Global Markets, Corporate Assurances, and the Legitimacy of State Intervention: Perceptions of Distant Labor and Environmental Problems

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Collective perceptions of harm and impropriety channel the evolution of capitalism, as shown by research on the moral boundaries of markets. But how are boundaries perceived when harms are distant and observers face competing claims from advocacy organizations and corporations? These conditions are particularly salient in global supply chains, where private voluntary initiatives have been formed to address labor exploitation and environmental degradation. We argue that state intervention is now on the rise and that popular judgments about state intervention carry new insights for the sociology of markets, morality, policy, and globalization. Analyzing data from a conjoint survey experiment, we find that distant labor and environmental problems (e.g., forced labor, natural resource depletion) provoke varied levels of interest in state intervention as well as different justifications for state intervention. We also find an asymmetry of influence by strategic actors: transnational advocacy frames shape judgments to some degree, but they fall flat or backfire among conservatives. Corporate promises of reform reduce the perceived importance of state intervention—across political-ideological divides and regardless of credibility. Moving beyond stylized pro-/anti-trade attitudes, these findings reveal implicit logics of a contested moral field and the legitimacy of state intervention at a formative moment.

Related collections

Most cited references 116

Record: found
Abstract: found
Article: not found

Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto (2014)

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

0 comments Cited 345 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Validating vignette and conjoint survey experiments against real-world behavior.

Jens Hainmueller, Dominik Hangartner, Teppei Yamamoto (2015)

Survey experiments, like vignette and conjoint analyses, are widely used in the social sciences to elicit stated preferences and study how humans make multidimensional choices. However, there is a paucity of research on the external validity of these methods that examines whether the determinants that explain hypothetical choices made by survey respondents match the determinants that explain what subjects actually do when making similar choices in real-world situations. This study compares results from conjoint and vignette analyses on which immigrant attributes generate support for naturalization with closely corresponding behavioral data from a natural experiment in Switzerland, where some municipalities used referendums to decide on the citizenship applications of foreign residents. Using a representative sample from the same population and the official descriptions of applicant characteristics that voters received before each referendum as a behavioral benchmark, we find that the effects of the applicant attributes estimated from the survey experiments perform remarkably well in recovering the effects of the same attributes in the behavioral benchmark. We also find important differences in the relative performances of the different designs. Overall, the paired conjoint design, where respondents evaluate two immigrants side by side, comes closest to the behavioral benchmark; on average, its estimates are within 2% percentage points of the effects in the behavioral benchmark.