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Millions of children learn only very little. How can the world provide a better education to the next generation?

This figure and the figures in the following bar chart are from João Pedro Azevedo, Diana Goldemberg, Silvia Montoya, Reema Nayar, Halsey Rogers, Jaim

  • This figure and the figures in the following bar chart are from João Pedro Azevedo, Diana Goldemberg, Silvia Montoya, Reema Nayar, Halsey Rogers, Jaime Saavedra, Brian William Stacy (2021) – “Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.” World Bank Policy Research Working Paper 9588, March 2021.

  • You find these estimates for particular countries in the previously cited study and updates for some countries can be found in João Pedro Azevedo, Silvia Montoya, Maryam Akmal, Yi Ning Wong, Laura Gregory, Koen Martijn Geven, Marie-Helene Cloutier, Syedah Aroob Iqbal, Adolfo Gustavo Imhof, Natasha de Andrade Falcão, Cristelle Kouame, Mahesh Dahal, Tihtina Zenebe Gebre, and Maria Jose Vargas Mancera (2021) – Learning Poverty Updates and Revisions What’s New?. July 2021

    One country that does very well is the Netherlands. 98.4% of all children read with comprehension by the end of primary school. Other countries have also very low (2-3%) of children who don’t learn how to read with comprehension by that age: Austria, Finland, Hong Kong, Italy, Kazakhstan, Lithuania, Russia, Sweden, Singapore, and the UK are among them.

  • Lant Pritchett (2013) – The Rebirth of Education: Schooling ain’t Learning (CGD Books, 2013).

  • See the UNDP’s page on the Human Development Index and our own site on the HDI.

  • Existing large testing efforts are restricted to particular world regions [To give two examples: SACMEQ – the Southern and Eastern Africa Consortium for Monitoring Education Quality – focuses on that region of the world while the OECD’s PISA test focuses largely on high-income countries.]. The key difficulty that these researchers have to find solutions for is to bring these regional results together to obtain a global perspective through harmonized test scores.

    Three recent key efforts in this area are:

  • Dev Patel and Justin Sandefur (2020) – A Rosetta Stone for Human Capital. Working Paper.

    Data and Code for this research paper are made available by Dev Patel on his website. The authors also summarized their findings in a blog post. Many thanks to Dev Patel who helped me to access and understand the data.

  • On this aspect see for example: Alex Bell, Raj Chetty, Xavier Jaravel, Neviana Petkova, and John Van Reenen (2019) – Who Becomes an Inventor in America? The Importance of Exposure to Innovation. In The Quarterly Journal of Economics, Volume 134, Issue 2, May 2019, Pages 647–713, https://doi.org/10.1093/qje/qjy028 Alex Bell makes the research available on his website.

    See my summary of this research article in Talent is everywhere, Opportunity is not.

  • Another way to make these test score differences relatable is to relate them to changes over time. Patel and Sandefur have converted the international data on test scores into the TIMSS scale. Most countries have made progress in the TIMSS study. The US average score for students in grade 4 increased by 23 points over the course of the last generation (from 492 points in 1995 to 515 points in 2019). This means that a 165-point difference is more than 7-times larger than the progress the US made in the last generation.

  • The strength of those country effects is very large. Patel and Sandefur write: “Controlling for a household income as flexibly as possible, we still find that country fixed effects explain over half of the pupil-level variation in reading scores, and about two-thirds of the variation in math scores.”

  • The evidence shows that it is education in the form of skills and learning – rather than mere school attendance – that matters for individual earnings and economic growth.

    On the impact of education on economic growth see the research by Hanushek and Woessman:

    Eric A Hanushek and Ludger Woessmann (2008) – The Role of Cognitive Skills in Economic Development. In Journal of Economic Literature 46 (3): 607–68.

    Eric A. Hanushek, Ludger Woessmann (2010) – Education and Economic Growth. In Economics of Education (Amsterdam: Elsevier, 2010), Pages: pp. 60-67

    Eric A Hanushek and Ludger Woessmann (2012) – Do Better Schools Lead to More Growth? Cognitive Skills, Economic Outcomes, and Causation. In Journal of Economic Growth 17 (4): 267–321.

    And for a more detailed account: Eric A Hanushek and Ludger Woessmann (2015) – The Knowledge Capital of Nations: Education and the Economics of Growth. MIT Press.

    See also:

    Pritchett, L. (2006) – Chapter 11 Does learning to add up add up? The returns to schooling in aggregate data. Handb. Econ. Educ. 1, 635–695 (2006).

    Alan B. Krueger and Lindahl, M. (2001) – Education for growth: why and for whom? In J. Econ. Lit. 39, 1101–1136 (2001).

    And our section Education outcomes predict economic growth.

  • Literacy is a skill that is distributed along a continuum, to turn it into a binary variable a cutoff has to be chosen and there are different reasonable ways to choose that cutoff. In this statistic here the cutoff for what it means to be literate is lower than in the study that I cited first in this text (that’s why I emphasized the comprehension aspect in that study there). We explain this in more detail in How is literacy measured?

  • See the relevant section in the post ‘Global education quality in 4 charts’ by my colleague Esteban Ortiz-Ospina.

    And for more recent data read this paper in Nature: Angrist, N., Djankov, S., Goldberg, P.K. et al. (2021) – Measuring human capital using global learning data. In Nature 592, 403–408 (2021). doi.org/10.1038/s41586-021-03323-7

  • McCoy, Dana Charles, Evan D. Peet, Majid Ezzati, Goodarz Danaei, Maureen M. Black, Christopher R. Sudfeld, Wafaie Fawzi, et al. (2016) – Early Childhood Developmental Status in Low- and Middle-Income Countries: National, Regional, and Global Prevalence Estimates Using Predictive Modeling. PLOS Medicine 13 (6): e1002034.

    Walker, Susan P., Theodore D. Wachs, Julie Meeks Gardner, Betsy Lozoff, Gail A. Wasserman, Ernesto Pollitt, Julie A. Carter, et al. (2007) – Child Development: Risk Factors for Adverse Outcomes in Developing Countries. In Lancet 369 (9556): 145–57.

    For an overview see “SPOTLIGHT 2 – Poverty hinders biological development and undermines learning” in World Bank (2018) – World Development Report 2018: Learning to Realize Education’s Promise. Washington, DC: World Bank. doi:10.1596/978-1-4648-1096-1.

  • Peter Boone, Ila Fazzio, Kameshwari Jandhyala, Chitra Jayanty, Gangadhar Jayanty, Simon Johnson, Vimala Ramachandrin, Filipa Silva & Zhaoguo Zhan (2013) – The Surprisingly Dire Situation of Children’s Education in Rural West Africa: Results from the CREO Study in Guinea-Bissau (Comprehensive Review of Education Outcomes). NBER Working Paper 18971. They have also summarized their findings in an article for VoxEU.

  • Fazzio, I., Eble, A., Lumsdaine, R. L., Boone, P., Bouy, B., Hsieh, P.-T. J., Jayanty, C., Johnson, S., & Silva, A. F. (2021) – Large learning gains in pockets of extreme poverty: Experimental evidence from Guinea Bissau. In Journal of Public Economics, 199, 104385.

  • In these places teaching has to come from the school, there is little chance for parents to reinforce learning, the literacy rates among parents is very low. What makes the situation additionally hard is that in this region multiple languages are spoken, none of which have their own script. The students in this study therefore first learned Portuguese (the country’s official language) in the first year of the program, before they attended three years of primary school within the study’s schools.

  • Eble, A., Frost, C., Camara, A., Bouy, B., Bah, M., Sivaraman, M., Hsieh, P.-T. J., Jayanty, C., Brady, T., Gawron, P., Vansteelandt, S., Boone, P., & Elbourne, D. (2021) – How much can we remedy very low learning levels in rural parts of low-income countries? Impact and generalizability of a multi-pronged para-teacher intervention from a cluster-randomized trial in the Gambia. Journal of Development Economics, 148, 102539. 

    Banerjee, Abhijit, Rukmini Banerji, James Berry, Esther Duflo, Harini Kannan, Shobhini Mukerji, Marc Shotland, and Michael Walton (2017) – From Proof of Concept to Scalable Policies: Challenges and Solutions, with an Application. In Journal of Economic Perspectives, 31 (4): 73-102.

    Gertler, Paul J., James J. Heckman, Rodrigo Pinto, Arianna Zanolini, Christel Vermeersch, Susan Walker, Susan M. Chang, et al. (2014) – Labor Market Returns to an Early Childhood Stimulation Intervention in Jamaica. In Science 344 (6187): 998–1001.

  • The authors find the intervention to be cost-effective, which could mean that some well-resourced organizations and the governments in some countries that are richer than Guinea-Bissau can adopt it. The authors also suggest that it would be valuable to find out exactly which aspect of these schools was so very important. That might offer the opportunity to leave out some expensive yet less-important aspects of the school program and achieve similar results for a smaller cost. This connects to the next section in my text that focuses on cost-effective small interventions rather than the bundled intervention that this study conducted.

  • The differences in spending on education are vast. According to the latest data Guinea-Bissau spends about int.-$ 66 per primary school student per year. High-income countries spend more than 150-times more on each child.

    The latest data for Guinea-Bissau is for 2010, a long time ago, but unfortunately the country has only had very little economic growth since then. Back then the government spending per primary school student was international-$ 66.41. In a high-income country like Austria the spending at the same time was international-$ 10,469 per student per year. The ratio is 10,469/66.41=157.6. Other high-income countries spend even more than Austria.

  • Large implementations like the one in Guinea-Bissau can be a first step in that direction. Research in Kenya tried to identify exactly what of the Tusome program was crucial. Piper, B., Destefano, J., Kinyanjui, E.M. et al. (2018) – Scaling up successfully: Lessons from Kenya’s Tusome national literacy program. J Educ Change 19, 293–321 (2018). https://doi.org/10.1007/s10833-018-9325-4

  • On the point that many social science findings don’t generalize well see the research by Eva Vivalt.

  • See: Noam Angrist; Evans, David K.; Filmer, Deon; Glennerster, Rachel; Rogers, F. Halsey; Sabarwal, Shwetlena (2020) – How to Improve Education Outcomes Most Efficiently? A Comparison of 150 Interventions Using the New Learning-Adjusted Years of Schooling Metric. Policy Research Working Paper; No. 9450. World Bank.

  • See: Noam Angrist; Evans, David K.; Filmer, Deon; Glennerster, Rachel; Rogers, F. Halsey; Sabarwal, Shwetlena (2020) – How to Improve Education Outcomes Most Efficiently? A Comparison of 150 Interventions Using the New Learning-Adjusted Years of Schooling Metric. Policy Research Working Paper; No. 9450. World Bank.

  • There is a very wide literature on this fact. For a recent major paper see the following (and the references therein): Banerjee, Abhijit, Rukmini Banerji, James Berry, Esther Duflo, Harini Kannan, Shobhini Mukerji, Marc Shotland, and Michael Walton (2017) – From Proof of Concept to Scalable Policies: Challenges and Solutions, with an Application. In Journal of Economic Perspectives, 31 (4): 73-102.

    And see the references in the previously cited Angrist et al. (2020) paper.

  • On ‘Structured Pedagogy’ see: Chakera, S., Haffner, D., Harrop, E., (2020) – UNICEF Eastern and Southern Africa Region Working Paper – Structured Pedagogy: For Real-Time Equitable Improvements in Learning Outcomes. UNICEF: Nairobi.

    On its cost-effectiveness see: Angrist, Noam; Evans, David K.; Filmer, Deon; Glennerster, Rachel; Rogers, F. Halsey; Sabarwal, Shwetlena (2020) – How to Improve Education Outcomes Most Efficiently? A Comparison of 150 Interventions Using the New Learning-Adjusted Years of Schooling Metric. Policy Research Working Paper; No. 9450. World Bank.

    Interesting in this context is also the evidence on ‘Direct Instruction’ – on this see José Luis Ricón’s On Bloom’s two sigma problem: A systematic review of the effectiveness of mastery learning, tutoring, and direct instruction

  • Muralidharan, Karthik, Abhijeet Singh, and Alejandro J. Ganimian – (2019) – Disrupting Education? Experimental Evidence on Technology-Aided Instruction in India. In American Economic Review, 109 (4): 1426-60.

  • See Angrist et al. (2020) for the overview.
    Robert Jensen (2010) – The (perceived) returns to education and the demand for schooling. In The Quarterly Journal of Economics 125 (2), 515-548
    Trang Nguyen (2008) – Information, Role Models and Perceived Returns to Education: Experimental Evidence from Madagascar

    Angrist et al (2020) cite also other studies on whether these types of interventions work (for these they unfortunately lack information on costs so that effectiveness can be established, but the cost-effectiveness is unknown). See: Tahir Andrabi, Jishnu Das, and Asim Ijaz Khwaja (2017) – Report Cards: The Impact of Providing School and Child Test Scores on Educational Markets. In American Economic Review vol. 107, no. 6, June 2017 (pp. 1535-63).

  • On this point I recommend the excellent book by David Deutsch. Deutsch (2011) – The Beginning of Infinity: Explanations that Transform the World

  • Two texts that give a background on the overall problem are:

    Michael Kremer, Conner Brannen, Rachel Glennerster (2013) – The challenge of education and learning in the developing world. In Science, 340 (6130) (2013), pp. 297-300

    Lant Pritchett (2013) – The Rebirth of Education: Schooling Ain’t Learning. CGD Books. 

    More recent literature on specific interventions or overviews that are relevant:

    Paul Glewwe, Karthik Muralidharan (2016) – Improving education outcomes in developing countries: evidence, knowledge gaps, and policy implications. In Handbook of the Economics of Education, 5, Elsevier, Amsterdam, Holland (2016), pp. 653-743

    Bold, Tessa, Kimenyi, Mwangi, Mwabu, Germano, Ng’ang’a, Alice, Sandefur, Justin (2018) – Experimental evidence on scaling up education reforms in Kenya. J. Public Econ. 168 (December): 1–20.

    Abhijit Banerjee, Rukmini Banerji, James Berry, Esther Duflo, Harini Kannan, Shobhini Mukerji, Marc Shotland, Michael Walton (2017) – From proof of concept to scalable policies: challenges and solutions, with an application. J. Econ. Perspect., 31 (4) (2017), pp. 73-102

    Dana Burde, Linden, L. Leigh (2013) – Bringing education to Afghan girls: a randomized controlled trial of village-based schools. In Am. Econ. J.: Appl. Econ., 5 (3) (2013), pp. 27-40

    GiveWell has studied the cost-effectiveness of programs that focus on ‘Education in developing countries’. It was written in 2018 and therefore doesn’t take the recent literature into account.

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