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Shaoming Cheng

Publications and source records attributed to Shaoming Cheng.

4 recordsLinked to original sources

Capitalizing Risk and Regulation: Sequential Shocks in Florida's Condominium Market

Housing markets capitalize new information regarding future risks and ownership costs, yet little is known about how markets respond when sequential shocks differ fundamentally in nature. This study examines how Florida's condominium market capitalized two temporally adjacent but distinct shocks: the unexpected collapse of the Surfside condominium building, which revised perceptions of structural risk, and the subsequent enactment of Senate Bill 4-D (SB4D), which introduced mandatory milestone inspections, Structural Integrity Reserve Studies (SIRS), reserve funding requirements, and related compliance obligations that increased anticipated future ownership costs. Using more than one million condominium transactions across Florida between 2020 and 2024, the study employs a three-period difference-in-differences design, complemented by monthly event-study analyses, to distinguish the initial capitalization following the Surfside information shock from the subsequent capitalization observed after SB4D. Condominium sales prices declined significantly following the Surfside collapse and experienced an additional, statistically significant decline subsequent to SB4D. Monthly event-study estimates show two distinct phases of price adjustment corresponding closely to the sequential shocks. Heterogeneous analyses indicate that capitalization varied systematically across building age and condominium value but exhibited comparatively modest and less systematic variation across coastal proximity. The findings demonstrate that housing markets capitalize revised perceptions of future risk and anticipated future ownership costs through distinct economic channels. The magnitude of capitalization depends on the extent to which each sequential shock revises buyers' prior assessments, rather than on the underlying level of risk or ownership costs alone.

econ.GN

When AI Classifies: What Counts as Public Administration?

This study examines how alternative systems of scholarly representation identify and characterize broad public administration (PA) and artificial intelligence related public administration (AI-in-PA) scholarship. Using Web of Science and OpenAlex, it compares five approaches based on author-defined, citation-driven, and AI-assisted representations. The results highlight substantial differences in corpus size, publication types, publishing outlets, temporal development, and thematic clustering and structure. The alternative approaches often identify different knowledge domains instead of varied subsets of the same scholarship and therefore produce distinct representations, as evidenced by no overlap in publications and publishing outlets across representations. The findings suggest that algorithmic knowledge organization increasingly influences how interdisciplinary scholarship is classified, structured, and understood and, epistemologically, how its visibility, intellectual structure, and boundaries are represented. AI-enabled scholarly classifications and representations are not neutral but interpretative, likely self-reinforcing, and potentially constrain the evolution and adaptation of disciplinary boundaries. Human disciplinary judgment is essential and is complemented rather than replaced.

cs.DL

A Mediation Analysis of the Relationship Between Land Use Regulation Stringency and Employment Dynamics

The paper examines the effects of stringent land use regulations, measured using the Wharton Residential Land Use Regulatory Index (WRLURI), on employment growth during the period 2010-2020 in the Retail, Professional, and Information sectors across 878 local jurisdictions in the United States. All the local jurisdictions exist in both (2006 and 2018) waves of the WRLURI surveys and hence constitute a unique panel data. We apply a mediation analytical framework to decompose the direct and indirect effects of land use regulation stringency on sectoral employment growth and specialization. Our analysis suggests a fully mediated pattern in the relationship between excessive land use regulations and employment growth, with housing cost burden as the mediator. Specifically, a one standard deviation increase in the WRLURI index is associated with an approximate increase of 0.8 percentage point in the proportion of cost burdened renters. Relatedly, higher prevalence of cost-burdened renters has moderate adverse effects on employment growth in two sectors. A one percentage point increase in the proportion of cost burdened renters is associated with 0.04 and 0.017 percentage point decreases in the Professional and Information sectors, respectively.

econ.GN

Centrally Administered State-Owned Enterprises' Engagement in China's Public-Private Partnerships: A Social Network Analysis

A salient characteristic of China's public-private partnerships (PPPs) is the deep involvement of state-owned enterprises (SOEs), particularly those administered by the central/national government (CSOEs). This paper integrates the approaches of resource-based view and resource-dependency theory to explain CSOEs' involvement in PPP networks. Built upon a network perspective, this paper differs from earlier studies in that it investigates the entire PPP governance network as a whole and all PPP participants' embedded network positions, rather than individual, isolated PPP transactions. Using a novel data source on PPP projects in the period of 2012-2017, social network analysis is conducted to test hypothesized network dominance of CSOEs' in forming PPPs, in light of CSOEs' superior possession of and access to strategic assets. Research findings suggest that CSOEs have a dominant influence and control power in PPP networks across sectors, over time, and throughout geographic space. It is also suggested that policy makers should reduce resource gaps between SOEs and private businesses, and only in so doing, presence and involvement of non-SOEs in China's PPPs can be enhanced.

cs.SI