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

The Gender Economic Visibility Index (GEVI) asks whether women’s economic activity is present, measured and recognised in the systems that set policy, budgets and taxes. This paper applies the index to Papua New Guinea, the pilot economy for the index as a data-science measurement study: 13 indicators, each converted to a parity score and graded for evidence quality, are combined into five dimensions and aggregated by a geometric mean into a score out of 100, which is then tested for uncertainty (10,000 joint Monte Carlo draws), sensitivity (seven weighting schemes and ten alternative specifications), attribution of the gap to full parity (Shapley values), peer rank, scenarios and a pathway to the 65-point Emerging threshold, and value of information for the data strategy.

Papua New Guinea scores 47.2 out of 100 (90% interval 41.4 to 53.0), in the Hidden band, ranking 13th of 15 economies (5th to 95th percentile of the rank: 9 to 14). Women take part in work at almost the same rate as men (D1 Labour participation, 0.912, is the strongest dimension), but almost everything that would make that work visible to policy is missing: D4 Institutional visibility scores 0.172 and accounts for 18.5 of the 52.8-point gap to full parity (35%). All 13 inputs are measured grade A statistics (blind-spot ratio 0.00). The rank runs from 8 to 15 across the seven weighting schemes and the score moves by at most 3.6 points across the ten alternative specifications. The largest single-lever scenario, stronger supportive frameworks and more women ministers, is worth +6.4 points; a greedy pathway of +0.10 steps on measured indicators reaches the Emerging threshold after 17 steps, or about 9 years at an illustrative 0.02 a year. The findings bear directly on tax administration and fiscal transparency, since indicators that are missing from official statistics cannot inform gender-responsive revenue policy. All numbers are research estimates, not official statistics, and are fully reproducible from the accompanying scripts with a fixed random seed.

The paper is organised so that every number can be traced and challenged. It begins with the purpose and scope (Chapter 1), a data audit of sources, input ledger, data age and coverage (Chapter 2), and the measurement model (Chapter 3), followed by indicator-by-indicator construction (Chapter 4) and dimension scores with a full aggregation trace and Shapley attribution of the gap (Chapter 5). It then reports the uncertainty analysis (Chapter 6), sensitivity and robustness tests (Chapter 7), peer benchmarking and rank uncertainty (Chapter 8), and scenario simulation and pathways (Chapter 9), before turning to the value of information and a data roadmap (Chapter 10), findings, recommendations and a twelve-month implementation plan for Papua New Guinea (Chapter 11), and the limitations and validity threats of the approach (Chapter 12).