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    <link>https://hdl.handle.net/10419/273420</link>
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        <rdf:li rdf:resource="https://hdl.handle.net/10419/277606.2" />
        <rdf:li rdf:resource="https://hdl.handle.net/10419/290195" />
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    <dc:date>2026-04-29T22:04:10Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/277606.2">
    <title>Executive Compensation and Secured Debt: Evidence from REITs</title>
    <link>https://hdl.handle.net/10419/277606.2</link>
    <description>Title: Executive Compensation and Secured Debt: Evidence from REITs
Authors: Li, Ying; Li, Lingxiao; Zhu, Bing
Abstract: This paper explores the impact of executive compensation structure on firm debt choices. To analyze the relationship between executive compensation and firm debt structure via the managerial effort channel, we extend the theoretical model developed by Boot et al. (1991) by incorporating an agent-principal model. We employ data from US Equity Real Estate Investment Trusts (REITs) to empirically assess the model's implications. The evidence presented in this study reveals that when executive compensation exhibits a higher sensitivity to the firm's stock price (represented by a higher Delta), the firm tends to use a greater proportion of secured debt within its overall debt structure. This phenomenon can be attributed to the managerial effort channel: firms with higher Delta values tend to engage in investments that are more effort-sensitive, and these investment choices are positively associated with increased utilization of secured debt, where the collateral plays the role of incentivizing the manager to put more effort into projects - an "effort-lifting" behavior. These findings hold for an expanded sample consisting of firms from all industries. Our analysis offers a fresh perspective on the use of collateral and executive compensation as a tool to mitigate principal-agent problems.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/290195">
    <title>Artificial Intelligence and its Effect on Competition and Factor Income Shares</title>
    <link>https://hdl.handle.net/10419/290195</link>
    <description>Title: Artificial Intelligence and its Effect on Competition and Factor Income Shares
Authors: von Maydell, Richard
Abstract: We examine the impact of self-learning Artificial Intelligence (AI) on firm competition in a growth model with endogenous labor supply and heterogeneous agents. AI possesses the ability to improve autonomously through application, testing, and training. When firms incorporate AI into their production processes, they incur variable costs for acquiring the necessary software as well as fixed costs for installing AI infrastructure. The latter indeed drive productivity increases and economic growth but can also serve as an entry barrier for competing firms. Therefore, we examine how the rise of AI affects market concentration, firm competition, firm productivity and income inequality. We discuss potential policy interventions such as a profit tax or the modernization of competition and merger laws to prevent a significant increase in market concentration and income inequality within AIintensive industries and to foster economic growth.</description>
    <dc:date>2024-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/277669">
    <title>Changes in Occupational Tasks and the Costs of Job Loss</title>
    <link>https://hdl.handle.net/10419/277669</link>
    <description>Title: Changes in Occupational Tasks and the Costs of Job Loss
Authors: Ivanov, Boris</description>
    <dc:date>2023-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/277708">
    <title>Incentives for Collective Innovation</title>
    <link>https://hdl.handle.net/10419/277708</link>
    <description>Title: Incentives for Collective Innovation
Authors: Curello, Gregorio</description>
    <dc:date>2023-01-01T00:00:00Z</dc:date>
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