This page provides guidance on how to start a SAE case study. Case studies are also compiled for indicators under relevant Sustainable Development Goals. Case studies are not available for all SDGs yet, but more cases will be added continuously.

Case studies for the estimation of disaggregated SDG indicators

In the following, case studies are summarized for the different SDGs. The descriptions are short and usually refer to a publicly available longer description of the study. The tables sum up user needs (indicators and disaggregation level), data availability and the specified estimation approach. The idea is to learn from other cases since some problems occur in different applications and thus for some problems, solutions may be found in another application.


User needs

  • Goal
  • Indicator of interest
  • Disaggregation level

  • What are the key policies or funding decisions?
  • What questions need to be answered?
  • What are you trying to measure?
  • What type of indicator is the indicator of interest?
  • What is the relevant dimension of disaggregation?
Data availability
  • Which survey data is available for the estimation of the indicator?
  • What are the data challenges?
  • Which additional data sources can be used?
SAE methods/Specification
  • Which SAE approach can be used based on the inputs above?
  • Which approaches are available in statistical software?
  • What is the available expertise to do the computation, analysis and interpretation?
Model validation
  • What is the plan for data validation?
  • What data sources will be used for benchmarking?
  • Are there any plans for an external review process?
Model refining
  • Plan to refine the model
Extend case study to official production
  • Plan or roadmap to extend the case study for official data production 

Goal 1. End poverty in all its forms everywhere

Poverty mapping is one of most common applications in small area estimation. Many examples are available for the proportion of population living below the international or national poverty line (indicators 1.1.1 and 1.2.1).

World Bank applications

The World Bank proposed a poverty mapping process that was conducted in several countries. Based on surveys and additional data sources, various poverty and inequality estimates such as the Foster-Greer-Thorbecke poverty estimates and the Gini coefficient were derived.

The report More than a pretty picture - Using poverty maps to design better policies and interventions published in 2007 shows case studies for the countries Albania, Bolivia, Bulgaria, Cambodia, Yunnan Province (China), Ecuador, Indonesia, Mexico, Morocco, Sri Lanka, Thailand and Vietnam that describe all poverty mapping steps and also lessons learned. Hence this can be a good starting point for a new poverty mapping study. 

In 2005, the World Bank provided technical assistance to the Philippine national statistical system to leverage on small area estimation techniques to produce municipality- and city-level poverty statistics. The Philippine Statistics Authority conducts the Family Income and Expenditure Survey (FIES), which is the main source of official poverty statistics in the country, every three years. The small area estimation technique used in the Philippines is based on the ELL method. It entails regressing (log) per capita household income from the FIES with auxiliary information from the FIES, the Labor Force Survey, and the Census of Population and Housing. The model regressors include survey-obtainable variables such as educational attainment of the household head and other household characteristics, and census-derivable information like average family size in a village, and other village-level information. Since small area poverty statistics became available in 2005, numerous government agencies have used these data as inputs for formulating and implementing poverty reduction programs.  For example, the Philippine Department of Social Welfare and Development (DSWD) used the estimates to identify poor municipalities for its National Household Targeting System for Poverty Reduction (NHTS-PR) data collection.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel


SpatialUnit-level survey and auxiliary dataModel-based estimationELL

Poverty estimation in Chilean comunas

To improve fund allocations among comunas, the Chilean Ministerio de Desarrollo Social (in the following the ministry) is required to provide poverty estimates for all 345 comunas in Chile which is the smallest territorial entity. After the evaluation of various options, the ministry decided to combine the National Socioeconomic Characterization Survey (CASEN), which is Chile's official data source for poverty statistics, with relevant administrative records. Since 2011, model-based poverty statistics are obtained for Chilean comunas.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel
1.2.1SpatialUnit-level survey and area-level auxiliary dataModel-based estimationArcsin-transformed area-level model

Small Area Income and Poverty Estimates (SAIPE) program by the U.S. Census Bureau

The SAIPE program produces small area estimates of income and poverty statistics for all school districts, counties, and states. The estimates are based on several data sources such as the American Community Survey and Federal Income Tax Returns. The produced indicators do not exactly follow the definition of the SDGs but the example is added since the SAIPE program is continuously improving their approach and the disaggregation is not only spatial but also by age groups.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel

Spatial and ageUnit-level survey and area-level auxiliary dataModel-based estimationLog-transformed area-level model

Mean income in Middle-layer Super Output Areas in England and Wales

Super Output Areas (SOAs) are a geographic hierarchy introduced for the reporting of small area estimates. The mean population of Middle-layer SOAs (MSOAs) ranges from a minimum of 5,000 to 7,200. To obtain different income estimates (e.g., equivalised and unequivalised) for the MSOAs, data from the Family Resources Data is combined with additional data including Census information, energy consumption and house price statistics using a linear mixed model.

The case study is explained on the homepage with more methodological details in the technical report.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel

SpatialUnit-level survey and area-level auxiliary dataModel-based estimationLog-transformed unit-level model
Goal 2. End hunger, achieve food security and improved nutrition and promote sustainable agriculture

Estimation of food insecurity in Malawi

FAO’s case study illustrates the implementation of a model-assisted estimation approach proposed by Kim and Rao (2012) to produce disaggregated estimates of  SDG Indicator 2.1.2, “Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)”. FAO (2022) considered the case of two independent surveys conducted in Malawi in 2016: (i) the Fourth Integrated Household Survey (IHS4) and (ii) Gallup World Poll survey (GWP). IHS4 with a large sample collected only auxiliary information and GWP with much smaller sample provided information on both the variable of interest and the auxiliary variables.

The model-assisted projection method is based on a working model which results in asymptotically unbiased projection estimates. Synthetic values of SDG 2.1.2 are generated by first fitting the working model, relating the variable of interest to the auxiliary variables, to the data from GWP and then predicting the variable of interest associated with the auxiliary variables observed in IHS4. The projection estimators are obtained from GWP and associated synthetic values.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel


Age, Sex, Income groups, rural/urban

Unit-level survey and auxiliary dataModel-assisted projection methodApproach following Kim, J. K. and Rao, J. N. K. (2012)

Estimation of productivity and income of small-scale food producers in Mali

FAO’s case study discusses the implementation of a Fay-Herriot small area estimation model to produce granular sub-national estimates of SDG Indicators 2.3.1 and 2.3.2 in Mali, by integrating area-level direct estimates with auxiliary information retrieved from various trustworthy geospatial information systems.The case study is presented both in an article (Khalil et al. 2022), and a FAO technical report (Khalil, C.A & di Candia, Stefano. 2023). The latter document provides practical guidance to national statistical offices and other institutions wanting to implement small area estimation techniques on indicators monitoring target 2.3 or similar measures based on survey microdata.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel
2.3.1 and 2.3.2Spatial and farm sizeArea-level survey and auxiliary dataModel-based estimationFay-Herriot model
Goal 3. Ensure healthy lives and promote well-being for all at all ages

Small Area Health Insurance Estimates (SAHIE) by the U.S. Census Bureau

The SAHIE program produces small area estimates of population with and without health insurance coverage for counties, states and the cross-classification of age, sex and income categories as well as ethnicities. The estimates are based on several data sources such as the American Community Survey and Medicaid participation.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel

Related to 3.8.1

Spatial, age, sex, income groups, ethnicities

Unit-level survey and auxiliary dataModel-based estimationUnmatched area-level model
Goal 4. Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

Estimation of  literacy rates in Senegal

Senegal has been devoting efforts to improve the literacy rate in the country, especially for women. Estimated literacy rates for men and women in 2017 were 64.8% and 51.9% , respectively, according to UNESCO ( ).  Areas of high illiteracy must be identified for targeting public policies. Schmid at al.(2017) developed a small area estimation model for deriving small area literacy rate estimates for the 431 communes in Senegal by gender, using mobile phone auxiliary data. The authors propose an area level model with alternative of sources data as auxiliary variables. The estimates are based on 2011 Demographic Health Survey (DHS) carried out by the National Agency of Statistics and Demography of Senegal (ANSD, Agence Nationale de Statistique et de la Démographie)  and mobile phone data covering the year 2013 (provided by the Senegalese telecommunication company Sonatel ). The small area estimates are benchmarked such that the aggregated small area estimates agree with the national estimate for the country.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel

Related to 4.6.1

Spatial and sex

Area-level survey and auxiliary data

Model-based estimation

Inverse sine transformed (FH) area-level model

Goal 5. Achieve gender equality and empower all women and girls

Estimating women's ownership or secure rights over agricultural land in Uganda

FAO's case study facilitates the use of small area estimation approaches to produce disaggregated estimates of SDG indicator 5.a.1 by sex and at granular sub-national level. In particular, after introducing the framework to implement SAE techniques, the report (FAO, 2022) discusses a possible unit-level SAE approach to integrate a household or agricultural survey measuring the indicator of interest with census microdata, in order to borrow strength from a more comprehensive data source and increase estimates precision.

For the implementation of the case study, the Uganda National Panel Survey (2013-14) - providing microdata to estimate the indicator of interest at the national level - was integrated with auxiliary unit-level information from the Uganda Population and Housing Census (2014). 

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel
5.a.1Spatial (sub-national) and sexUnit level survey and auxiliary dataModel-based estimationbasic unit-level model with EBLUP estimator
Goal 6. Ensure availability and sustainable management of water and sanitation for all
No case studies yet
Goal 7. Ensure access to affordable, reliable, sustainable and modern energy for all

Estimation of fuel poverty at different geographic levels in UK

The Office for National Statistics (ONS) provides estimates of the proportion of households in fuel poverty at local levels. Fuel poverty statistics are required  by central and local governments and may be used to target energy efficiency policies. It is determined by the household income, household energy requirements and fuel prices. Household-level data from the English Housing Survey (EHS) is combined with aggregated household-level information from the Census and Experian data by a binomial model using the logit link function that considers that each household belongs to a specific area.

The description of the case study comprises the data and methodological setup, maps with estimates and an evaluation.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel

Related to 7.1.1 and 7.1.2


Unit-level survey and area-level auxiliary dataModel-based estimationBinomial model
Goal 8.Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

Estimation of the unemployment rate in Canada

Statistics Canada uses the Fay-Herriot model to obtain the unemployment rate for 149 areas (cities) in Canada. The data used is Statistics Canada's Labour Force Survey (LFS) which is a monthly survey that is designed to produce reliable unemployment rate estimates for the 55 Employment Insurance Economic Regions (EIER).  

The case study is an application of the small area estimation system developed by Statistics Canada (Hidiroglou, 2019). The description of the case study is extensive and it gives good insights on how to evaluate the small area estimates.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel



Unit-level survey and area-level auxiliary dataModel-based estimationFay-Herriot model

Economic activity rate at commune level in Switzerland

The structural population survey is a survey covering about 200,000 people each year in Switzerland and enables the estimation of economic activity rate for groups of 15,000 inhabitants. The combination of this survey with additional data, for example old-age and survivors' insurance, makes it possible estimate the annual economic activity rate for groups of at least 100 inhabitants.

For the experimental statistics of economic activity in Swiss communes, a linear mixed model that combines the survey data with additional data is used. The executive summary gives a first overview. Extensive simulations are available in the report part 1.1. and the description of the application in the report part 1.2.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel

Related to 8.5.2


Unit-level survey and auxiliary dataModel-based estimationBasic unit-level model

Inbound travel spending estimates at sub-provincial level in Canada

The Visitor Travel Survey (VTS) enables the estimation of statistics on the volume of international visitors to Canada and detailed characteristics of their trips such as expenditures, activities, places visited and length of stay. It can also be used to estimate inbound travel spending at sub-provincial levels. However, the direct estimates may be unreliable when the sample size is not large enough.

Statistics Canada uses an adjusted area-level model to obtain the inbound travel spending for a combination of 11 country groups and 22 tourism regions. As additional data source, Payment processors' data is included.  

A short description of the case study is available that describes but does not show the validation of the model.

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel

Related to 8.9.1

Origin and Spatial

Unit-level survey and area-level auxiliary dataModel-based estimationPiecewise area-level model
Goal 9. Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
No case studies yet
Goal 10. Reduce inequality within and among countries
No case studies yet
Goal 11. Make cities and human settlements inclusive, safe, resilient and sustainable
No case studies yet
Goal 12. Ensure sustainable consumption and production patterns
No case studies yet
Goal 13. Take urgent action to combat climate change and its impacts
No case studies yet
Goal 14. Conserve and sustainably use the oceans, seas and marine resources for sustainable development
No case studies yet
Goal 15. Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
No case studies yet
Goal 16. Promote peaceful and inclusive societies for sustainable development, provide access to justice for all andbuild effective, accountable and inclusive institutions at all levels
No case studies yet
Goal 17. Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development

Estimation of ICT indicators in Brazilian states

A department of the Brazilian Network Information Center ( called Regional Center for Studies on the Development of the Information Society ( collects data about access and use of information and communication technologies (ICT) in Brazil. Data users are interested in timely publication of main ICT indicators for the 27 Brazilian states. The estimated indicators include the proportion of households with computers, and proportion of households with Internet access (which is related to 17.8.1 Proportion of individuals using the Internet). The data source is the Brazilian Annual Household Survey on ICT whose sample contains almost 33,000 households. Reliable estimates for the five larger regions North, Northeast, Southeast, South and Center-West can be produced. Direct estimation approaches by averaging estimates of consecutive years and, by pooling samples of consecutive years are employed. In addition, a single-year composite estimator considering the regions are tested for yielding synthetic estimates; and a composite estimator based on pooling samples from two consecutive years, and using the regions. The simpler approaches are chosen due to a wide range of indicators that need to be produced in a timely manner after data collection. For more information, see Bertolini Coelho et al. (2020).

IndicatorsDisaggregation dimensionData availabilityEstimation approachModel

Related to 17.8.1

SpatialUnit-level survey dataDirect estimation

Case study submission

In order to provide a wide range of SAE applications for the SDGs, you can submit your own case study with help of the downloadable template.


Bertolini Coelho, I., Trindade Pitta, M. and do Nascimento Silva, P. S. (2020). Estimating state level indicators from ICT household surveys in Brazil, Statistical Journal of the IAOS 36, 495–508.

FAO. 2021. Guidelines on data disaggregation for SDG Indicators using survey data. Rome.

Hidiroglou, M. A., Beaumont, J-F. and Yung, W. (2019) Survey Methodology, 2019 (special issue) 101 Vol. 45, No. 1, pp. 101-126 Statistics Canada, Catalogue No. 12-001-X

Kim, J. K. and Rao, J. N. K. (2012). Combining data from two independent surveys: a model-assisted approach. Biometrika, 99(1), 85–100.

Schmid, T., Burckschen, F., Salvati, N. and T. Zbiranski. 2017. Constructing Socio-Demographic Indicators for National Institutes Using Mobile Phone Data: Estimating Literacy Rates in Senegal, Journal of the Royal Statistical Society, Series A, Vol. 180 Issue 4 pp. 1163–1190.

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