# RBM Logic Model Sample: Youth Employment and SME Skills Pathways (Jordan, 2026-2028)

## Theory of Change

If youth aged 18-29 in Jordan receive market-relevant technical and employability training, and if SMEs co-design internships and entry pathways with implementing partners, then more youth (especially women and vulnerable groups) will secure and retain decent work. Over a 3-5 year horizon, this contributes to more inclusive labor-market participation and improved household income resilience.

Causal pathway:

`Inputs -> Activities -> Outputs -> Outcomes (placement + retention + income) -> Impact`

Key assumptions:
- Employer demand remains stable in target sectors.
- Transport/care barriers are partially mitigated for priority participants.
- SMEs sustain internship and placement participation through project cycles.

## Executive Summary

This sample RBM model sets out a 30-month, donor-style youth employment pathway for Jordan with explicit inclusion priorities (women, vulnerable youth, governorates outside Amman). The model links training and employer engagement to concrete employment and retention outcomes, then to longer-term labor-market impact. Indicators are SMART, disaggregated, and paired with a practical monitoring cadence.

## Logic Model Table (Inputs to Impact)

| Level | Statements |
| --- | --- |
| Inputs | EU funding; accredited training providers; employer network; placement coordinators; M&E staff; inclusion support budget (transport/childcare subsidies where feasible). |
| Activities | Labor-demand-informed curriculum design; technical and soft-skills training cohorts; career coaching and placement support; SME internships; retention mentoring; employer feedback cycles. |
| Outputs | 1) Youth complete certified tracks. 2) Internships/interviews are facilitated. 3) Job-readiness portfolios and career plans completed. 4) Employer feedback mechanism established. |
| Outcomes (short/medium term) | O1 (by month 30): Graduate placement rate improves within 6 months post-training. O2 (by month 30): Employment retention at 12 months improves. O3 (by month 30): Median earnings of employed graduates increase. |
| Impact (3-5 years) | More inclusive labor-force participation and reduced youth unemployment pressure in target governorates, with improved economic resilience for participating households. |

## Outcome Indicators (grouped by outcome)

### Outcome 1: Improved placement within 6 months

| Indicator | Definition / Formula | Baseline | Target | Frequency | Data source | Disaggregation |
| --- | --- | --- | --- | --- | --- | --- |
| O1.1 Graduate placement rate within 6 months | `(# graduates employed/self-employed within 6 months) / (total graduates) * 100` | 28% (assumption; verify baseline) | >= 65% by month 30 | Quarterly | Tracer surveys + employer verification | Sex, age band, governorate, disability status |
| O1.2 Women graduate placement rate within 6 months | `(# women graduates employed/self-employed within 6 months) / (women graduates) * 100` | 20% (assumption) | >= 55% by month 30 | Quarterly | Tracer surveys + HR confirmations | Governorate, sector |
| O1.3 SME satisfaction with graduate job readiness | `(# SMEs rating readiness >= 4/5) / (responding SMEs) * 100` | 35% (assumption) | >= 80% by month 30 | Semi-annual | Employer feedback survey | Sector, firm size |

### Outcome 2: Improved retention and earnings quality

| Indicator | Definition / Formula | Baseline | Target | Frequency | Data source | Disaggregation |
| --- | --- | --- | --- | --- | --- | --- |
| O2.1 12-month employment retention rate | `(# placed graduates still employed at month 12) / (placed graduates) * 100` | 45% (assumption) | >= 75% by month 30 | Semi-annual | Tracer surveys + employer confirmation | Sex, governorate, sector |
| O2.2 Median earnings change among employed graduates | `((follow-up median earnings - baseline median earnings) / baseline median earnings) * 100` | 0% change | >= +30% by month 30 | Semi-annual | Participant earnings survey | Sex, governorate, sector |
| O2.3 Share employed in field aligned with training | `(# employed graduates in trained occupation) / (employed graduates) * 100` | 40% (assumption) | >= 70% by month 30 | Quarterly | Tracer survey + role verification | Sex, training track |

## SDG Alignment

| Results level | SDG goal/target | Rationale |
| --- | --- | --- |
| Outcome 1 | SDG 8.6 | Supports reduction in youth NEET through placement pathways. |
| Outcome 2 | SDG 8.5 | Promotes productive employment and decent work outcomes. |
| Outcome 1/2 inclusion lens | SDG 5.5 | Strengthens women's participation in economic life and decision space. |
| Impact | SDG 1.2 | Earnings gains can contribute to multidimensional poverty reduction. |

## Data Collection Plan

| Component | Method | Frequency | Owner |
| --- | --- | --- | --- |
| Baseline scan | Labor-market analysis + participant baseline survey | Months 1-3 | M&E team |
| Cohort monitoring | Attendance, completion, practical assessments | Monthly | Training partners |
| Placement tracking | Tracer follow-up + employer confirmation | Quarterly | Placement coordinators |
| Retention follow-up | 3/6/12-month windows after placement | Rolling | M&E team |
| Midline review | Outcome trend check and adaptive management | Month 15 | Program manager + M&E lead |
| Endline review | Final outcome analysis vs baseline/targets | Month 30 | External evaluator + M&E lead |
| Data quality assurance | Spot checks, duplicate checks, source verification logs | Quarterly | M&E lead |

## Assumptions and Risks

Assumptions due to missing baseline detail:
- Baseline values for placement, retention, and earnings are provisional.
- Sector mix and employer demand profile are assumed and should be validated at inception.

Key risks and mitigations:
- Macroeconomic shocks reduce hiring demand.
  - Mitigation: diversify sectors and increase self-employment support modules.
- Gender and mobility barriers suppress women's placement outcomes.
  - Mitigation: targeted support packages and flexible training schedules.
- Weak employer follow-up reduces retention data quality.
  - Mitigation: signed data-sharing protocols and routine verification cadence.
