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    <responseDate>2026-10-15T09:48:28Z</responseDate>
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                <identifier>oai:data.mendeley.com/nn8c63wccr.1</identifier>
                <datestamp>2026-09-03T18:54:54Z</datestamp>
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    <dc:creator>Cassidy, William</dc:creator>
    <dc:title>Elections Have Consequences: The Impact of Political Agency on Climate Policy and Asset Prices</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>Replication package for &quot;Elections Have Consequences: The Impact of Political Agency on Climate Policy and Asset Prices&quot; (William Cassidy), forthcoming in the Journal of Financial Economics.

This package contains the code and author-generated data needed to reproduce the tables and figures in the paper. The paper studies how political agency shapes the origination and pricing of climate transition risk, combining a structural model of elections, climate policy, and asset prices with an empirical analysis of high-frequency asset-price reactions to presidential climate-policy announcements.

Contents:
- Full analysis code (Python, R, Stata, MATLAB, SAS) for the data pipeline, the option-implied expected-return (SVIX) construction, the regressions, and all figures/tables.
- A README following the Social Science Data Editors template, documenting software requirements, execution order, and a mapping from each manuscript table/figure to the program that produces it.
- Author-derived intermediate data that may be redistributed: the public-domain presidential-remark transcripts and the fitted 260-topic LDA weights used to construct the climate/energy text measures.

Proprietary source data are NOT included and cannot be redistributed under their license terms: NYSE TAQ Millisecond, OptionMetrics IvyDB US, and CRSP (all via WRDS subscription), and Gallup polling series. The README gives full acquisition instructions, variable definitions, and the exact datasets used so that a researcher with the corresponding subscriptions can regenerate every intermediate file. The high-frequency market panel is rebuilt from WRDS TAQ; the option-implied expected returns follow Martin (2017) and Martin–Wagner (2019).

 Software: Python 3.10 (pandas 1.5.3, numpy 1.24.2), R 4.3.x (fixest, data.table), and MATLAB and SAS (for the option-implied SVIX construction). The only stochastic step (the LDA topic model) uses a fixed seed and is deterministic. Approximate end-to-end runtime is 1–3 days.

License: This dataset is released under CC BY 4.0. The author-generated code in the package is additionally released under the MIT license (see LICENSE.txt); neither license extends to the proprietary source data (WRDS, Gallup), which are not included.</dc:description>
    <dc:subject>Finance</dc:subject>
    <dc:subject>Asset Pricing</dc:subject>
    <dc:subject>Climate</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/nn8c63wccr.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/nn8c63wccr.1</dc:identifier>
    <dc:rights>MIT License</dc:rights>
    <dc:rights>http://opensource.org/licenses/MIT</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/nn8c63wccr</dc:relation>
    <dc:date>2026-09-03T18:54:54Z</dc:date>
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