1. Background
Leaving no one behind (LNOB) was the central, transformative promise of the 2030 Agenda for Sustainable Development and its Sustainable Development Goals. LNOB meant moving beyond assessing average and aggregate progress, towards identifying progress for different population groups, including with intersecting vulnerabilities, to ultimately ensure all human beings can fulfil their potential in dignity and equality.
To support governments and the United Nations system in the Asia-Pacific region, the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP) had developed a user-friendly diagnostic tool called Leave No One Behind (LNOB) platform. The tool supported understanding of how various circumstances intersected and created inequalities in access to basic opportunities covered by the Sustainable Development Goals. Building on empirical methodologies such as Classification and Regress Trees (CART) and the Dissimilarity index (D-index), it used data and statistics at national and sub-national levels to identify groups left furthest behind and the circumstances they share.
In this technical R training, ESCAP took participants through the inner workings of its LNOB platform, using the statistical R code that prepared, analyzed and produced evidence on 16 SDG indicators based on the Nepal Demographic and Health Survey (DHS) from 2022 and Multiple Indicator Cluster Survey (MICS) from 2019. In addition to replicating the results hosted in LNOB platform, participants also learned how to operationalize the LNOB methodology on indicators other than the SDG indicators currently available on the platform. Equipped with this knowledge, participants could then analyze inequality based on other nationally representative household surveys.
2. Training Objectives
The primary objective of this statistical training was to strengthen national capacities in building the evidence base in support of policies that reduce inequality and accelerate the implementation of the Sustainable Development Goals, by making sure that policies and programmes are inclusive and reach the furthest behind and support equal opportunity permitting the full realization of human potential and contributing to shared prosperity.
At the end of the training, technical officials and specialists would be able to map out inequalities in access to opportunities and prevalence of barriers for which they had data and identify furthest behind and furthest ahead and quantify the level of inequality at national and sub-national levels through the D-Index. The training also aimed to raise awareness about the important role that NSO played in enhancing the use of SDG indicators for decision making.
3. Target Audience
The statistical training drew participation from statisticians and technical specialists in ministries, departments and agencies, academia, think-thanks, research institution and the United Nations system entities. Given the complexity of the code and the short duration of the training, a basic level of understanding of and experience with statistical software programmes such as R and Stata would be helpful to follow the instructions. Participants were recommended to follow ESCAP guidelines for installing and preparing the R Studio environment before the training begins. It might be beneficial, but was not required, for participants to review basic R skills which could be found by a simple search on the internet.