Build a Cross-Country Output and Living-Standards Comparison Tool

A country research analyst preparing a comparative briefing.

Compare economic size, output growth, and output per person without mixing market exchange rates, purchasing-power measures, or population vintages.

Key: country_code + year + indicator_code + download_snapshot.

Fields: value, unit, price_basis, conversion_basis, population_definition, missing_status.

Record territorial coverage and revisions. Do not substitute missing purchasing-power values with market-rate values.

Create separate comparison panels for incompatible concepts.

Align population denominators to the selected output measure.

Calculate rankings and denominator sensitivity.

Export a briefing with coverage and comparability limitations.

A country comparison table with explicit measure selectors and blocked-comparison explanations.

A metadata-aware country panel.

A comparison report that distinguishes economic scale from individual welfare.

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.

Market-rate and purchasing-power rankings remain separate.

A population revision changes per-capita values without changing total output.

Missing observations never become zero-output countries.

Do not describe GDP per capita as household income or a full welfare measure.

Two economies have identical total output but different populations. Their per-capita results must differ.

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

Full target: Acquire ten countries across 15 years with nominal, real, purchasing-power, and population indicators. Retain metadata for every selected series.

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

pandas, Pandera, 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.

No claims about national welfare follow from one output ranking.

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.