The Impact of School Operational Assistance Program Implementation at School Level on Senior Secondary Education Enrollment by Households: Evidence from Indonesia in 2007 and 2014

Fairuzah Pertiwi Kartasasmita, Eny Sulistyaningrum


Education is recognized worldwide as one of the key elements in developing the human capital of a nation for a prosperous future. Given an almost universal enrollment in primary education, many governments have shifted their focus on students’ motivation to continue to and finish their secondary education. The government of Indonesia has made extensive efforts in widening participation in education. With a growing budget for educational expenditure, various government programs have been implemented to assist students in their learning. One such program is the School Operational Assistance Program (BOS), which has been running for two decades. This paper reports on a study aimed to investigate the impact of the implementation of BOS at a school level on senior secondary school enrollment by households using data obtained from the Indonesia Family Life Survey (IFLS) recorded in 2007 and 2014. By using Propensity Score Matching (PSM), it was found that students whose schools received BOS during their primary education years were more likely to continue their education to senior secondary education than those whose schools did not receive BOS. This shows that a school subsidy could encourage students to continue their education, particularly for students coming from poorer households.


BOS; school subsidy; propensity score matching; enrolment

Full Text:



ACDP 2013, General senior secondary education financing in Indonesia, Education Sector Analytical and Capacity Development Partnership, Jakarta. viewed 13 June 2020,

Adewumi, B & Enebe, N 2019, ‘Government educational expenditure and human capital development in West African countries’, International Journal of Research and Innovation in Social Science (IJRISS), vol. 3, no. 6, pp. 546-556.

Al-Samarrai, S & Zaman, H 2007, ‘Abolishing school fees in Malawi: The impact on education access and equity’, Education Economics, vol. 15, no. 3, pp. 359-375. doi:

Attanasio, O, Fitzsimons, E, & Gomez, A 2005, ‘The impact of a conditional education subsidy on school enrolment in Colombia’, The Institute for Fiscal Studies. viewed 18 June 2020,

Barrera-Osorio, F 2007, The impact of private provision of public education: Empirical evidence from Bogota’s concession school’, Policy Research Working Paper Series 4121, The World Bank. viewed 18 June 2020,

Behrman, JR, Sengupta, P, & Todd, P 2005, ‘Progressing through PROGRESA: An impact assessment of a school subsidy experiment in rural Mexico’, Economic Development and Cultural Change, vol. 54, no. 1, pp. 237-275. doi:

Borkum, E 2012, ‘Can eliminating school fees in poor districts boost enrollment? Evidence from South Africa’, Economic Development and Cultural Change, vol. 60, no. 2, pp. 359-398. doi:

BPS-Statistics Indonesia 2019, Angka Partisipasi Sekolah (A P S), 2003-2019. viewed 7 March 2020,

Bui, TA, Nguyen, CV, Nguyen, KD, Nguyen, HH, & Pham, PT 2020, ‘The effect of tuition fee reduction and education subsidy on school enrollment: Evidence from Vietnam’, Children and Youth Services Review, vol. 108, p. 104536. doi:

Caliendo, M & Kopeinig, S 2008, ‘Some practical guidance for the implementation of propensity score matching’, Journal of Economic Surveys, vol. 22, no. 1, pp. 31-72. doi:

Chevalier, A, Harmon, C, O’Sullivan, V, & Walker, I 2005, ‘The impact of parental income and education on the schooling of their children’, IFS Working Papers W05/05, The Institute for Fiscal Studies. viewed 7 March 2020,

Dubois, P, de Janvry, A, & Sadoulet, E 2012, ‘Effects on school enrollment and performance of a conditional cash transfer program in Mexico’, Journal of Labor Economics, vol. 30, no. 3, pp. 555-589. doi:

Duflo, E 2001, ‘Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment’, American Economic Review, vol. 91, no. 4, pp. 795-813. doi: 10.1257/aer.91.4.795.

Duryea, S & Morrison, A 2004, ‘The effect of conditional transfers on school performance and child labor: Evidence from an ex-post impact evaluation in Costa Rica’, Working Paper 505, Inter-American Development Bank, Washington DC. viewed 1 May 2020,

Grimm, M 2011, ‘Does household income matter for children’s schooling? Evidence for rural Sub-Saharan Africa’, Economics of Education Review, vol. 30, no. 4, pp. 740-754. doi:

Guo, S & Fraser, MW 2015, Propensity score analysis: Statistical methods and applications (2nd edition), SAGE publications.

Heckman, JJ, Ichimura, H, & Todd, PE 1997, ‘Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme’, The Review of Economic Studies, vol. 64, no. 4, pp. 605-654. doi:

Hermida, P 2014, ‘Who benefits from the elimination of school enrolment fees? Evidence from Ecuador’, Revista Desarrollo y Sociedad, no. 74), pp. 69-132. doi:

Kharisma, B 2016, ‘Can a school operational assistance fund program (BOS) reduce school drop-outs during the post-rising fuel prices in Indonesia? Evidence from Indonesia’, MPRA Paper No. 70041, Munich Personal RePEc Archive. viewed 17 February 2020,

Khiem, PH, Linh, DH, & Dung, ND 2020, ‘Does tuition fee policy reform encourage poor children’s school enrolment? Evidence from Vietnam’, Economic Analysis and Policy, 66, pp.109-124. doi:

Loening, JL 2005, Effects of primary, secondary, and tertiary education on economic growth: Evidence from Guatemala’, Policy Research working Paper Series 3610, World Bank Publications. viewed 19 June 2020,

Martinelli, C & Parker, SW 2008, ‘Do school subsidies promote human capital investment among the poor?’, Scandinavian Journal of Economics, vol. 110, no. 2, pp. 261-276. doi:

Nowak, AZ & Dahal, G 2016, ‘The contribution of education to economic growth: Evidence from Nepal’, International Journal of Economic Sciences, vol. 5, no. 2, pp. 22-41. doi: 0.52950/ES.2016.5.2.002.

Ravallion, M 2007, ‘Evaluating anti-poverty programs’ in Handbook of Development Economics Vol. 4, eds TP Schultz & JA Strauss, North Holland, Amsterdam, pp. 3787-3846. doi:

Rosenbaum, PR & Rubin, DB 1983, ‘The central role of the propensity score in observational studies for causal effects’, Biometrika, vol. 70, no. 1, pp. 41-55. doi:

Schultz, TP 2004, ‘School subsidies for the poor: Evaluating the Mexican Progresa poverty program’, Journal of Development Economics, vol. 74, no. 1, pp. 199-250. doi:

Sparrow, R 2007, ‘Protecting education for the poor in times of crisis: An evaluation of a scholarship programme in Indonesia’, Oxford Bulletin of Economics and Statistics, vol. 69, no. 1, pp. 99-122. doi:

Shi, X 2016, ‘The impact of educational fee reduction reform on school enrolment in rural China’, The Journal of Development Studies, vol. 52, no. 12, pp. 1791-1809. doi:

Strauss, J, Witoelar, F, & Sikoki, B 2015, ‘User’s guide for the Indonesia Family Life Survey, Wave 5, Volume 2’, RAND Labor and Population Working Paper WR-1143/2-NIA/NICHD, RAND Corporation. viewed 22 July 2020,

Sulistyaningrum, E 2016, ‘Impact evaluation of the school operational assistance program (BOS) using the matching method’, Journal of Indonesian Economy and Business, vol. 31, no. 1, pp. 33-62.

Suryadarma, D, Widyanti, W, Suryahadi, A, & Sumarto, S 2006, ‘From access to income: Regional and ethnic inequality in Indonesia’, SMERU Working Paper, May 2006, SMERU Research Institute. viewed 14 August 2020,

Suryahadi, A, Sumarto, S, & Pritchett, L 2003, ‘Evolution of poverty during the crisis in Indonesia’, Asian Economic Journal, vol. 17, no. 3, pp. 221-241. doi:

Thomas, D, Beegle, K, Frankenberg, E, Sikoki, B, Strauss, J, & Teruel, G 2004, ‘Education in a crisis’, Journal of Development Economics, vol. 74, no. 1, pp. 53-85. doi:

Todaro, M 2009, Economic development (10th edition), Pearson.

World Bank 2014, ‘Assessing the role of the School Operational Grant Program (BOS) in improving education outcomes in Indonesia’, Report No: AUS4133, World Bank. viewed 14 August 2020,

Zhao, Z 2004, ‘Using matching to estimate treatment effects: Data requirements, matching metrics, and Monte Carlo evidence’, Review of Economics and Statistics, vol. 86, no. 1, pp. 91-107. doi:


  • There are currently no refbacks.