We examine how search frictions impact labor market sorting by constructing a model consistent with evidence that employers interview a subset of a pool of applicants. We derive necessary and sufficient conditions for sorting in applications and matches. Positive sorting is obtained when production complementarities outweigh a counterforce measured by a (novel) quality-quantity elasticity. Interestingly, the threshold for the complementarities depends on the fraction of high-type workers and can be increasing in the number of interviews. Our model shows how policies like Ban the Box can backfire because when screening workers becomes harder, firms may discourage certain workers from applying.

Affirmative action in India reserves explicit proportions of seats and jobs in publicly funded institutions for various beneficiary groups. Because seats are indivisible and arise in small numbers over time, implementation of this policy requires that beneficiary groups take turns claiming seats, for which purpose India relies on a device called a roster. We study the problem of constructing a roster, which involves addressing a series of connected apportionment problems. To identify suitable apportionment methods, six essential requirements direct our search to a large class of divisor methods. We show that the Webster–Sainte-Laguë method is the unique divisor method that satisfies several practical properties and fairness criteria. Comparative analysis between an existing Indian roster and the application of the Webster–Sainte-Laguë method highlights that method’s benefits.

Using data from a nationally representative survey of U.S. adults, this study explores how trust in key actors to responsibly manage artificial intelligence (AI) develops among members of the U.S. population and how trust, along with other key factors, shapes public attitudes toward AI. Greater trust is linked to stronger support for AI, both directly and indirectly (through risk and benefit perceptions). Furthermore, the strength or direction of the link between trust and support—as well as media diets and trust—differs significantly for liberals and conservatives, suggesting that Americans are indeed beginning to process AI-related information through a political lens.

The voluminous strategy research on the determinants of corporate scope is often premised on a well-established property rights regime, which contrasts with the weak property rights protection that still characterizes most countries today. We address this gap by applying property rights theory to theorize and empirically examine how the strengthening of the property rights regime affects corporate scope. Our analysis exploits the enactment of a property law that enhanced the formal protection of private properties in China as a quasi-experiment. We show that with a strengthened property rights regime, the horizontal relatedness among private firms’ businesses increases, but their vertical relatedness decreases, compared with state-owned firms. Further, these effects are less prominent for politically connected firms that are afforded informal protection of property rights. Our findings shed new light on the property rights regime as a critical determinant of firms’ horizontal and vertical scope.

In striking a balance between attracting more product reviews versus maintaining review quality, online platforms have started to label reviews with whether they are associated with verifiable purchases. This paper examines the impact of such disclosure policy on the strategic behavior of review writers and the helpfulness of verified reviews (VRs) and non-verified reviews (NVRs) for review users. We propose that the introduction of the verified purchase tag induces two competing effects for VRs, increased credibility and concerns for acquisition bias, which in turn influence the behaviors of both writers and users. By exploiting the exogenous shock resulting from a policy change on Amazon, we find that, after the disclosure, NVRs became longer in length and VRs started to contain more unique information. Surprisingly, we find strong evidence that VRs receive fewer helpfulness votes than NVRs. We further explore the underlying mechanism, namely review users' concerns about acquisition bias associated with VRs

Objective The COVID-19 pandemic has deeply impacted mental health, especially among young people, driven by extended social isolation, routine disruptions, and uncertainties about health and the future. While rising levels of anxiety and depression in this group are well-documented, little is known about their online information-seeking patterns during this prolonged crisis. Exploring these patterns is vital for understanding how individuals navigate mental health challenges and seek support in times of uncertainty. Method This cross-sectional study investigates the online mental health information-seeking behaviors of young people in China during the COVID-19 pandemic. Using content analysis, we examined 1211 questions and 2303 responses from a popular Chinese social Q&A platform, Zhihu. Among these, 691 questions were identified as originating from young people, with the remainder attributed to adults. The analysis focused on the types of information sought, the effectiveness of responses, and the responsib

With new technologies emerging, productivity is increasing, while environmental issues are being addressed. Studies focus on the benefits of technological progress but pay limited attention to potential risks. This paper uses data from 286 Chinese cities to explore the impact of industrial robots on urban green total factor productivity. We find that industrial robots significantly reduces urban green total factor productivity. Mechanism analysis shows the main effect is driven by manufacturing agglomeration and crowding out environmental investment. Urban green total factor productivity decomposition shows that industrial robots block green technical progress and efficiency. Heterogeneity analysis shows that industrial robots significantly impact urban green total factor productivity in developed, highly market-oriented, and industrialized regions. Our findings contribute to a deeper understanding of the role of industrial robots in urban green development and provide corresponding policy implications for gu

We examine the impact of China's de-politicization regulation Rule 18 on corporate risk-taking. This rule mandates government officials to step down from their positions on the boards of public firms, thereby severing the political ties through official directors. Employing a staggered difference-in-differences design, our study reveals that the collapse of political connections results in a significant decrease in the level of risk-taking among politically connected firms. Furthermore, we identify bank credit and direct government support as plausible channels through which these effects manifest and highlight the presence of heterogeneous effects across different contextual factors.