# Building the feedback loop: how M&E systems turn reporting into better decisions for children

Theme: M&E frameworks  
Audience: Political + Technical  
Reading time: 10 min read  
Updated: 2026-08-22  
Canonical: https://childguarantee.eu/briefs/me-systems/  
Publisher: UNICEF (Child Guarantee Learning Lab), funded by the European Union

## Summary

A monitoring system exists to tell whether measures are reaching children, and to allow a change of course when they are not. The countries that built one sequenced it: governance first, then a results chain, then a phased indicator set.

## In brief

A monitoring system's function is to tell whether measures are reaching children, and to allow a change of course when they are not. The countries that built systems that do this sequenced the build: governance first, then a results chain, then a phased indicator set, and treated every reporting cycle as a chance to learn. Romania created a sequenced roadmap and a dashboard concept that tracks key indicators and progressively links them to financial monitoring; Luxembourg made its data constraints explicit and put its statistical office in charge of the data; Ireland linked a national outcomes framework to practical measurement and learning tools. The recurring trap mostly goes unnoticed: an indicator set chosen for what is easy to collect rather than what is meaningful to track.

## The problem

A biennial progress report goes to the European Commission on time, full of indicators. Asked in
a committee whether any specific group of children is measurably better off than two years ago,
the honest answer is that no one can say. The indicators were chosen because the data happened
to exist, not because they track what the measures were meant to improve. The numbers that would
answer the question sit in health, education and social-protection systems that do not
communicate with one another. The report is complete. It demonstrates little to the country.

Three structural reasons keep recurring.

**First, indicators are driven by what can be collected.** When indicator sets are built from
what is easy to collect rather than from a theory of how measures produce outcomes, the system
measures activity, not results. The logic of intervention has to be reconstructed later, at much
greater cost.

**Second, data are fragmented and non-interoperable.** Sectoral administrative data, with no
interoperability across ministries and levels of government, break the monitoring chain: even
good indicators cannot be compiled or compared if the underlying data never connect.

**Third, M&E is built as a reporting afterthought, separate from planning and from decisions.**
When monitoring is designed in isolation from the plan and disconnected from how decisions are
made, evidence rarely leads back into revising measures.

The rest of this brief addresses one question: what does a monitoring system that actually
changes decisions require, and in what sequence?

> "Countries that make progress do so not because they have perfect systems from the outset, but because they establish sound governance foundations, invest in a deep understanding of the situation of children, develop their indicator frameworks iteratively, and treat each reporting cycle as an opportunity to learn and improve."
>
> (ECG Learning Lab, M&E Systems Synthesis, 2026)

**Ask your team:** For one measure in our plan, can we name the indicator that would show it is producing results? Is that indicator built from a results chain, or from the data we happened to have?

## What the working systems share

Across the documented countries, the most functional systems share one characteristic: two
governance tiers and a clear data owner. A political-strategic level, a high-level
inter-institutional group that holds cross-ministerial accountability; and a
technical-operational level, a permanent expert working group including statistical, research
and civil-society actors that runs the day-to-day monitoring, data collection and reporting.
Bulgaria's two-level model is the reference: a high-level group provides overall coordination
and monitoring, while a permanent expert group develops two-year operational plans and progress
reports. The other critical component is naming a data owner: Luxembourg formally mandated its
statistical office, STATEC, as the central data-governance institution for the whole monitoring
framework, putting standardisation and quality assurance with the institution best equipped to
hold them. None of this requires new legislation; it can be established through mandates, terms
of reference and clear reporting cycles.

### Case: Romania: Diagnose first, standardise everything, then design the dashboard

*A roadmap built from a mapping of what already existed, and fiches for over 200 indicators.*

Romania began with a diagnostic mapping of its M&E components, data flows, overlaps and gaps
against the ECG Monitoring Framework, then built a roadmap. A first initiative produced
standardised fiches for 146 indicators; a second, TSI-funded, extended the scope to over 200
indicators assessed against a nine-criteria matrix (data routine, availability, periodicity,
definition clarity, formula clarity, biennial-reporting compliance, disaggregation,
territorial coverage, interoperability). It mapped data flows across child protection, health
and education, and proposed interim data-sharing protocols, feeding a curated shortlist into
a dashboard concept.

**What transfers:** where data systems are fragmented, a country can start from ECG-aligned
indicators and progressively integrate national indicators, disaggregation needs and
financial linkages, supporting decision-making, accountability and the EU reporting cycles.

**The catch:** Romania's documentation openly acknowledges gaps in state-budgeted M&E
capacity. The process was possible with TSI and UNICEF support, and durability depends on
internalising it into the national budget.

### Case: Luxembourg: Logic before data, and the statistical office in charge

*Theories of change first, then an indicator set that is transparent about its own gaps.*

Luxembourg built theories of change for each domain before selecting indicators, then
classified the whole set into three groups: indicators available with the minimum required
breakdowns, indicators available but lacking the required breakdowns, and placeholders that
are not yet available but are considered critical. It mandated its statistical office,
STATEC, to ensure data management, and paired a high-level Steering Committee with Technical
Working Groups spanning ministries, local authorities, observatories, research institutes and
civil society.

**What transfers:** indicator design from a theory of change; a classification that keeps
gaps transparent and stakeholders engaged while they are worked on; data governance assigned
to the national statistical authority.

**The catch:** it relies on a capable, well-resourced statistical office and structured
technical facilitation. Luxembourg's own lessons point at what to do earlier: clearer
technical profiles for the indicator working groups, structured input from children on
priority domains, and interim dissemination products that demonstrate value between
milestones.

### Case: Ireland: Permanent units and an evidence habit built over a decade

*Not a project: standing research infrastructure funded from the national budget.*

Ireland's Department of Children (DCEDIY) sustains two permanent internal units: a Research
and Evaluation Unit for standards, guidance and training, and a Data and Analytics Unit for
integration, analysis and reporting. The research infrastructure supports them: the Growing
Up in Ireland longitudinal study, and the What Works Evidence Hub curating programmes with
evidence and cost ratings, provide the monitoring and evidence foundations for implementing
and tracking the NAP. Capacity is funded from domestic sources alongside EU support, and the
outcomes framework dates back to the mid-2010s.

**What transfers:** survey-based and longitudinal evidence assets that complement
administrative data and support policy learning over time; an evidence hub for continuous
learning; dashboard-style reporting that improves the accessibility and comparability of
indicators.

**The catch:** this is a long-term trajectory backed by sustained domestic funding, dedicated
in-house analytical capacity, and prior institutional maturity.

## What makes it work

- **A robust, inclusive governance architecture.** A high-level political coordination body
  plus a permanent technical working group responsible for M&E operations, data governance and
  capacity-building, with all ECG domains represented in both tiers.
- **A formal, active role for the statistical authority.** Placing definitions, quality
  assurance and interoperability with the national statistical office gives standardisation a
  durability that a ministry unit alone rarely sustains.
- **Capacity built in two directions at once.** Systems need both the ability to produce
  rigorous, disaggregated data and the ability to use it: revising plans, designing new
  interventions, and reallocating resources toward what works.
- **Plan and monitor together, from the start.** M&E cannot be designed in isolation from
  planning: each measure should trace back to a diagnostic finding and forward to an indicator
  that captures the change it is meant to produce.
- **Start from the ECG Monitoring Framework and what is already collected with rigour.**
  Develop the indicator architecture with top-down logic, derived from the theory of change and
  the results chain, combined with bottom-up knowledge of what is already routinely collected
  at national and local level.

## What gets in the way

**The recurring failure modes.** Named and specific, drawn from the documented systems rather than from principle.

- **Indicators chosen for availability, not meaning.** When the set is built from what is easy
  to collect rather than from a theory of change, the system measures activity, and the
  intervention logic must be reconstructed later.
- **A weak indicator framework undermines attribution.** If indicators are inconsistent,
  monitoring data cannot be compared across institutions, evaluation cannot attribute findings
  to measures, and reporting to the Commission cannot rest on evidence of progress.
- **Relying on projects and external funds.** Most documented systems still depend on
  time-limited external support (TSI, UNICEF). Without internalised, state-budgeted units, the
  system cannot become sustainable.
- **Monitoring built apart from planning and from decisions.** Where M&E is a separate
  reporting track, evidence rarely reaches the people revising the plan.

**Ask your team:** If our external technical support ended next year, which parts of our monitoring system would keep running on the national budget?

## What transfers, and how

**Two-tier governance needs mandate, not legislation.** A political-strategic layer and a
permanent technical-operational layer can be established through ministerial orders, terms of
reference and a fixed reporting cycle: Bulgaria, Luxembourg and Spain built theirs from very
different starting points. A constant across Member States is that statistical institutes and
research bodies anchor the technical layer, most explicitly in Luxembourg's formal mandate for
STATEC as the data-governance hub of the entire monitoring framework.

**The three-group indicator classification allows for improvement.** Organising indicators by
immediate availability, partial availability and placeholder makes gaps transparent and
delivers early results while methodological work continues. Luxembourg's practice indicates it
is highly replicable: a foundation for iterative improvement that keeps stakeholders engaged.

**Standardised fiches reduce ambiguity across institutions.** Documenting every indicator to a
common template forces methodological consensus exactly where definitions diverge between
institutions.

**Evaluability assessment comes before evaluation.** Checking that a measure has a clear
results chain, measurable indicators and available data before commissioning an evaluation, as
Croatia and Greece sequenced explicitly, is what makes evaluation findings usable.

## What transfers

| Component | Transfers | Effort | Note |
|---|---|---|---|
| Two-tier governance | yes | low | Viable in most contexts: built through mandates and a reporting cycle, not legislation or special infrastructure. |
| Three-group indicator classification | yes | low | Needs analytical discipline, and lets a country report now and improve iteratively. |
| Standardised indicator fiches | yes | medium | Labour-intensive to write but low-cost, and an effective quality-control step. |
| Statistical authority as data hub | context-dependent |  | Transformative where a capable, independent statistical office exists; requires prior capacity-building where it does not. |
| Internalised permanent units | yes | high | Ireland's two-unit model rests on a decade-plus trajectory and sustained domestic funding. |
| Longitudinal and research infrastructure | yes | high | Cohort studies and evidence hubs are best-practice endpoints, not entry requirements. |

## How this applies by country

Views written for candidate and potential candidate countries. No country is ranked against another.

### Albania (AL)

**Albania: build the M&E system alongside the NAP drafting, not after it.**

Albania's NAP drafting is live and a deep dive is committed, so the single highest-value move now is to build the M&E system alongside: a two-tier governance structure and INSTAT as data hub, rather than treating monitoring as a later add-on once measures are written.

- Children AROPE (2024): 44.1%
- Candidate status: 2014
- Funding instruments: IPA III + RGF
- Data hub: INSTAT

### Moldova (MD)

**Moldova is exactly at the point this brief is written for.**

With the deep dive complete and the NAP underway, the work now is to turn the diagnostic into a results chain and a phased indicator set while the plan is still being drafted, and to mandate the National Bureau of Statistics as the data hub before indicators multiply.

- Children in poverty (proxy, 2022): 30.2%
- Candidate status: 2022
- NAP status: Underway
- Data hub: NBS

### Serbia (RS)

**Serbia: build the monitoring system with the plan.**

Serbia is still early on ECG alignment, which makes this the moment to adopt M&E as a design principle: plan and monitor together, and give the Statistical Office of the Republic of Serbia the data-governance role from the start. Start from what is already reported to Eurostat and build a results chain before selecting indicators.

- Children AROPE (2023): 25.7%
- Candidate status: 2012
- Funding instruments: IPA III + RGF
- Data hub: SORS

### Türkiye (TR)

**Türkiye: the case for a monitoring system is domestic, whatever the accession timeline.**

With negotiations frozen, Türkiye's reason to build M&E is its own: better targeting of vulnerable children. TurkStat is a strong data-hub candidate, and the transferable practices are the same: a results chain first, then indicators drawn from what is already collected with rigour.

- Children AROPE: 36.9%
- Candidate status: 1999
- Funding instrument: IPA III
- Data hub: TurkStat

### Ukraine (UA)

**Ukraine has an approved NAP; the phased indicator approach matters most here.**

Ukraine developed and approved its NAP, so the brief's three-group indicator approach is the working method: report on what already exists, flag the critical gaps as placeholders, and lean on the State Statistics Service to hold definitions steady as conditions change.

- Children below national poverty line: 38%
- Candidate status: 2022
- Funding 2024-2027: Ukraine Facility
- Data hub: Ukrstat

## Applied scenarios

Build the system that learns: Three applied activities from the same evidence base: map the twelve components of a working M&E system, sequence your own build against the country that started where you are, and lead five decisions over 36 months without taking the plausible shortcut. https://childguarantee.eu/modules/me-systems/

## Sources

- ECG M&E Systems Synthesis (2026) (The evidence base behind this brief, with the documented country practices. Link pending publication.)
- European Child Guarantee Monitoring Framework https://employment-social-affairs.ec.europa.eu/policies-and-activities/social-protection-social-inclusion/social-protection-committee/indicators-sub-group/monitoring-and-benchmarking-frameworks_en (Social Protection Committee Indicators Sub-Group.)
- Making Monitoring and Evaluation Systems Work (Görgens & Kusek, 2009) https://openknowledge.worldbank.org/entities/publication/b4f8f831-9cf9-5825-a0dc-d2d8c48c6bdd (The twelve-component framework used in the companion module.)
- EU Technical Support Instrument (DG REFORM) https://reforms-investments.ec.europa.eu/technical-support-instrument-0_en (The instrument behind Romania's indicator-standardisation work.)