Why do firms adopt stock options and who benefits? A natural experiment in China

Lin Jiang* (Corresponding Author), Gerhard Kling, Hong Bo, Ciaran Driver

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


A regulatory change in 2006, permitting equity compensations in China, offers a natural experiment to investigate drivers and outcomes of stock options. There are two unique features. First, adoption of stock options occurred rapidly compared to the US, where stock options have been around for more than 100 years with periods of high (1990s) and low (before 1950s) adoption. Second, stock options have been issued by state-owned enterprises (SOEs), an unusual aspect. This study analyzes all listed companies in China from 2004 to 2014, testing two competing theories: optimal contracting and managerial power. If managers own more equity, if the CEO also serves as board chairman and if compensation committees exist, managers are more likely to receive stock options. Ownership type and firm characteristics are also essential factors in granting stock options. In non-SOEs, evidence suggests that controlling shareholders award stock options less frequently but if they do they seem to induce managers to collude in tunneling. Applying a propensity score matching approach to account for an alleged self-selection bias, we do not observe any improvements in firm performance or shareholder value after stock options have been issued. Accordingly, managerial power seems to be the predominant driver for the introduction of stock options. Hence, managerial accountability and better disclosure are essential to ensure that stock options do contribute to value creation.
Original languageEnglish
Pages (from-to)124-140
Number of pages17
JournalPacific-Basin Finance Journal
Issue numberPart A
Early online date20 Sept 2017
Publication statusPublished - Dec 2017


  • Bayesian statistics
  • binary dependent variables
  • causality
  • longitundinal research
  • vector autoregression


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