Alex Nowrasteh, the immigration policy analyst at the Cato Institute, recently authored a paper entitled “Terrorism and Immigration: A Risk Analysis.” It is not an analysis of risk in the traditional sense; it has little interest in causes and none in mitigation strategy. A risk-reward study, it argues from observed frequencies of terrorism incidents in the US that restricting immigration is a poor means of reducing terrorism and that the huge economic benefits of immigration outweigh the small costs of terrorism.
That may be true – even if we adjust for the gross logical errors and abuse of statistics in the paper.
Nowrasteh admits that in the developing world, heavy refugee flows are correlated with increased terrorism. He also observes that, since 2001, “only three years were marred by successful foreign-born attacks.” Given his focus on what he calls the chance of being murdered in a terrorist attack (based solely on historical frequencies since 1975), the fact that successful terrorism occurred in only three years seems oddly polemical. What follows? By his lights, the probability of terrorist death stems only from historical frequencies. While honest people disagree about Bayesian probability theory, surely we owe the Bayesians more than blinding ourselves to all but brute averages over a 40-year interval. I.e., having noted that heavy refugee flows correlate with terrorism elsewhere, he doesn’t update his prior at all. Further, unsuccessful terrorist attempts have no influence.
Nowrasteh writes, “government officials frequently remind the public that we live in a post-9/11 world where the risk of terrorism is so extraordinarily high that it justifies enormous security expenditures.” I don’t know his mindset, but writing this in a “risk analysis” seems poorly motivated at best. He seems to be saying that given the low rate of successful terrorism, security efforts are a big waste. The social justice warriors repeating this quote from his analysis clearly think so:
“The chance that an American would be killed in a terrorist attack committed by a refugee was 1 in 3.64 billion a year.” [emphasis in original]
Nowrasteh develops a line of argument around the cost of a disrupted economy resulting from terrorism events using the 1993 and 2001 WTC attacks and the Boston Marathon bombing, finding that cost to be relatively small. He doesn’t address the possibility that a cluster of related successful attacks might have disproportionately disruptive economic effects.
He makes much of the distinctions between various Visa categories (e.g., tourist, refugee, student) – way too much given that the rate of terrorism in each is tiny to start with, and they vary only by an order of magnitude or so.
These are trifles. Two aspects of the analysis are shocking. First, Nowrasteh repeatedly reports the calculated probabilities of being killed by foreigners of various Visa categories – emphasizing their extreme smallness – with no consideration to base rate. I.e. the probability of being murdered is already tiny. Many of us might be more interested in a conditional probability – what is the probability that if you were murdered, the murderer would be an immigrant terrorist. Or perhaps, if you were murdered by an immigrant terrorist, how likely is it that the immigrant terrorist arrived on a refugee Visa.
Finally, Nowrasteh makes this dazzling claim:
“The attacks (9/11) were a horrendous crime, but they were also a dramatic outlier.”
Dramatic outlier? An outlier is a datum that lies far outside a known distribution. Does Nowrasteh know of a distribution of terrorist events that nature supplies? What could possibly motivate such an observation. We call a measurement an outlier when its accuracy is in doubt because of prior knowledge about its population. Outliers cannot exist in sparse data. Saying so is absurd. Utterly.
“I wouldn’t believe it even if Cato told me so.” That is how, we are told, an ancient Roman senator would express incredulity, since Marcus Porcius Cato was the archetype of truthfulness. Well, Cato has said it, and I’m bewildered.