How Data Manipulation Mitigates Authoritarian Risks: Evidence from China

• In Event: Authoritarianism in the Past & Present

Abstract

Data manipulation in authoritarian regimes has long been perceived as a catalyst for political radicalism that leads to disastrous livelihood consequences, with career advancement identified as the only incentive for the political agents in question. Behind such an argument is the tendency to view authoritarian officials as purely upward accountable. Nevertheless, rich evidence from contemporary China points to the presence of basic downward accountability among many subnational officials, motivated by their practical need for local support to facilitate policy implementation and material benefits to supplement income. Such downward accountability explains many officials’ attempts to prevent and mitigate disasters under their jurisdictions and is more likely to be found in regimes with lower top-down administrative capacity.

By considering downward and upward accountability simultaneously, this article proposes a novel but widely applicable model illustrating what motivates bottom-up data manipulation: to minimize the harm of misinformed central policies on local livelihood. Our model shows that data manipulation tends to be the only solution to the impossible dilemma that local officials are in when facing central tasks that could potentially cause local disasters, especially when upward communication is ineffective. Under such conditions, the local officials would opt for a low amount of effort to implement the central policy and manipulate data to fulfill the objective, even when they believe their behaviors would certainly be brought to light afterwards. In that way, bottom-up data manipulation paradoxically mitigates authoritarian risks. The results of this model categorize authoritarian regimes into four quadrants according to the administrative capacity and the leaders’ expectation of local compliance. I predict that as long as a regime does not score highly in both factors, data manipulation of this kind is likely to be observed.

My model is illustrated by China’s local officials’ behaviors on the eve of the Great Leap Famine (1959-61), China’s worst central policy failure that would eventually cost 30 million lives. Through studying local government archives, I find that upon receiving a central order to carry out schistosomiasis control, a public health exigency that required an excessive diversion of agricultural labor, some officials chose a low amount of effort in schistosomiasis control and overstated their achievements to buffer against the imminent food shortage.

My theory goes beyond existing theories of authoritarian regimes that portray local officials as purely upward accountable or reduce their incentives to solely promotion-seeking; it provides an alternative explanation for what motivates data manipulation and selective policy implementation in authoritarian regimes. Furthermore, this study supplies the long-standing literature on data manipulation in authoritarian regimes with a rare micro-level case study showing how data can be falsified, by whom, and for what purpose.

Author

• Jingyang Rui, University of Michigan - Ann Arbor