ItalyEconomic Visibility
Shape. The strongest dimension is D3 Enterprise and legal rights (0.866); the weakest is D4 Institutional visibility (0.640). A Shapley attribution of the 19.0-point gap to full parity assigns the largest share, 6.0 points (32%), to D4 Institutional visibility.
Evidence. Of 13 inputs, 13 are grade A, 0 are grade B and 0 are grade C (imputed), a blind-spot ratio of 0.00. Every input is measured.
Reported research measures. Animation reveals the graphic; it does not represent change over time.
Abstract
Result. Italy scores 81.0 out of 100 on the Gender Economic Visibility Index, in the Visible band. The 90% uncertainty interval from 10,000 joint Monte Carlo draws is 78.3 to 82.3 (median 80.5). Its point rank is 7 of 19, and across the same draws the rank has a median of 7 with a 5th to 95th percentile range of 6 to 8.
Shape. The strongest dimension is D3 Enterprise and legal rights (0.866); the weakest is D4 Institutional visibility (0.640). A Shapley attribution of the 19.0-point gap to full parity assigns the largest share, 6.0 points (32%), to D4 Institutional visibility.
Evidence. Of 13 inputs, 13 are grade A, 0 are grade B and 0 are grade C (imputed), a blind-spot ratio of 0.00. Every input is measured.
Download full paper PDFInputs and parity calculations
Inputs and parity scores
| ID | Indicator | Raw | Score | Grade | SD |
|---|---|---|---|---|---|
| I1 | Labour force participation | women 40.8, men 58.3 (2025) | 0.6998 | A | 0.02 |
| I2 | Youth NEET | women 11.6, men 12.3 (2024) | 1.0000 | A | 0.02 |
| I3 | Youth unemployment | women 22.6, men 19.3 (2025) | 0.8540 | A | 0.02 |
| I4 | Non-vulnerable employment | women 11.8, men 16.2 (2025) | 1.0000 | A | 0.02 |
| I5 | Senior and middle management | 24.8% (2023) | 0.4960 | A | 0.02 |
| I6 | Wage and salaried employment | women 84.1, men 75.0 (2025) | 1.0000 | A | 0.02 |
| I7 | Firm ownership | 52.9% (2024) | 1.0000 | A | 0.02 |
| I8 | WBL legal frameworks | 92.9 (2025) | 0.9290 | A | 0.02 |
| I9 | WBL enforcement | 67 (2026) | 0.6700 | A | 0.02 |
| I10 | WBL supportive frameworks | 78 (2026) | 0.7800 | A | 0.02 |
| I11 | Women ministers | 25.0% (2024) | 0.5000 | A | 0.02 |
| I12 | Women MPs | 32.8% (2026) | 0.6560 | A | 0.02 |
| I13 | Data availability | 12 of 12 rows | 1.0000 | A | 0.02 |
The labour, management, enterprise and legal inputs (I1 to I11) come from the World Bank Gender Data Landscape for this economy (April 2026 edition, including Women, Business and the Law 2026). The parliamentary share (I12) comes from the Inter-Parliamentary Union monthly ranking as of 1 August 2026.
Step 1: Indicator calculations
GEVI has three layers: 13 indicators, 5 dimensions and one composite. Each indicator is converted to a parity score, each dimension is the simple mean of its indicators, and the composite is a weighted geometric mean of the dimensions. Three principles govern the design: parity scores avoid arbitrary goalposts; substitutability is allowed within a dimension but not across dimensions; and every input carries an evidence grade that sets its uncertainty.
| ID | Calculation | Score |
|---|---|---|
| I1 | min(40.8 / 58.3, 1) = 0.700 | 0.700 |
| I2 | min(12.3 / 11.6, 1) = 1.000 | 1.000 |
| I3 | min(19.3 / 22.6, 1) = 0.854 | 0.854 |
| I4 | min((100 - 11.8) / (100 - 16.2), 1) = min(88.2 / 83.8, 1) = 1.000 | 1.000 |
| I5 | min(24.8 / 50, 1) = 0.496 | 0.496 |
| I6 | min(84.1 / 75.0, 1) = 1.000 | 1.000 |
| I7 | min(52.9 / 50, 1) = 1.000 | 1.000 |
| I8 | 92.9 / 100 = 0.929 | 0.929 |
| I9 | 67 / 100 = 0.670 | 0.670 |
| I10 | 78 / 100 = 0.780 | 0.780 |
| I11 | min(25.0 / 50, 1) = 0.500 | 0.500 |
| I12 | min(32.8 / 50, 1) = 0.656 | 0.656 |
| I13 | 12 / 12 = 1.000 | 1.000 |
Step 2: Dimension scores
| Dim | Calculation | Score |
|---|---|---|
| D1 | 0.700 + 1.000 + 0.854 = 2.554; / 3 | 0.8513 |
| D2 | 1.000 + 0.496 + 1.000 = 2.496; / 3 | 0.8320 |
| D3 | 1.000 + 0.929 + 0.670 = 2.599; / 3 | 0.8663 |
| D4 | 0.780 + 0.500 = 1.280; / 2 | 0.6400 |
| D5 | 0.656 + 1.000 = 1.656; / 2 | 0.8280 |
The base weights (0.25, 0.20, 0.20, 0.15, 0.20) reflect the emphasis on labour participation and on statistical voice. They are a prior, not an estimate, which is why the paper tests seven weighting schemes, including data-driven entropy and principal-component weights computed from the 15-economy dimension matrix.
Aggregation, uncertainty and sensitivity
Step 3: Aggregation trace
| Term | Calculation | Value |
|---|---|---|
| D1 | 0.8513 ^ 0.25 | 0.96054 |
| D2 | 0.8320 ^ 0.20 | 0.96388 |
| D3 | 0.8663 ^ 0.20 | 0.97171 |
| D4 | 0.6400 ^ 0.15 | 0.93525 |
| D5 | 0.8280 ^ 0.20 | 0.96296 |
| Product | 0.9605 x 0.9639 x 0.9717 x 0.9352 x 0.9630 | 0.81024 |
| GEVI | 100 x product | 81.02 |
Check in logs: sum of w ln D = -0.21043; exp = 0.81024; times 100 = 81.02.
Step 4: Uncertainty and sensitivity
For each of 10,000 draws the model samples all 13 indicator scores from truncated normal distributions and the five dimension weights from a Dirichlet distribution, then recomputes the index. The same seed (2026) is used for every economy, which means all economies share the same weight draws. That is intentional: weight uncertainty is common to the whole series, so rank comparisons are made under identical weights in each draw.
Each input has a grade with a standard deviation applied to its score: A = 0.02, B = 0.06, C = 0.12. The standard deviations are judgemental settings that express how much a reader should trust a value; they are not estimated from replications. Section 7.5 shows the effect of changing the settings that matter most.
Robustness. Across seven weighting schemes the rank runs from 7 to 7; across ten alternative specifications (benchmark, parity cap and imputation rule) it runs from 7 to 7. The score changes by at most 6.7 points across those specifications.
| p1 | p5 | p10 | p25 | p50 | p75 | p90 | p95 | p99 | Mean | SD |
|---|---|---|---|---|---|---|---|---|---|---|
| 77.20 | 78.28 | 78.83 | 79.64 | 80.47 | 81.27 | 81.92 | 82.26 | 82.92 | 80.41 | 1.22 |
Weight uncertainty explains 68.7% of the output variance; the grade C (imputed) inputs explain 0.0%. Weights and measured inputs, not imputation, drive the spread.
Gaps, scenarios and priorities
Step 5: Where the gap is
The gap to full parity is 19.0 points. Because the geometric mean is non-additive, the contribution of one dimension depends on the state of the others. Shapley values average each dimension’s marginal contribution over all 5! orderings of the dimensions, so they sum exactly to the gap. v(S) is the GEVI obtained when the dimensions in S are set to 1.
| Dim | Shapley points | Share of gap | Shortfall of score (x100) |
|---|---|---|---|
| D4 | 6.03 | 31.8% | 36.0 |
| D1 | 3.63 | 19.1% | 14.9 |
| D5 | 3.40 | 17.9% | 17.2 |
| D2 | 3.32 | 17.5% | 16.8 |
| D3 | 2.59 | 13.6% | 13.4 |
Scenario simulation and pathways
Levers. The largest single-lever scenario among S1 to S6 is "S4 Labour access: halve the gap to parity", worth +1.7 points. A greedy pathway of +0.10 steps on measured indicators reaches the next threshold, 90 points, after 11 steps; at an illustrative improvement of 0.02 a year on every measured indicator it takes 8 years.
| Scenario | GEVI | Gain | p5 | Median | p95 |
|---|---|---|---|---|---|
| S1 Data coverage to 9 of 12 rows | 81.0 | +0.0 | 78.3 | 80.5 | 82.3 |
| S2 Institutions: supportive frameworks 50%, ministers 15% | 81.0 | +0.0 | 78.3 | 80.5 | 82.3 |
| S3 Legal: legal and enforcement scores +0.10 | 82.1 | +1.0 | 79.1 | 81.4 | 83.4 |
| S4 Labour access: halve the gap to parity | 82.7 | +1.7 | 79.7 | 82.2 | 84.4 |
| S5 Politics: women 15% of parliament | 81.0 | +0.0 | 78.3 | 80.5 | 82.3 |
| S6 Enterprise: management and firm ownership to 0.75 | 82.6 | +1.6 | 79.7 | 82.1 | 84.1 |
| S7 Combined S1 to S6 | 85.4 | +4.4 | 82.0 | 84.8 | 87.1 |
| S8 Series best-in-class on every measured indicator | 96.0 | +15.0 | 94.1 | 95.0 | 95.9 |
The pathway adds 0.10 to one measured indicator at a time, always choosing the step that raises GEVI most, until the composite reaches the next threshold above the current score (50, 65, 80, 90 or 95) or no measured indicator can rise further. It is an ordering of priorities, not a forecast.
| Step | ID | Indicator | Score change | GEVI after |
|---|---|---|---|---|
| 1 | I12 | Women MPs | 0.66 to 0.76 | 82.0 |
| 2 | I10 | WBL supportive frameworks | 0.78 to 0.88 | 82.9 |
| 3 | I12 | Women MPs | 0.76 to 0.86 | 83.8 |
| 4 | I12 | Women MPs | 0.86 to 0.96 | 84.7 |
| 5 | I10 | WBL supportive frameworks | 0.88 to 0.98 | 85.6 |
| 6 | I11 | Women ministers | 0.50 to 0.60 | 86.5 |
| 7 | I1 | Labour force participation | 0.70 to 0.80 | 87.3 |
| 8 | I1 | Labour force participation | 0.80 to 0.90 | 88.1 |
| 9 | I11 | Women ministers | 0.60 to 0.70 | 88.9 |
| 10 | I1 | Labour force participation | 0.90 to 1.00 | 89.7 |
| 11 | I11 | Women ministers | 0.70 to 0.80 | 90.5 |
Priorities
I5 Senior and middle management. Publish women’s share of senior and middle management from payroll or company records with a common definition.
I11 Women ministers. Publish an annual appointments series by sex for ministerial and board posts.
I12 Women MPs. Treat parliamentary representation as a policy question for parliament and parties; a revenue authority can supply the data but not the remedy.
Restore a fiscal indicator. Add a sex field to wage-tax and taxpayer records and publish an annual gender tax profile, so that D4 can include a direct measure of visibility in the tax system.
Practical use and data strategy
How to use this data
| Data product | Primary user | Decision it informs | Cadence |
|---|---|---|---|
| Management share by sex from employer returns | Revenue authority, company registry, labour ministry | Where women are missing from senior roles | Annual |
| Ministerial and board appointments by sex | Cabinet office, public service commission | A measured record of decision-making representation | Annual |
| Candidate and elected members by sex by party | Parliament, electoral commission | Baseline for any special-measures discussion | After each election |
| Sex-disaggregated taxpayer and wage-tax statistics | Revenue authority, treasury | Who is in the formal tax base and who receives refunds and credits | Annual |
Italy’s use case is to turn parity gaps into named data products with named owners, so that each year’s re-run of the index shows which product moved which indicator.
Data strategy
| Data product | Owner | Indicators | Effect |
|---|---|---|---|
| Sex field on wage-tax returns and taxpayer registrations | Revenue authority | Adds a fiscal indicator (I14, proposed) | Restores the fiscal indicator the index was designed to have |
| Keep the core 12-row table current and add a fiscal (taxpayer) row | National statistics office and revenue authority | I13 and a proposed I14 | Coverage is already 12 of 12; the gain is a new fiscal indicator, not more rows |
Scope and limitations
Weights are a prior. The weights express a view, not an estimate. Chapter 7 shows their effect, but no data can identify the right weights.
Uncertainty settings are judgemental. The standard deviations 0.02, 0.06 and 0.12 are settings, not measurements. If they were larger the intervals would widen.
Source heterogeneity. Inputs come from different years and sources; older values carry a wider standard deviation, but a level shift caused by definitions is not modelled.
No causal claim. The scenarios show what the index would do if an indicator changed. They do not show that a policy would change the indicator.
Fiscal indicator absent. No sex-disaggregated taxpayer statistic was found, so the index does not yet measure visibility in the tax system directly.