1. Rachel Snow United Nations Population Fund
Capacity strengthening to estimate SDG indicators at lower geographic levels – Small Area Estimation (SAE)
Rachel Snow
United Nations Population Fund

2. SDG Indicator : proportion of women age years who have their need for family planning satisfied with modern methods
Data sources: Nationally representative population surveys
[including: Contraceptive Prevalence Surveys (CPS), Demographic and Health Surveys (DHS), Fertility and Family Surveys (FFS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Performance Monitoring and Accountability 2020 surveys (PMA), World Fertility Surveys (WFS), etc..]
Challenge: With sample sizes - level of geographic disaggregation limited - often to one level below national (e.g. regional, provincial etc.).
Opportunity:
Censuses lack data on family planning, but include data related to use of family planning e.g. level of education, occupation, residence, hh wealth, children ever born, children surviving.
When a national household survey is conducted close to the census – opportunity to combine sources for SAE of Indicator at lower geographic level

3. Key Steps of Small Area Estimation
Small Area Estimation (SAE)
Data Analysis and Assessment
Regression model coefficients for predicting probability of using contraception for an individual woman obtained from DHS data
The national coefficients from DHS are applied to Census Data to predict the probability of using contraception for individual women
The individual contraceptive use probabilities from census data are aggregated (using average) to district level
Application of SAE
Estimate the number of women aged (married or in union) in need of contraception
Identify priority districts
Major Steps:
1. Identify common variables associated with contraceptive indicators in DHS and census data
DHS
CENSUS
2. Develop a model for predicting the probability of individual contraceptive use using DHS data
3. Apply the model to census and estimate the probability of individual contraceptive use
4. Aggregate the estimation of individual contraceptive use to small area administrative level and map results
Variable 1
Variable 2
Variable 3 ……
Data Analysis and Assessment
Initial variables identification
2011 NDHS and 2011 Census data omparison
Final variable identification
Use SAE to produce estimates of FP indicators for each one of the 75 districts in Nepal
The SAE technique takes advantage of the existing correlation between a set of common variables in the 2011DHS and the 2011 Population Census (age, number of children, urban/rural residence, education, water and sanitation, etc.) to predict values for contraceptive dynamics indicators at the district level using regression models.

4. Small Area Estimation using Census and DHS- district level
Estimating proportion of women with need for family planning satisfied with modern methods (SDG Indicator 3.7.1) at district level, -Nepal
DHS Data- regional level
Note: Census year is same year as DHS year or close
Small Area Estimation using Census and DHS- district level

5. Wider Applications
SAE approach can be used to estimate other SDG indicators at lower geographic levels – FGM, gender based violence etc.
Applicability depends on:
Census and survey data collected around the same time
Relevant variables and data compatibility between census and survey-
Suitability of the prediction model- analysis of model fitness

6. Lessons Learned: Strengthening capacity of NSOs to use SAE
Various training modalities
Week-long hands-on regional training in Asia Pacific Region in 2016
Shorter workshops in 2017 in Abuja, the Global Data Forum, Kenya, and SDG Training in Korea last week - have touched NSOs from a range of countries
Background of participants crucial – Ease using both data sets
Preloading census data and DHS data
Multiple representatives from a given institution improves retention and use
Readiness for follow-up -

7. Approach going forward
2018 training of trainers – cascade approach
National hosting within each UNFPA region – where sufficient expertise is already present to support others
Preloaded data from one country – simplifies approach
Partnerships sought with other institutions – UNICEF, World Bank?
Common methods – different applications

8. Thank you !