Build a Real Wage and Workforce Composition Analyzer

A labor-market researcher preparing a distributional briefing.

Distinguish wage changes within worker groups from changes in workforce composition. Compare nominal wages, price-adjusted wages, and fixed-composition results.

Key: period + group_definition_version + group_id.

Fields: mean_wage, employment_count, survey_weight, price_index, wage_frequency.

Do not infer individual wage mobility from repeated cross-sections.

Reconcile wage periods and price-index periods.

Compare observed and fixed-composition mean wages.

Estimate uncertainty with the documented survey design where available.

Export decomposition tables and limits on interpretation.

A group comparison explorer with fixed-composition controls and uncertainty bands.

A wage and composition decomposition notebook.

A briefing distinguishing purchasing power from workforce selection.

Runnable local application, setup commands, and a dependency lockfile.

Versioned input data, data dictionary, acquisition instructions, and rights notes.

Calculation traces, source references, assumption records, and exported review artifacts.

A deterministic baseline and tests for normal, incomplete, contradictory, and failure-recovery cases.

A recorded complete workflow with a reproducible manifest and explicit limitations.

Real growth uses the ratio of nominal and price growth factors.

Changing group shares cannot be called within-worker wage growth.

Missing survey design metadata blocks design-based interval claims.

Group totals reconcile to the selected population.

Hold nominal wage growth constant and change inflation. Purchasing-power growth must change sign when inflation exceeds wage growth.

The attached starter files are prototype inputs. Expected starter answers are visible. They are not independent holdouts.

Full target: Use permitted public-use records or synthetic grouped wages, employment shares, and a documented price index. Keep sampling weights and period definitions.

Complete target contracts and expert review remain pending. Public observations are not historical release vintages.

pandas, statsmodels, Plotly

Use an available model appropriate to the task. Record provider, model identifier, version or retrieval date, settings, cost, and latency. Use deterministic code for calculations. Models may extract, retrieve, or explain evidence. They must not invent missing records, override approval rules, or claim professional authority.

Do not infer individual outcomes from aggregate groups.

No fabricated records, source citations, professional endorsements, or expert-adjudication claims.

No future-data leakage into dated decisions, model fitting, or historical valuation inputs.

No unauthorized payments, account restrictions, regulatory filings, or production changes.

No automated appraisal certification, tax-compliance determination, or unsupported fraud accusation.

No concealed uncertainty or silently invented inputs required for a numerical conclusion.

Correct domain calculations and justified methods with documented assumptions. Weight: 30.

Source lineage, temporal integrity, rights checks, and explicit synthetic-data labels. Weight: 25.

A working application with review, export, error handling, and reproducible state. Weight: 20.

Adversarial cases, uncertainty, missing-data behavior, and sensitivity analysis. Weight: 15.

Evidence-linked conclusions that distinguish facts, assumptions, and unresolved judgments. Weight: 10. Score each dimension from zero to two. Weighted score = sum(weight * score / 2). A failed safety gate blocks acceptance.