Quantitative Service Delivery Survey in Education 2003
Indonesia, 2002 - 2003
Created on
Apr 25, 2019
Last modified
Apr 25, 2019
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Identification
Survey ID Number
IDN_2003_QSDS_v01_M
Title
Quantitative Service Delivery Survey in Education 2003
Country
| Name | Country code |
|---|---|
| Indonesia | IDN |
Study type
Quantitative Service Delivery Survey (QSDS)
Series Information
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).
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).
Abstract
This survey is the first detailed study on the phenomena of teacher absenteeism in Indonesia obtained from two unannounced visits to 147 sample schools in October 2002 and March 2003. The study was conducted by the SMERU Research Institute and the World Bank, affiliated with the Global Development Network (GDN). Similar surveys were carried out at the same time in seven other developing countries: Bangladesh, Ecuador, India, Papua New Guinea, Peru, Uganda, and Zambia.
This research focuses on primary school teacher absence rates and their relations to individual teacher characteristics, conditions of the community and its institutions, and the education policy at various levels of authority. A teacher was considered as absent if at the time of the visit the researcher could not find the sample teacher in the school.
This survey was conducted in randomly selected 10 districts/cities in four Indonesian regions: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara.
This research focuses on primary school teacher absence rates and their relations to individual teacher characteristics, conditions of the community and its institutions, and the education policy at various levels of authority. A teacher was considered as absent if at the time of the visit the researcher could not find the sample teacher in the school.
This survey was conducted in randomly selected 10 districts/cities in four Indonesian regions: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Teachers
- Schools
- Schools
Version
Version Description
v01 - Final, edited dataset.
Documented here is final, cleaned dataset prepared by the World Bank based on the raw dataset provided by the study researchers.
The description of the difference between raw and edited datasets is taken from "Data Cleaning Guide for PETS/QSDS Surveys" (p.10):
"Each country set includes two data files. The first file, the "raw" data file, presents the data as collected and entered by the survey teams. While field teams do conduct very high-level coherence tests with regards to responses collected, the data contained therein has generally not been thoroughly checked for internal coherence across questions, variable outliers and other such involved data cleaning procedures.
The second file, the "final" data file, has been reviewed in order to ensure consistency both within and across single observations. While the sanctity of data is paramount, such that no changes are made if it cannot be asserted that the edited value is closer to the "true" value than the previous entry, data edits are introduced into the final data set. The list of edits applied are listed in the available Stata 10 © do-file associated with each data set. Furthermore, each do-file includes other tests that were applied to the data set. In addition, basic statistical analysis is applied to variables in order to identify potential statistical outliers. Outlier values that cannot be explained are replaced by missing values in the "final" data set; these changes are reported both in the do-file and in the Data Quality Report.
Finally, independently of the values presented in the questionnaires, missing values are replaced across all "final" data sets to ensure consistency across countries. Following industry best practices, negative 3-digit integers are used in order to ensure there is no confusion between missing values and valid data points. "
"Data Cleaning Guide for PETS/QSDS Surveys" is available in external resources.
Documented here is final, cleaned dataset prepared by the World Bank based on the raw dataset provided by the study researchers.
The description of the difference between raw and edited datasets is taken from "Data Cleaning Guide for PETS/QSDS Surveys" (p.10):
"Each country set includes two data files. The first file, the "raw" data file, presents the data as collected and entered by the survey teams. While field teams do conduct very high-level coherence tests with regards to responses collected, the data contained therein has generally not been thoroughly checked for internal coherence across questions, variable outliers and other such involved data cleaning procedures.
The second file, the "final" data file, has been reviewed in order to ensure consistency both within and across single observations. While the sanctity of data is paramount, such that no changes are made if it cannot be asserted that the edited value is closer to the "true" value than the previous entry, data edits are introduced into the final data set. The list of edits applied are listed in the available Stata 10 © do-file associated with each data set. Furthermore, each do-file includes other tests that were applied to the data set. In addition, basic statistical analysis is applied to variables in order to identify potential statistical outliers. Outlier values that cannot be explained are replaced by missing values in the "final" data set; these changes are reported both in the do-file and in the Data Quality Report.
Finally, independently of the values presented in the questionnaires, missing values are replaced across all "final" data sets to ensure consistency across countries. Following industry best practices, negative 3-digit integers are used in order to ensure there is no confusion between missing values and valid data points. "
"Data Cleaning Guide for PETS/QSDS Surveys" is available in external resources.
Scope
Notes
The scope of the study includes:
a) School Level:
- Facility size, number of teachers, and teachers' names;
- School hours and the time when the visit was conducted;
- Attendance or absence of all the teachers stated in the sample list (identifying those who were present and absent);
- Reasons for absences for different teachers (for those that were absent, researchers asked the reasons for the absence, such as, for work-related reasons, attending training sessions or meetings, assisting in census-gathering activities, or immunizations);
- Activities performed by each teacher at the time of the visit;
- School's relative remoteness (e.g., distance from paved roads and the distance from the city center);
- Distances from the nearest bank (in some countries teachers have to travel a great distance to collect their salaries from banks, therefore causing their absence from schools).
- The language used by the students (to see the possibility of language differences between teachers and students, relating to teachers' absences).
- Community involvement (e.g., the existence of a parents association, the number of parents attending school meetings, and the number of parents making financial contributions to the school);
- Monitoring and supervision (teachers' monitoring methods and if available, the frequency of the department of education officers' visits to the school);
- Discipline (the number of teachers who were given warnings or were dismissed because of their lack of discipline, and the reasons for being absent);
- The procedures for replacing teachers who were absent (were there any substitute teachers, were the classes merged, or were the students sent home);
- Use of school facilities (data of students who were actually present compared to the student data taken from available archives);
- Schools' finances (sources of teachers' salaries, such as from the government, parents' contribution, or donations from non-profit organizations);
- Educational background of teachers;
- School characteristics (e.g., whether the surveyed school has a library, blackboards, public bathrooms, and electricity).
b) Teacher Level:
- Distance and transportation means to schools (the distance between teachers' residence and the school and the means of transportation used);
- Linkages with surrounding communities (were the teachers born and raised near the schools where they worked and where their families lived);
- Title/position of the teachers (headmasters, regular teachers, part-time teachers, etc.);
- Demographic characteristics of teachers (e.g., age and gender);
- Teachers' ethnic group and mother tongue (compared with those of the local community);
- Training and education related to teachers' job (teachers' education level);
- Teachers' marital status and the number of children they have (including the number of children who attended the school where the teacher works);
- Teachers' extra income/earnings (from other jobs/sources);
- Teachers' tenure and work experiences;
- Salary information (e.g., how teachers received their salaries, do the salaries always arrive on time);
- Teachers' career choice motivation and their job satisfaction at the present.
a) School Level:
- Facility size, number of teachers, and teachers' names;
- School hours and the time when the visit was conducted;
- Attendance or absence of all the teachers stated in the sample list (identifying those who were present and absent);
- Reasons for absences for different teachers (for those that were absent, researchers asked the reasons for the absence, such as, for work-related reasons, attending training sessions or meetings, assisting in census-gathering activities, or immunizations);
- Activities performed by each teacher at the time of the visit;
- School's relative remoteness (e.g., distance from paved roads and the distance from the city center);
- Distances from the nearest bank (in some countries teachers have to travel a great distance to collect their salaries from banks, therefore causing their absence from schools).
- The language used by the students (to see the possibility of language differences between teachers and students, relating to teachers' absences).
- Community involvement (e.g., the existence of a parents association, the number of parents attending school meetings, and the number of parents making financial contributions to the school);
- Monitoring and supervision (teachers' monitoring methods and if available, the frequency of the department of education officers' visits to the school);
- Discipline (the number of teachers who were given warnings or were dismissed because of their lack of discipline, and the reasons for being absent);
- The procedures for replacing teachers who were absent (were there any substitute teachers, were the classes merged, or were the students sent home);
- Use of school facilities (data of students who were actually present compared to the student data taken from available archives);
- Schools' finances (sources of teachers' salaries, such as from the government, parents' contribution, or donations from non-profit organizations);
- Educational background of teachers;
- School characteristics (e.g., whether the surveyed school has a library, blackboards, public bathrooms, and electricity).
b) Teacher Level:
- Distance and transportation means to schools (the distance between teachers' residence and the school and the means of transportation used);
- Linkages with surrounding communities (were the teachers born and raised near the schools where they worked and where their families lived);
- Title/position of the teachers (headmasters, regular teachers, part-time teachers, etc.);
- Demographic characteristics of teachers (e.g., age and gender);
- Teachers' ethnic group and mother tongue (compared with those of the local community);
- Training and education related to teachers' job (teachers' education level);
- Teachers' marital status and the number of children they have (including the number of children who attended the school where the teacher works);
- Teachers' extra income/earnings (from other jobs/sources);
- Teachers' tenure and work experiences;
- Salary information (e.g., how teachers received their salaries, do the salaries always arrive on time);
- Teachers' career choice motivation and their job satisfaction at the present.
Topics
| Topic | Vocabulary |
|---|---|
| Primary Education | World Bank |
Coverage
Geographic Coverage
Java-Bali, Sumatera, Kalimantan-Sulawesi and Nusa Tenggara regions
Producers and sponsors
Primary investigators
| Name |
|---|
| SMERU Research Institute, Indonesia |
| World Bank |
Sampling
Sampling Procedure
Information from Indonesian Statistics Agency (BPS) and the Ministry of Education was used as a basis to build a sample frame. The data gathered included the amount of total population, a list of villages and primary school facilities in each district/city. Due to limited time and resources, this research only focused on primary schools. In Indonesia, there are two types of primary education facilities: primary schools and primary madrasah. Primary schools are regulated by the Ministry of National Education, using the general curriculum, while primary madrasah are regulated by the Ministry of Religious Affairs, using a mixed (general and Islamic) curriculum.
A sample of districts/cities and schools (consisting of primary schools and primary madrasah) were selected using the following steps. First, Indonesia was divided into several regions based on the number of total population: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara. Indonesian provinces that were suffering from various conflicts (such as Aceh, Central Sulawesi, Maluku, North Maluku, and Papua) were removed from the sample selection process. Then, from each region, a total of five districts and cities were randomly selected, taking into account the population of each district/city.
Second, 12 schools were selected in each district/city. Before choosing sampled schools, researchers randomly selected 10 villages in each district/city to be sampled, taking into account the location of these villages (in urban or rural areas). One of the 10 villages was a backup village to anticipate the possibility of a village that was too difficult to reach. In each village sampled, researchers asked residents about the location of primary schools/madrasah (both public and private) in these villages. They started visiting schools, giving priority to public primary schools/madrasahs. To meet the number of samples in each district/city, additional samples were selected from private schools.
Third, in each school sampled, the researcher would request a list of teachers. If a school visited was considered to be large, such as schools with more than 15 teachers, then the researcher would only interview 15 teachers chosen randomly to ensure that survey quality could be maintained despite the limited time and resources. Each school was visited twice, both on an unannounced date. From the 147 primary schools/madrasah in the sample, 1,441 teachers were selected in each visit (because this is a panel study, the teacher absence data that were used were taken only from teachers that could be interviewed or whose data were obtained from both visits). If there were teachers whose information was only obtained from one of the visits, then their data was not included in the dataset panel.
A sample of districts/cities and schools (consisting of primary schools and primary madrasah) were selected using the following steps. First, Indonesia was divided into several regions based on the number of total population: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara. Indonesian provinces that were suffering from various conflicts (such as Aceh, Central Sulawesi, Maluku, North Maluku, and Papua) were removed from the sample selection process. Then, from each region, a total of five districts and cities were randomly selected, taking into account the population of each district/city.
Second, 12 schools were selected in each district/city. Before choosing sampled schools, researchers randomly selected 10 villages in each district/city to be sampled, taking into account the location of these villages (in urban or rural areas). One of the 10 villages was a backup village to anticipate the possibility of a village that was too difficult to reach. In each village sampled, researchers asked residents about the location of primary schools/madrasah (both public and private) in these villages. They started visiting schools, giving priority to public primary schools/madrasahs. To meet the number of samples in each district/city, additional samples were selected from private schools.
Third, in each school sampled, the researcher would request a list of teachers. If a school visited was considered to be large, such as schools with more than 15 teachers, then the researcher would only interview 15 teachers chosen randomly to ensure that survey quality could be maintained despite the limited time and resources. Each school was visited twice, both on an unannounced date. From the 147 primary schools/madrasah in the sample, 1,441 teachers were selected in each visit (because this is a panel study, the teacher absence data that were used were taken only from teachers that could be interviewed or whose data were obtained from both visits). If there were teachers whose information was only obtained from one of the visits, then their data was not included in the dataset panel.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2002-10 | 2003-03 |
Data Collection Mode
Face-to-face [f2f]
Data Collection Notes
Data was collected during school sessions in two visits. The second visit not only increased the accuracy of the estimated absence rate, but was also important for gathering specific individual data of the teachers who were called on during the first visit.
Questionnaires
Questionnaires
The following survey instruments are available:
- Teacher Questionnaire, First Visit
- Teacher Questionnaire, Second Visit.
- Teacher Questionnaire, First Visit
- Teacher Questionnaire, Second Visit.
Data Processing
Data Editing
Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.
The STATA cleaning do-file and the data quality report on the dataset can also be found in external resources.
The STATA cleaning do-file and the data quality report on the dataset can also be found in external resources.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Hooman Dabidian | World Bank | hdabidian@worldbank.org |
| Cindy Audiguier | World Bank | caudiguier@worldbank.org |
Access conditions
Public use file
Citation requirements
The use of this dataset must be acknowledged using a citation which would include:
- the identification of the Primary Investigator (including country name)
- the full title of the survey and its acronym (when available), and the year(s) of implementation
- the survey reference number
- the source and date of download (for datasets disseminated online)
Example:
SMERU Research Institute and the World Bank. Quantitative Service Delivery Survey in Education (QSDS) 2003, Ref. IDN_2003_QSDS_v01_M. Dataset downloaded from http://microdata.worldbank.org on [date].
- the identification of the Primary Investigator (including country name)
- the full title of the survey and its acronym (when available), and the year(s) of implementation
- the survey reference number
- the source and date of download (for datasets disseminated online)
Example:
SMERU Research Institute and the World Bank. Quantitative Service Delivery Survey in Education (QSDS) 2003, Ref. IDN_2003_QSDS_v01_M. Dataset downloaded from http://microdata.worldbank.org on [date].
Disclaimer and copyrights
Disclaimer
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.
Metadata production
DDI Document ID
DDI_IDN_2003_QSDS_v01_M
Producers
| Name | Affiliation | Role |
|---|---|---|
| Antonina Redko | DECDG, World Bank | DDI documentation |
Date of Metadata Production
2011-08-03
DDI Document version
v01 (August, 2011)