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National Income, Political Freedom, and Investments in R&D and Education: A Comparative Analysis of the Second Digital Divide Among 15-Year-Old Students

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Abstract

Digital technology has become an indispensable component in education around the world. Despite its growing importance, a gap in students’ digital skills and usage based on their socioeconomic status—known as the second digital divide—has been identified in a wide range of countries. Using data from the 2009 OECD Programme for International Student Assessment, we consider two aspects of the second digital divide for 15-year-olds across 55 countries: the gaps in use of educational software at home and Internet literacy. Specifically, we ask whether national income, political freedom, and national investments in research and development (R&D) and secondary education are associated with the second digital divide. We find that national income predicts the digital divide and that national investments have differential effects depending upon a country’s income. R&D spending reduces the socioeconomic gap in educational software use only in low-income countries. Educational expenditures reduce the Internet literacy gap in high-income countries while exacerbating it in low-income ones. Additional analyses suggest that income inequality increases the digital divide, but like political freedom, the effects become non-significant when national income is considered. We conclude by discussing the implications of these findings for policymakers interested in reducing the digital divide.

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Notes

  1. Research from Australia (OECD 2011) and Cyprus (Milioni et al. 2014) suggests a “reverse digital divide” between immigrants and nonimmigrants; that is, there is a “compensatory or remedial use of the Internet (Milioni et al. 2014, p. 333)” by racial minority immigrants in order to overcome their existing racial and social barriers.

  2. The gross national income per capita for low-income countries is below $4570 in U.S. dollars; medium-income countries are between $4571 and $14,090; and upper- and high-income countries are above $14,091.

  3. To account for the possibility that countries with large sample sizes may disproportionately affect parameter estimates, we run supplementary analyses with a variable measuring country sample size and find our results to be unchanged.

  4. Following previous studies (Chiu 2010; Decancq and Schokkaert 2016), we present the unlogged values in the appendix to give the reader a better sense of the actual range of values for the country-level variables.

  5. Seven online activities are listed in the questionnaire. We excluded two of these activities—reading emails and chatting online—as they are less relevant to students’ online literacy and reading proficiency (OECD 2011).

  6. Some studies use the number of books at home as a proxy for family SES or social class (Carnoy and Rothstein 2013). We consider this alternative in supplementary analyses and find the results to be substantively the same.

  7. For countries that have missing data on country-level variables in 2009, we utilize data from the closest adjacent year in which data are available (see Table 6 in the Appendix).

  8. Because the composite polity score ranges from − 10, to 10, we take a linear transition by adding 11 before logging to ensure that all values are positive.

  9. To avoid over-parameterization, we do not consider the random slopes of other individual-level variables. In models not shown, we find the inclusion of additional random slopes does not influence the main results reported here. Additionally, we find family SES to be much more important than other variables in capturing cross-cluster heterogeneity. These models are available upon request.

  10. In supplementary analyses including both R&D and educational expenditures, the significant effect of R&D disappears because of the high correlation between the two variables (r = .48), but the general patterns remain the same.

  11. Trinidad & Tobago and Macao are potential outliers in Fig. 2 with regard to the effect of R&D for high-income nations. Supplementary analyses excluding these two countries show patterns consistent with those reported here.

  12. Results from supplementary analyses are not shown here, but are available from the authors upon request.

  13. Based on the World Bank, there are 6 lower-middle-income countries, 15 upper-middle-income countries, and 34 high-income countries.

  14. The Gini index data are obtained from the World Income Inequality Database (UNU-WIDER 2008). Secondary education enrollment rates are derived from the World Bank (2015a).

  15. While educational expenditures have a clear and direct relationship to students, countries may distribute their educational resources in ways that are unrelated to digital technology use. The distribution of educational investments within a country could also be biased by social status, with newer technologies going only to schools in the most affluent areas. Bearing these possibilities in mind, we examine whether or not these investments, when used appropriately, may serve as tools to reduce the second digital divide.

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Acknowledgements

The authors would like to thank Brian Powell, Michael Wallace, Jeremy Pais, Mary Fischer, David Weakliem, Thung-Hong Lin, and several anonymous reviewers for valuable comments on previous versions of this paper.

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Authors and Affiliations

  1. Department of Sociology, National Taipei University, Taipei, Taiwan

    Josef Kuo-Hsun Ma

  2. Department of Labor Studies and Employment Relations, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA

    Todd E. Vachon

  3. Department of Sociology, University of Connecticut, Storrs, CT, USA

    Simon Cheng

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Correspondence to Josef Kuo-Hsun Ma.

Appendix

Appendix

See Tables 5 and 6 and Fig. 3.

Table 5 Sample size and descriptive statistics for key individual-level variables in 55 countries
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Table 6 Country-level variables: 55 countries
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Fig. 3
figure 3

Effects of R&D and secondary educational expenditures on family SES slopes by country income group: digital use for schoolwork at home vs. use of educational software at homea. Note Predicted family SES slopes are based on the same model specifications in Table 4 but limited to only 36 countries with valid data for the two dependent variables. a Standardized composite measure based on five items: (1) browsing the Internet for schoolwork, (2) using e-mail to communicate with other students about schoolwork, (3) using e-mail to communicate with teachers and for submission of schoolwork, (4) using material from the school’s website, and (5) checking the school’s website for announcements

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Ma, J.KH., Vachon, T.E. & Cheng, S. National Income, Political Freedom, and Investments in R&D and Education: A Comparative Analysis of the Second Digital Divide Among 15-Year-Old Students. Soc Indic Res 144, 133–166 (2019). https://doi.org/10.1007/s11205-018-2030-0

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