Today’s pandemic caused by COVID-19 brought to light intensive discussions on the ways in which it challenged the healthcare systems all over the world. However, despite the risks imposed to public health on the global scale, there is also a wide list of indirect effects of the pandemic, and the consequences of these effects might be way more long-lasting than national quarantines. In this context, the impact of the coronavirus on education cannot be discounted. On the one hand, it caused mobilization of the IT resources, e-learning and incorporation of digital technologies into the educational process. On the other hand, one must account that not all national systems of education are at the point where implementation of the facilitated and inclusive distant learning, as well as other relevant processes can be provided to mitigate the effects of the current outbreak. Indeed, national responses in the sphere of education can vary substantially and sometimes be insufficient in tackling the today’s situation. In addition to that, the effect of the existing inequalities in wealth, as well as inequality in the access to digital technology (caused in many ways by the differences in socio-economic status) impose radical challenges to the educational equities and inclusiveness, widening the existing gaps in learning and escalating situation in the national contexts where learning crisis was present even before the pandemic.
Needs to be noted that today, when more than 180 countries reported temporarily school closures due to the COVID-19 outbreak, not only the schools might lack the resources to implement the technologies of digital learning, but the children from the poorer households in many countries simply do not have internet access to remain included into the educational process. Since the access to internet at home is in many ways defined by the position in the socio-economic ladder, an important goal in this light is to understand, what is the share of those left behind and excluded due to lack of opportunities in using digital technology. In this event, it appears necessary to estimate, how the current pandemic could sharpen the existing inequalities and inequities of national education systems, potentially widen the gap between learning skills of the rich and poor, thus increasing the share and extent of those who are already excluded and leading to the even higher number of children left behind.
The data from MICS6 show that in countries like Bangladesh, DR Congo, Lao PDR, Lesotho, Madagascar, Mongolia, Pakistan (Punjab), Sierra Leone, Togo, Tunisia, and Zimbabwe more than 50% of children in the age between 5-17 who are enrolled at school do not have access to internet at their households. Moreover, truly critical situation emerges in countries like Congo DR and Lao PDR, where only less than 2% of children attending school have internet access at home. On the opposite, only 3 countries such as Georgia, Kyrgyzstan, and Montenegro are ready to meet the challenges of the outbreak since in these countries at least 70% of children attending school have internet access at home. All in all, if to estimate based on the given selection of 18 countries, on average, about 55% of students in these countries will be strongly affected by the COVID-19 outbreak by being unable to continue learning using digital technologies.
The statement regarding the strong link between the internet access and socio-economic status is substantially supported by the national data from different countries. As such, out of those children attending school who also have internet access at home, 20% of the richest ones are the major social group that will be able to continue learning in a distant mode: in all the countries, they take at least 20% of the internet access within the respective samples. When we look at the opposite side of the wealth distribution, namely, children at school belonging to 20% of the poorest families, the data show that the internet access amongst these households accounts between 0.3% (Sierra Leone) and 16.3% (Kyrgyzstan).
However, the most interesting patterns are to be found when we look at both figures 1 and 2 in their interlinkage. As such, some countries where the share of children with internet access is substantially low (i.e., below 30%), such as Madagascar, Sierra Leone, and Togo, demonstrate also highest inequality in the distribution of internet access by the wealth groups. In all the three countries, children enrolled at school from the families belonging to the top 20% by wealth take more than 60% of internet access provided to the households (the values by countries are 76.5%, 60.5%, and 61.3% respectively). On the other hand, the children at school belonging to the poorest 40% of households in these countries take less than 2.5% of total internet access. It means that a low level of internet access leads to even greater inequality in distribution of the respective service. This pattern is opposite to the one where some resource or an asset, being in scarce or on the low level in society, can be distributed relatively even amongst different hierarchical groups. In other words, sometimes even in low or lower-middle income economies, level of inequality can also be low, which means that people are relatively equal in their conditions. However, in the countries mentioned above, it does not seem to be the case. If to put this argument into the considerations of educational equity, it highlights additional risks for those children who belong to the poorer households, substantially limiting opportunities for them to participate in the learning process. It can be added that to a relatively softer extent, a similar pattern in distribution by wealth quintile groups is observed in such countries with low internet access as Congo DR, Lao PDR, Pakistan (Punjab), and Zimbabwe. Children at school from the 20% of richest families take from 40.6% (Zimbabwe) to 50.7% (Lao PDR) of internet access there, whereas for the poorest 40% the share varies from 10.3% (Congo DR) to 16% (Lao PDR).
To sum it up so far, the observed patterns in data testify that the lower access to internet on the national level is, the further from egalitarian and equitable conditions it is distributed amongst the children in the society. In other words, the low level of internet access leads to even a greater inequality of distribution in the light of wealth groups. These arguments provide a mounting evidence that internet cannot be regarded as a substantial tool in ensuring education on the inclusive and equitable basis for poor children, vulnerable groups and all those standing at the lower hierarchical levels of the social ladder.
Despite no reason exists to claim that skills in learning are somehow shaped by access to internet, in the lens of the current discussion it makes sense to examine, what are the differences in foundational learning skills (on the example of reading) among children attending school by internet access at home.
The MICS6 data on 14 countries show that in all the cases, the average score of foundational reading skills disaggregated by access to internet at home is substantially higher for the group of children with internet. Whereas such countries as Kiribati and Mongolia demonstrate the low percentage difference between two groups of children (below 3.5%), severe discrepancies could be identified in Congo DR, Madagascar, Sierra Leone, Togo, and Zimbabwe: the share of children with foundational reading skills is at least twice higher for those having internet access at home than for children without it.
As was mentioned earlier, the graph should not be misinterpreted, and the revealed statistics does not imply that the access to internet determines the ability to learning. It appears clear though that in the current situation of the global pandemic, the gap explicitly identified on the graph above will grow further. In other words, children without internet access, mostly belonging to the lower levels of the social hierarchy (as was shown previously on the chart 2), will face even greater risk in not receiving education that they were able to get while attending school.
Despite all countries are differently affected by the COVID-19 pandemic, the negative effects on national education systems are inevitable. By shedding light on the available data, we could estimate potential risks and clearly understand that solutions for children from the poor families need to be found in ensuring inclusive and equitable education for all.