My research agenda currently focuses on technological change and Artificial Intelligence.
WORKING PAPERS
WORKING PAPERS
balancing progress and protection: public support for technology regulation, With Alexander Kuo
The rapid progression of the fourth industrial revolution has sparked debate about its economic impacts, and prompted calls for increased government steering of technological advancements. This study analyzes the foundations of public support for policies that regulate and tax new technologies. We propose that policy preferences are shaped by two prevailing narratives about technology. The 'pro-technology' narrative emphasizes the benefits for economic growth and consumers, while the 'anti-technology' narrative highlights potential harms to certain workers and communities. To investigate the influence of these narratives on public opinion, we design novel survey questions about six pertinent policies, and we experimentally vary the arguments presented to respondents. We embed our experiments in large, representative samples from European Union countries and measure objective and subjective technological risks, including of job substitution by artificial intelligence (AI). We find substantial support for technology regulation. Pro-technology claims diminish support significantly but anti-technology arguments enhance support only modestly. We discuss the implications of our results in light of growing concern about AI.
Draft available on request
Draft available on request
Policy Responses to Digitalization-related risks, With Nicolas Bicchi and Alexander Kuo (R&R)
What policies do individuals prefer in response to the labor market risks related to the ongoing processes of digitalization and automation? To what extent does being exposed to different forms of “technological risk” condition such preferences? In this paper, we advance existing research on this topic by distinguishing between among three main dimensions of technological risks (general concern about negative technological impacts, concern about tasks in one’s job being automated, and stress about learning new technology “technostress”), as well as and preferences for three types of policies related to these risks (compensation, retraining, and protectionist policies intended to slow down or prevent technological change). Using new survey evidence from Spain, we find little evidence that technological risks matter for preferences for compensation or retraining, but they do condition support for protectionist policies. We conclude with implications for politics in the current context of rapid digitalization.
Published as a working paper by the EU Science Hub.
Published as a working paper by the EU Science Hub.
Scaling POlitical texts with Large Language Models: Asking a chatbot might be all you need, With Gaël Le Mens (R&R)
We use instruction-tuned Large Language Models (LLMs) such as GPT-4, MiXtral, and Llama 3 to position political texts within policy and ideological spaces. We directly ask the LLMs where a text document or its author stand on the focal policy dimension. We illustrate and validate the approach by scaling British party manifestos on the economic, social, and immigration policy dimensions; speeches from a European Parliament debate in 10 languages on the anti- to pro-subsidy dimension; Senators of the 117th US Congress based on their tweets on the left-right ideological spectrum; and tweets published by US Representatives and Senators after the training cutoff date of GPT-4. The correlation between the position estimates obtained with the best LLMs and benchmarks based on coding by experts, crowdworkers or roll call votes exceeds .90. This training-free approach also outperforms supervised classifiers trained on large amounts of data. Using instruction-tuned LLMs to scale texts in policy and ideological spaces is fast, cost-efficient, reliable, and reproducible (in the case of open LLMs) even if the texts are short and written in different languages. We conclude with cautionary notes about the need for empirical validation.
Working paper here
Working paper here
Historical Family types and female political representation: Persistence and changE, With Dídac Queralt and Ana Tur-Prats (R&R)
We argue that different historical family configurations shaped the gendered division of labor within the household, gender norms, and female political representation in the long run. Our main evidence draws from geographic variation in historical family types in Spain and municipality-level electoral data from 1978 to 2015 and earlier democratic spells. We find that areas where the stem family was prevalent-meaning that multiple generations of women lived together and shared domestic work-show higher female political representation than areas with nuclear-family tradition. Still, history is not destiny, and the impact of historical legacies can fade. In our mechanisms analyses, we demonstrate that the introduction of party-list gender quotas balanced off the main effect, although they did not erase underlying differences between regions in gender attitudes and female paid employment. Our research contributes to the study of historical persistence by assessing what institutions can and cannot do to combat patriarchal prejudice.
Working paper here
Working paper here
OTHER WORK IN PROGRESS
Who Wants the Knowledge Economy?
With Alexander Kuo, Silja Häusermann and Reto Bürgisser
Slides available on request.
Public Opinion about the Regulation of Artificial Intelligence
With Alexander Kuo and Shir Raviv
Data collection in progress
What if you see it? Workers' perceptions of and reactions to LLMs
With Massimo Anelli, Italo Colantone, and Piero Stanig
Data collection in progress
AI and Political Attitudes: Psychological outcomes as mediators
With Alexander Kuo
Data collection in progress
CURRENT FUNDED RESEARCH PROJECTS
Transforming European Work and Social Protection (TRANSEUROWORKS) as PI of the Spanish team
Awarded by the European Commission, H2020 program
November 2022 - October 2026
Citizen Attitudes toward Artificial Intelligence (CATAI) as PI
Awarded by the Spanish Ministry of Science and Innovation
January 2023 - December 2026
Who Wants the Knowledge Economy?
With Alexander Kuo, Silja Häusermann and Reto Bürgisser
Slides available on request.
Public Opinion about the Regulation of Artificial Intelligence
With Alexander Kuo and Shir Raviv
Data collection in progress
What if you see it? Workers' perceptions of and reactions to LLMs
With Massimo Anelli, Italo Colantone, and Piero Stanig
Data collection in progress
AI and Political Attitudes: Psychological outcomes as mediators
With Alexander Kuo
Data collection in progress
CURRENT FUNDED RESEARCH PROJECTS
Transforming European Work and Social Protection (TRANSEUROWORKS) as PI of the Spanish team
Awarded by the European Commission, H2020 program
November 2022 - October 2026
Citizen Attitudes toward Artificial Intelligence (CATAI) as PI
Awarded by the Spanish Ministry of Science and Innovation
January 2023 - December 2026