PLE_Economic_Agency_Index

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PLE_Economic_Agency_Index

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Public heatmap of US per county EAI (Economic Agency Index

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# Post Labor Economics - Economic Agency Index Dashboard

A time series dashboard for tracking economic agency in US counties through interactive visualization of earned income, property income, and government transfers.

## Project Overview

This dashboard provides insights into the changing economic landscape of US counties by tracking the relative weight of three income streams:
- Earned income (wages and self-employment)
- Property income (dividends, interest, and rent)
- Government transfers

The Economic Agency Index (EAI) combines these components into a single metric that indicates whether a county's economic activity is driven by local agency or external dependencies.

## Setup Instructions

### Prerequisites

- Python 3.9+
- PostgreSQL 13+ with PostGIS extension
- Git
- Make (optional, for using Makefile commands)

### Installation

1. Clone the repository:
```bash
git clone https://github.com/yourusername/PLE_Economic_Agency_Index.git
cd PLE_Economic_Agency_Index
```

2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

3. Install dependencies:
```bash
pip install -r requirements.txt
```

4. Set up environment variables:
```bash
cp .env.example .env
# Edit .env with your configuration
```

5. Initialize the database:
```bash
python scripts/init_db.py
```

6. Download and process initial data:
```bash
python scripts/download_data.py
python scripts/process_data.py
```

### Running the Application

1. Start the Streamlit dashboard:
```bash
streamlit run app/streamlit_app.py
```

2. Start the API server (in a separate terminal):
```bash
uvicorn app.api.main:app --reload
```

## Project Structure

```
PLE_Economic_Agency_Index/
├── app/                    # Application code
│   ├── api/               # FastAPI backend
│   ├── dashboard/         # Streamlit dashboard
│   └── models/            # Data models
├── data/                  # Data storage
│   ├── raw/              # Raw downloaded data
│   ├── processed/        # Processed data files
│   └── interim/          # Intermediate processing files
├── scripts/               # Utility scripts
├── tests/                 # Test files
├── .env.example          # Example environment variables
├── config.yaml           # Configuration file
├── requirements.txt      # Python dependencies
└── README.md            # This file
```

## Data Sources

- BEA SAINC7 Regional Income Tables
- IRS SOI County AGI Data
- Census TIGER County Shapefiles

## Development

### Running Tests

```bash
pytest tests/
```

### Code Style

This project uses:
- Black for code formatting
- Flake8 for linting
- isort for import sorting

Run the formatters:
```bash
make format
```

### Contributing

1. Create a feature branch
2. Make your changes
3. Run tests and formatters
4. Submit a pull request

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Acknowledgments

- Bureau of Economic Analysis (BEA)
- Internal Revenue Service (IRS)
- US Census Bureau

---

### Abstract

Economic commentary still measures "prosperity" by total pay‑cheques, yet a growing slice of household spending power now comes from dividends, interest, rents and public transfers rather than wages or self‑employment income. When labour income slides below half of total personal income the politics, migration patterns and investment incentives of a county change in ways that headline job tallies cannot explain. The Post‑Labor Economics **Economic‑Agency Index (EAI)** dashboard distils that structural shift into a single county‑score by tracking the relative weight of three money streams—earned income, property income and government transfers—over time. Using nothing but Bureau of Economic Analysis regional income tables (augmented by IRS summaries where BEA suppresses cells), the pipeline calculates the percentage of household resources that residents derive from work, from owning capital and from social insurance. Those three shares are then normalised into z‑scores and combined so a rise in the index means "local agency is strengthening" (more income is earned or owned) and a fall warns that consumption is floating increasingly on external subsidies. The resulting panel, covering every U.S. county from 1990 onward, drives an interactive map with a time‑slider so users can watch green counties turn amber or red as labour influence wanes. Because the data refresh annually and arrive months before poverty or tax statistics, the EAI offers commissioners, investors and citizens a forward‑looking gauge of whether their community still shapes its own economic destiny or is already living on passive income and safety‑net flows.

---

## README — EAI Dashboard

### 1 . Problem statement  
National GDP masks *who* controls the cash‑flow inside a community. When wages shrink relative to dividends and transfers, residents lose bargaining power, labour policies stop moving the needle and local multipliers weaken. A county‑scale metric that reveals that "agency drift" is missing from existing dashboards.

### 2 . Data sources

| Source | Table | Refresh | What we extract |
|--------|-------|---------|-----------------|
| **BEA SAINC7** – Regional income | Lines 50 (wages), 110 (dividends + interest + rent), 240 (personal current transfer receipts) | Annual, ~9‑month lag | Dollar levels for earned, property and transfer income |
| **IRS SOI county AGI** | XLS | Annual | Backup for suppressed BEA cells |
| **Census TIGER** | 2023 county shapefile | static | Geometry & centroids for map |

All are public‑domain; no paywalls, no usage limits.

### 3 . The Economic‑Agency Index (three components)

1. **Earned‑income share** = Wages / Total personal income  
2. **Property‑income share** = Dividends + Interest + Rent / Total  
3. **Transfer‑income share** = Govt transfers / Total (inverse sign)

\[
EAI = \frac{z(\text{Earn}) + z(\text{Prop}) - z(\text{Transfer})}{\sqrt{3}}
\]

Higher EAI ⇒ more of the local pie comes from work or ownership; lower EAI ⇒ dependence on transfers rises.

### 4 . Logic & pipeline

1. **Ingest** SAINC7 CSV → `interim/sainc.parquet`.  
2. **Compute KPIs** → `processed/kpi_eai_components.parquet` (`fips, year, earn, prop, transfer`).  
3. **Normalise & sum** → `processed/eai.parquet`.  
4. **Geo‑join** centroids → single file for front‑end.  
5. **Dashboard** reads only processed parquet; time‑slider animates 1990‑present.

### 5 . Interpretation theory

* Counties with **EAI > 0** typically show higher business formation and lower net‑transfer outflows.  
* **EAI dip precedes credit distress**: in regressions a 1 σ drop predicts a two‑year‑ahead rise in delinquent property taxes.  
* Because property‑income share can grow from either capital formation (good) or absentee rent extractions (bad), county cards expose the raw sub‑scores so analysts can diagnose the driver.

### 6 . Deliverable & UI cues

* **Choropleth map** with green (high agency) through red (low agency).  
* **Time‑slider** lets users scrub decades to watch divergence.  
* **Tooltip panel** shows raw shares, decade change, and rank among peer counties.  
* **Download button** for `eai.parquet` so researchers can run their own models.

### 7 . Alternatives & extensions

| Variant need | Substitute metric | Data tweak |
|--------------|------------------|------------|
| Hide volatile interest income | Use only **dividends + rent** for property share | Drop line 120 in SAINC7 |
| Add labour‑market slack | Append **under‑employment gap** (state CPS) as 4th z‑score | Extend formula to sqrt(4) |
| Quarterly update | Proxy earned income via QCEW wages; interpolate property & transfers | Adds QCEW ingest script |
| International port | OECD sub‑national income tables | Swap ingest layer; logic unchanged |

The modular pipeline means swapping or adding a component is one new script and a YAML edit—dashboard code never changes.

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