52 M. Irhamni and G. A. Sahadewo
“Usually, a month, I will spend Rp 65,000, but now it is doubled.” Teacher in public primary school, high poverty school
Figure 3.8 shows school supports to teachers by schools’ poverty level. About a fifth of teachers receives device support from schools, while less than a fifth of teachers receives expense coverage for the internet and given a subscription to rele- vant software. We find significant differences in school support for these inputs across low and high-poverty schools. Therefore, we find that the proportion of teachers who receive training for distance learning is higher in low-poverty schools compared to in high-poverty schools.
Summarizing, we find evidence of inequality in teachers’ capabilities in imple- menting online learning. Teachers in high-poverty schools are less likely to have expe- rience and capacity using online teaching platforms and techniques in conducting online teaching. These teachers are also more likely to report having no decent internet connection. Despite these challenges, we find that teachers adopt their teaching strategies to accommodate disadvantaged students.
3 COVID-19 Widening the Gap in Education: Evidence from Urban Jakarta 53
Fig. 3.8 School supports to teachers during PJJ by schools’ poverty level (Note The 95% confidence interval estimation is clustered at the school level. Starting in the top left and in clockwise order, the p-values from student t-tests between low and high poverty are 0.398, 0.093, 0.697, 0.000, 0.008, and 0.005. Source Authors’ calculation)
54 M. Irhamni and G. A. Sahadewo children’s ability to focus during learning from home. The lower time spent with children in early grades may affect their learning of fundamentals important for learning in later grades.
Data from the online survey and in-depth interviews with teachers also show evidence of inequality in educational inputs. Teachers’ capabilities in implementing online learning differ significantly between teachers in low-poverty schools and those in high-poverty schools. Teachers in high-poverty schools are less likely to have experience, training, and capacity in using online teaching platforms and techniques in conducting online teaching. Teachers from high-poverty schools are also facing internet connection issues, which would affect the quality of delivery of learning materials.
We find that teachers adopt their teaching strategies to accommodate learning from home strategy. Teachers spend more time planning and preparing teaching materials as well as communicating with parents on academic matters. We also find that teachers initiate offline learning to facilitate disadvantaged students.
There are several policy implications due to the results of this study. First, the government should continue to provide public internet and subsidies for internet connection. The government should also establish a mechanism for parents to borrow or purchase affordable gadgets and essentials for learning from home. Second, the government should establish and deliver guidance to parents on how to accompany the process of learning from home. The materials should not be based on grades, but on key competencies particularly math and literacy. This is particularly impor- tant for children from lower income households whose parents may not have the educational background needed to facilitate their children’s learning. The guidance should also involve parents in evaluating students’ learning of key competencies.
To avoid perverse incentives, the evaluation process should not be linked to grades.
The process should rather be aimed for teachers and parents to understand students’
development.
Third, teachers should collaborate with parents and use results from learning evaluations to tailor materials for students. Specifically, if a Grade 2 student has not mastered competencies expected of Grade 1, teachers should provide relevant materials from Grade 1. This approach is referred to as teaching at the right level (TaRL) which ensures that students obtain competencies essential for later stages of learning. Rethinking pedagogy is particularly important for students in early grades.
Lastly, the government should conduct training in using Information Technology (IT) for teachers. The training includes choosing and preparing learning materials, conducting synchronous and asynchronous learning methods, as well as designing learning evaluations.
Ethical Clearance We obtained ethics committee approval with reference number KE/FK/0561/EC/2020 from the Medical and Health Research Ethics Committee (MHREC), Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada.
Funding This research is funded by Australian Government through J-PAL SEA-IRF Fund.
Acknowledgments We would like to express our gratitude for the outstanding research assistance from Terry Muttahhari, Ma’rifatul Amalia, Elghafiky Bimardhika, and Chaerudin Kodir. We also
3 COVID-19 Widening the Gap in Education: Evidence from Urban Jakarta 55 thank Lina Marliani and Buhat Yulianto for their support during the study. We also express our gratitude for the support given by DKI Jakarta Provincial Government, represented by Mr. Momon Sulaeman, Mr. Suyoto, and all the school supervisors from the DKI Jakarta Provincial Education Agency. We thank Dr. Totok Amin Soefijanto and Mrs. Qonita Beldatis from the Governor’s Team for Accelerated Development for their constructive feedback. We thank the following institutions for their support: J-PAL Southeast Asia, the Australian Government, and LPEM FEB UI.
References
Ager, P., Eriksson, K., Karger, E., Nencka, P., & Thomasson, M. A. (2020). School closures during the 1918 flu pandemic. National Bureau of Economic Research.
Agostinelli, F., Doepke, M., Sorrenti, G., & Zilibotti, F. (2020). It takes a village: The economics of parenting with neighborhood and peer effects. National Bureau of Economic Research.
Alifia, U., Barasa, A. R., Bima, L., Pramana, R. P., Revina, S., & Tresnatri, F. A. (2020). Learning from home: A portrait of teaching and learning inequalities in times of the COVID-19 pandemic.
Smeru Research Institute.
Avvisati, F., Gurgand, M., Guyon, N., & Maurin, E. (2014). Getting parents involved: A field experiment in deprived schools. Review of Economic Studies, 81(1), 57–83.
Bacher-Hicks, A., Goodman, J., & Mulhern, C. (2021). Inequality in household adaptation to schooling shocks: Covid-induced online learning engagement in real time. Journal of Public Economics, 193, 104345. https://doi.org/10.1016/j.jpubeco.2020.104345
Beuermann, D. W., Cristia, J., Cueto, S., Malamud, O., & Cruz-Aguayo, Y. (2015). One laptop per child at home: Short-term impacts from a randomized experiment in Peru. American Economic Journal: Applied Economics, 7(2), 53–80.
Bol, T. (2020). Inequality in homeschooling during the Corona crisis in the Netherlands. First results from the LISS Panel. https://doi.org/10.31235/osf.io/hf32q
Catsambis, S., & Beveridge, A. A. (2001). Does neighborhood matter? Family, neighborhood, and school influences on eighth-grade mathematics achievement. Sociological Focus, 34(4), 435–457. https://doi.org/10.1080/00380237.2001.10571212
Cristia, J., Ibarrarán, P., Cueto, S., Santiago, A., & Severín, E. (2017). Technology and child devel- opment: Evidence from the one laptop per child program. American Economic Journal: Applied Economics, 9(3), 295–320.
Davis-Kean, P. E. (2005). The influence of parent education and family income on child achieve- ment: The indirect role of parental expectations and the home environment. Journal of Family Psychology, 19(2), 294.
Fairlie, R. W. (2005). The effects of home computers on school enrollment. Economics of Education Review, 24(5), 533–547.
Fairlie, R. W., Beltran, D. O., & Das, K. K. (2010). Home computers and educational outcomes:
Evidence from the NLSY97 and CPS. Economic Inquiry, 48(3), 771–792.
Fairlie, R. W., & Robinson, J. (2013). Experimental evidence on the effects of home computers on academic achievement among schoolchildren. American Economic Journal: Applied Economics, 5(3), 211–240.
Fryer, R. G., Jr. (2017). The production of human capital in developed countries: Evidence from 196 randomized field experiments. In Handbook of economic field experiments (Vol. 2, pp. 95–322).
Elsevier.
Glewwe, P., & Lambert, S. (2010). Education production functions: Evidence from developing countries. In International encyclopedia of education (pp. 412–422). https://doi.org/10.1016/
B978-0-08-044894-7.01232-X
56 M. Irhamni and G. A. Sahadewo Guimbeau, A., Menon, N., & Musacchio, A. (2020). The Brazilian bombshell? The long-term impact of the 1918 Influenza Pandemic the South American way (NBER Working Paper Series).
https://doi.org/10.3386/w26929
Hanushek, E. A. (2010). Education production functions: Evidence from developed countries. In D. J. Brewer & P. J. McEwan (Eds.), Economics of education (pp. 132–136). Elsevier.
Jæger, M. M., & Blaabæk, E. H. (2020). Inequality in learning opportunities during Covid-19:
Evidence from library takeout. Research in Social Stratification and Mobility, 68, 100524. https://
doi.org/10.1016/j.rssm.2020.100524
Jeynes, W. H. (2007). The impact of school, family, and community connections on student achievement. Urban Education, 42(1), 82–110.
Kraft, M. A., & Monti-Nussbaum, M. (2017). Can schools enable parents to prevent summer learning loss? A text-messaging field experiment to promote literacy skills. The ANNALS of the American Academy of Political and Social Science, 674(1), 85–112.
Malamud, O., & Pop-Eleches, C. (2011). Home computer use and the development of human capital.
The Quarterly Journal of Economics, 126(2), 987–1027.
McBride, B. A., Schoppe-Sullivan, S. J., & Ho, M. H. (2005). The mediating role of fathers’ school involvement on student achievement. Journal of Applied Developmental Psychology, 26(2), 201–216. https://doi.org/10.1016/j.appdev.2004.12.007
Meyers, K., & Thomasson, M. A. (2017). Paralyzed by panic: Measuring the effect of school closures during the 1916 polio pandemic on educational attainment (NBER Working Paper Series, 30).
Ming Chiu, M., & Sui Chu Ho, E. (2006). Family effects on student achievement in Hong Kong.
Asia Pacific Journal of Education, 26(1), 21–35. https://doi.org/10.1080/02188790600607846 Muralidharan, K. (2017). Field experiments in education in developing countries (Vol. 2, pp. 323–
385). North-Holland. https://doi.org/10.1016/bs.hefe.2016.09.004
Muralidharan, K., Singh, A., & Ganimian, A. J. (2016). Teaching all students, and not just the top of the class: Experimental evidence on technology-led education in India. Unpublished Manuscript. University of California.
Orlov, G., McKee, D., Berry, J., Boyle, A., DiCiccio, T., Ransom, T., Rees-Jones, A., & Stoye, J.
(2020). Learning during the COVID-19 pandemic: It is not who you teach, but how you teach (National Bureau of Economic Research, 11).
Rockoff, J. E. (2004). The impact of individual teachers on student achievement: Evidence from panel data. American Economic Review, 94(2), 247–252. https://doi.org/10.1257/000282804130 2244
Sparrow, R., Dartanto, T., & Hartwig, R. (2020). Indonesia under the new normal: Challenges and the way ahead. Bulletin of Indonesian Economic Studies (Vol. 56:3, pp. 269–299). https://doi.
org/10.1080/00074918.2020.1854079
Vigdor, J. L., & Ladd, H. F. (2010). Scaling the digital divide: Home computer technology and student achievement (Working Paper 48. National Center for Analysis of Longitudinal Data in Education Research).
Wilder, S. (2014). Effects of parental involvement on academic achievement: A meta-synthesis.
Educational Review, 66(3), 377–397. https://doi.org/10.1080/00131911.2013.780009
Milda Irhamni is an Adjunct Researcher at Pusat Riset Ilmu Sosial dan Budaya, Universitas Syiah Kuala. She was the Associate Director of Research at J-PAL Southeast Asia. Her research interests include the economics of the environment in developing countries, education, early child- hood development and gender dimension in development. Milda earned her Doctoral degree from the University of Minnesota Twin Cities. Prior to J-PAL, she worked at the ILO as a national economist, University of Indonesia as a lecturer, and at the World Bank as a research analyst.
3 COVID-19 Widening the Gap in Education: Evidence from Urban Jakarta 57 Gumilang Aryo Sahadewo is an Associate Professor at the Department of Economics, Univer- sitas Gadjah Mada, and an Invited Researcher in the Abdul Latif Jameel Poverty Action Lab Southeast Asia. He earned his Ph.D. in economics from the University of Pittsburgh, Penn- sylvania, USA. His research interests are development economics, experimental economics, economics of education, and the economics of tobacco control. His previous projects include investigation on the effects of a deposit insurance scheme on moral hazard behaviors among banks (with Indonesian Deposit Insurance Corporation), the effects of religious messages on the choice of Islamic financing (NYU Abu Dhabi research grant), the relationship between school resources and labor market earnings, the impact of the Dell Scholarship Program on various college outcomes, household’s preferences regarding the fuel subsidy elimination in Indonesia (with EEPSEA and IDRC Canada) and tobacco control economics (with the World Bank). He has published articles in the Journal of Human Resources, Journal of Development Studies, Tobacco Control, and International Journal of Environmental Research and Public Health.