Background
Leaving no one behind (LNOB) was the central, transformative promise of the 2030 Agenda for Sustainable Development and its Sustainable Development Goals (SDGs). 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 could 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. The evidence generated by this platform could inform evidence-based inclusive policies not only at the design phase but also during the implementation and monitoring and evaluation phases. The evidence was also highly informative for Voluntary National Reviews (VNR) which took stock of progress in SDGs.
Against this background and in response to the official request received from the Principal Coordinator for SDG Affairs at the Chief Adviser’s Office, ESCAP in partnership with United Nations Resident Coordinator’s Office in Bangladesh organized a technical workshop to build capacity to generate LNOB evidence to enrich the 2025 VNR of Bangladesh. In this technical R training, ESCAP took participants through the inner workings of its LNOB platform, using the statistical R code that prepares, analyzed and produced evidence on SDG indicators. In addition, participants had hands-on experience to run the LNOB algorithm on selected SDG or national indicators based on Bangladesh’s latest Demographic and Health Survey from 2022. Equipped with this knowledge, participants could then analyze inequality based on any other nationally representative household surveys.
Training Objectives
The primary objective of this statistical training was to strengthen national capacities in building the evidence base in support of policies that reduced inequality and accelerated the implementation of the SDGs, by making sure that policies and programmes were inclusive and reached the furthest behind and supported equal opportunity permitting the full realization of human potential and contributing to shared prosperity.
At the end of this training, technical officials and specialists were able to map out inequalities in access to opportunities and prevalence of barriers for which they had data and identified 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.
Target Audience
The statistical training could accommodate up to 25 participants and drew participation from statisticians and technical specialists with relevant background from the SDG Coordination Office, Bangladesh Bureau of Statistics (BBS), other ministries, departments and agencies. Upon discussion with the SDG Coordination Office, representatives from academia, think-thanks, civil society, research institutions and the United Nations system entities were invited to participate.
Given the complexity of the code and the short duration of the training, the following criteria applied in selecting the participants:
An intermediate level of work experience in analyzing data and drafting empirical reports in line with indicators pertaining to inclusive and sustainable development
A basic level of understanding of and experience with statistical software programmes such as R, SPSS or Stata
A basic level of coding experience in statistical software programmes
Participants were recommended to follow ESCAP guidelines for installing and preparing the R Studio environment before the training begins.
Organization and Participation