Moral Foresight in the Age of Backlash: Populism, Polarization, and the Ethical Responsibility of Firms
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2025
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| author | Brown, Scott |
| author_facet | Brown, Scott |
| contents | <p>This study utilizes data from the <strong>General Social Survey (GSS)</strong>, a nationally representative, repeated cross-sectional survey administered by <strong>NORC at the University of Chicago</strong>. 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.</p> <p>For the purposes of this analysis, the dataset was restricted to survey waves from <strong>2000 to 2022</strong>, 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 <strong>GSS Data Explorer</strong> (<a href="https://gssdataexplorer.norc.org/" target="_new" rel="noopener">https://gssdataexplorer.norc.org/</a>), using the platform’s variable filtering and trend tools.</p> <p>Key variables used in the analysis include:</p> <ul> <li> <p><strong>Dependent variable</strong>: <em>Support for redistribution</em>, measured by agreement with the statement “The government should reduce income differences between the rich and the poor.”</p> </li> <li> <p><strong>Independent variables</strong>:</p> <ul> <li> <p><em>Party identification</em> (Democrat, Republican, Independent/Other)</p> </li> <li> <p><em>Racial resentment indicators</em>, including agreement with items such as “Blacks should work their way up without special favors”</p> </li> <li> <p><em>Year</em> (centered for interaction and trend modeling)</p> </li> <li> <p><em>Demographic controls</em>: age, gender, income, education, and geographic region</p> </li> </ul> </li> </ul> <p>The analytic sample includes respondents with valid responses to all core variables, totaling <strong>5,483 observations</strong> 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.</p> <p>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.</p> <h3>How to Replicate the Analysis</h3> <p>To replicate the statistical models presented in this study, follow these steps:</p> <ol> <li> <p><strong>Upload the Dataset to Google Colab</strong></p> <ul> <li> <p>Open <a target="_new" rel="noopener">Google Colab</a></p> </li> <li> <p>Create a new notebook</p> </li> <li> <p>Click the folder icon on the left sidebar</p> </li> <li> <p>Upload the Excel file (<code>MAGADATA.xlsx</code>) containing the cleaned General Social Survey (GSS) data used in this study</p> </li> </ul> </li> <li> <p><strong>Load the Analysis Code</strong></p> <ul> <li> <p>Open the accompanying Word document (<code>MAGACode.docx</code>)</p> </li> <li> <p>Copy the code blocks written in R and Python from the document</p> </li> <li> <p>Paste the code into the Colab notebook code cells</p> </li> </ul> </li> <li> <p><strong>Run the Notebook</strong></p> <ul> <li> <p>Click the “▶️” (Run) button at the top left of each code cell</p> </li> <li> <p>Ensure all packages load successfully (Colab will install them if not preloaded)</p> </li> <li> <p>Once the notebook runs, it will execute:</p> <ul> <li> <p>Logistic regression on redistribution preferences</p> </li> <li> <p>Interaction models between racial resentment and party ID</p> </li> <li> <p>Rolling OLS trend models by year</p> </li> <li> <p>Summary statistics and plots</p> </li> </ul> </li> </ul> </li> </ol> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15862115 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Moral Foresight in the Age of Backlash: Populism, Polarization, and the Ethical Responsibility of Firms Brown, Scott <p>This study utilizes data from the <strong>General Social Survey (GSS)</strong>, a nationally representative, repeated cross-sectional survey administered by <strong>NORC at the University of Chicago</strong>. 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.</p> <p>For the purposes of this analysis, the dataset was restricted to survey waves from <strong>2000 to 2022</strong>, 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 <strong>GSS Data Explorer</strong> (<a href="https://gssdataexplorer.norc.org/" target="_new" rel="noopener">https://gssdataexplorer.norc.org/</a>), using the platform’s variable filtering and trend tools.</p> <p>Key variables used in the analysis include:</p> <ul> <li> <p><strong>Dependent variable</strong>: <em>Support for redistribution</em>, measured by agreement with the statement “The government should reduce income differences between the rich and the poor.”</p> </li> <li> <p><strong>Independent variables</strong>:</p> <ul> <li> <p><em>Party identification</em> (Democrat, Republican, Independent/Other)</p> </li> <li> <p><em>Racial resentment indicators</em>, including agreement with items such as “Blacks should work their way up without special favors”</p> </li> <li> <p><em>Year</em> (centered for interaction and trend modeling)</p> </li> <li> <p><em>Demographic controls</em>: age, gender, income, education, and geographic region</p> </li> </ul> </li> </ul> <p>The analytic sample includes respondents with valid responses to all core variables, totaling <strong>5,483 observations</strong> 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.</p> <p>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.</p> <h3>How to Replicate the Analysis</h3> <p>To replicate the statistical models presented in this study, follow these steps:</p> <ol> <li> <p><strong>Upload the Dataset to Google Colab</strong></p> <ul> <li> <p>Open <a target="_new" rel="noopener">Google Colab</a></p> </li> <li> <p>Create a new notebook</p> </li> <li> <p>Click the folder icon on the left sidebar</p> </li> <li> <p>Upload the Excel file (<code>MAGADATA.xlsx</code>) containing the cleaned General Social Survey (GSS) data used in this study</p> </li> </ul> </li> <li> <p><strong>Load the Analysis Code</strong></p> <ul> <li> <p>Open the accompanying Word document (<code>MAGACode.docx</code>)</p> </li> <li> <p>Copy the code blocks written in R and Python from the document</p> </li> <li> <p>Paste the code into the Colab notebook code cells</p> </li> </ul> </li> <li> <p><strong>Run the Notebook</strong></p> <ul> <li> <p>Click the “▶️” (Run) button at the top left of each code cell</p> </li> <li> <p>Ensure all packages load successfully (Colab will install them if not preloaded)</p> </li> <li> <p>Once the notebook runs, it will execute:</p> <ul> <li> <p>Logistic regression on redistribution preferences</p> </li> <li> <p>Interaction models between racial resentment and party ID</p> </li> <li> <p>Rolling OLS trend models by year</p> </li> <li> <p>Summary statistics and plots</p> </li> </ul> </li> </ul> </li> </ol> |
| title | Moral Foresight in the Age of Backlash: Populism, Polarization, and the Ethical Responsibility of Firms |
| url | https://doi.org/10.5281/zenodo.15862115 |