Build a Tax-and-Transfer Distribution Scenario Microsimulator

A policy analyst comparing proposed household support designs.

Estimate mechanical household effects of fictional tax and transfer proposals. Show distributional results, survey weights, eligibility assumptions, and what the model cannot say about behavior.

Key: household_id + policy_version.

Fields: income, household_size, eligibility_fields, sample_weight, baseline_transfer, proposed_transfer.

Survey fields are not verified benefit eligibility. Preserve missingness and imputation flags.

Encode explicit fictional policy rules and boundary cases.

Calculate household-level mechanical changes.

Aggregate weighted fiscal and distributional results.

Export incidence tables with behavioral exclusions and data limits.

A rule editor, weighted distribution charts, household examples, and eligibility-uncertainty panel.

A versioned rule engine and microsimulation pipeline.

A distribution report separating mechanical effects from behavioral predictions.

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.

Household weights are used in population totals.

Threshold boundaries follow the declared inclusive or exclusive rule.

Unknown eligibility does not become ineligible by default.

Do not label mechanical transfers as estimated welfare or labor-supply effects.

The same household benefit with different sampling weight changes aggregate cost, not the household payment.

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

Full target: Use permitted survey microdata or clearly synthetic households with weights. Define fictional policy rules in a versioned configuration, not as current law.

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

pandas, NumPy, 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.

Rules are fictional. No benefits are awarded and no current tax liability is calculated.

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.