Inside mathematics learning inequality: an analysis of Young Lives Survey data, India

References

• Ammermueller, A. (2011). PISA: What makes the difference? Explaining the gap in pisa test scores between Finland and Germany. Empirical Economics, 33(2), 263–287. https://doi.org/10.1007/s00181-006-0102-5
   Google Scholar
• Annual Status of Education Report. (2005). Pratham Resource Centre.
   Google Scholar
• Annual Status of Education Report. (2010). Pratham Resource Centre.
   Google Scholar
• Annual Status of Education Report. (2017). Pratham Resource Centre.
   Google Scholar
• Arteaga, I., & Glewwe, P. (2014). Achievement gap between indigenous and non-indigenous children in Peru. Young Lives Working Paper 130.
   Google Scholar
• Asif, R., Merceron, A., Abbas, S., & Ghani, N. (2017). Computers & education analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007
   Web of Science ®Google Scholar
• Atherton, P., & Kingdon, G. G. (2010). The relative effectiveness and costs of contract and regular teachers in India by. Institute of Education, University of London, 256–276.
   Google Scholar
• Azubuike, O. B., & Briones, K. (2016). Young lives’ constructed files. Oxford Review of Economic Policy, 23(2)( 7483), 168–195. https://www.younglives.org.uk/publications/young-lives-rounds-1-4-constructed-files
   Google Scholar
• Azubuike, O. B., Moore, R., & Iyer, P. (2017). Young lives school surveys, 2016–17: The design and development of cross-country maths and English tests in Ethiopia, India & Vietnam. Young Lives.
   Google Scholar
• Baird, K. (2012). Class in the classroom: The relationship between school resources and math performance among low socioeconomic status students in 19 rich countries. Education Economics, 20(5), 484–509. https://doi.org/10.1080/09645292.2010.511848
   Google Scholar
• Barrera-Osorio, F., García-Moreno, V., Patrinos, H. A., & Porta, E. (2011). Using the Oaxaca-Blinder decomposition technique to analyze learning outcomes changes over time: An application to Indonesia’s results in PISA mathematics. World Bank Policy Research Working Paper, (5584).
   Google Scholar
• Biewen, M. (2012). Additive decompositions with interaction effects. IZA Discussion Paper, (6730).
   Google Scholar
• Blinder, A. S. (1973). Wage discrimination: Reduced form and structural estimates. The Journal of Human Resources, 8(4), 436–455. https://doi.org/10.2307/144855
   Web of Science ®Google Scholar
• Burger, R. (2011). School effectiveness in Zambia: The origins of differences between rural and urban outcomes. Development Southern Africa, 28(2), 157–176. https://doi.org/10.1080/0376835X.2011.570064
   Web of Science ®Google Scholar
• Chetty, R., Friedman, J. N., & Rockoff, J. E. (2011). The long-term impacts of teachers: Teacher value-added and student outcomes in adulthood. National Bureau of Economic Research Working Paper, (17699).
   Google Scholar
• Chudgar, A., & Quin, E. (2012). Relationship between private schooling and achievement: Results from rural and urban India. Economics of Education Review, 31(4), 376–390. https://doi.org/10.1016/j.econedurev.2011.12.003
   Web of Science ®Google Scholar
• Cobb-Clark, D. A., & Gørgens, T. (2014). Parents’ economic support of young-adult children: Do socioeconomic circumstances matter? Journal of Population Economics, 27(2), 447–471. https://doi.org/10.1007/s00148-013-0484-6
   Web of Science ®Google Scholar
• Crane, J. (1996). Effects of home environment, SES, and maternal test scores on mathematics achievement. The Journal of Educational Research, 89(5), 305–314. https://doi.org/10.1080/00220671.1996.9941332
   Web of Science ®Google Scholar
• Crookston, B. T., Forste, R., McClellan, C., Georgiadis, A., & Heaton, T. B. (2014). Factors associated with cognitive achievement in late childhood and adolescence: The young lives cohort study of children in Ethiopia, India, Peru, and Vietnam. BMC Pediatrics, 14(1), 1–9. https://doi.org/10.1186/1471-2431-14-253
   PubMedGoogle Scholar
• Cueto, S., & Leon, S. (2012). Psychometric characteristics of cognitive development and achievement instruments in round 3 of young lives (young lives technical no notes) young lives.
   Google Scholar
• Currie, J., & Yelowitz, A. (2000). Are public housing projects good for kids? Journal of Public Economics, 75(1), 99–124. https://doi.org/10.1016/S0047-2727(99)00065-1
   Web of Science ®Google Scholar
• Davis-Kean, P. E. (2005). The influence of parent education and family income on child achievement: The indirect role of parental expectations and the home environment. Journal of Family Psychology, 19(2), 294–304. https://doi.org/10.1037/0893-3200.19.2.294
   PubMed Web of Science ®Google Scholar
• Dawes, A. (2020). Measuring the development of cognitive skills across time and context: reflections from young lives. Young Lives.
   Google Scholar
• Dercon, S., & Krishnan, P. (2009). Poverty and the psychosocial competencies of children: Evidence from the young lives sample in four developing countries. Children, Youth and Environments, 19(2), 138–163. https://www.jstor.org/stable/10.7721/chilyoutenvi.19.2.0138
   Google Scholar
• Desai, S., Dubey, A., Vanneman, R., & Banerji, R. (2008). Private schooling in India: A new educational landscape. India Policy Forum, 5(1), 1–58. https://www.ncaer.org/publication/india-policy-forum-2008-09
   Google Scholar
• Galab, S., Prudhvikar, R., & Singh, R. (2014) Young lives survey design and sampling in Andhra Pradesh, India
   Google Scholar
• Galab, S., Prudhvikar, R., & Singh, R. (2014a). Education and learning: round 4 preliminary findings. Round 4 Fact Sheet. Young Lives
   Google Scholar
• Glewwe, P., Chen, Q., & Katare, B. (2015). What determines learning among kinh and ethnic minority students in Vietnam? An analysis of the round 2 young lives data. Asia and the Pacific Policy Studies, 2(3), 494–516. https://doi.org/10.1002/app5.102
   Web of Science ®Google Scholar
• Goyal, S. (2009). Inside the house of learning: The relative performance of public and private schools in Orissa. Education Economics, 17(3), 315–327. https://doi.org/10.1080/09645290903142577
   Google Scholar
• Goyal, S., & Pandey, P. (2009). How do government and private schools differ? Findings from two large Indian states. International Bank for Reconstruction and Development/The World Bank.
   Google Scholar
• Holmlund, H., Lindahl, M., & Plug, E. (2011). The causal effect of parents’ schooling on children’s schooling: A comparison of estimation methods. Journal of Economic Literature, 49(3), 615–651. https://doi.org/10.1257/jel.49.3.615
   Web of Science ®Google Scholar
• Jann, B. (2008). The Blinder–Oaxaca decomposition for linear regression models. The Stata Journal, 8(4), 453–479. https://doi.org/10.1177/1536867X0800800401
   Web of Science ®Google Scholar
• Keane, M. P., Krutikova, S., & Neal, T. (2018). The impact of child work on cognitive development: Results from four low to middle income countries. (No. W18/29) IFS Working Papers.
   Google Scholar
• Kingdon, G. G. (1996). The quality and efficiency of public and private schooling: A case study of India. Oxford Bulletin of Economics and Statistics, 58(1), 57–82. https://doi.org/10.1111/j.1468-0084.1996.mp58001004.x
   Web of Science ®Google Scholar
• Kingdon, G. G. (2007). The progress of school education in India. Oxford Review of Economic Policy, 23(2), 168–195. https://doi.org/10.1093/oxrep/grm015
   Web of Science ®Google Scholar
• Kingdon, G. G., & Teal, F. (2007). Does performance related pay for teachers improve student performance? Some evidence from India. Economics of Education Review, 26(4), 473–486. https://doi.org/10.1016/j.econedurev.2006.02.007
   Web of Science ®Google Scholar
• Koenker, R., & Hallock, K. F. (2001). Quantile Regression. Journal of Economic Perspectives, 15(4), 143–156. https://doi.org/10.1257/jep.15.4.143
   Web of Science ®Google Scholar
• Leibowitz, A. (1977). Parental inputs and children’s achievement. The Journal of Human Resources, 12(2), 242–251. https://doi.org/10.2307/145387
   Web of Science ®Google Scholar
• Le, H. T., & Nguyen, H. T. (2018). The evolution of the gender test score gap through seventh grade: New insights from Australia using unconditional quantile regression and decomposition. IZA Journal of Labor Economics, 7(1). https://doi.org/10.1186/s40172-018-0062-y
   Google Scholar
• Lipovetsky, S. (2012). Regression split by levels of the dependent variable. Journal of Modern Applied Statistical Methods, 11(2), 319–324. https://doi.org/10.22237/jmasm/1351742580
   Google Scholar
• Li, Z., & Qiu, Z. (2018). How does family background affect children’s educational achievement? Evidence from contemporary China. Journal of Chinese Sociology, 5(1), 1–21. https://doi.org/10.1186/s40711-018-0083-8
   Google Scholar
• Machado, J. A. F., & Mata, J. (2005). Counterfactual decomposition changes in wage distributions using quantile regression. Journal of Applied Econometrics, 20(4), 445–465. https://doi.org/10.1002/jae.788
   Web of Science ®Google Scholar
• Melly, B. (2005). Decomposition of differences in distribution using quantile regression. Labour Economics, 12(4), 577–590. https://doi.org/10.1016/j.labeco.2005.05.006
   Web of Science ®Google Scholar
• Montes-Rojas, G., Siga, L., & Mainali, R. (2017). Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination. Journal of Economic Inequality, 15(3), 245–255. https://doi.org/10.1007/s10888-017-9355-9
   Web of Science ®Google Scholar
• Montes-Rojas, G., Siga, L., & Mainali, R. (2017). Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination. Journal of Economic Inequality, 15, 245–255.
   Web of Science ®Google Scholar
• Muralidharan, K., & Sundararaman, V. (2015). The aggregate effect of school choice: Evidence from a two-stage experiment in India. The Quarterly Journal of Economics, 130(3), 1011–1066. https://doi.org/10.1093/qje/qjv013
   Web of Science ®Google Scholar
• Oaxaca, R. (1973). Male-female wage differentials in urban labor markets. International Economic Review, 14(3), 693–709. https://doi.org/10.2307/2525981
   Google Scholar
• Parvez, A., & Laxminarayana, K. (2022). Mathematics learning inequality among children of private and public schools. Asia Pacific Education Review, 23, 257–269. https://doi.org/10.1007/s12564-021-09733-6
   Web of Science ®Google Scholar
• Paxson, C., & Schady, N. (2007). Cognitive development among young children in Ecuador: The roles of wealth, health, and parenting. The Journal of Human Resources, XLII(1), 49–84. https://doi.org/10.3368/jhr.XLII.1.49
   Google Scholar
• Reeves, E. B., & Lowe, J. (2009). Quantile regression: An education policy research tool. Journal of Rural Social Sciences, 24(1). https://egrove.olemiss.edu/jrss/vol24/iss1/10
   Google Scholar
• Rolleston, C. (2014). Learning profiles and the “skills gap” in four developing countries: A comparative analysis of schooling and skills development. Oxford Review of Education, 40(1), 132–150. https://doi.org/10.1080/03054985.2013.873528
   Web of Science ®Google Scholar
• Sakellariou, C. (2008). Peer effects and the indigenous/non-indigenous early test-score gap in Peru. Education Economics, 16(4), 371–390. https://doi.org/10.1080/09645290802133065
   Google Scholar
• Sanfo, J. B. M. B., & Ogawa, K. (2021). Explaining the rural-urban learning achievements gap in Ethiopian primary education: A re-centered influence function decomposition using young lives data. Education Economics, 29(3), 1–29. https://doi.org/10.1080/09645292.2021.1872504.
   Web of Science ®Google Scholar
• Sanjay, S., Gupta, R., Gautam, V., & Meena, S. H. R. (2014). Educational statistics at a glance. MHRD, Bureau of Planning, Monitoring & Statistics.
   Google Scholar
• Singh, A. (2011). Family background, academic ability and associated inequality of opportunity in India. Economics Bulletin, 31(2), 1463–1473. http://www.accessecon.com/pubs/eb/default.aspx?page=Currentcontent&Show=Issue&Value=2&Year=2011
   Google Scholar
• Singh, A. (2012, June). Reducing gender based inequalities in employment and wages: Is India on right track? Indira Gandhi Institute of Development Research.
   Google Scholar
• Singh, A. (2015). Private school effects in urban and rural India: Panel estimates at primary and secondary school ages. Journal of Development Economics, 113(2015), 16–32. https://doi.org/10.1016/j.jdeveco.2014.10.004
   Google Scholar
• Singh, R., & Mukherjee, P. (2019). Effect of preschool education on cognitive achievement and subjective wellbeing at age 12: Evidence from India. Compare: A Journal of Comparative and International Education, 49(5), 723–741. https://doi.org/10.1080/03057925.2018.1453349
   Web of Science ®Google Scholar
• Tian, M. (2006). A quantile regression analysis of family background factor effects on mathematical achievement. Journal of Data Science, 4(4), 461–478. https://doi.org/10.6339/JDS.2006.04(4).283
   Google Scholar
• Tighe, E. L., & Schatschneider, C. (2014). A quantile regression approach to understanding the relations among morphological awareness, vocabulary, and reading comprehension in adult basic education students. Journal of Learning Disabilities, 49(4), 1–13. https://doi.org/10.1177/0022219414556771
   Web of Science ®Google Scholar
• Todd, P. E., & Wolpin, K. I. (2007). The production of cognitive achievement in children: Home, school, and racial test score gaps. Journal of Human Capital, 1(1), 91–136. https://doi.org/10.1086/526401
   Google Scholar
• Wadhwa, W. (2009). Are private schools really performing better than government schools? Annual Status of Education Report (Rural) New Delhi.
   Google Scholar
• Wamalwa, F. M., & Burns, J. (2018). Private schools and student learning achievements in Kenya. Economics of Education Review, 66(July), 114–124. https://doi.org/10.1016/j.econedurev.2018.07.004
   Google Scholar
• Zhang, Y. (2006). Urban ‐ rural literacy gaps in sub ‐ Saharan Africa: The roles of socioeconomic status and school quality. Comparative Education Review, 50(4), 581–602. https://doi.org/10.1086/507056
   Web of Science ®Google Scholar
• Zhang, L., & Lee, K. A. (2011). Decomposing achievement gaps among OECD countries. Asia Pacific Education Review, 12(3), 463–474. https://doi.org/10.1007/s12564-011-9151-3
   Web of Science ®Google Scholar