US State Socio-Economic Metrics: Governance Analysis: Seeking Your Expertise & Feedback

Hello r/artificial Community,

I've recently concluded an in-depth analysis, with the help of ChatGPT, focused on socio-economic metrics across various US states, categorized by governance aspects like governorship and state legislature composition. This project aimed to understand how governance might influence socio-economic factors. The dataset encompassed a variety of metrics, including GDP, Gini Coefficient, health, education, poverty, incarceration and violent crime rates, and air quality, all normalized per capita for accurate comparisons.

Your feedback and suggestions are invaluable to me, and I look forward to engaging with your expertise. Thank you for your time and insights!

Findings:

1. States under Democrat control generally showed higher composite scores.
2. Score variations suggest differences in policy priorities across governance types.
3. States with mixed political control displayed intermediate scores, possibly reflecting balanced policy decisions.

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https://preview.redd.it/5q9ucdkganec1.png?width=1709&format=png&auto=webp&s=9fbbaab1d39c87e23f2b10243549d16b2f9d0fce

Metric Correlation Matrix

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https://preview.redd.it/syjmdrahanec1.png?width=2635&format=png&auto=webp&s=c02e8c9e7fca6db75d89eba3db186c1dd617f387

Key Stages of the Analysis:

1. Selected diverse socio-economic and governance metrics.
2. Conducted thorough data cleaning and normalization.
3. Adjusted metrics per capita for accuracy.
4. Categorized states based on political control.
5. Applied equal weighting to all metrics in the composite score calculation.
6. Created rolled-up metrics for education and air quality.
7. Calculated normalized composite scores for each state.

Limitations:

1. Correlation vs. Causation: The analysis shows correlations, not causation. External factors not included in the dataset might also influence these metrics.
2. Scope of Metrics: The exclusion of other metrics (e.g., infrastructure, employment rates) means the analysis might not capture the full spectrum of socio-economic conditions.
3. Temporal Limitations: The data represents a snapshot in time and does not account for long-term trends or recent changes.
4. Normalization and Weighting: The method may oversimplify or skew the representation of states' performance. Different weighting schemes could lead to different results.

Data:

• Data File: https://docs.google.com/spreadsheets/d/e/2PACX-1vREIDxphExg4OBkaVy5nbS_WNvRXaVgcRZAii4Rx0WRuztG_AG0nUujDmiISySOFmozNibVHeXeUu4K/pub?output=csv
• Bibliography: https://docs.google.com/spreadsheets/d/e/2PACX-1vQb_jZOaE4JIYfeqWFL0gg_h2n_IvygROZhKxzxQ8lrc18sdOW40UC4A1fG_C7sKVRn3Ve_E5Do3NLk/pub?output=csv
• ChatGPT chat: ​​https://chat.openai.com/share/ba4d4d46-eff2-40fa-9b44-98f7d7146bd3

Request for Feedback:

I'm reaching out for your expert feedback:

• Methodological Improvements: Suggestions to enhance the analysis methodology.
• Additional Metrics: Insights into other metrics or data sources that could enrich this study.
• Interpretation for Policy-making: Thoughts on interpreting these findings in a policy-making context.

Goal:

To ensure that this analysis aligns with professional standards in data science and policy analysis, incorporating data integrity, appropriate statistical techniques, and an unbiased approach, while also acknowledging its limitations.

If you made it this far, thank you! I greatly value your input and am eager to benefit from your expertise. Thank you for dedicating your time and sharing your insights!

P.S. This project marks the beginning of a broader discussion and dissemination of these findings. Your input will be instrumental in refining and sharing this analysis with a wider audience.