverty in India," World Development, Elsevier, vol. 87(C), pages 349-359.
    4. Zilian, Stella Sophie & Zilian, Laura Samantha, 2020. "Digital inequality in Austria: Empirical evidence from the survey of the OECD “Programme for the International Assessment of Adult Competencies”," Technology in Society, Elsevier, vol. 63(C).
    5. W. J. Browne & S. V. Subramanian & K. Jones & H. Goldstein, 2005. "Variance partitioning in multilevel logistic models that exhibit overdispersion," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 599-613, July.
    6. Lopez-Sintas, Jordi & Lamberti, Giuseppe & Sukphan, Jakkapong, 2020. "The social structuring of the digital gap in a developing country. The impact of computer and internet access opportunities on internet use in Thailand," Technology in Society, Elsevier, vol. 63(C).
    7. Michelle Baddeley & Kirsty McNay & Robert Cassen, 2006. "Divergence in India: Income differentials at the state level, 1970-97," Journal of Development Studies, Taylor & Francis Journals, vol. 42(6), pages 1000-1022.
    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. Anoop Jain & Lia C.H. Fernald & Kirk R. Smith & S.V. Subramanian, 2019. "Sanitation in Rural India: Exploring the Associations between Dwelling Space and Household Latrine Ownership," IJERPH, MDPI, vol. 16(5), pages 1-14, February.
    2. Wang, Shangrui & Cao, Anran & Wang, Guohua & Xiao, Yiming, 2022. "The Impact of energy poverty on the digital divide: The mediating effect of depression and Internet perception," Technology in Society, Elsevier, vol. 68(C).
    3. Sanjay K. Mohanty & Guru Vasishtha, 2021. "Contextualizing multidimensional poverty in urban India," Poverty & Public Policy, John Wiley & Sons, vol. 13(3), pages 234-253, September.
    4. Kim, Rockli & Mohanty, Sanjay K. & Subramanian, S.V., 2016. "Multilevel Geographies of Poverty in India," World Development, Elsevier, vol. 87(C), pages 349-359.
    5. Anh Thu Quang Pham & Pundarik Mukhopadhaya & Ha Vu, 2020. "Targeting Administrative Regions for Multidimensional Poverty Alleviation: A Study on Vietnam," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 150(1), pages 143-189, July.
    6. Tarasova, Ekaterina & Rohracher, Harald, 2023. "Marginalising household users in smart grids," Technology in Society, Elsevier, vol. 72(C).
    7. Cuesta, Lizeth & Ruiz, Yomara, 2021. "Efecto de la globalización sobre la desigualdad. Un estudio global para 104 países usando regresiones cuantílicas [Effect of globalization on inequality. A global study for 104 countries using quan," MPRA Paper 111022, University Library of Munich, Germany.
    8. Gabriele B. Durrant & Sylke V. Schnepf, 2018. "Which schools and pupils respond to educational achievement surveys?: a focus on the English Programme for International Student Assessment sample," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1057-1075, October.
    9. Elias Giannakis & Sophia Efstratoglou & Artemis Antoniades, 2018. "Off-Farm Employment and Economic Crisis: Evidence from Cyprus," Agriculture, MDPI, vol. 8(3), pages 1-11, March.
    10. Ana Maria Santacreu & Michael Sposi & Jing Zhang, 2021. "What Determines State Heterogeneity in Response to US Tariff Changes?," Working Papers 2021-007, Federal Reserve Bank of St. Louis, revised 08 Mar 2023.
    11. Amjad, Rashid, 2017. "Remittances and Poverty: A Comparison of Bangladesh and Pakistan, 2000–2016," Bangladesh Development Studies, Bangladesh Institute of Development Studies (BIDS), vol. 40(3-4), pages 75-104, Sep-Dec.
    12. Xin Luo & Shubin Zhu & Zhenjiang Song, 2023. "Quantifying the Income-Increasing Effect of Digital Agriculture: Take the New Agricultural Tools of Smartphone as an Example," IJERPH, MDPI, vol. 20(4), pages 1-16, February.
    13. Kelvyn Jones & David Manley & Ron Johnston & Dewi Owen, 2018. "Modelling residential segregation as unevenness and clustering: A multilevel modelling approach incorporating spatial dependence and tackling the MAUP," Environment and Planning B, , vol. 45(6), pages 1122-1141, November.
    14. da Silva Neto, Victo José & Chiarini, Tulio, 2021. "Technological progress and political systems: Non-institutional digital platforms and political transformation," Technology in Society, Elsevier, vol. 64(C).
    15. Hermann Ndoya & Simplice A. Asongu, 2022. "Digital divide, globalization and income inequality in sub-Saharan African countries: analysing cross-country heterogeneity," Social Responsibility Journal, Emerald Group Publishing Limited, vol. 20(1), pages 1-19, October.
    16. Muhammad Ahsan Rana & Muhammad Nadeem Malik, 2021. "Friendly Fire: Wheat Subsidy in Punjab, Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 60(2), pages 153-174.
    17. Gaurav Nayyar, 2017. "Economic Growth and the Income-Consumption Disconnect: Evidence from Indian States," Economics Bulletin, AccessEcon, vol. 37(1), pages 264-281.
    18. Fikru, Mahelet G., 2020. "Determinants of electricity bill savings for residential solar panel adopters in the U.S.: A multilevel modeling approach," Energy Policy, Elsevier, vol. 139(C).
    19. Venera Timiryanova & Dina Krasnoselskaya & Natalia Kuzminykh, 2022. "Applying the Multilevel Approach in Estimation of Income Population Differences," Stats, MDPI, vol. 6(1), pages 1-32, December.
    20. Omer Siddique & Hanzla Jalil, 2018. "Exploring the Structure and Performance of Petroleum Retail Outlets in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 57(2), pages 223-247.

    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:eee:teinso:v:64:y:2021:i:c:s0160791x20312859. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    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.