Lao PDR - SABER Service Delivery 2018, Measuring Education Service Delivery

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The SABER Service Delivery survey tool was developed in 2016 in the Global Engagement and Knowledge Unit of the Education Global Practice (GP) at the World Bank, as an initiative to uncover bottlenecks that inhibit student learning in low and middle income countries and to better understand the quality of education service delivery in a country as well as gaps in policy implementation. The SABER SD survey collects strategic information on school inputs and processes that influence learning outcomes. The data collected aims to uncover the extent to which policies translate into implementation and practice. As a global initiative, SABER SD provides data for the new global lead indicator on learning, which makes it easier to monitor the Sustainable Development Goal of achieving universal primary education. SABER SD was created using knowledge and expertise from two major initiatives at the World Bank: SABER (Systems Approach for Better Education Results) and the SDI (Service Delivery Indicators) tools. The SABER program conducts research and knowledge from leading expertise in various themes of education. Using diagnostic tools and detailed policy information, the SABER program collects and analyzes comparative data and knowledge on education systems around the world and highlights the policies and institutions that matter most to promote learning for all children and youth. The SDI program is a large-scale survey of education and health facilities across Africa. The new SABER SD tool builds on and contributes to the growing SABER evidence base by capturing policy implementation measures identified as important in the frameworks of the core SABER domains of School Autonomy and Accountability, Student Assessment, Teachers, Finance, Education Management Information Systems, and Education Resilience. The SABER SD instrument collects data at the school level and asks questions related to the roles of all levels of government (including local and regional). The tool provides comprehensive data on teacher effort and ability; principal leadership; school governance, management, and finances; community participation; and student performance in math and language and includes a classroom observation module.

Type: 
Microdata
Acronym: 
SABER SD 2018
Languages Supported: 
English
Topics: 
Topic not specified
Tags: 
Geographical Coverage: 
Lao PDR
Economy Coverage: 
Economy Coverage not specified
Release Date: 
April 4, 2019

Last Updated

Last Updated: 
April 4, 2019

Harvest System ID

Harvest System ID: 
Microdata

Harvest Source ID

Harvest Source ID: 
10400
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.
Version Description: 
Version 01 (April 2019)
Publisher Name: 

Development Economics Data Group; The World Bank

Funding Name, Abbreviation, Role: 
SABER Trust Fund, Second Global Partnership for Education Project in Lao PDR
Other Acknowledgments: 
Lao PDR Ministry of Education and Sports (MOES), IndoChina Research Limited (IRL)
Study Type: 
Service Provision Assessments
Series Information: 
Systems Approach to Better Education Results - Service Delivery (SABER SD) consists of a collection of school-based surveys implemented globally. The SABER SD survey work began in 2016. So far, three countries are included in the series: Lao PDR, Afghanistan, and the province of Punjab in Pakistan.
Universe: 
The target was to have a nationally representative sample. All primary schools in Lao PDR were included in the original sample. The final pool of primary schools from which the sample was drawn included those with a grade 4 population of students.
Primary Investigator Name, Affiliation: 
World Bank; World Bank
Sampling Procedure: 
The SABER Service Delivery survey was implemented in primary schools across Lao PDR, with detailed information being collected from Grade 4. According to official records from the Ministry of Education and Sports (MOES) education management information system for the school year 2015-2016, 8,864 primary schools exist across the country. The sample was created using probability proportional to size (PPS) according to the size of students enrolled in Grade 4. The target population of the survey was Grade 4 students, so all schools with at least one student enrolled in Grade 4 were considered in the sample. Schools were stratified for sampling along four dimensions to ensure representation. For each of these, stratification was done on a discrete variable. The four sampling strata used for this survey with a target sample size of 200 schools across Lao PDR are the following: Urban/Rural, Public/Private, Single grade/Multi-grade, Priority/Non-Priority. Multiple sampling scenarios were created according to the number of schools within a stratum. The final sample option was selected based on the standard errors of the sample as a whole and the errors within a subgroup. Please see the sampling appendix in the final report for more information (Appendix A).
Response Rates: 
100% response rate from all 200 schools in sample. No reserve schools were activated.
Weighting: 
The final weighting was conducted at the school level. Since private schools were oversampled in the final sample drawn, other public schools were underrepresented slightly in the final sample. The true approximate percentage of private schools in Lao PDR is 10 percent, whereas in the final sample of the survey, private schools were represented at roughly 17.5 percent. Therefore, the schools in various strata were weighted to match the true spread of public and private schools across Lao PDR. Additional weighting information is available in Appendix A of the final report, and individual weights for each of the strata are available in the data deposit, which is available for download.
Data Collector(s) Name: 
IndoChina Research Ltd.
Supervision: 
Fieldwork supervision was conducted by IRL and the World Bank team based in both Vientiane, Laos and Washington, DC. A detailed fieldwork manual was supplied to all enumerators. Each enumerator team consisted of three members with one as the team leader. Since the data was automatically uploaded via internet connection from the tablets (enabled with SIM cards), each dataset was checked and verified by each team leader the day it was collected. Once approved, the datasets were sent to the central survey firm office, where the data management team ran a series of checks for inconsistencies and missing values. Each time an error was flagged, the World Bank team was alerted and teams worked collectively to solve problems. Every two weeks during fieldwork, the survey firm provided a large data deposit of raw survey data collected to the World Bank team, where high-frequency data check were again conducted for verification. In addition to data, the World Bank team in Washington, DC and Vientiane, Lao PDR were in constant communication with the survey firm, supervisors, and enumerators to ensure that all fieldwork protocols were being followed. Additional field supervision details can be found in the annexes of the final report.
Data Editing: 
After a first round of cleaning and editing carried out by IRL, the raw data was sent to the World Bank team by the survey firm. The World Bank team ran data checks on the raw data files, with comments and questions sent back to the survey firm on inconsistencies and/or missing data. The survey firm then responded to the questions, if any data are missing, the field team collects the data again or corrects the incorrect information. This happened in a few cases where the principals of the schools were contacted again to confirm and verify certain answers from the school. Once the data was finalized, the weights were attached back to the dataset. The weighting procedure was done by the Development Economics Vice Presidency (DEC) team at the World Bank. Finally, with weights attached, the final datasets for each module (1 through 6) were produced. For this data, many modules were also merged together to run analysis across different school components. There is one final data file which has merged modules 1, 2, 3, 5, and 6.
Other Processing: 
The scope of the survey includes information on the following education variables among others: - School facility and infrastructure - Teacher background - Teacher absenteeism - Teacher knowledge - Principal background - School management and accountability - School finance - Community participation - Classroom observation - Student background - Student test scores Data was collected across the following strata: urban/rural; priority/non-priority; single grade/multi-grade; public/private.

No Visualizations Available.

Use of the dataset must be acknowledged using a citation which would include: - the Identification of the Primary Investigator - the title of the survey (including country, acronym and year of implementation) - the survey reference number - the source and date of download Example: World Bank. SABER Service Delivery, Lao PDR 2017. Ref. LAO_2017_SABER-SD_v01_M. Dataset downloaded from [URL] on [date]. In addition, if you use the dataset for published papers, it would greatly help us maintain a citation database if you would inform the primary investigators.

The SABER Service Delivery survey tool was developed in 2016 in the Global Engagement and Knowledge Unit of the Education Global Practice (GP) at the World Bank, as an initiative to uncover bottlenecks that inhibit student learning in low and middle income countries and to better understand the quality of education service delivery in a country as well as gaps in policy implementation. The SABER SD survey collects strategic information on school inputs and processes that influence learning outcomes. The data collected aims to uncover the extent to which policies translate into implementation and practice. As a global initiative, SABER SD provides data for the new global lead indicator on learning, which makes it easier to monitor the Sustainable Development Goal of achieving universal primary education. SABER SD was created using knowledge and expertise from two major initiatives at the World Bank: SABER (Systems Approach for Better Education Results) and the SDI (Service Delivery Indicators) tools. The SABER program conducts research and knowledge from leading expertise in various themes of education. Using diagnostic tools and detailed policy information, the SABER program collects and analyzes comparative data and knowledge on education systems around the world and highlights the policies and institutions that matter most to promote learning for all children and youth. The SDI program is a large-scale survey of education and health facilities across Africa. The new SABER SD tool builds on and contributes to the growing SABER evidence base by capturing policy implementation measures identified as important in the frameworks of the core SABER domains of School Autonomy and Accountability, Student Assessment, Teachers, Finance, Education Management Information Systems, and Education Resilience. The SABER SD instrument collects data at the school level and asks questions related to the roles of all levels of government (including local and regional). The tool provides comprehensive data on teacher effort and ability; principal leadership; school governance, management, and finances; community participation; and student performance in math and language and includes a classroom observation module.

FieldValue
Modified Date
2019-12-17
Release Date
Identifier
cc86427a-178a-4de9-9580-b410c19cd35c
License
License Not Specified
Public Access Level
Public
Rating: 
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No votes yet
Acronym: 
SABER SD 2018
Type: 
Languages Supported: 
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.
Response Rates: 
100% response rate from all 200 schools in sample. No reserve schools were activated.
Weighting: 
The final weighting was conducted at the school level. Since private schools were oversampled in the final sample drawn, other public schools were underrepresented slightly in the final sample. The true approximate percentage of private schools in Lao PDR is 10 percent, whereas in the final sample of the survey, private schools were represented at roughly 17.5 percent. Therefore, the schools in various strata were weighted to match the true spread of public and private schools across Lao PDR. Additional weighting information is available in Appendix A of the final report, and individual weights for each of the strata are available in the data deposit, which is available for download.
Data Collector(s) Name: 
IndoChina Research Ltd.
Supervision: 
Fieldwork supervision was conducted by IRL and the World Bank team based in both Vientiane, Laos and Washington, DC. A detailed fieldwork manual was supplied to all enumerators. Each enumerator team consisted of three members with one as the team leader. Since the data was automatically uploaded via internet connection from the tablets (enabled with SIM cards), each dataset was checked and verified by each team leader the day it was collected. Once approved, the datasets were sent to the central survey firm office, where the data management team ran a series of checks for inconsistencies and missing values. Each time an error was flagged, the World Bank team was alerted and teams worked collectively to solve problems. Every two weeks during fieldwork, the survey firm provided a large data deposit of raw survey data collected to the World Bank team, where high-frequency data check were again conducted for verification. In addition to data, the World Bank team in Washington, DC and Vientiane, Lao PDR were in constant communication with the survey firm, supervisors, and enumerators to ensure that all fieldwork protocols were being followed. Additional field supervision details can be found in the annexes of the final report.
Economy Coverage: 
Other Acknowledgments: 
Lao PDR Ministry of Education and Sports (MOES), IndoChina Research Limited (IRL)
Primary Investigator Name, Affiliation: 
World Bank; World Bank
Funding Name, Abbreviation, Role: 
SABER Trust Fund, Second Global Partnership for Education Project in Lao PDR
Publisher Name: 
Development Economics Data Group; The World Bank
Version Description: 
Version 01 (April 2019)
Subtitle: 
Measuring Education Service Delivery
Universe: 
The target was to have a nationally representative sample. All primary schools in Lao PDR were included in the original sample. The final pool of primary schools from which the sample was drawn included those with a grade 4 population of students.
Geographical Coverage: 
Data Classification of a Dataset: 
Series Information: 
Systems Approach to Better Education Results - Service Delivery (SABER SD) consists of a collection of school-based surveys implemented globally. The SABER SD survey work began in 2016. So far, three countries are included in the series: Lao PDR, Afghanistan, and the province of Punjab in Pakistan.
Sampling Procedure: 
The SABER Service Delivery survey was implemented in primary schools across Lao PDR, with detailed information being collected from Grade 4. According to official records from the Ministry of Education and Sports (MOES) education management information system for the school year 2015-2016, 8,864 primary schools exist across the country. The sample was created using probability proportional to size (PPS) according to the size of students enrolled in Grade 4. The target population of the survey was Grade 4 students, so all schools with at least one student enrolled in Grade 4 were considered in the sample. Schools were stratified for sampling along four dimensions to ensure representation. For each of these, stratification was done on a discrete variable. The four sampling strata used for this survey with a target sample size of 200 schools across Lao PDR are the following: Urban/Rural, Public/Private, Single grade/Multi-grade, Priority/Non-Priority. Multiple sampling scenarios were created according to the number of schools within a stratum. The final sample option was selected based on the standard errors of the sample as a whole and the errors within a subgroup. Please see the sampling appendix in the final report for more information (Appendix A).
Release Date: 
Thursday, April 4, 2019
Last Updated Date: 
Thursday, April 4, 2019
Data Editing: 
After a first round of cleaning and editing carried out by IRL, the raw data was sent to the World Bank team by the survey firm. The World Bank team ran data checks on the raw data files, with comments and questions sent back to the survey firm on inconsistencies and/or missing data. The survey firm then responded to the questions, if any data are missing, the field team collects the data again or corrects the incorrect information. This happened in a few cases where the principals of the schools were contacted again to confirm and verify certain answers from the school. Once the data was finalized, the weights were attached back to the dataset. The weighting procedure was done by the Development Economics Vice Presidency (DEC) team at the World Bank. Finally, with weights attached, the final datasets for each module (1 through 6) were produced. For this data, many modules were also merged together to run analysis across different school components. There is one final data file which has merged modules 1, 2, 3, 5, and 6.
Other Processing: 
The scope of the survey includes information on the following education variables among others: - School facility and infrastructure - Teacher background - Teacher absenteeism - Teacher knowledge - Principal background - School management and accountability - School finance - Community participation - Classroom observation - Student background - Student test scores Data was collected across the following strata: urban/rural; priority/non-priority; single grade/multi-grade; public/private.
Harvest Source: 
Harvest System ID: 
10400
Citation Text: 
Use of the dataset must be acknowledged using a citation which would include: - the Identification of the Primary Investigator - the title of the survey (including country, acronym and year of implementation) - the survey reference number - the source and date of download Example: World Bank. SABER Service Delivery, Lao PDR 2017. Ref. LAO_2017_SABER-SD_v01_M. Dataset downloaded from [URL] on [date]. In addition, if you use the dataset for published papers, it would greatly help us maintain a citation database if you would inform the primary investigators.
Modified date: 
17990
Study Type: 
Service Provision Assessments
Primary Dataset: 
Yes

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