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Access to digital education and learning in the times of COVID-19: a latent class analysis

Santanu Pramanik, National Council of Applied Economic Research, India
Reem Ashraf, National Council of Applied Economic Research
Bijay Chouhan, National Council of Applied Economic Research
Sonalde B. Desai, University of Maryland

Months of school closure during the COVID-19 pandemic can have serious negative impact on children’s education, learning and labour market prospects. Prior studies suggest that the shift of responsibility of learning from schools to households often disproportionately affect those on the margins. Learning gaps widen as remote learning methods remain inaccessible to the marginalized. In order to understand the impact of school closure on children’s learning, as an intermediate step it is crucial to estimate the level of access to digital learning for school-age children. Surveys collecting data on actual use of digital learning during periods of school closure are sparse in India. We predicted level of digital access using latent class analysis. We used data on explanatory variables determining the level of access from existing surveys. Once the final latent class model is identified, we used latent class regression model to predict individuals’ class membership based on socio-demographic variables.

Keywords: Children and youth, COVID-19, Latent class analysis, Methodology

See extended abstract.

  Presented in Session P14.