How COVID-19 caused a global learning crisis

COVID-19 can undermine life-long perspectives of young people. Students globally lost eight months of learning and the impact varies widely from staggering 12 months in South Asia and Latin America and Caribbean to modest 4 months in North America and Europe and Central Asia. Recent McKinsey study identifies three archetypes of countries:
🚩 Most affected countries with moderate levels of pre-COVID-19 learning and significant delays in education, where students may be nine to 15 months behind.
🚩 Prepandemic-challenged countries, with very low levels of pre-COVID-19 learning, where losses were daunting but not so dramatic in absolute terms, about three to eight months
🚩 Least affected are high-performing countries, with relatively high levels of pre-COVID-19 performance, where losses were limited to one to five months.

Lower levels of learning translate into lower future earnings potential for students and lower economic productivity for nations (📉 losing 1 percent of global GDP annually, according to McKinsey estimates). By using scenario modelling UNDP came to similar conclusions. The study shows how governments can make choices today that have the greatest potential to boost progress in the future. School systems can respond across multiple horizons to help students get back on track: ⭐Resilience, 🔁Reenrollment, 🔼Recovery, and 💡Reimagining.

Do we measure right inequalities

Can we consider any society developed if the people have a deep sense of unfairness and believe that the ‘system is rigged’? Recent chapter by Avidit Acharya and John E. Roemer in “The Great Upheaval” argues that fairness entails equalizing opportunities rather than equalizing something else.

However, do we measure what matters?

In the end, all inequalities are unequal, but some are more unequal than others. We still use only one indicator—the Gini coefficient of income inequality—to judge them all. This chart illustrates possible approach in measuring equality of opportunity. It shows distributions of income among people, grouped by the levels of education of their two parents. These curves summarize the income opportunities available to its members. Inequality of opportunity for income appears to be a good deal higher in Indonesia than in Germany around a similar time. Measuring right inequalities could help policymakers to shape right policies.

New-old human-machine interactions

Nowadays we store more than 99% of information in digital form, comparing to just 1% a couple of decades ago. Just 60% of Internet traffic is currently generated by human, the rest is coming from bots, good 15% and bad 25%. This is a new reality, which pose a lot of questions–how we, human, interact with algorithms? with each other using the technologies? what does this mean for human development and human security? This video illustrates new-old interactions–a grandma helping a small robot to cross a street, by holding cars.