Consultancy: Enhancing the UIS Methodology for Calculating Regional Aggregates (Re-advertised)

OVERVIEW

Parent Sector:UNESCO Institute for Statistics (UIS)

Duty Station:Montreal

Classification of duty station:filter12

Standard Duration of Assignement :filter13

Job Family:Education

Type of contract:Non Staff

Duration of contract:From 1 to 6 months

Recruitment open to:External candidates

Application Deadline (Midnight Paris Time):23/09/2025

UNESCO Core Values: Commitment to the Organization Integrity Respect for Diversity Professionalism

OVERVIEW OF THE FUNCTIONS OF THE POST

Background

Every year in August the UNESCO Institute for Statistics (UIS) produces regional aggregates for a variety of education indicators. These groupings (regional averages) may be based on geography income levels or other classifications requested by UIS partners such as the Global Partnership for Education (GPE).
These indicators feed into global monitoring databases and reports and are disseminated as part of the official international education statistics. The national data underpinning these aggregates are sourced from publicly available official statistics.
The quality of these aggregates is strongly influenced by the completeness of country-level data. This is particularly important for weighted averages where countries with larger populations have a higher impact on the final aggregate. Missing data from populous countries is frequent and this can significantly impact the results. Further challenges arise from using population-based indicators that rely on population data from sources different from those used for education data leading to occasional inconsistencies (e.g. population denominators exceeding education-related numerators in cases where this should not occur).
Moreover as many education indicators are interrelated calculating them in isolation may result in internal inconsistencies. The current process for producing aggregates is time-consuming inflexible and limits the ability to respond quickly to emerging issues or data corrections.
To address these concerns UIS has initiated a review of its aggregation methodology and seeks to enhance the quality reliability transparency and efficiency of its procedures.

UIS seeks to engage a Senior Expert Consultant to develop the module on Legal Framework and Institutional Setup regarding Education Administrative data.

Long Description

Deliverables

Deliverable 1 (duration: 1 month)
Comprehensive review of the current UIS aggregation methodology and its implementation within the new UIS education data architecture and coding system (aligning with specifications from UIS Multi-Year Dynamic Template data collection).
Identification of methodological weaknesses and development of a detailed set of recommendations for programming using Python (or R) codes.
A proposed roadmap for an enhanced aggregation methodology using Python or R codes and its implementation including:
oEstimation methods to improve data coverage at the national level;
oEstimation and imputation approaches for indicators (at national level) with partial or missing data;
oMechanisms to ensure consistency across interrelated indicators;
oIncorporation of confidence intervals;
oConsideration of external shocks (e.g. COVID-19 natural disasters or socio-political crises).

Deliverable 2 (duration: 1 month)
Development of a pilot framework including scripts (preferably in Python) and quality rating methods for calculating aggregates focused on a core set of indicators.
Sensitivity analysis and testing of thresholds for data coverage quality rating of aggregates and confidence intervals.
Production of associated metadata for each aggregate including:
oNumber of observed estimated and imputed data points;
oConfidence interval.
Proposal for scaling the approach across all indicators produced through the UIS education data collection.

Deliverable 3 (duration: 1.5 months)
Full-scale implementation of the new aggregation methodology.
Complete integration with the new UIS education data processes and associated data repository systems.
Delivery of documentation and tools supporting the new methodology.

COMPETENCIES (Core / Managerial)

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REQUIRED QUALIFICATIONS

Education

• Advanced degree statistics mathematics education economics or a related field.

Experience

• Proven knowledge of international education statistics including data sources indicator methodologies estimation techniques and limitations;
• Extensive experience in statistical modeling data analysis large education databases estimation and imputation of national-level data and aggregation at international level;

Language

• Excellent verbal and written communication skills in English.

DESIRABLE QUALIFICATIONS

Experience

• Familiarity with the International Standard Classification of Education (ISCED) and its application.

Skills

• Ability to work effectively in a remote environment.

APPLICATION PROCESS

Interested candidates should complete the on-line application download and complete the Employment History form (Word file). At the end of the Word file insert extra pages with the following required information:

1. A cover letter outlining the candidates interest qualifications availability and proposed consultancy fee;
2. An updated CV
3. Two samples of relevant work in statistical modeling estimation/imputation for missing data and aggregation particularly in the context of international data or education.

Only the proposals that include all the four aforementioned elements will be considered. Failure to provide any of those will result in an incomplete application which will not be considered.

UNESCO places significant importance on achieving the objectives outlined in the Terms of Reference. Consequently the evaluation of proposals will prioritize the technical components. From the proposals that meet the criteria specified in the Terms of Reference UNESCO will select the one that provides the best value for money for the Organization.

SELECTION AND RECRUITMENT PROCESS

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