Compensation or reproduction? The implications of online learning for socio-economic equalisation in urban China

Abstract

This study explores the implications of promoting online learning for equalisation, focusing on urban China where online learning is promoted to alleviate socio-economic gaps between rural migrants and urban residents. To achieve equalisation, online learning should benefit disadvantaged individuals as least as much as, if not more than, the advantaged counterparts, as greater returns for the advantaged can reproduce pre-existing inequalities. This study examines the interaction effect between hukou (household registration) origin and daily online learning on occupational mobility and income growth, using data from the China Family Panel Studies. Findings reveal modest economic returns to online learning. While not found to benefit urban residents more, the most ideal scenario for equalisation, greater online learning benefits for rural migrants, is limited, except for the increased downward occupational mobility avoidance effect for rural origin women. These results underscore the deficiency of solely relying on online learning to challenge inequalities.

Keywords:

• Inequality
• lifelong learning
• internet
• hukou
• rural migrant
• China

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The imputation drew on the use of package MICE in R.

2 The variable for individual wage income in CFPS has an extremely high rate of missing or invalid data (33% in 2014, 85.18% in 2016, 52.86% in 2018), while the variable for family income per capita has fewer missing data (8.23% in 2014, 16.10% in 2016, 15.27% in 2018). To address the issue of unreliable estimation caused by the lack of natural variation due to the high rate of missingness, the latter variable was used. Nonetheless, sensitivity analysis was conducted using individual wage income as an alternative measurement, and the results showed similar patterns.

3 Ln (Income_16/18) – Ln (Income₁₄) = Ln ($\frac{\mathit{\text{Income}}16/18}{\mathit{\text{Income}}14}$) .

4 The analysis used grf package (version 2.1.0) in R (Version 3.6.0).

5 Average treatment effects (ATEs) refer to the mean outcome difference between the treated and control groups across an entire population, while conditional average treatment effects (CATEs) refer to the average treatment effect conditioned on specific covariate values.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.