arXiv subjects

Julia Manso

Publications and source records attributed to Julia Manso.

4 recordsLinked to original sources

arXiv2026

Anticorruption Enforcement and Sale Mechanism Choice in China's Land Market

Upon taking office in late 2012, Chinese President Xi Jinping launched one of the most intensive anticorruption campaigns in the history of the People's Republic of China. Prior to the campaign, China's land market suffered from corruption, particularly surrounding sale method selection (auction versus listing). Listing is a two-stage sale mechanism that prior research has identified as more susceptible to corruption, leading to lower prices. This paper examines the campaign's impact on land allocation, focusing on whether corruption influences the choice of sale method and, in turn, land sale prices. This paper is the first to utilize Blackwell and Yamauchi (2021, 2024)'s marginal structural model with fixed effects in the inverse probability of treatment weighting model; absorbing time-invariant unobserved confounding and utilizing a set of time-varying covariates as controls, this model can estimate causal effects in the land sale case. I find that indictments in a prefecture cause a statistically significant drop in the probability that land is sold via listing$\unicode{x2014}$an effect that is further compounded when indictments occur in consecutive months. Sensitivity analyses indicate that any violations of the identification assumptions would bias estimates towards zero, confirming the negative effect. A second marginal structural model shows that both mean and median land sale prices increase in the presence of indictments. Together, these results suggest that the anticorruption campaign not only deterred actual corrupt allocation practices, but also impacted the discretionary use of listings.

econ.EM↗

arXiv2026

Volatility Spillovers in China's Real Estate Crisis: A Network Approach

Sentiment towards the Chinese real estate sector has deteriorated following the introduction of financing constraints in 2020 with the ''three red lines." Forcing developers to restructure their debt, the policy triggered a cascade of financing troubles, defaults, and reduced housing demand, ultimately culminating in a prolonged real estate crisis. This paper utilizes a network approach in line with Demirer et al. (2018) and Diebold and Yilmaz (2014) to measure daily time-varying connectedness in the stock return volatilities of major Chinese real estate developers throughout the crisis. Focusing on spillover between companies as reflected by market perception, this paper examines how connectedness evolves over time across firms with different regional exposures and state-ownership statuses, filling a gap in the literature to elucidate where property demand and real estate firm trustworthiness have deteriorated most. An event-study analysis of four key moments of the crisis outlines distinct phases of market sentiment: with the introduction of the three red lines, connectedness primarily reflects shared exposure and a uniform shock to the market. Then, the early unrest surrounding Evergrande exposes strong regional differentiation, with firms concentrated in less developed regions receiving significant spillover. By one year into the crisis, previously stable regions receive higher levels of spillover, and there is evidence of a substitution effect towards private developers. Two years into the crisis, the market has much less homogeneity in effects across regions and state-ownership status: major shocks induce minimal network changes, reflecting how investors have already priced in their beliefs. This paper also offers one of the most extensive timelines of the Chinese real estate crisis to date, and a new R package, GephiForR, was created for the network visualization in this paper.

econ.EM↗

arXiv2025

Are Princelings Truly Busted? Evaluating Transaction Discounts in China's Land Market

This paper replicates Chen and Kung's 2019 analysis ($The$ $Quarterly$ $Journal$ $of$ $Economics$ 134(1): 185-226). Inspecting the data reveals that nearly one-third of transactions (388,903 out of 1,208,621) are perfect duplicates of other rows, excluding the transaction number. The analysis on the data sans duplicates replicates their statistically significant princeling effect, robust across various specifications. Further analysis reveals a disagreement between Chen and Kung's text and code: the paper's ''logarithm of area'' is actually area ($\text{m}^2$) divided by one million. This therefore necessitates a reinterpretation of the estimation results, revealing that the princeling effect is extremely large.

econ.GN↗

arXiv2024

GephiForR: An R package for creating Gephi-style network visualizations

This paper introduces GephiForR, an R package designed to replicate Java-based Gephi's key plotting tools in R. The package is accessible to those with minimal R experience and, in particular, implements ForceAtlas2, the key layout feature developed for Gephi by Jacomy et al. (2014). The most significant advancement is the ability to pass previous positions into ForceAtlas2 as baselines, a particularly useful feature for plotting the evolution of network layouts for time series data. GephiForR is especially suited for networks of less than 1000 nodes, simply because R's dependence on single-thread computation means that larger networks take longer to compute, but the package can handle these larger networks as well. I demonstrate the package's capabilities through various examples and comparisons with existing tools and Gephi itself, assessing performance and speed.

stat.CO↗