Political Language in Economics

Jelveh, Zubin; Kogut, Bruce; Naidu, Suresh

doi:10.2139/ssrn.2535453

Cited by 50 publications

(51 citation statements)

References 26 publications

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“…In a companion paper (Jelveh et al, 2014) we further demonstrate how this tool can be used to aid policymakers in de-biasing research findings. When compared to domains where ideological language is expected, our predictive ability is reduced.…”

Section: Discussionmentioning

confidence: 96%

Detecting Latent Ideology in Expert Text: Evidence From Academic Papers in Economics

Jelveh¹,

Kogut²,

Naidu³

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Previous work on extracting ideology from text has focused on domains where expression of political views is expected, but it's unclear if current technology can work in domains where displays of ideology are considered inappropriate. We present a supervised ensemble n-gram model for ideology extraction with topic adjustments and apply it to one such domain: research papers written by academic economists. We show economists' political leanings can be correctly predicted, that our predictions generalize to new domains, and that they correlate with public policy-relevant research findings. We also present evidence that unsupervised models can under-perform in domains where ideological expression is discouraged.

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Section: Discussionmentioning

confidence: 96%

Detecting Latent Ideology in Expert Text: Evidence From Academic Papers in Economics

Jelveh¹,

Kogut²,

Naidu³

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Self Cite

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“…3 Notable exceptions are Jelveh, Kogut, and Naidu (2018), who document partisan bias in economic research, and Posner (2008), McKenzie (2012), and Chen (2019), who document partisan bias among judges. 1 political affiliation.…”

Section: Introductionmentioning

confidence: 99%

Partisan Professionals: Evidence from Credit Rating Analysts

Kempf¹,

Tsoutsoura²

2018

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Partisan perception affects the actions of professionals in the financial sector. Using a novel dataset linking credit rating analysts to party affiliations from voter records, we show that analysts who are not affiliated with the U.S. president's party downward-adjust corporate credit ratings more frequently. By comparing analysts with different party affiliations covering the same firm in the same quarter, we ensure that differences in firm fundamentals cannot explain the results. We also find a sharp divergence in the rating actions of Democratic and Republican analysts around the 2016 presidential election. Our results show analysts' partisan perception has sizable price effects on rated firms and may influence firms' investment policies.

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“…As there are improvements in storage and computer processing power, more refined representations of language may be useful in future research. 11 The basic methods on tokenizing text and representing documents as frequency distributions over tokens has become relatively standardized in the literature on political text analysis (Gentzkow and Shapiro, 2010;Quinn et al, 2010;Jensen et al, 2012;Gentzkow et al, 2014;Ash et al, 2015;Gentzkow et al, 2015;Jelveh et al, 2015). A script removes upper-case, splits text into sentences, and removes punctuation.…”

Section: Processing Text Featuresmentioning

confidence: 99%

“…The key difference from PCA is that PLS is a supervised technique: Components are constructed to maximize the predictiveness for an outcome variable (Chun and Keleş, 2010). Previous examples of PLS in social-science text analysis include Jensen et al (2012) and Jelveh et al (2015).…”

Section: Out-of-sample Prediction Of Revenue With the Effective Tax Codementioning

confidence: 99%

Case Vectors: Spatial Representations of the Law Using Document Embeddings

Ash¹,

Chen²

2019

Law as Data

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This paper contributes to recent work in political economy and public finance that focuses on how details of the tax code, rather than tax rates, are used to implement redistributive fiscal policies. I use tools from natural language processing to construct a high-dimensional representation of tax code changes from the text of 1.6 million statutes enacted by state legislatures since 1963. A data-driven approach is taken to recover the effective tax code -the set of legal phrases in tax law that have the largest impact on revenues, holding major tax rates constant.Exogenous variation in tax legislation from judicial districts is used to capture revenue impacts that are solely due to changes in the tax code language, with the resulting phrases providing a robust out-of-sample predictor of tax collections. I then test whether political parties differ in patterns of effective tax code changes when they control state government. Relative to Republicans, Democrats use revenue-increasing language for income taxes but use revenue-decreasing language for sales taxes -consistent with a more redistributive fiscal policy -despite making no changes on average to statutory tax rates. These results are consistent with the view that due to their relative salience, changing tax rates is politically more difficult than changing the tax code.

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Political Language in Economics

Cited by 50 publications

References 26 publications

Detecting Latent Ideology in Expert Text: Evidence From Academic Papers in Economics

Detecting Latent Ideology in Expert Text: Evidence From Academic Papers in Economics

Partisan Professionals: Evidence from Credit Rating Analysts

Case Vectors: Spatial Representations of the Law Using Document Embeddings

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