State of Gender Data Financing 2021 Methodology Note

This methodology note describes the gender-relevant statistical instrument inventory constructed as part of the State of Gender Data Financing 2021 in order to measure the capacity of gender data systems in IDA-eligible countries in section 3b, as well as to estimate the costs to complete the gender data system in section 3c. The inventory captures gender-relevant survey and census instruments conducted over the period 2010 through 2020 across 74 IDA-eligible countries according to World Bank FY2021 country groups in October 2020 and consists of the following instruments:

Censuses and surveys

Population and housing censuses, which detail basic population demographics and household composition to understand communities. Assessors recorded censuses available in the 2010 and 2020 rounds from the UN Statistics Division’s World Population and Housing Census Programme, though only the 2020 census round was included for the purposes of costing investments. In the instance where there is no information for a country (as in the case of Kosovo), assessors looked at the NSO website to find censuses. Assessors also recorded the status of population and housing censuses as a result of the COVID-19 pandemic through personal correspondence with UNSD and UNFPA, but for the purposes of costing census instruments, the census count reflects planned censuses as of Fall 2020.

Household health and women’s wellbeing, which detail health, demographic and other information on well-being at the household level. Assessors looked at the DHS, MICS, NSO and other ministries. Additionally, assessors looked through microdata catalogs of the World Bank, IHSN, and NADA. Assessors also noted whether there is a scheduled DHS or MICS survey.

Income or expenditure and multi-topic household surveys, which detail poverty and distribution of income or wealth. These surveys typically include the Living Standards Measurement Surveys or other household, income, expenditure surveys. Assessors looked through microdata catalogs of the World Bank, IHSN, and NADA. Additionally, assessors also looked through NSO websites to see if more recent surveys are available.

Labor force surveys or other survey modules, which detail the paid and unpaid work of men and women at the household level. Assessors searched for labor force surveys on microdata catalogs of ILO, World Bank, IHSN, and NADA. Additionally, assessors looked through the NSO and ministry of labor websites if more recent surveys are available. In this section, Open Data Watch also received supplementary information from ILO, particularly on the status and frequency of labor force surveys or labor force modules.

Time use surveys or modules, which detail the paid and unpaid work of men and women at the household level. Assessors referenced the UN Statistics Division’s time use portal to find whether or not a country has conducted a time use survey.

Agricultural censuses or surveys, which detail farm production, income and paid and unpaid activities of family members. Assessors looked at the microdata catalogues of the World Bank, IHSN, and NADA; assessors also looked at the NSO website. When searching for agricultural censuses and surveys, assessors would go through the survey report and questionnaires to make sure that it records basic information on household members or the agricultural population.

Administrative systems

Civil registration and vital statistics systems: The designation of low, medium, and high functionality was based on SDG indicator 16.9.1 “Proportion of children under 5 years of age whose births have been registered with a civil authority.” This indicator is usually estimated from surveys that ask mothers whether their children’s births have been registered. In cases where birth registration is missing, qualitative information is used to assign a functionality score.

The coverage threshold is as follows:

Low coverage: 0-44%
Medium coverage: 45-74%
High coverage: 75-100%

There were 12 countries where indicator 16.9.1 was not available. In those instances, Open Data Watch assessed whether a country had data for ODIN indicators 1.2 and 1.3 (number of births and deaths, respectively). See ODIN methodology and indicators for details. From there, additional qualitative information on the country’s CRVS was assessed. For most countries, qualitative information was obtained from Get in the Picture, UNICEF, and other publications from IGOs and academia.

Education management and information systems: The designation of low, medium, and high functionality was based on the availability of ODIN indicator 3.1 – enrollment rate. As a first step, the ODW assessors recorded whether data are available for ODIN indicator 3.1. If data are available, then the years were recorded. There were a few countries, such as Tuvalu and Kiribati, that were not included in ODIN. In this case, assessors then tried to find data for ODIN indicator 3.1 on the country’s NSO website.

ODIN alone cannot assess a country’s education management information system. For example, countries may have data on school enrollments, but lack a management information system. Their data collection methods could be decentralized and be paper based. Therefore, another layer of research was added. Assessors also looked at qualitative information about a country’s education management information system. Qualitative information was primarily found on IGO websites (such as UNICEF, UNESCO), bilateral agencies (USAID), NGOs, and academic articles.

From assessing ODIN indicator 3.1 and qualitative information on a country’s EMIS, a functionality score was created:

Score of 0: No ODIN data; no MIS
Score of 0.5: limited ODIN data; weak MIS
Score of 1: Some ODIN data; weak MIS
Score of 1.5: Most ODIN data available; partially functioning MIS
Score of 2: ODIN data fully available (at least 9 of 10 years); functioning MIS

The functionality score was then converted to a scale:

Score of: 0 – 1: Low
Score of 1.5: Medium
Score of 2: High

Health management and information systems: The designation of low, medium, and high functionality was based on the availability of ODIN indicator 4.1 – number of health facilities. ODIN indicator 4.1 was selected because this indicator overwhelmingly relies on administrative sources. There are other ODIN indicators such as disease prevalence or immunization rate, however, depending on the country, those indicators can use administrative sources or health surveys (such as DHS/MICS).

As a first step, the ODW assessors recorded whether data are available for ODIN indicator 4.1. If data are available, then the years are recorded. There were a few countries, such as Tuvalu and Kiribati, that were not included in ODIN. In this case, assessors then tried to find data for ODIN indicator 4.1 on the country’s NSO website.

ODIN alone cannot assess a country’s health management information system. For example, countries can have data on health facilities, but lack a management information system. Their data collection methods could be decentralized and be paper based. Therefore, another layer of research was added. Assessors also looked at qualitative information about a country’s health management information system. Qualitative information was primarily found on WHO, Measure Evaluation, bilateral aid agencies, governmental reports, and other publications from academia or from other NGOs and IGOs.

From assessing ODIN indicator 4.1 and qualitative information on a country’s HMIS, a functionality score was created:

Score of 0: No ODIN data; no MIS
Score of 0.5: limited ODIN data; weak MIS
Score of 1: Some ODIN data; weak MIS
Score of 1.5: Most ODIN data available; partially functioning MIS
Score of 2: ODIN data fully available (at least 9 of 10 years); functioning MIS

The functionality score was then converted to a scale:

Score of: 0 – 1: Low
Score of 1.5: Medium
Score of 2: High

In Table 1 below, we display the distribution of the 74 IDA-eligible countries across the three administrative data systems and three functionality grades.

Table 1: Functionality of administrative data systems

Administrative Systems	No. of countries
Administrative Systems	High	Medium	Low
Civil registration and vital statistics	26	27	21
Education management information system	8	29	37
Health management information systems	12	14	48