Are Princelings Truly Busted? Evaluating Transaction Discounts in China's Land Market
Julia Manso
TL;DR
This study tackles the question of whether princeling-connected firms receive discounts in China's land market and whether anticorruption reforms reduce this advantage. It reproduces CK (2019) but uncovers major data-quality issues: extensive duplicates in the transaction data and a mis-specified transformation of area, which inflates estimated princeling effects when interpreted as a log. Even after removing duplicates and correcting the area interpretation, princeling discounts remain large under several specifications, though their magnitude and interpretation depend on the transformation and model chosen; findings on the anticorruption campaign's causal impact are inconsistent and often implausible. The work highlights how data quality and variable construction critically shape conclusions in political economy studies of corruption and advocates more rigorous data validation and alternative estimation strategies.
Abstract
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.