Economic Substance Behind Texas Political Anti-ESG Sanctions
Bibliographic record
Abstract
A stark contrast exists between the stated preferences of politicians in the so-called blue states (Democrats) and those in red states (Republicans) on environmental, social, and governance (ESG) matters. We examine whether these polarized political stances are reflected in the investment strategies of respective states’ pension funds. We examine a Texas directive that the state agencies divest from investment companies that profess a pro-ESG stance and allegedly “boycott” energy stocks. We find that funds banned by the Texas directive, despite carrying ESG-focused titles, are largely indexers with a tilt slightly away from energy stocks and slightly toward technology stocks. Banning such funds would make little difference to Texas pensioners or Texas energy companies, because the returns and stock holdings of banned funds are not meaningfully different from those of size-matched funds that do not proclaim an ESG focus. Pension funds in red states do not act per their politicians’ stance and largely follow market trends in their investment strategies. They have similar exposures to technology and energy stocks, as do pension funds in blue states. We conclude that the vehement pro– and anti–fossil fuel proclamations of blue and red states’ politicians, respectively, are not observed in their own state pension funds’ investment policies over which politicians have better control than on external funds. This paper was accepted by Ranjani Krishnan, accounting. Funding: The authors acknowledge financial support from the Social Sciences and Humanities Research Council of Canada. A. Srivastava acknowledges financial support from the Canada Research Chairs Program of the Government of Canada. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05180 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".