Senior Consultant - An Integrated Model of School Attendance, Completion and Attainment

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 7 to 11 months

Recruitment open to : External candidates

Application Deadline (Midnight Paris Time) : 10-APR-2023

 

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

 

Long Description

BACKGROUND

The UNESCO Institute for Statistics (UIS) is the statistical office of UNESCO and is the UN depository for global statistics in the fields of education, science, technology and innovation, culture and communication. The UIS was established in 1999. It was created to improve UNESCO’s statistical programme and to develop and deliver the timely, accurate and policy-relevant statistics needed in today’s increasingly complex and rapidly changing social, political and economic environments.

The Education 2030 Incheon Declaration and Framework for Action specifies that the mandate of the Global Education Monitoring (GEM) Report is to be 'the mechanism for monitoring and reporting on SDG 4 and on education in the other SDGs' with the responsibility to 'report on the implementation of national and international strategies to help hold all relevant partners to account for their commitments as part of the overall SDG follow-up and review'. It is prepared by an independent team hosted by UNESCO. 

As part of its 2020 review of the SDG indicator framework, the Inter-Agency and Expert Group on SDG indicators approved the completion rate by level of education as additional global indicator 4.1.2 for monitoring target 4.1 on universal primary and secondary schooling. In addition, the Technical Cooperation Group on the indicators for SDG 4 (TCG) endorsed the development of model-based estimates for this indicator to overcome issues of infrequent and inconsistent survey data on which its calculation is based. A Bayesian estimation model (Dharamshi et al., 2022a) has been developed that uses multiple sources of data to address these issues (e.g. efficient use of information, survey data sampling and non-sampling biases, back- and nowcasting etc.) in order to produce consistent time series for most countries in the world. Following a further endorsement by the TCG, model results are now being used to report on regional and global aggregates annually to the United Nations.

Long Description

The need to develop a methodology that combines multiple data sources was first recognized in education almost 20 years ago, when it was acknowledged that ‘some sort of composite approach may be needed for estimating time series and producing estimates for the most recent year’ to estimate the other flagship education indicator, namely the out-of-school rate (UIS and UNICEF, 2005). Around the same time, facing a similar challenge to estimate indicators based on multiple sources, the international health community developed models to estimate under-5, neo-natal, and maternal mortality rates, stillbirth rates and sex-ratios at birth. Using somewhat related techniques, a new cohort-based model combines data from administrative and survey sources (Dharamshi et al., 2022b) to estimate latent out-of-school rates that mirror the natural progression of students through a school cycle. 

OBJECTIVE OF THE WORK

Building on these two developments, the objective of the work is to integrate and develop the models in two ways. First, integrate the completion and out-of-school rate models to develop a joint cohort-based model that tracks the flow of individuals through the education system by age and grade so that the estimated eventual completion rates are the result of observed data on the timing of entry, repetition, dropout and other relevant education transitions. The approach will trace cohorts through their school lives as they move between early, timely and late in-school and out-of-school status, with those movements becoming eventually constrained by completion estimates. This approach will help decompose out-of-school rates by reason and will also help estimate other system-level indicators, such as enrolment ratios and transition rates.

The model may be based on a system of equations that set the rules of student progression through a school cycle and optimized to estimate events like dropout and completion. Using estimated out-of-school rates and completion rates, the objective of the model would be to estimate the optimal values that correspond to early/late entry, repetition and dropout rates that lead from the school attendance to the completion values. Key challenges to overcome, in addition to problems already encountered in the out-of-school and completion rate models, are inaccurate repetition values and consistency between cohorts. 

Long Description

Second, integrate the results of completion rate model, which focuses on children, adolescents and youth at least three to five years older than their expected graduation age with the attainment levels of adult populations to generate datasets on the distribution of populations by each level of attainment. This would require the inclusion of a measure of post-secondary education attainment, for which the data exist in the same sources but have not been modelled so far. 

Under the supervision of the UIS Director and the GEM Report Director, the consultant shall carry out the following activities to:

Integrate the completion and out-of-school rate models into a single joint model:

• Identify the commonalities and the differences between the completion rate and out-of-school rate models to propose a basis for modelling the two indicators jointly.
• Integrate male/female/total completion rates into a joint estimation model that that estimates the true completion rates while ensuring the results are consistent with the school attendance trajectories by age and grade that generated this completion rate outcome.
• Perform validation checks, including by comparing the joint model estimates with those of the two constituent models.

Generate datasets on the distribution of the adult population by age and attainment:

• Extend the calculation of completion rate estimates to post-secondary education completion.
• Align the model completion rate estimates with attainment rates for adults aged older than 22 years to develop an articulated and internally consistent (between younger and older adults) dataset of population distribution. Past work by the UIS on attainment rates may be consulted.

Summarize the findings into key indicators and interactive visualizations:

• Estimate the system indicators that are consistent with the model:
  • Percentage of individuals who never go to school.
  • Enrolment/attendance rates by level of education.
  • Percentage of individuals who are at least two years too old for their grade.
  • Transition rates into lower, upper and post-secondary education.
  • Dropout rates.
  • Completion rates. 
• Develop an interactive visual dashboard of lifecycle trajectory by education (attendance and attainment) status by age and level that allows (a) efficient visual identification for continuous model improvement, and (b) exploration of results by the public.
• Prepare guidelines for input data quality requirements (with respect to completeness, sample size etc.)

COMPETENCIES (Core / Managerial) Accountability (C) Communication (C) Innovation (C) Knowledge sharing and continuous improvement (C) Planning and organizing (C) Results focus (C) Teamwork (C) Professionalism (C) Building partnerships (M) Driving and managing change (M) Leading and empowering others (M) Making quality decisions (M) Managing performance (M) Strategic thinking (M) - For detailed information, please consult the UNESCO Competency Framework. REQUIRED QUALIFICATIONS

Education

• Advanced university degree in statistics or quantitative social sciences.  

Experience

• At least 10 years professional experience in statistical analysis of administrative and household survey data, preferably on education;
• Advanced skills and experience in Bayesian modelling is required;
• Experience working with household surveys and large databases in general, especially in the production of education-related indicators. In particular, experience using MICS, DHS and learning assessments is required.
• Proficiency in R and Stan is required.
• Familiarity with high-performance cloud computing and associated R packages is desirable.
• Strong communication and writing skills in English and ability to work effectively through email and other means of remote communications.
• Work experience with the UN is an asset.

APPLICATION PROCESS

All submissions should include:

• A statement indicating how their qualifications and experience make them suitable for the assignment.
• A Technical Written proposal consisting of:

1. A description of the proposed approach and methodology for undertaking the assignment.
2. A work plan with detailed milestones and key deliverables/activities as per the terms of reference.
3. Comments on the Terms of Reference, if any (in brief).
4. An up-to-date curriculum vitae (CV).
5. The amount to be charged for the assignment per month. Candidates should design the payment scheme against clear and specific set of deliverables and milestones as reflected in the work plan.
6. A list of work assignments and THREE (03) contacts as references as part of the submission. 

NB: Applications submitted without the above will not be considered.

SELECTION AND RECRUITMENT PROCESS

After the opening, each proposal will be assessed first on its technical merits and subsequently on its financial value price. The proposal with the best overall value, composed of technical merit and price, will be recommended for approval. UNESCO will set up an evaluation panel composed of technical and procurement staff and their conclusions will be forwarded to the internal UNESCO Contracts Committee or other relevant approving authority. The evaluation panel will first evaluate each response for compliance with the requirements of this Terms of Reference. Responses deemed not to meet all the mandatory requirements will be considered non-compliant and rejected at this stage without further consideration. Failure to comply with any of the terms and conditions contained in these Terms of Reference, including provision of all required information, may result in a response or proposal being disqualified from further consideration.

The overall weighting between technical and price evaluation will be based on the predefined criteria. The technical component will account for 70% of the total points allocated and the financial component will account for 30% of the total points allocated.

The currency of this proposal shall be in US Dollars. All quoted prices/rates must be exclusive of all taxes since UNESCO is exempted from government taxes, levies and duties.

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