The political conditioning of subjective economic evaluations: the role of party discourse

Classic and revisionist perspectives on economic voting have thoroughly analyzed the role of macroeconomic indicators and individual partisanship as determinants of subjective evaluations of the national economy. Surprisingly, however, top-down analysis of parties’ capacity to cue and persuade voters about national economic conditions is absent in the debate. This study uses a novel dataset containing monthly economic salience in party parliamentary speeches, macroeconomic indicators and individual survey data covering the four last electoral cycles in Spain (1996–2011). The results show that the salience of economic issues in the challenger’s discourse substantially increases negative evaluations of performance when this challenger is the owner of the economic issue. While a challenger’s conditioning of public economic evaluations is independent of the state of the economy (and can affect citizens with different ideological orientations), incumbent parties are more constrained by the true state of the economy in their ability to persuade the electorate on this issue

The Dynamic Impact of Periodic Review on Women’s Rights

Human rights treaty bodies have been frequently criticized as useless and the regime’s self-reporting procedure widely viewed as a whitewash. Yet very little research explores what, if any, influence this periodic review process has on governments’ implementation of and compliance with treaty obligations. We argue oversight committees may play an important role in improving rights on the ground by providing information for international and primarily domestic audiences. This paper examines the cumulative effects on women’s rights of self-reporting and oversight review, using original data on the history of state reporting to and review by the Committee on the Elimination of Discrimination against Women (CmEDAW). Using a dynamic approach to study the effects of the periodic review process, we find that self-reporting has a significant positive effect on women’s rights. We explore three clusters of evidence for the domestic mobilization mechanism: information provision through domestic civil society organizations; publicity and critique through the domestic media; and parliamentary attention, debate, and implementation of recommendations. This is the first study to present positive evidence on the effects of self-reporting on rights and to describe the mechanisms that link Geneva bodies with local politics. Our findings challenge the received wisdom that the process of reporting to these treaty bodies is basically useless

CausalNLP: A Practical Toolkit for Causal Inference with Text

The vast majority of existing methods and systems for causal inference assume that all variables under consideration are categorical or numerical (e.g., gender, price, blood pressure, enrollment). In this paper, we present CausalNLP, a toolkit for inferring causality from observational data that includes text in addition to traditional numerical and categorical variables. CausalNLP employs the use of meta-learners for treatment effect estimation and supports using raw text and its linguistic properties as both a treatment and a "controlled-for" variable (e.g., confounder). The library is open-source and available at: https://github.com/amaiya/causalnlp.Comment: 7 page

Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures

The rapid advancement of Large Language Models (LLMs) has sparked intense debate regarding their ability to perceive and interpret complex socio-political landscapes. In this study, we undertake an exploration of decision-making processes and inherent biases within LLMs, exemplified by ChatGPT, specifically contextualizing our analysis within political debates. We aim not to critique or validate LLMs' values, but rather to discern how they interpret and adjudicate "good arguments." By applying Activity Dependency Networks (ADNs), we extract the LLMs' implicit criteria for such assessments and illustrate how normative values influence these perceptions. We discuss the consequences of our findings for human-AI alignment and bias mitigation. Our code and data at https://github.com/david-jenny/LLM-Political-Study