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https://github.com/iroy2000/one-big-beautiful-bill-impact-analysis

Distributional Effects Across Different Demographic Groups. Purely data driven and no political agenda.
https://github.com/iroy2000/one-big-beautiful-bill-impact-analysis

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Distributional Effects Across Different Demographic Groups. Purely data driven and no political agenda.

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README 

          
# H.R. 1 "One Big Beautiful Bill Act" Impact Analysis Dashboard



## Overview



This repository contains a comprehensive, **data-driven analysis** of the distributional effects of H.R. 1, the "One Big Beautiful Bill Act," across various demographic groups in the United States. The analysis is designed to provide government officials, policymakers, advocacy groups, and the public with actionable insights into how this legislation impacts different communities.



## Purpose & Mission



### For Government Officials & Policymakers

- **Evidence-Based Decision Making**: Provides objective, quantitative analysis to inform policy discussions and amendments

- **Constituent Impact Assessment**: Helps representatives understand how the bill affects their specific districts and demographics

- **Resource Allocation**: Identifies which communities may need additional support or mitigation measures



### For Public Awareness & Advocacy

- **Transparency**: Makes complex policy impacts accessible to the general public

- **Community Organizing**: Enables advocacy groups to understand and communicate impacts to their constituencies

- **Civic Engagement**: Empowers citizens with data to engage meaningfully in the democratic process



## Data Sources & Methodology



### Primary Data Sources

- **Congressional Budget Office (CBO)** - Official distributional analysis of H.R. 1

- **Joint Committee on Taxation (JCT)** - Tax impact assessments

- **Census Bureau** - Demographic participation rates in federal programs

- **Bureau of Labor Statistics** - Employment and wage data



### Analytical Framework

1. **Quantitative Analysis**: Based on CBO's household resource allocation models

2. **Demographic Correlation**: Cross-referenced with program participation rates

3. **Geographic Distribution**: State-level analysis of policy impacts

4. **Temporal Modeling**: 9-year projection period (2026-2034)



### Key Provisions Analyzed

- Extension of 2017 Tax Cuts and Jobs Act provisions

- Changes to Medicaid eligibility and work requirements

- SNAP (Supplemental Nutrition Assistance Program) modifications

- ACA health insurance subsidy reductions

- Student loan program changes

- Immigration enforcement and border security funding



## Call to Action



### For Government Officials

1. **Review constituency impacts** in your district

2. **Consider mitigation measures** for adversely affected groups

3. **Engage with community stakeholders** using this data

4. **Support data-driven policy discussions** in legislative sessions



### For Advocacy Groups

1. **Use visualizations** in community presentations

2. **Share findings** with affected constituencies

3. **Advocate for policy amendments** based on impact data

4. **Organize community responses** to policy changes



### For Researchers & Academics

1. **Validate findings** with independent analysis

2. **Contribute improvements** to the methodology

3. **Extend analysis** to additional demographic factors

4. **Collaborate on policy impact studies**



### For Citizens

1. **Understand how policies affect you** and your community

2. **Contact your representatives** with data-backed concerns

3. **Share information** with friends and family

4. **Participate in civic engagement** with informed perspectives



## Dashboard Sections



### Demographic Analysis (Charts 1-12)

- Income distribution effects

- Racial and ethnic impact analysis

- Age group comparisons

- Political affiliation correlations

- Geographic regional differences

- Education level impacts

- Employment sector effects

- Family structure analysis

- Immigration status impacts

- Disability status effects

- Health insurance coverage impacts

- Housing status comparisons



### Policy-Specific Impact Analysis (Charts 13-20)

- **Tax Burden Changes by Income Bracket**: Effective tax rate changes across income levels

- **State & Local Tax Revenue Impact**: SALT deduction cap effects by state

- **Health Insurance Coverage Changes**: Projected coverage loss by insurance type

- **Out-of-Pocket Healthcare Costs**: Average cost increases by family size

- **Border Security Investment Breakdown**: Allocation of border security funding

- **Urban vs Rural Impact Comparison**: Differential effects by community type

- **Small Business vs Large Corporation Impact**: Tax and regulatory burden changes

- **SNAP Benefit Changes by Household Type**: Food assistance impact analysis



### Economic & Budget Analysis (Charts 21-23)

- **Economic Sector Impact**: Financial impact and job changes by sector (in billions)

- **Industry Employment Changes**: Current vs. projected employment levels by industry



## Quick Start



### Local Development

1. Clone the repository

2. Open `index.html` in a web browser

3. No build process required - uses CDN for Chart.js



### GitHub Pages Deployment

1. Push to GitHub repository

2. Enable GitHub Pages in repository settings

3. Set source to main branch

4. Dashboard will be available at `https://iroy2000.github.io/one-big-beautiful-bill-impact-analysis`



## Project Structure



```

one-big-beautiful-bill-impact-analysis/

├── index.html          # Main dashboard HTML

├── css/

│   └── styles.css      # All dashboard styling

├── js/

│   └── dashboard.js    # Chart configurations and logic

└── README.md           # Project documentation

```



## Technical Details



### Browser Compatibility

- Modern browsers (Chrome, Firefox, Safari, Edge)

- Mobile responsive design

- Optimized for various screen sizes



### Chart Types Used

- Bar charts for comparative data

- Line charts for trends over time

- Doughnut charts for proportional data

- Radar charts for multi-dimensional comparisons

- Polar area charts for categorical data

- Mixed charts for dual-axis visualizations



## Responsive Features



- **Desktop**: Full grid layout with hover effects

- **Tablet**: Adjusted grid spacing and font sizes

- **Mobile**: Single-column layout with optimized touch targets

- **Adaptive Text**: Font sizes scale with screen size

- **Flexible Charts**: Maintain readability across all devices



## Data Sources



Based on Congressional Budget Office (CBO) analysis covering:

- Tax Cuts and Jobs Act provisions

- ACA health insurance subsidy changes

- Medicaid eligibility modifications

- SNAP program adjustments

- Student loan program changes

- Border security funding

- Federal spending adjustments



## Customization and Contribution



### Adding New Charts

1. Add chart container to `index.html`

2. Implement chart in `js/dashboard.js`

3. Update summary statistics if needed



### Styling Changes

- Modify `css/styles.css` for visual updates

- All responsive breakpoints included

- CSS variables for easy color scheme changes



### Data Updates

- Update data arrays in `js/dashboard.js`

- Summary statistics auto-calculate from chart data

- Maintain existing data structure for compatibility



## Chart Configuration



All charts share a common configuration base with:

- Responsive sizing

- Consistent font scaling

- Mobile-optimized rotations

- Unified color schemes

- Accessibility considerations



## GitHub Pages Optimization



- Relative file paths

- CDN dependencies

- No build process required

- Optimized asset loading

- Mobile-first responsive design



## License



This project is open source and available under the MIT License.



## Contributing



Contributions are welcome! Please feel free to submit pull requests or open issues for improvements and bug fixes.



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**Note**: This analysis represents estimated effects based on CBO projections and demographic correlations. Actual impacts may vary based on implementation details and economic conditions.