Developing artificial intelligence (AI) technology requires data. In many domains, government data far exceed in magnitude and scope data collected by the private sector, and AI firms often gain access to such data when providing services to the state. We argue that such access can stimulate commercial AI innovation in part because data and trained algorithms are shareable across government and commercial uses. We gather comprehensive information on firms and public security procurement contracts in China’s facial recognition AI industry. We quantify the data accessible through contracts by measuring public security agencies’ capacity to collect surveillance video. Using a triple-differences strategy, we find that data-rich contracts, compared to data-scarce ones, lead recipient firms to develop significantly and substantially more commercial AI software. Our analysis suggests a contribution of government data to the rise of China’s facial recognition AI firms, and that states’ data collection and provision policies could shape AI innovation.

Media censorship is a hallmark of authoritarian regimes. We conduct a field experiment in China to measure the effects of providing citizens with access to an uncensored internet. We track subjects’ media consumption, beliefs regarding the media, economic beliefs, political attitudes, and behaviors over 18 months. We find four main results: (i) free access alone does not induce subjects to acquire politically sensitive information; (ii) temporary encouragement leads to a persistent increase in acquisition, indicating that demand is not permanently low; (iii) acquisition brings broad, substantial, and persistent changes to knowledge, beliefs, attitudes, and intended behaviors; and (iv) social transmission of information is statistically significant but small in magnitude. We calibrate a simple model to show that the combination of low demand for uncensored information and the moderate social transmission means China’s censorship apparatus may remain robust to a large number of citizens receiving access to an uncensored internet.

Recent scholarship has suggested that artificial intelligence (AI) technology and autocratic regimes may be mutually reinforcing. We test for a mutually reinforcing relationship in the context of facial-recognition AI in China. To do so, we gather comprehensive data on AI firms and government procurement contracts, as well as on social unrest across China since the early 2010s. We first show that autocrats benefit from AI: local unrest leads to greater government procurement of facial-recognition AI as a new technology of political control, and increased AI procurement indeed suppresses subsequent unrest. We show that AI innovation benefits from autocrats’ suppression of unrest: the contracted AI firms innovate more both for the government and commercial markets and are more likely to export their products; noncontracted AI firms do not experience detectable negative spillovers. Taken together, these results suggest the possibility of sustained AI innovation under the Chinese regime: AI innovation entrenches the regime, and the regime’s investment in AI for political control stimulates further frontier innovation.

Social scientists have long viewed the decision to protest as strategic, with an individual's participation a function of their beliefs about others' turnout. We conduct a framed field experiment that recalibrates individuals' beliefs about others' protest participation, in the context of Hong Kong's ongoing antiauthoritarian movement. We elicit subjects' planned participation in an upcoming protest and their prior beliefs about others' participation, in an incentivized manner. One day before the protest, we randomly provide a subset of subjects with truthful information about others' protest plans and elicit posterior beliefs about protest turnout, again in an incentivized manner. After the protest, we elicit subjects' actual participation. This allows us to identify the causal effects of positively and negatively updated beliefs about others' protest participation on subjects' own turnout. In contrast with the assumptions of many recent models of protest participation, we consistently find evidence of strategic substitutability. We provide guidance regarding plausible sources of strategic substitutability that can be incorporated into theoretical models of protests.

We study the causal effect of school curricula on students? political attitudes, exploiting a major textbook reform in China between 2004 and 2010. The sharp, staggered introduction of the new curriculum across provinces allows us to identify its causal effects. We examine government documents articulating desired consequences of the reform and identify changes in textbooks reflecting these aims. A survey we conducted reveals that the reform was often successful in shaping attitudes, while evidence on behavior is mixed. Studying the new curriculum led to more positive views of China?s governance, changed views on democracy, and increased skepticism toward free markets.