For a cluster-robust t-statistic under cluster heterogeneity we establish that the cluster-robust t-statistic has a gaussian asymptotic null distribution and develop the effective number of clusters, which scales down the actual number of clusters, as a guide to the behavior of the test statistic. The implications for hypothesis testing in applied work are that the number of clusters, rather than the number of observations, should be reported as the sample size, and the effective number of clusters should be reported to guide inference. If the effective number of clusters is large, testing based on critical values from a normal distribution is appropriate.
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Fake antivirus (AV) programs have been utilized to defraud millions of computer users into paying as much as one hundred dollars for a phony software license. As a result, fake AV software has evolved into one of the most lucrative criminal operations on the Internet. In this paper, we examine the operations of three large-scale fake AV businesses, lasting from three months to more than two years. More precisely, we present the results of our analysis on a trove of data obtained from several backend servers that the cybercriminals used to drive their scam operations. Our investigations reveal that these three fake AV businesses had earned a combined revenue of more than $130 million dollars.A particular focus of our analysis is on the financial and economic aspects of the scam, which involves legitimate credit card networks as well as more dubious payment processors. In particular, we present an economic model that demonstrates that fake AV companies are actively monitoring the refunds (chargebacks) that customers demand from their credit card providers. When the number of chargebacks increases in a short interval, the fake AV companies react to customer complaints by granting more refunds. This lowers the rate of chargebacks and ensures that a fake AV company can stay in business for a longer period of time. However, this behavior also leads to unusual patterns in chargebacks, which can potentially be leveraged by vigilant payment processors and credit card companies to identify and ban fraudulent firms.
-We study the effect of income uncertainty on consumption in a model that includes precautionary saving. In contrast to previous studies, we focus on time-series variation in income uncertainty. Our time-series measure of income uncertainty is constructed from a panel of forecasts. We find evidence of precautionary saving in that increases in income uncertainty are related to increases in aggregate rates of saving. We also find evidence that anticipated income growth rates have less explanatory power for consumption growth rates after conditioning on income uncertainty. The evidence indicates the presence of forward-looking consumers who gradually adjust precautionary savings in response to changing income uncertainty.
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