Research

Publications

Young, Kayla, Kayla Gurganus, and Leigh Raymond. “Promoting Market-based vs. Regulatory Climate Policies: A Comparative Analysis.” Review of Policy Research. (doi: http://doi.org/10.1111/ropr.12493).

An active debate has emerged about the political viability of market-based versus non-market-based policies to address climate change. As carbon pricing policies face significant political challenges, some have argued that regulatory policies are a better political option because they do not highlight consumer energy prices and can be linked to other economic and social priorities. Yet, no study has compared communication strategies for regulatory versus price-based climate policies in practice. This paper fills that gap through a qualitative content analysis of framing strategies for Ontario’s 2016 cap-and-trade program for greenhouse gas emissions, and Virginia’s 2020 clean energy mandate. Results largely confirm the paper’s primary hypothesis that similar positive and negative economic frames will be used as or more frequently for the regulatory policy as for the price-based policy, complicating any theory that regulatory policies will face an easier political path due to their different messaging options.

Visconti, Giancarlo, Owura Kuffuor, and Kayla Young. “Constructing Generalizable Geographic Natural Experiments.” Research & Politics. (doi: https://doi.org/10.1177/20531680221113763).

A natural experiment is a real-world situation that generates as-if or haphazard assignment to treatment. Geographic or administrative boundaries can be exploited as natural experiments to construct treated and control groups. Previous research has demonstrated that matching can help enhance these designs by reducing imbalances on observed covariates. An important limitation of this empirical approach, however, is that the results are inherently local. While the treated and control groups may be quite similar to each other, they could be substantially different from the target population of interest (e.g., the country). We propose a design inspired by the idea of template matching to construct generalizable geographic natural experiments. By matching our treated and control groups to a template (i.e., the target population), we obtain groups that are similar to the target population of interest and to each other, which can increase both the internal and external validity of the study.

Works in Progress

Visconti, Giancarlo, and Kayla Young. “Do Extreme Weather Events Change Public Support for Climate Policy?”

Can exposure to disasters change political preferences about climate change? Although there is a growing literature on both the effects of disasters and the factors explaining attitudes towards global warming, there remains a lack of consensus about whether and how disasters influence public opinion about climate change. We study the effects of exposure to nine hazards associated with climate variability, including floods, fires, and hurricanes, on support for climate change mitigation. Specifically, we use different empirical strategies to analyze public opinion data at the individual level in the US: namely, a difference-in-differences design and cardinality matching. We find that exposure to extreme weather events has a significant effect on acknowledging the existence of climate change and supporting the need for action. These findings reveal that exposure to certain hazards may change attitudes toward a changing global climate, which can have important policy implications.

Young, Kayla. “Can a New Generation of Activists Change Public Opinion and Policy Preferences about Climate Change? Evidence from a Survey Experiment.”

Youth activists were fundamental in organizing some of the largest-ever climate strikes in recent years, and many young people continue to mobilize for the climate today. While the social movement literature suggests that organized dissent could help marginalized groups achieve their political goals, there is relatively little information on the efficacy of youthactivism. I use a 2×3 survey experiment (n = 995) to explore the influence of (i) messaging with youth-centered and intergenerational justice framing and (ii) protesters of different ages on public opinion and policy preferences about climate change in the United States. While I do not find evidence of main effects, the results suggest important interaction effects – a youth activist using youth-centered messaging may increase public support for climate mitigation. My findings also indicate that youth-based appeals to intergenerational justice might be most effective among a younger audience. This study contributes to the social movement literature by exploring the efficacy of youth climate activism and by providing additional insights into the relationship between protests and public opinion. It may also be relevant for a broader audience, including activists and policy practitioners.

Young, Kayla, Joan Timoneda, and Christina Walker. “Is More Time Always Better? Revisiting the No Time-Varying Confounder Assumption for Unit Fixed Effects Models.”

Including unit fixed effects in regression models can be a powerful strategy for causal inference. Many researchers seem to apply this approach without critical reflection on its causal identification assumptions, however, including the no time-varying confounder assumption that is among the most important for using unit fixed effects models. In this paper, I revisit the no time-varying confounder assumption, arguing that it warrants closer scrutiny from researchers, particularly when considering how much data over time to include in a unit fixed effects analysis. Drawing on theoretical simulations, I suggest that unobserved confounders may often have an uneven distribution over time, which can create significant biases for models relying on unit fixed effects. I contend that such biases may be especially problematic when researchers draw on data across substantial amounts of time, as every additional unit of time in a unit fixed effects analysis could make the no time-varying confounder assumption less likely to hold. Somewhat in contrast to the standard practice of including as much data across time as possible, this suggests that scholars should be cautious about the time span of data included in their fixed effects models – more data across time may not always make for more robust and precise estimates. I recommend that scholars using unit fixed effects provide a theoretical justification for how many time periods are included in their analysis and evaluate estimates using different subsets of data, which I illustrate using a dataset of complaints against New York City police officers filed from 2000 to 2020.

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