IDN_2000_QSDS_v01_M
Quantitative Service Delivery Survey in Education 2000
Second Round
Name | Country code |
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Indonesia | IDN |
Quantitative Service Delivery Survey (QSDS)
Quantitative Service Delivery Surveys (QSDS) are multi-purpose surveys that assess quality and performance in resource usage at the frontline facility level, such as schools, health clinics and hospitals. QSDS collect information on characteristics and activities of service providers and on various agents in the system, on a sample basis, in order to examine the quality, efficiency and equity of service delivery on the frontline.
QSDS are often combined with Public Expenditure Tracking Surveys (PETS) in order to obtain a more complete picture of the efficiency and equity of a public allocation system, activities at the provider level, as well as various agents involved in the process of service delivery.
While most of PETS and QSDS have been conducted in the health and education sectors, a few have also covered other sectors, such as justice, Early Childhood Programs, water, agriculture, and rural roads.
In the past decade, about 40 PETS and QSDS have been implemented in about 30 countries. While a large majority of these surveys have been conducted in Africa, which currently accounts for 66 percent of the total number of studies, PETS/QSDS have been implemented in all six regions of the World Bank (East Asia and Pacific, Europe and Central Asia, Latin America and Caribbean, Middle East and North Africa, South Asia and Sub-Saharan Africa).
In the end of 1990s Indonesia had been experiencing a severe economic crisis. The Government of Indonesia, with the support of the Asian Development Bank and The World Bank, launched a "stay in school" media campaign, a program to provide block grants to schools to offset the shortfalls resulting from parents' lessened ability to pay fees, and a program to provide scholarships to poor students to compensate the direct costs of schooling.
To assess the impact of the crisis from schools' perspective, the World Bank and the Ministry of National Education launched a Quantitative Service Delivery Survey (QSDS) of primary and secondary schools. This research is also known as Crisis Impact School Survey (CISS).
The main approach used in the study was to collect historical data on enrollment trends and analyze the variation in those trends by types of schools across different regions. The first round of the survey was carried out in 1998. Described here is the second round, conducted from April 10 to May 7, 2000, with the goal to follow up the original study.
The 2000 survey covered enrollment, perceptions of the crisis from school staff, the distribution and use of school grants that were implemented by the government of Indonesia to mitigate the effects of the crisis, as well as school fees and school financing more generally.
The second round covered 600 schools (479 primary and 121 junior secondary) in five provinces: North Sumatra, DKI Jakarta, Central Java, South Sulawesi and East Nusa Tenggara.
Sample survey data [ssd]
Topic | Vocabulary |
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Education | World Bank |
Primary Education | World Bank |
Secondary Education | World Bank |
Five provinces: North Sumatra, DKI Jakarta, Central Java, South Sulawesi and East Nusa Tenggara.
Name | Affiliation |
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World Bank | |
Research and Development Department | Ministry of National Education (MoNE), Indonesia |
Name |
---|
World Bank |
Ministry of National Education (MoNE), Indonesia |
In 1998 the schools included in the research were from five provinces: North Sumatra, DKI Jakarta, Central Java, South Sulawesi, and Maluku. In 2000 investigators could not cover Maluku because of security concerns and East Nusa Tenggara (NTT) was chosen as a replacement because of its general similarity (geographic, religious, and location in the eastern part of Indonesia).
In NTT the school selection process was the same as that used in the other provinces in 1998: within each province three districts, two rural (Kabupaten) and one urban (Kotamadya) were selected with Probability Proportional to population Size - PPS (in the case of Jakarta, three Kotamadya were selected). Within each district level, four sub-districts (Kecamatan) were randomly selected, again with PPS. In each group of four sub-districts, forty schools were randomly selected by type - public/private, Sekolah Dasar (SD) / Madrasah Ibtidayah (MI), Sekolah Lanjutan Tingkat Pertama (SLTP) / Madrasah Tsanawiyah (MTs) in proportion to their actual distribution in the four sub-districts. SD (Sekolah Dasar) and SLTP (Sekolah Lanjutan Tingkat Pertama) are secular schools, MI (Madrasah Ibtidayah) and MTs (Madrasah Tsanawiyah) are religious schools. Each of these schools could be either public or private.
The resulting target sample consisted of 40 schools per district, 120 per province, and 600 schools in total. Outside of East Nusa Tenggara the schools in QSDS 2000 were the same as those in the original 1998 study.
Although 120 schools were covered in each province, the results are generally presented by averaging across school type (public-private, SD/SLTP-MI/MTs) at each level (primary, junior secondary) in three areas (rural, urban non-Jakarta and Jakarta) in order to achieve sample sizes that produce robust estimates. Despite the fact that schools from various areas were surveyed, the data are not designed to be statistically representative of Indonesia as a whole. The coverage was designed, however, to capture regional variation within Indonesia by surveying schools in different provinces both on- and off-Java, ensure coverage of the eastern islands, as well as schools in urban and rural areas.
Start | End |
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2000-04 | 2000-05 |
The data were collected between April 10 - May 7, 2000, and the interviewing involved more than 75 data collectors, mostly students from local universities. Each data collector was expected to visit one school per day.
Public use file
Use of the survey data must be acknowledged using a citation which would include:
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Name | Affiliation | |
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Hooman Dabidian | World Bank | hdabidian@worldbank.org |
Cindy Audiguier | World Bank | caudiguier@worldbank.org |
DDI_IDN_2000_QSDS_v01_M
Name | Affiliation | Role |
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Antonina Redko | DECDG, World Bank | DDI documentation |
2011-09-23
v01 (September 2011)