Justin Gallagher

Who they are

He is an economist. The vault holds no biography and no confirmed current post, and none is claimed here. The paper is sole-authored and sits in an applied economics journal, which is the setting in which the question about flood insurance was posed: how do people update their expectation of an uncertain and infrequently observed event?

The question is the economist's counterpart of the psychologist's availability heuristic. Both ask why a rare event, once it has happened nearby, changes behaviour. The economist's answer is framed in terms of learning and forgetting. The psychologist's is framed in terms of recall. Gallagher's data can be read either way, and his own preferred reading is the economic one.

Key papers

Gallagher, J. (2014). Learning about an infrequent event: Evidence from flood insurance take-up in the United States. American Economic Journal: Applied Economics, 6(3), 206-233. https://doi.org/10.1257/app.6.3.206

The paper uses a new nationwide panel dataset of large regional floods and flood insurance policies. Its abstract reports that insurance take-up spikes the year after a flood and then steadily declines to baseline, and that residents of non-flooded communities in the same television media market increase take-up at one-third the rate of flooded communities. It finds that take-up is most consistent with a Bayesian learning model that allows for forgetting or incomplete information about past floods.

The American Economic Association's research summary adds figures. About 8 per cent of United States counties are covered by at least one Presidential Disaster Declaration for severe flooding in a typical year. In the year after a county is hit by a declared flood, take-up rises by nearly 10 per cent. Under the forgetting model the effect fades over roughly 15 years. A model that weighs all past floods equally would predict take-up taking about 15 more years to return to its previous level. Floods in close succession produce a sawtooth pattern of repeated spikes. Neighbouring counties see a smaller spike after a nearby flood, and the proximity effect largely disappears once shared media markets are accounted for.

Key ideas

The paper's value for a reader of the availability literature is that it measures a purchase. Most availability studies measure judgements. This one measures policies bought, across the United States, over a long period, and ties them to specific, dated events.

Three features of the pattern stand out. The response is immediate, arriving the year after the flood. It is local, strongest where the flood happened. And it decays, which is the sign that recent events are weighted more than older ones.

The media market finding is the most distinctive. Communities that did not flood still raised take-up when they shared a television market with communities that did. Distance by itself mattered much less once the shared market was taken into account. That is consistent with news coverage carrying the signal, though the paper does not claim to test the news channel directly.

Gallagher's own reading is Bayesian learning with forgetting: buyers update their beliefs about flood risk when floods occur and let the information lapse. A rational buyer could produce that pattern as readily as a biased one, so the data do not isolate the availability heuristic. They show that a vivid, nearby, recent event moves purchases and that the movement fades.

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