Analyze Affordability Rebates reform on enhanced_cps_2024 dataset

[DTrim99]

Overview

Analyze the enhanced_cps_2024 dataset and compute the impact of an "Affordability Rebates" reform using the Recovery Rebate Credit mechanism.

Reform Specification

The reform modifies the ARPA Recovery Rebate Credit parameters for 2026:

from policyengine_us import Microsimulation
from policyengine_core.reforms import Reform

reform = Reform.from_dict({
  "gov.irs.credits.recovery_rebate_credit.arpa.max.adult": {
    "2026-01-01.2026-12-31": 3000,
    "2027-01-01.2035-12-31": 0
  },
  "gov.irs.credits.recovery_rebate_credit.arpa.max.dependent": {
    "2026-01-01.2026-12-31": 3000,
    "2027-01-01.2100-12-31": 0
  },
  "gov.irs.credits.recovery_rebate_credit.arpa.phase_out.threshold.JOINT": {
    "2026-01-01.2026-12-31": 150000,
    "2027-01-01.2035-12-31": 0
  },
  "gov.irs.credits.recovery_rebate_credit.arpa.phase_out.threshold.SINGLE": {
    "2026-01-01.2026-12-31": 75000,
    "2027-01-01.2035-12-31": 0
  },
  "gov.irs.credits.recovery_rebate_credit.arpa.phase_out.threshold.SEPARATE": {
    "2026-01-01.2026-12-31": 75000,
    "2027-01-01.2035-12-31": 0
  },
  "gov.irs.credits.recovery_rebate_credit.arpa.phase_out.threshold.SURVIVING_SPOUSE": {
    "2026-01-01.2026-12-31": 75000,
    "2027-01-01.2035-12-31": 0
  },
  "gov.irs.credits.recovery_rebate_credit.arpa.phase_out.threshold.HEAD_OF_HOUSEHOLD": {
    "2026-01-01.2026-12-31": 112500,
    "2027-01-01.2035-12-31": 0
  }
}, country_id="us")

Analysis Requirements

1. Dataset Exploration (similar to us/states/ut/data_exploration.ipynb)

• Household and person counts (weighted)
• AGI distribution (median, 75th, 90th, 95th percentiles, max)
• Households with children breakdown
• Children by age groups
• Income bracket analysis
• Total employment income (weighted)
• Employment income before LSR (labor supply response)

2. Reform Impact Analysis

baseline = Microsimulation(dataset="enhanced_cps_2024")
reformed = Microsimulation(reform=reform, dataset="enhanced_cps_2024")

baseline_income = baseline.calculate("household_net_income", period=2026)
reformed_income = reformed.calculate("household_net_income", period=2026)
difference_income = reformed_income - baseline_income

# Employment income metrics
baseline_employment = baseline.calculate("employment_income", period=2026)
baseline_employment_before_lsr = baseline.calculate("employment_income_before_lsr", period=2026)

3. Key Metrics to Compute

• Total cost of the rebate program (weighted)
• Number of households receiving benefits
• Average benefit per household
• Distribution of benefits by income bracket
• Distribution by filing status
• Impact on households with/without children
• Total employment income aggregates
• Comparison of employment income vs employment income before LSR

Output

• Jupyter notebook in us/federal/ directory
• Summary CSV files with key statistics

Reference

Analysis style based on: us/states/ut/data_exploration.ipynb