A data-science study of how visible women's economic activity is: measurement model, uncertainty, sensitivity, scenarios and value of information - Russia

This paper studies how visible women’s economic activity is in Russia, one of the 19 sovereign G20 members  scored in the series, using the Gender Economic Visibility Index (GEVI). The index converts 13 indicators  into parity scores, averages them into five dimensions (labour participation, job quality and seniority,  enterprise and legal rights, institutional visibility, and statistical and political voice) and combines the  dimensions with a weighted geometric mean, so that a strong dimension cannot fully compensate for a weak  one. Every input carries an evidence grade that sets its uncertainty. Russia scores 69.0 out of 100, in the  Emerging band, with a 90% uncertainty interval of 63.8 to 73.2 from 10,000 joint Monte Carlo draws  (median 68.8) and a point rank of 14 of 19 (rank median 14; 5th to 95th percentile range 13 to 15). The  strongest dimension is D2 Job quality and seniority (0.939); the weakest is D4 Institutional visibility (0.365).  A Shapley attribution of the 31.0-point gap to full parity assigns the largest share, 12.6 points (41%), to D4  Institutional visibility. All 13 inputs are grade A and measured, so the blind-spot ratio is 0.00, and weight  uncertainty explains 78.7% of simulated output variance. Rank runs from 13 to 16 across seven weighting  schemes and from 13 to 15 across ten alternative specifications (benchmark, parity cap and imputation  rule), with scores moving by at most 6.1 points. The largest single-lever scenario is halving the gap to parity  in labour access (S4, +1.8 points), and a greedy pathway of +0.10 steps reaches the 80-point threshold after  eleven steps. These pathways order priorities; they are not forecasts. The index contains no direct measure  of women’s visibility in taxpayer records, so the paper proposes adding a sex field to wage-tax and taxpayer  records and publishing an annual gender tax profile; this would extend the model and is not part of the  current score. The priority data actions are to publish an annual appointments series by sex (I11), treat  parliamentary representation (I12) as a matter for parliament and parties, and fund the missing supporting  mechanisms behind legal frameworks (I10). The weights and uncertainty settings are judgemental  assumptions, the study is cross-sectional, and the scenarios describe index arithmetic rather than policy  effects. All results are reproducible from the accompanying Python scripts with a fixed random seed of 2026;  scores and intervals are research estimates, not official statistics.