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    <title>EconStor Collection:</title>
    <link>https://hdl.handle.net/10419/31</link>
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    <pubDate>Thu, 07 May 2026 04:52:56 GMT</pubDate>
    <dc:date>2026-05-07T04:52:56Z</dc:date>
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      <title>Broadcasting change: India's community radio policy and women's empowerment</title>
      <link>https://hdl.handle.net/10419/318543</link>
      <description>Title: Broadcasting change: India's community radio policy and women's empowerment
Authors: Rusche, Felix
Abstract: In poor countries, the interaction of early marriage, early motherhood, and low educational attainment disempowers women and limits their life opportunities. Even as countries grow richer, gender inequality is often sustained by social norms, thereby limiting welfare gains from women's empowerment. I investigate the use of media as a cheap and scalable policy to empower women. In 2006, India enacted a community radio policy that grants radio licenses to NGOs and educational institutions with the aim to foster local development. I collect original data on the content and coverage areas of all 250+ radio stations. I uncover women's empowerment as a key theme through topic modeling and ChatGPT-based analyses of radio show recordings. For identification, I exploit topography-driven variation in radio access and develop a novel econometric approach to deal with randomly displaced geolocated household data. The results show that women exposed to radio gain an additional 0.3 years of education and are 4.1pp (11%) more likely to obtain a secondary degree. In line with increased education, exposure reduces child marriages by 1.4pp (22%) and fertility of young women by around 10% while they are 11pp more likely to exhibit autonomy in household decisions. The findings demonstrate that community media can effectively address gender inequality.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
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      <dc:date>2025-01-01T00:00:00Z</dc:date>
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      <title>Reporting big news, missing the big picture? Stock market performance in the media</title>
      <link>https://hdl.handle.net/10419/318542</link>
      <description>Title: Reporting big news, missing the big picture? Stock market performance in the media
Authors: Ciccone, Antonio; Rusche, Felix
Abstract: Between 2017 and 2024, the main national stock market indices rose in the US and the five largest European economies. However, the average daily performance of all six indices turns from positive to negative when weighted by daily media coverage. A case in point is the average daily performance of Germany's DAX index on days it was reported on the country's most-watched nightly news. While the DAX increased by more than 4 index points per day over the period, the index dropped by more than 10 points on days it was reported- news was bad news. On days the DAX wasn't covered on the nightly news, the index rose by around 10 points-no news was good news. About half of the worse daily performance when the DAX was covered is accounted for by a greater focus on negative news. The other half stems from a novel big news bias: a greater focus on large index changes, whether positive or negative, combined with a negative skew in the daily performance of the index. We show that the big news bias extends to other national stock market indices.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
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      <dc:date>2025-01-01T00:00:00Z</dc:date>
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      <title>The democracy premium in expressive law: An experiment</title>
      <link>https://hdl.handle.net/10419/318544</link>
      <description>Title: The democracy premium in expressive law: An experiment
Authors: Hermstrüwer, Yoan; Khesali, Mahdi
Abstract: Why do people obey the law when it is not formally enforced? In this study, we explore the expressive power of democracy as a behavioral channel of compliance with the law. Using a modified version of the stealing game, we examine the effect of two distinct democratic interventions on stealing under normative ambiguity: a voting procedure in which the outcome is revealed, and a voting procedure in which the outcome of the vote remains unknown. We find that revealing the outcome of a vote significantly reduces stealing relative to a baseline treatment without a vote and the treatment in which the outcome of the vote remains unknown. We also observe suggestive evidence that participants who support the social norm proscribing theft are more likely to steal nonetheless when the outcome remains unknown. Our findings have important implications for the design of expressive law and of democratic voting procedures.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
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      <dc:date>2025-01-01T00:00:00Z</dc:date>
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      <title>Human realignment: An empirical study of LLMs as legal decision-aids in moral dilemmas</title>
      <link>https://hdl.handle.net/10419/318541</link>
      <description>Title: Human realignment: An empirical study of LLMs as legal decision-aids in moral dilemmas
Authors: Engel, Christoph; Hermstrüwer, Yoan; Kim, Alison
Abstract: Recent advances in AI create possibilities for delegating legal decision-making to machines or enhancing human adjudication through AI assistance. Using classic normative conflicts - the trolley problem and similar moral dilemmas - as a proof of concept, we examine the alignment between AI legal reasoning and human judgment. In our baseline experiment, we find a pronounced mismatch between decisions made by GPT and those of human subjects. This misalignment raises substantive concerns for AI-powered legal decision-aids. We investigate whether explicit normative guidance can address this misalignment, with mixed results. GPT-3.5 is susceptible to such intervention, but frequently refuses to decide when faced with a moral dilemma. GPT-4 is outright utilitarian, and essentially ignores the instruction to decide on deontological grounds. GPT-o3-mini faithfully implements this instruction, but is unwilling to balance deontological and utilitarian concerns if instructed to do so. At least for the time being, explicit normative instructions are not fully able to realign AI advice with the normative convictions of the legislator.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/318541</guid>
      <dc:date>2025-01-01T00:00:00Z</dc:date>
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