Person:

Roberts, Margaret Earling

"Robust standard errors'" are used in a vast array of scholarship to correct standard errors for model misspecification. However, when misspecification is bad enough to make classical and robust standard errors diverge, assuming that it is nevertheless not so bad as to bias everything else requires considerable optimism. And even if the optimism is warranted, settling for a misspecified model, with or without robust standard errors, will still bias estimators of all but a few quantities of interest. Even though this message is well known to methodologists and has appeared in the literature in several forms, it has failed to reach most applied researchers. The resulting cavernous gap between theory and practice suggests that considerable gains in applied statistics may be possible. We seek to help applied researchers realize these gains via an alternative perspective that offers a productive way to use robust standard errors; a new general and easier-to-use information test statistic which is easier to apply appropriately; and practical illustrations via simulations and real examples from published research. Instead of jettisoning this extremely popular tool, as some suggest, we show how robust and classical standard error differences can provide effective clues about model misspecification, likely biases, and a guide to more reliable inferences.

Many scholars have speculated that censorship efforts will be ineffective in the information age, where the possibility of accessing incriminating information about almost any political entity will benefit the masses at the expense of the powerful. Others have speculated that while information can now move instantly across borders, autocrats can still use fear and intimidation to encourage citizens to keep quiet. This manuscript demonstrates that the deluge of information in fact still benefits those in power by observing that the degree of accessibility of information is still determined by organized groups and governments. Even though most information is possible to access, as normal citizens get lost in the cacophony of information available to them, their consumption of information is highly influenced by the costs of obtaining it. Much information is either disaggregated online or somewhat inaccessible, and organized groups, with resources and incentives to control this information, use information flooding and information friction as methods of controlling the cost of information for consumers. I demonstrate in China that fear is not the primary deterrent for the spread of information; instead, there are massively different political implications of having certain information completely free and easy to obtain as compared to being available, but slightly more difficult to access.

We offer the first large scale, multiple source analysis of the outcome of what may be the most extensive effort to selectively censor human expression ever implemented. To do this, we have devised a system to locate, download, and analyze the content of millions of social media posts originating from nearly 1,400 different social media services all over China before the Chinese government is able to find, evaluate, and censor (i.e., remove from the Internet) the large subset they deem objectionable. Using modern computer-assisted text analytic methods that we adapt to and validate in the Chinese language, we compare the substantive content of posts censored to those not censored over time in each of 85 topic areas. Contrary to previous understandings, posts with negative, even vitriolic, criticism of the state, its leaders, and its policies are not more likely to be censored. Instead, we show that the censorship program is aimed at curtailing collective action by silencing comments that represent, reinforce, or spur social mobilization, regardless of content. Censorship is oriented toward attempting to forestall collective activities that are occurring now or may occur in the future --- and, as such, seem to clearly expose government intent.