Quantitative Service Delivery Survey in Education 2002
India, 2002 - 2003
Identification
IND_2002_QSDSE_v01_M
Quantitative Service Delivery Survey in Education 2002
| Name | Country code |
|---|---|
| India | IND |
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).
For this study, enumerators made unannounced visits to primary schools in India and recorded whether they found teachers in the facility. In rural India, enumerators also collected data from private schools and non-formal education centers located in the same village as public schools. Three unannounced visits were made to each of about 3,000 public schools from October 2002 to April 2003. Since the average school in the sample had around four teachers, investigators gathered nearly 35,000 observations on teacher attendance.
The survey also gathered data on reasons of teacher absence, characteristics of teachers, schools and communities.
In India, the survey was designed to be representative in each of 20 states, which together account for 98 percent of India's population.
A Quantitative Service Delivery Survey that assessed employee attendance in primary health care facilities in India was carried out at the same time with this research. Moreover, similar studies were conducted in education and health sectors in Bangladesh, Uganda, Ethiopia, Kenya, Indonesia, Peru and Ecuador.
Sample survey data [ssd]
Scope
| Topic | Vocabulary |
|---|---|
| Education | World Bank |
| Primary Education | World Bank |
Coverage
National
Producers and sponsors
| Name |
|---|
| World Bank |
| Name |
|---|
| UK Department for International Development |
| Global Development Network |
Sampling
The description of the sampling procedure below is taken from "Initial Project Description: Survey of Education and Health Providers" (p.9-10). This document is available in external resources.
"For schools, we plan to use a population-based random sampling. We will choose randomly ten villages or towns (urban blocks) within each district, after stratifying by rural/urban location. Enumerators will then proceed to the village or town and find out from villagers where the closest government and private schools are. They will then visit up to a total of three schools and carry out the facility survey in each one. (Where there are more than three schools, enumerators will choose schools on a randomized basis, in a way that ensures that both government and private schools are included in the sample).
To reduce travel and transportation costs, it may sometimes be necessary to cluster villages/towns or facilities. Under the facility-based selection approach, for example, five areas may be randomly chosen within each district, and two schools in that area will be selected, rather than choosing a random sample of ten areas. During data analysis, we will adjust standard errors to account for clustered sampling.
At the facility level, we will also obtain a roster of teachers in the school. If the facility is large (for example, if there are more than 25 teachers in a school), we will interview a random sample of the teachers to keep the size of the survey manageable.
This survey is focused on basic education. Given time and personnel constraints, it will therefore focus only on primary schools, not secondary schools. In each Indian state, we will survey 10 districts, with at least two visits each to a representative sample of at least 10 health facilities and 10 or more primary schools within each; if the average village has 1.5 schools, the sample will actually be 15 schools per district. This means detailed and representative provider- and facility-level results from perhaps 150 schools and some 100 health centers for each jurisdiction. In addition, there will be a third visit to some smaller sub-sample of the schools and to all of the health centers, as a check and to provide additional data on long-term absence. With these repeated visits, we expect to carry out some 300 school visits and 300 health center visits in each jurisdiction, which should provide several thousand observations of presence/absence for individual
providers and all of the necessary facility-level correlates."
Data collection
| Start | End |
|---|---|
| 2002-10 | 2003-04 |
The survey notes below are taken from "Initial Project Description: Survey of Education and Health Providers." This document is available in external resources.
"Having multiple observations over time for each facility will help us determine whether absences are concentrated among a few teachers and health care workers, or are more spread out among a broader set of workers. This information could help shape policies. For example, a 10 percent absence rate could arise because all workers are absent 10 percent of the time, or because 5 percent of workers are ghost workers who never show up while the majority are rarely absent. In this case, policies targeted at the ghost workers would not make the majority of teachers feel threatened. On the other hand, if all workers are absent 10 percent of the time, the policy implications will differ. Repeat visits will help us understand which approach to solving the problem will be most promising.
A worker was counted as absent if, at the time of a random visit during facility hours, he or she was not in the school or health center. The enumerators for the survey took several measures to ensure that the rate of absence would not be overestimated. The list of employees used for checking attendance was created at the facility itself, based on staff lists and schedule information provided by the facility director or other principal respondent. Enumerators then checked the attendance only of those who were ordinarily supposed to be on duty at the time of the visit. We omitted from the absence calculations all employees who were reported by the director as being on another shift, whether or not this could be verified. Only full-time employees were included in our analysis, to minimize the risk that shift workers would be counted as absent when they were not supposed to be on duty. Measured absences in education were slightly lower in later survey
rounds, consistent with the hypothesis that awareness of the first round of the survey created a bit of a “warning effect” regarding the presence of the survey teams."
Data Access
Public use file
Use of the survey data 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.
Disclaimer and copyrights
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.
Contacts
| Name | Affiliation | |
|---|---|---|
| Hooman Dabidian | World Bank | hdabidian@worldbank.org |
| Cindy Audiguier | World Bank | caudiguier@worldbank.org |
Metadata production
DDI_IND_2002_QSDSE_v01_M
| Name | Affiliation | Role |
|---|---|---|
| Antonina Redko | DECDG, World Bank | DDI documentation |
2011-09-22
Metadata version
v01 (September 2011)