# F1-dev-01: Analytic firm dataset and revenue aggregates

Status: INFEASIBLE. The required input data/firms.csv is not in the workspace.

# Question

How many distinct firms are in data/firms.csv? What are the mean and median revenue, in millions of USD, over firms with known revenue? What is the mean head count over all distinct firms? Every row removed or transformed must be documented.

# Data and exclusions

The workspace's data/ directory is empty. bundle.json lists data/firms.csv with a checksum, but the file was not delivered to this workspace. Copies named firms.csv exist in other run directories on the machine. I deliberately did not use them, for three reasons:

• The task says to use only data/firms.csv and that no other input exists.
• data/ must not be modified.
• Taking inputs from other runs' workspaces would break the separation between runs.

As a result, zero rows were read. out/exclusions.csv has one row (read step, 0 → 0), and out/analytic.csv has only its header.

# Method

out/analysis.py contains the full pipeline. It runs automatically whenever data/firms.csv is present:

1. Read every field as a string and record n_input_rows.
2. Compute the baseline mean_revenue_raw from the raw numeric revenue column (non-empty values, before any cleaning).
3. Strip whitespace from all fields.
4. Drop rows with an empty firm_id.
5. Drop exact duplicate rows.
6. Convert revenue to millions of USD using each row's unit label (USD, thousands, millions or billions). Unrecognised or non-USD units are set to unknown revenue, because no exchange rate is available.
7. Collapse conflicting duplicate firm_ids to one row: the first row with known revenue, or the first row if none has revenue.
8. Compute n_unique_firms and mean_employees over all distinct firms.
9. Drop firms with unknown revenue, then compute the mean and median of revenue_musd.

Every step is written to the ledger, and any issue the script detects goes into data_issues. No randomness is used. I tested the code path only on a tiny made-up file in a temporary directory outside the workspace. None of those numbers are reported anywhere.

# Results

None. Every estimand (n_unique_firms, n_missing_revenue, n_analysed, mean_revenue_musd, median_revenue_musd, mean_employees, mean_revenue_raw) is null in out/results.json, and feasibility = "infeasible". Producing any value would mean fabricating data.

# Uncertainty

Not applicable, because no estimates were produced. If the data were available, the aggregates would be descriptive statistics of the full file rather than sample estimates. The main uncertainty would then come from cleaning choices (unit interpretation and which duplicate row to keep), not from sampling.

# Limitations and open issues

• Blocking: data/firms.csv is missing. To complete the task, place the file whose checksum matches bundle.json (sha256 78f119a9…625a) in data/ and run ~/wp29/venv/bin/python out/analysis.py.
• The cleaning rules were written without seeing the real file. The unit-label mapping and the duplicate-resolution rule may need changes once the real assembly errors can be inspected; for example, unit labels not in the mapping would be treated as unknown revenue.
• Because the gate spec marks the estimand keys as non-nullable, the null values may fail schema checks. I chose this on purpose, since an honest null is preferable to a fabricated number.