Moral Infrastructure: Soft Skills, Technocratic Blindness, and the Ethics of Systemic Foresight

Description This dataset and accompanying code support the study titled “Soft Skills, Trust, and Income: GSS-Based Empirical Replication”, which investigates the predictive power of interpersonal soft skills—such as trust, job satisfaction, and financial satisfaction—on income using General Social Survey (GSS) data. The study is part of a broader project on ethical capacities and economic outcomes in behavioral finance and soft skills education. The results reinforce the hypothesis that non-cognitive, relational capacities meaningfully predict real-world income variance. 🔍 Contents This Zenodo repository includes: Controls.xlsx: GSS demographic and background variables. Core.xlsx: Core relational and attitudinal variables (e.g., trust, fairness). Institutional.xlsx: Measures of institutional trust and confidence in government. satisfaction.xlsx: Recoded ordinal responses for job and financial satisfaction. soft.ipynb: A fully executable Google Colab notebook containing data cleaning, regression code, and result interpretation. 🧪 How to Run the Analysis in Google Colab Open Google Colab. Upload the files from this Zenodo record into your Colab environment using the file upload interface (left sidebar > Files > Upload). Open the soft.ipynb notebook. Run each cell in sequence: The first cell loads all five .xlsx files using pandas. The data is merged and cleaned to remove missing and inapplicable responses. Satisfaction variables are numerically encoded. A regression model is specified using statsmodels with log_income as the dependent variable. Review the printed OLS regression summary, which reports coefficient estimates, p-values, and confidence intervals. 🛠 Programming Environment Language: Python 3.10+ Libraries: pandas, numpy, statsmodels, openpyxl Runtime: Google Colab, recommended for reproducibility and accessibility.