1# Contract (P0) — F1-dev-0123**Question.** Turn the synthetic firm cross-section `data/firms.csv` into a documented, analysis-ready dataset and report basic aggregates.45**Estimands.**6- `n_unique_firms`: number of distinct `firm_id` values.7- `n_missing_revenue`: number of distinct firms whose revenue is unknown (empty cell).8- `n_analysed`: number of firms with known revenue that are used in the revenue aggregates.9- `mean_revenue_musd`, `median_revenue_musd`: mean and median revenue in **millions of USD**. This needs every row's `unit` converted to USD millions; the conversion is meaningful only if each unit maps unambiguously to a USD-million scale.10- `mean_employees`: mean head count over all distinct firms (after deduplication).11- `mean_revenue_raw`: baseline mean of the raw `revenue` column over non-empty rows, before any cleaning.1213**Outputs.** `out/results.json` (keys task_id, n_unique_firms, n_missing_revenue, n_analysed, mean_revenue_musd, median_revenue_musd, mean_employees, mean_revenue_raw, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); `out/analytic.csv` (firm_id, sector, revenue_musd, employees; exactly n_analysed rows); `out/exclusions.csv` (step, rule, n_before, n_after, n_removed, rationale; a chained ledger); `out/report.md` (6 sections); `out/analysis.py`; `out/claims.json`.1415**Information rules.** Use only `data/firms.csv`. Report revenue in USD millions. Rows with unknown revenue are left out of the revenue aggregates and are listed in the ledger.1617**What makes the task impossible or the result invalid.** The input file is absent or unreadable; units cannot be converted to USD millions; duplicates with conflicting values cannot be resolved.1819**Finding at P0/P1.** `data/` is empty. `data/firms.csv` is listed in `bundle.json` (sha256 78f119a9…) but does not exist in the workspace. The task is therefore infeasible.20