Marshall IslandsTwo Gaps, One Score
D4 (0.150) and D5 (0.204) together explain the score.
The Marshall Islands score a GEVI of 44.7 (90% interval 37.5 to 52.0), in the Hidden band and 14th of 15. Two things pull the score down together: there are no women in ministerial positions, and only 2 of 12 core economic rows have a sex-disaggregated value, so 5 of 13 inputs are imputed (blind-spot ratio 0.38).
Source: the attached country paper. Animation reveals reported measures; it does not show change over time.
Abstract
The Marshall Islands score a GEVI of 44.7 (90% interval 37.5 to 52.0), in the Hidden band and 14th of 15. Two things pull the score down together: there are no women in ministerial positions, and only 2 of 12 core economic rows have a sex-disaggregated value, so 5 of 13 inputs are imputed (blind-spot ratio 0.38).
D4 (0.150) and D5 (0.204) together explain the score. A ministerial share of 15%, up from zero, would add 4.9 points to 49.6; lifting data coverage to 9 of 12 rows would add 8.7 to 53.4. Raising the supportive-frameworks score from 30% to 50% adds 3.6 points.
Priorities include Fill the labour rows first; Publish the ministerial and board appointments series so I11 is tracked annually rather than read from a single year; Use administrative registers.
Download the paper PDFHeadline result
The Marshall Islands score a GEVI of 44.7 (90% interval 37.5 to 52.0), in the Hidden band and 14th of 15. Two things pull the score down together: there are no women in ministerial positions, and only 2 of 12 core economic rows have a sex-disaggregated value, so 5 of 13 inputs are imputed (blind-spot ratio 0.38).
90% interval 37.5–52.0.
Rank 14 of 15 · Blind-spot ratio 0.38.
versus D1 at 0.815
How the index is built
Dimensions are simple means of their indicators. The index uses a weighted geometric mean. Imputed inputs use the series medians; these are placeholders, not observed country outcomes.
| Dimension | Score D | Weight w | D^w |
|---|---|---|---|
| D1 Labour participation | 0.815 | 0.25 | 0.9501 |
| D2 Job quality and seniority | 0.787 | 0.20 | 0.9533 |
| D3 Enterprise and legal rights | 0.594 | 0.20 | 0.9010 |
| D4 Institutional visibility | 0.150 | 0.15 | 0.7523 |
| D5 Statistical and political voice | 0.204 | 0.20 | 0.7279 |
= 44.7
Product of the five terms = 0.4469, so GEVI = 100 × 0.4469 = 44.7, rank 14 of 15. Values are rounded as reported in the source.
Uncertainty and rank sensitivity
The 10,000-draw simulation samples each score with the standard deviation of its grade (8 grade A at 0.02, 5 grade C at 0.12) and draws dimension weights from a Dirichlet distribution centred on the base weights. Median 44.5, 5th to 95th percentile 37.5 to 52.0; blind-spot ratio 5/13 = 0.38. Under 5,000 random weight sets alone, the rank has a median of 14 with a range of 13 to 14.
Where the gap is
Each scenario raises one dimension to full parity, holding the others fixed.
| Dimension raised to 1.0 | GEVI | Gain |
|---|---|---|
| D1 | 47.0 | +2.3 |
| D2 | 46.9 | +2.2 |
| D3 | 49.6 | +4.9 |
| D4 | 59.4 | +14.7 |
| D5 | 61.4 | +16.7 |
Single-indicator scenarios
Single-indicator scenarios, each lifting one measured or coverage indicator to the upper quartile of countries with data (imputed indicators excluded):
| Indicator lifted | From | To | GEVI | Gain |
|---|---|---|---|---|
| I13 Data availability | 0.167 | 0.750 | 53.4 | +8.7 |
| I11 Women ministers | 0.000 | 0.373 | 50.4 | +5.8 |
| I12 Women MPs | 0.242 | 0.382 | 47.4 | +2.7 |
| I5 Senior and middle management | 0.654 | 0.746 | 45.0 | +0.3 |
Scenario values and gains are reproduced as printed; rounding can affect the subtraction of displayed values. These are conditional model scenarios, not forecasts or causal estimates.
Reading the result
D4 (0.150) and D5 (0.204) together explain the score. A ministerial share of 15%, up from zero, would add 4.9 points to 49.6; lifting data coverage to 9 of 12 rows would add 8.7 to 53.4. Raising the supportive-frameworks score from 30% to 50% adds 3.6 points.
The imputed labour indicators (I1, I3, I4, I6, I7) are placeholders. The ranking of 14th is therefore provisional in the sense that it could move by several places once measured values exist.
Priorities and practical use
The priorities connect the index to actions and named users.
Fill the labour rows first. A labour force or census tabulation by sex would replace five imputed inputs at once.
Publish the ministerial and board appointments series so I11 is tracked annually rather than read from a single year.
Use administrative registers. With a small formal sector, employer and business registers with an owner-sex field would give a full count without a survey.
Who uses the data, and for what decision?
| Data product | Primary user | Decision it informs | Cadence |
|---|---|---|---|
| I11 women in ministerial posts (none) | Cabinet office, public service commission | An annual appointments series by sex; lifting I11 to the series upper quartile is worth 5.8 points | Annual |
| I13 coverage (2 of 12 rows) | Statistics office, development partners | Which of the missing labour and finance rows to collect first; full lift to 9 rows is worth 8.7 points | Survey plan |
| I1, I3, I4, I6 and I7 by sex from any labour or business tabulation | Statistics office, revenue authority | Replace five imputed inputs with measured values | Next census or survey release |
| I10 supportive frameworks (30%) | Women's ministry, finance ministry | A costed gap list; a lift to 50% is worth 3.6 points | Budget cycle |
For the Marshall Islands the most direct use is as a scorecard: a small number of measurable, annually reportable changes (appointments, one labour tabulation, one survey module) would each move the index by roughly 6 to 9 points.
Series: GEVI Research Series · Paper 9 · DOI: 10.5281/zenodo.23208751