Service Delivery Indicators Health Survey 2013-2014 - Harmonized Public Use Data
The SDI provides a set of metrics to benchmark the performance of schools and health facilities in Africa. The Indicators can be used to track progress within and across countries over time, and aim to enhance active monitoring of service delivery to increase public accountability and good governance. Ultimately, the goal of this effort is to help policymakers, citizens, service providers, donors, and other stakeholders enhance the quality of services and improve development outcomes. The perspective adopted by the Indicators is that of citizens accessing a service. The Indicators assemble objective and quantitative information from a survey of frontline service delivery units, using modules from the Public Expenditure Tracking Survey (PETS), Quantitative Service Delivery Survey (QSDS), and Staff Absence Survey (SAS). The SDI initiative is a partnership of the World Bank, the African Economic Research Consortium (AERC), and the African Development Bank. More information on the SDI survey instruments and data, and more generally on the SDI initiative can be found at: www.worldbank.org/sdi, or by contacting SDI@worldbank.org <mailto:SDI@worldbank.org>.
Kind of data
Sample survey data [ssd]
- v01: Harmonized, anonymous dataset for public distribution.
The data cover twelve states: Anambra, Bauchi, Bayelsa, Cross River, Ekiti, Imo, Kaduna, Kebbi, Kogi, Niger, Osun, and Taraba. The data may be combined to produce a joint estimate.
Unit of analysis
The facility list was restricted to three major categories: health posts, health centers (including medical clinics), and first-level hospitals.
Producers and sponsors
World Bank Group
Hanovia Medical Limited
Data collection and processing
Bill and Melinda Gates Foundation
William and Flora Hewlett Foundation
Hanovia Medical Limited
Dr. Opeyemi Fadeyebi
Dr. Ngozi Agbanusi
Ayodeji Oluwole Odutolu
Project task team leader
The sampling strategy was designed with the dual aims of producing nationally representative estimates and having a minimum power of 80 percent with 0.05 significance level for comparison of key service delivery indicators. For example, provider absence rates will be estimated with sufficient precision to identify changes in the indicator of 4.4 percentage points at the state level. The sampling strategy also allows for disaggregation by geographic location (rural/urban) and facility-type categories. The sampling approach is a multistage, cluster sampling.
The sampling strategy represents a trade-off: for a fixed sample size and the quality of comparisons between sub-state level facilities. There were also practical considerations such as cost and logistical effort. A simple random sample would imply added costs of travel and administration. With this in mind the first stratification was by LGAs (versus facilities) in order to manage the geographic spread of the sample. Backup facilities were drawn from each location in case the sampling frame includes facilities that no longer exist, are not functional or are inaccessible due to security or extreme weather conditions. Note, these back-up facilities are not to be used for logistical replacement; facilities were selected in keeping with the probability sampling approach.
The target population is the population of selected states in Nigeria (Anambra, Bauchi, Bayelsa, Cross River, Ekiti, Imo, Kaduna, Kebbi, Kogi, Niger, Osun and Taraba). Four data sources were used in developing the sampling frame:
(i) Public facilities: Ministries of Health;
(ii) Location-specific data on the fraction of the local population living in poverty was obtained from the Nigeria national statistical authority; and
(iii) The fraction living in urban areas, was obtained from the national statistical authority.
This note assumes that the sampling frame provided by the Ministry of Health is complete, and that the poverty data are the latest available.
Population estimates were obtained from the latest population projections provided by the National Population Commission (NPC), using the latest census data.
There are numerous types of facilities. The facility list was restricted to three major categories: Health Posts; Health centers (including medical clinics); First-level hospitals. Taking ownership into account, the facilities were then aggregated into six categories.
Based on the most recent available from national statistical authority, the facilities were categorize as rural or urban and poor on non-poor. These two binary distinctions yield four strata within which to sample facilities. Within each stratum, facilities are selected randomly.
Sample Size and Level of Power
To anticipate the statistical properties of the sample, an intra-cluster correlation of selected service delivery indicators from other service delivery surveys. This was used to generate various scenarios (number of facilities, statistical properties associated with selected indicators for state-level and health center-level comparisons). The minimum detectable effect (in terms of percentage points) shown in the scenarios is what can be detected with power 80 percent and confidence level 95 percent.]
Deviations from sample design
Weights are provided to estimate population parameters.
Dates of collection
Mode of data collection
Four modules were used, with multiple sections under the following themes:
• Module 1: Facility information
• Module 2: Staff roster
• Module 3: Patient case simulations (to measure provider knowledge)
• Module 4: Facility expenditure, resources and governance (data not included in public release files)
Data sets are harmonized to a common questionnaire and subsequently anonymized. For more information on both procedures, please refer to the technical documents: Note to Users (ReadMe\ReadMe_FR.pdf), Nigeria Health-20150824.xlsx as well as the SDC report provided as external resources.
Data has been anonymized, but users commit to not seeking to re-identify statistical units in the data set.
The harmonized, anonymized datasets are available as public use files.
Researchers who feel that they need non-anonymized data should contact firstname.lastname@example.org with a statement of research objectives and a rationale for why they require such data. That will start the Research Use File discussion.
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. Nigeria Service Delivery Indicators Health Survey 2013-2014. Ref. NGA_2013_SDI-H_v01_M_v01_A_PUF. Dataset downloaded from [URL] on [date]
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.
Service Delivery Indicators Team
Development Economics Data Group
The World Bank
Documentation of the DDI
Version 01 (March 2016)
Version 02 (January 2017)
The following changes were made in this version:
- Survey title changed from Service Delivery Indicators Health Survey 2013-2014 to Service Delivery Indicators Health Survey 2013-2014 - Harmonized Public Use Data
- Series Information added
- Scope edited