Risk Anal. 2016 Feb;36(2):302-19. doi: 10.1111/risa.12449.

An Empirical Analysis of Life Jacket Effectiveness in Recreational Boating.

Viauroux C1, Gungor A2.
  • 1Department of Economics, University of Maryland-Baltimore County, Baltimore, MD, USA.
  • 2Standards Evaluation & Analysis Division, United States Coast Guard Headquarters, 2703 Martin Luther King Jr. Ave. S.E., Washington, DC, USA.


This paper gives a measure of Life Jacket (LJ) effectiveness in U.S. recreational boating. Using the US Coast Guard’s Boating Accident Report Database (BARD) from 2008 to 2011, we find that life jacket wear is one of the most important determinants influencing the number of recreational boating fatalities, together with the number of vessels involved, and the type and engine of the vessel(s). We estimate a decrease in the number of deceased per vessel of about 80% when the operator wears their life jacket- compared to when they do not. The odds of dying are 86% higher than average if the accident involves a canoe or kayak, but 80% lower than average when more than one vessel is involved in the accident and 34%lower than average when the operator involved in the accident has more than 100 hours of boating experience. Interestingly, we find that LJ effectiveness decreases significantly as the length of the boat increases and decreases slightly as water temperature increases. However, it increases slightly as the operator’s age increases. We find that between 2008 and 2011, a LJ regulation that requires all operators to wear their life jacket representing a 20% increase in wear rate – would have saved 1,721 (out of 3047) boaters or 1,234 out of 2,185 drowning victims. The same policy restricted to boats 16 to 30 feet in length would have saved approximately 778 victims. Finally, we find that such a policy would reduce the percentage of drowning victims compared to other causes of death. © 2015 Society for Risk Analysis.


Life jacket effectiveness; Poisson model; U.S. Coast Guard

PMID: 26206494



1-Life jacket effectiveness is measured by the difference between the current estimated number of fatalities and the number of estimated fatalities under a mandatory life jacket wear policy for recreational boat operators.


2- To obtain estimates of the current number of fatalities and infer fatality estimates under the policy scenario, we construct an econometric model of the number of recreational accident fatalities using various reported characteristics of the accident. Our variables are extracted from the Coast Guard’s Boating Accident Report Database (BARD). They include information on the water temperature, the weather conditions, the time (day/night) of the accident but also some information on boat features: its length, its engine type… and some information on socioeconomic characteristics of the boat operator: for example, his/her age, experience or blood alcohol concentration.

The model used is the standard Poisson model, in which the expected average number of fatalities is expressed as a positive function of some characteristics of the accident, the people on board and some physical features of the vessel (see above). The statistical fit of the model is extremely good; an over-dispersion test excludes potential misuse of the Poisson regression assumptions.  The structure of the model is such that its results are best interpreted in terms of odds of dying. For a δ-change in characteristics x_{k}, the expected number of deceased per vessel increases by a factor of exp(β_{k}δ) or by a percentage equal to (exp(β_{k}δ)-1)*100%, holding all other variables constant, where β_{k} are the parameters of the model (associated to characteristics x_{k}) estimated in STATA. This factor is also known as the Incident Rate Ratio (I.R.R.). Note that if the change δ=1, then the I.R.R. is simply exp(β).


3- Results are striking: Boaters chances of survival are about 80% lower if they don’t wear their life jacket or if they boat on a canoe but 80% higher if more than one boat is involved in an accident. The boat operator’s experience, if it is greater than 100 hours, reduces the odds of dying in an accident by about 34%. Interestingly, we also find that the life jacket wear effect on fatalities wipes out the effect of alcohol concentration suggesting that alcohol addicts can mostly be identified by their life jacket wear decision.

Other interesting results include lower chances of fatalities if the boat is moving at the time of the accident, if the accident occurs during the day or a week-end or when the boat has been rented. Life jacket effectiveness is also found to increase, the longer the boat and decrease with increasing water temperature or the age of the operator.


4-We report the current estimated/predicted number of deceased per vessel as well as this predicted number under the policy that would make operators’ life jacket wear mandatory. The policy simulation targets operators because accident reports only include information on life jacket wear of operators. However, using an alternative database, we show that operators’ life jacket wear rate is similar to that of any other boater.

In order to infer how many lives could have been saved as the result of this policy, we compute the total number of boaters involved in an accident from the average number of people on board of each vessel involved in an accident and its number of operators.

We then assume that boaters’ wear rate is identical among operators and non-operators prior to the policy. This allows us to compute the new wear rate under full compliance of the policy. Finally, we link the change in total wear rate to the decrease in predicted number of fatalities obtained from simulating the policy (i.e. setting operators wear rate = 100%) using our adjusted Poisson model.


This study is important for two reasons:

It provides a comprehensive analysis of life jacket effectiveness by measuring the impact of usage/lack of usage of life jacket relative to many other factors that are present at the time of a recreational boating accident, whether environmental, mechanical or human.

It gives an econometric prediction of how efficient a policy on life jacket wear would be in terms of number of accident fatalities avoided.



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