DOI: 10.5281/zenodo.15268044
title: Moral Foresight in the Age of Backlash: Populism, Polarization, and the Ethical Responsibility of Firms

description: This study utilizes data from the General Social Survey (GSS), a nationally representative, repeated cross-sectional survey administered by NORC at the University of Chicago. The GSS is one of the most authoritative sources of longitudinal public opinion data in the United States, tracking American attitudes, beliefs, and behaviors across a wide range of social, political, and economic domains since 1972.

For the purposes of this analysis, the dataset was restricted to survey waves from 2000 to 2022, to capture contemporary patterns of polarization around economic redistribution and party identity, particularly during the post-9/11 and post-2016 political realignments. Data were accessed and downloaded through the GSS Data Explorer (https://gssdataexplorer.norc.org/), using the platform’s variable filtering and trend tools.

Key variables used in the analysis include:





Dependent variable: Support for redistribution, measured by agreement with the statement “The government should reduce income differences between the rich and the poor.”




Independent variables:





Party identification (Democrat, Republican, Independent/Other)




Racial resentment indicators, including agreement with items such as “Blacks should work their way up without special favors”




Year (centered for interaction and trend modeling)




Demographic controls: age, gender, income, education, and geographic region





The analytic sample includes respondents with valid responses to all core variables, totaling 5,483 observations after listwise deletion and multiple imputation for missing attitudinal items. All analyses were conducted using R and Python, with appropriate statistical methods for logistic regression, rolling OLS estimation, and interaction modeling. Attempts to estimate a Markov Switching model encountered convergence issues and are excluded from the final analysis.

The GSS sampling design includes multistage area probability sampling and post-stratification weights to ensure representativeness of the U.S. adult population. All interpretations in this study are based on weighted data unless otherwise noted.

How to Replicate the Analysis

To replicate the statistical models presented in this study, follow these steps:





Upload the Dataset to Google Colab





Open Google Colab




Create a new notebook




Click the folder icon on the left sidebar




Upload the Excel file (MAGADATA.xlsx) containing the cleaned General Social Survey (GSS) data used in this study






Load the Analysis Code





Open the accompanying Word document (MAGACode.docx)




Copy the code blocks written in R and Python from the document




Paste the code into the Colab notebook code cells






Run the Notebook





Click the “▶️” (Run) button at the top left of each code cell




Ensure all packages load successfully (Colab will install them if not preloaded)




Once the notebook runs, it will execute:





Logistic regression on redistribution preferences




Interaction models between racial resentment and party ID




Rolling OLS trend models by year




Summary statistics and plots