A Contingent Valuation of Hurricane Forecast

Improvement∗

Renato Molina1,2†, David Letson1, Brian McNoldy1, Pallab Mozumder3 and

Matthew Varkony1

1Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida2Miami Herbert Business School, University of Miami, Coral Gables, Florida

3Department of Earth & Environment and Department of Economics, Florida International

University, Miami, Florida

April 2020

PRELIMINARY DRAFT

Abstract

Hurricanes are the costliest type of natural disaster in the United States. Although the damage

caused by landfalling hurricanes will never be eliminated, the number of fatalities and the

cost of preparing for and evacuating from affected locations can be reduced through improved

forecasts. We integrate atmospheric modeling and econometrics to elicit the public willingness

to pay for more accurate hurricane forecasts through a large-scale double bounded dichotomous

choice experiment. Focusing on areas recently hit by Hurricanes Florence and Michael, survey

participants are asked to value improvements in storm track, wind speed, and precipitation

forecast precision. Our results indicate respondents value further improvements in forecast

accuracy, and that they value wind speed accuracy the most. In a world where the intensity

and the frequency of hurricanes is expected to increase and research funds are limited, these

results can inform relevant agencies regarding the effectiveness of different private and public

adaptive actions, and the value of publicly funded hurricane research programs.

JEL Codes: D23, P14, P48, Q15, Q25, Q28

∗We would like to thank Andrea Schumacher and Frank Marks for incredible support and help withatmospheric modeling. We would also like to thank David Roth for facilitating access to data and preparingvisuals on the precipitation for Florence and Michael. Finally, we would also like to thank Gina Eosco andparticipants of the Weather Program Office’s Weather Economic Research Workshop for valuable feedback.This project was funded by the National Atmospheric and Oceanographic Administration through NOAAgrant NA15OAR4320064.

†Corresponding author: renato.molina@rsmas.miami.edu

1 Introduction

Hurricanes are the costliest type of natural disaster in the United States (Weinkle et al.,

2018). While the damage caused by landfalling hurricanes will never be eliminated, the

number of fatalities and the cost of preparing for and evacuating from affected locations

can be reduced through improved forecasts. In this study, we propose and implement a

methodology to elicit preferences and estimate the willingness to pay for a better hurricane

forecast. Our results highlight that the demand for forecast improvements exists across the

ability to predict track, wind speed and precipitation, but it is also relatively elastic when

it comes to the actual rate of improvement.

To credibly achieve these measurements, this study builds on both atmospheric science

and the econometrics of stated preferences. More specifically, we create alternative scenarios

that relate to the decision-making process of the average user when faced with the threat

of a hurricane. We use a structural atmospheric model and the National Hurricane Center

(NHC) historical error statistics to construct plausible scenarios under different rates of im-

provement in the coming decade for track and wind speed, respectively. In addition, we also

propose a structural model of precipitation that allows us to construct alternative scenarios

for hurricane precipitation forecasts. We adapt these hypothetical improvements to standard

public dissemination products and construct alternative scenarios for two recent hurricanes.

Namely, Hurricanes Michael and Florence of 2018. We chose these different storms/regions

to help disentangle unobserved characteristics from other variables that condition survey

responses, but also because recently affected survey subjects would more likely be familiar

with NHC forecasts and how they might enter into adaptation decisions.

Based on these hypothetical improvements for Michael and Florence, we deploy a large-

scale randomized double-bounded dichotomous choice contingent valuation task to areas that

were affected by those hurricanes and derive their willingness to pay for error reduction in

track, wind speed, and precipitation. Our results cover more than 4500 responses and illus-

trate that improvements in the forecast are strictly positively valued among the population.

Moreover, these results are robust, even after controlling for multiple individual, household,

and geographical characteristics. These estimates suggest that the most valued attribute in

a forecast is the wind speed. While other attributes are also valued, the relative willingness

to pay is mostly associated with the experiences of different respondents. In addition, our

results also show that the respondents’ willingness to pay remains stable even after large

variations in the rate of improvement. In other words, the demand for forecast improvement

is relatively elastic, at least for fluctuations ±20% over the rate of improvement in prediction

error experienced over the last decade.

1

In a context where resources for improving hurricane forecast are limited, our results

provide several important insights that could help the public discussion on hurricane sci-

ence. First, despite the impressive improvement after the implementation of the Hurricane

Forecast Improvement Project (HFIP) in 2007, further improvements are still valued by the

general public (Gall et al., 2013). Nonetheless, our results also suggest that some of these

precision improvements may not be effectively differentiated by decision makers. This result

is consistent with Murphy (1993), who argues that skillful forecasts may not be valuable and

vice versa (i.e., value is not increasing monotonically in quality). The true value of a forecast

lies on its effectiveness at facilitating better adaptive behavior in the face of a hurricane, and

our results highlight the possibility that some of these technicals improvements may not be

perfectly internalized by the decision-makers -at least in the beginning.

Second, and while track and wind speed are the focus of HFIP (Gall et al., 2013), other

forecast attributes such as precipitation are also valued. These results highlight the tension

surrounding mandated improvements standards, and how future policies might be able to

balance costs and benefits of executive and legislative mandates on publicly funded research.

2 Background

2.1 The Economics of Hurricane Forecasts

Hurricanes threaten the status, sustainability and security of coastal communities more than

ever before (Gaddis et al., 2007). Accordingly, there is a growing demand for producing more

precise weather forecast information to make vulnerable communities better prepared and

resilient (Ewing et al., 2007). As the government responds to this demand, it becomes crucial

to understand how the public uses and values these actions. In particular, maintaining

and expanding the resources necessary to produce officially sanctioned hurricane forecasts

(Emanuel, 2005; Mozumder et al., 2015).

The literature has already explored this problem. Letson et al. (2007) provide a the-

oretical framework to analyze the economic value of hurricane forecasts and discussed the

research needs for producing reliable value estimates. Nonetheless, and despite the guidelines

from Letson et al. (2007), measuring the value of improved weather/hurricane forecasts can

be challenging. People may not have a well-defined preference for these products, and their

preferences may be influenced by their subjective risk perceptions. Moreover, people’s risk

averting behavior and the value they put on an accurate forecast are oftentimes intertwined.

To the best of our knowledge, only a handful of studies have previously attempted to

derive the value of either weather or hurricane forecasts. In particular, Lazo et al. (2008)

2

estimate that the general public in the USA values weather information provided by the

National Weather Service (NWS) at about $280/household/year. Their estimates can be

extrapolated to a value of more than $30 billion for the whole country, when considering all

households susceptible to hurricanes.

In addition, Lazo and Waldman (2011) conduct a stated preference study (choice exper-

iment) for valuing improved hurricane forecasts based on a small non-random sample. The

sample size was eighty (general public, above eighteen years, living within thirty miles of the

coast in the Miami - Fort Lauderdale Metropolitan Area). They estimate the willingness

to pay (WTP) for improvements in several forecast attributes, such as expected landfall

time, maximum wind speed, projected landfall location, and expected storm surge. Their

results suggest that the total WTP for forecast improvement is about $13.19 per household

per year. For an estimated population of 2.5 million in Miami-Dade County in 2009, they

extrapolate that the total value of the improved hurricane forecast would be about $11.6

million. Consequently, there were more than 73 million people living in hurricane-prone

areas in 2010, which would imply a total value of $340 million at a national scale for this

improved hurricane forecast.

Other studies have also explored this question outside the United States. One such study

is Ahsan et al. (2020), who estimate the household WTP for improved early warning services

(EWS) in coastal areas of Bangladesh. Based on a choice experiment conducted on 490

randomly selected households from Khulna, Satkhira, and Barguna districts, they estimate

household WTP for improved EWS at about $ 5.57/per year. The attributes they consider

include precise information on the landfall time of the cyclone1 with projected impacts, more

frequent radio forecasts, and voice messages via mobile phones. In another study, Nguyen

et al. (2013) conduct a similar choice experiment in Vietnam. They collect responses from

1014 Vietnamese households for the same attributes of a cyclone warning service. They

estimate a “one-time” payment WTP of $7.1-8.1/household. Similarly, Anaman et al. (1998)

estimate the WTP of Australian households in Queensland for access to cyclone warning

services, and their calculations suggest the mean annual WTP is about $45/household/year.

Noticeably, all of the aforementioned literature relies on stated preference studies. It is

only natural to question why. The answer is multifaceted, but the reasons can be traced to

the lack of reliable data on the actions of individuals as well as the lack of quasi-experimental

variation in the quality of hurricane forecast. Nonetheless, when revealed preference data

are inadequate to capture the behavioral responses to hurricane risk information and man-

agement issues, carefully-designed stated preference with granular household survey can be

1Essentially, cyclones, and typhoons are the same weather phenomenon. The only difference is wherethey take place geographically.

3

used to provide reliable estimates. More specifically, Whitehead (2005) shows that hypothet-

ical stated preferences can predict actual hurricane risk averting behaviors reasonably well,

which in turn implies that stated preference data regarding hurricanes are a valid proxy for

actual decisions on the ground.

Against this backdrop, we implement a contingent valuation survey based on a large

representative sample to understand public preferences for different attributes of improved

hurricane forecasts. In particular, we will focus on the areas affected by Hurricanes Florence

and Michael in 2018. These two prominent storms are described in the next section.

2.2 Hurricanes Florence and Michael

During the 2018 Atlantic hurricane season, Hurricane Florence and Hurricane Michael were

two significant landfalling hurricanes on the continental United States. Florence was a long-

track hurricane that formed on the west coast of Africa on the 30th of August, intensifying

to an upper-end Category 4 hurricane in the open ocean south of Bermuda, then weakening

to a Category 1 hurricane as it made landfall near Wilmington, North Carolina on the 14th

of September.2

The storm resulted in widespread flooding, which caused 52 fatalities and approximately

$24 billion in damage (Stewart and Berg, 2018). Michael, on the other hand, formed in the

western Caribbean Sea on the 6th of October, which was only four days prior to making

landfall. It tracked north into the Gulf of Mexico and it strengthened from a tropical storm

to a Category 5 hurricane in under sixty hours. It continued to strengthen up until landfall on

the 10th of October, when it reached its peak sustained wind speed of 161 mph. Michael made

landfall southeast of Panama City, Florida. The storm resulted in catastrophic damages to

structures and trees, and the Mexico Beach area was inundated by a 14-foot storm surge.

Michael was responsible for 59 fatalities and approximately $25 billion in damages (Beven

et al., 2018).

We display the estimated surface wind speed swaths and observed total rainfall accu-

mulation swaths for both storms in Figure 1. The wind swaths are generated using the

parametric model of Knaff et al. (2007) integrated along the observed track at five-minute

intervals, and a simple 10% reduction of surface wind speeds over land outside the radius

of maximum wind (Kaplan and DeMaria 1995). Storm intensity, track, and size parameters

are taken from the IBTrACS v4 database (Knapp et al., 2010). The rainfall swaths are

2As defined by the National Hurricane Center, the Saffir-Simpson Hurricane Wind Scale is a categoricalrating from 1 to 5 based on a hurricane’s peak sustained wind speed. The scale estimates potential prop-erty damage. Hurricanes reaching Category 3 and higher are considered major hurricanes because of theirpotential for significant loss of life and damage Simpson and Saffir (1974).

4

an interpolated blend of raingauge data compiled by David Roth at the NOAA Weather

Prediction Center.3

Figure 1: Wind and rain swaths for Hurricanes Florence and Michael. The esti-mated peak wind swaths are in the top two panels, shaded by Saffir-Simpson category; the observed rainfallaccumulation swaths are in the bottom two panels, shaded in inches. The wind swaths are generated usinga parametric model and uniform reduction over land.

3 Theory

This section illustrates a simple utility model that guides our empirical evaluation, and fol-

lows the approach proposed by Carson and Hanneman (2005) and Lazo and Waldman (2011).

Let u(x|f, h) be the utility function of an individual that enjoys a consumption bundle x in

the face of a hurricane with a given forecast accuracy, f , and probability of occurrence h.

A rational utility maximizing individual will choose bundle x, so as to maximize her utility

subject to her budget, y. Let v(p, y|f, h) denote the indirect utility of the individual under

price vector p. u(x|f, h) is increasing and quasi-concave in x, which implies v(p, y|f, h) is

decreasing in p and increasing in y.

3The archive can be found at: https://www.wpc.ncep.noaa.gov/tropical/rain/2018.html

5

Let f 0 and f 1 be two different forecast accuracy levels, such that f 0 < f 1.4 The dollar

value of the change in f , w, is then given by:

v(p, y|f 0, h) = v(p, y − w|f 1, h) (1)

Therefore, willingness to pay (WTP) for improving from f0 to f1 can be written as

w(f 0, f 1, p, y). Let m(p, f, u|f, h) be the expenditure function for the direct utility function

u(x|f, h). It follows that the expenditure function is increasing in u, nondecreasing, concave

and homogenous of degree 1 in p. Because we are implicitly assuming that improvements in

the forecast are desired, the expenditure function is also decreasing in f . The implication is

that m(p, f, u|f, h) > 0 for any f , and that w < y.

Depending on the structural assumptions on u(x|f, h), w(f 0, f 1, p, y) could be derived

in several different ways (Carson and Hanneman, 2005). Particularly for this study and

assuming competitive markets for the essential bundle of goods, x, we will assume that the

individual WTP w(f 0, f 1, p, y) can be represented by the following linear function:

wi(f0, f 1, p, y) = x′iβ + εi (2)

with x′i as a vector of observables at the individual level. ε is a zero-mean idiosyncratic

random component and it is additive to the difference in indirect utility. The data we use

to fit this relationship is described below.

4 Data

4.1 Constructing scenarios of hurricane forecast improvements

To construct the hypothetical scenarios, we use historical forecast errors from the National

Hurricane Center (NHC). NHC calculates and provides its annual average error statistics

of track and wind speed. These errors come from comparing the values in all of the real-

time forecasts against the corresponding observed “best-track” values.5 As of 2020, NHC

produces track and wind speed forecasts and error statistics for all tropical and subtropical

cyclones in the Atlantic and Eastern North Pacific basins out to 12, 24, 36, 48, 72, 96, and

120 hours. For this study, we use the 72-hour forecast errors from the Atlantic basin. Error

trends from the previous decade are then used to generate trends in the coming decade (i.e.,

4Note that our underlying assumption only covers the willingness to pay for a better forecast, and itexcludes the possibility of a willingness to accept for a worse forecast.

5See the historical forecast errors at https://www.nhc.noaa.gov/verification/pdfs/1990-present_

OFCL_ATL_annual_int_errors.pdf

6

2008-2018 and 2018-2028). Because we are concerned with the critical 72-hour lead time

before landfall (Regnier, 2008), we work with the forecast for Florence from 11 September

at 1200 UTC (landfall was 14 September at 1115 UTC) and the forecast for Michael from 7

October at 1800 UTC (landfall was 10 October at 1730 UTC).

For track errors, we use the “cone of uncertainty” (just “cone” hereafter) because of the

public’s familiarity with it since its introduction in 2002. The NHC updates the size of the

cone each year based on track errors over the past five hurricane seasons. It is designed to

contain the center (eye) of a storm 23

of the time, implying that historically there is a 13

chance that the storm will track outside the cone. Because of the sliding five-year averages,

variations in the size of the cone are quite smooth from year to year.

The observed and the three hypothetical trends of track forecast error are shown in panel

a) of Figure 2, with the most aggressive track forecast improvement as the maroon dashed

line, the status-quo rate of improvement as the red dashed line, and the reduced rate of

forecast improvement as the orange dashed line. The average trend of improvement over

the previous decade (2008-2018) is calculated to be 41.3%. When generating hypothetical

scenarios, we assume that the trend would be to continue that same rate of progress in

the coming decade (a status quo rate for the 2018-2028 period); this status quo is shown

by the dashed red line which reaches 67.2 miles in 2028. The other scenarios accelerate or

decelerate the rate of progress by 20%, producing decadal error reduction rates of 49.6%

or 33.0% (dashed maroon and orange lines, respectively). Accordingly, these varying rate

changes yield a 72-hour cone radius of 57.8 and 76.7 miles.

Wind speed errors are handled slightly differently. Rather than using a sequence of sliding

five-year averages, we calculate a linear trend through individual annual error values; the

2008-2018 trend line yields a 29.4% reduction in error. This improvement is shown in panel

b) of Figure 2 by the solid blue line. From the individual annual values denoted by light blue

dots, it can be seen that a substantial amount of inter-annual variability arises. Similar to

the treatment of the track forecast error improvements, we project into the coming decade

(2018-2028) at three varying rate changes; a 20% increase, a constant increase, and 20%

decrease relative to the previous decade’s improvement rate. This produces decadal error

reduction rates of 35.3%, 29.4%, or 23.5% (dashed maroon, red, or orange lines, respectively).

In 2018, the trend line has a value of 13.5 mph, then by 2028 the 72-hour wind speed errors

drop to 8.7, 9.5, or 10.3 mph, respectively.

Note that the hypothetical future errors are calculated using a percentage rather than a

static number because errors can only approach zero, not reach zero or become negative. So

the slope of the blue line and the slope of the red line are not equal by design. In addition

to reducing the future errors, we also adjust the forecast values closer to the observed values

7

Figure 2: Historical and hypothetical hurricane forecast errors. Panel a) shows thetrend in the size of the “cone of uncertainty” for 72-hour track forecasts, while panel b) shows the trend inerrors for wind speed. The same average percentage of improvement from 2008-2018 is extrapolated to 2028using the same rate of improvement (“Status Quo”, red dashed line), a 20% increase in that rate (“StatusQuo +20%”, maroon dashed line), and a 20% decrease in that rate (“Status Quo -20%”, orange dashed line).

by the same percentage. In other words, we expect that forecasts in future decades will be

more accurate and with less uncertainty surrounding them. As pointed out by Landsea and

Cangialosi (2018), forecasts have been generally improving over the past several decades, but

there will come a time when forecasts can no longer be improved due to the inherent limit

of predictability of chaotic systems such as the atmosphere. For the sake of this study, we

assume that limit will not be reached in the coming decade and that progress will continue.

While track and wind speed are fairly simple metrics to calculate and verify, rainfall

accumulation metrics are more complex. Rainfall is dependent on the hurricane track which

includes variables location and speed. In addition, rainfall accumulation depends on the

storm’s intensity and size, the topography of the affected area, and a number of other factors.

To generate rainfall swaths in an objective and uniform fashion, we use the parametric

hurricane rainfall model (PHRaM) of Lonfat et al. (2007). PHRaM accounts for storm size,

intensity, wind-shear-based storm asymmetry, and topographic effects. The model is run for

a given set of values including; latitude, longitude, intensity, wind shear magnitude, wind

shear direction, and radius of maximum wind. It can produce a full five-day forecast rainfall

swath beginning 72 hours before landfall. We utilize the hypothetical values of intensity and

location that were defined above, and use wind shear values from the operational Statistical

Hurricane Intensity Prediction Scheme (SHIPS) model output (DeMaria and Kaplan, 1994).

Finally, to address current and future uncertainty in the rainfall forecast, we use the

1000-member Monte Carlo ensemble that NHC creates every six hours for each active storm

8

to produce its suite of wind speed probability forecasts (DeMaria et al., 2009). PHRaM is

run on all 1000 realizations, which in turns allows us ask questions related to the probability

of over- or under-forecasting rainfall compared to a deterministic forecast. Further, we can

extend this analysis for all potential error reduction scenarios (Marks et al., 2020). In the

survey, using the forecast in 2018 as a benchmark, the respondents are shown a map with a

shaded region indicating the area where there was at least a 20% probability of the rainfall

amounts generated from the forecast storm attributes being less than the rainfall amounts

generated from the observed storm attributes (an under-forecast) by at least one inch. The

following section explains how we use NHC historical error data and rainfall predictions to

elicit the public’s willingness to pay for hurricane forecast improvements.

4.2 Survey implementation

To appropriately elicit the value of improved hurricane forecast, we implement atmospheric

model insights into a web based survey questionnaire. We target individuals recently affected

by our chosen hurricanes, Michael and Florence, so participants can compare the forecast

products they know with those derived from an improved forecast’s capability. Our sample is

divided between two communities: those affected by Florence and those affected by Michael.

More specifically, communities under FEMA advisories 56 and 15, respectively.6 That des-

ignation includes areas in Florida, Georgia, North and South Carolina. Respondents answer

the sequence of questions described in Figure 3, which seek to extract relevant information

on their backgrounds and attitudes towards forecast improvements.

Participants are initially screened by the zip code where they live. Following the intro-

duction, respondents are briefed on the nature of the survey. We include one more screening

question regarding the participants ability to provide thoughtful and honest answers in the

survey. The questions that follow, solicit participant information on residential living status

and the extent of insurance coverage for their homes. This set of questions asks respondents

to describe their familiarity with hurricane risk and the US governmental programs created

to protect against hurricane-related damages.

The next question set asks respondents to recount their experience with their respective

storm (i.e., Florence or Michael). These questions are preceded with a statement describing

the acceptable limits of hurricane experience, informing participants that their experience

is not only limited to physical impacts. Depending on an individual’s response to their

encounter with the most recent hurricane, a set of follow up questions is presented inquiring

6The official advisories for Florence and Michael are available at https://www.nhc.noaa.gov/archive/2018/FLORENCE_graphics.php and https://www.nhc.noaa.gov/archive/2018/MICHAEL_graphics.php,respectively

9

Figure 3: Survey Flow Chart. This schematic describes the structure of the survey. The rectan-gular sections represent questions pertaining to participant background. The middle of the diagram displaysour dichotomous choice design. A random attribute is matched with a random improvement rate. Eachattribute and each rate are only used once. The participants must vote in favor or against an annual taxbetween 1-50 dollars. The tax is then adjusted based on the previous answer, and the respondents are pre-sented with another yes/no vote. This design is repeated three times. Both hexagons represent the beginningand end of the survey. Respondents must enter their zip in both instances as a way of quality control.

about evacuation decisions and damages to property from the storm. The section concludes

with general questions about evacuation plans and the number of individuals living in the

residence.

After documenting the respondents’ experience with the past hurricane, the survey de-

scribes the role of federal agencies in providing hurricane information. We briefly explain

the process of fund allocation for hurricane research. In addition, we mention our motiva-

tion in collecting individual attitudes towards tax increases to support funding for hurricane

forecasting research. We ask respondents to rank, in order of importance, the value of hurri-

cane forecast information for their own decision making. These forecast components include

information on; hurricane track, wind speed, storm surge, and the level of anticipated rain-

fall. Given our atmospheric modeling possibilities, we focus on three of the four forecast

components for our contingent valuation study.

We provide respondents with a set of three random scenarios needed for the contingent

valuation. These three forecast components are presented in Figure 4. This figure represents

the status quo forecast improvements for each improvement scenario. Moving along columns

from left to right, these figures represent changes in decadal trends of 72-hour track forecast

accuracy, intensity forecast error, and accumulated precipitation. As shown in Figure 3,

the survey randomly generates a scenario combining one random forecast attribute with one

random rate of forecast change. The respondents are provided with a brief description of

the randomly selected forecast attribute. A visual is included in the description to demon-

10

Figure 4: Hypothetical Forecast Components. This figure presents the maps and chartsthat were presented to survey participants. The top row of panels (a,b,c) is included in the Florence surveyand the bottom row (d,e,f) is included for the Michael survey. Each of the three columns contain figuresrepresenting forecast improvements corresponding to the status quo improvement. Panels a) and d) show thetrack uncertainty defined by the size of the cone for 72-hour forecasts. Panels b) and e) are the average windspeed error, and are the same because average wind speed forecast error is the same regardless of location.Panels c) and f) are the rainfall under-forecast area.

strate the change in accuracy of the given forecast as a result of the randomly chosen rate

improvement. As stated earlier, the change in forecast abilities are related to the baseline

improvements from 2008 to 2018. The proposed improvements include a 20% increase, a

20% decrease, or a constant rate of improvement (relative to the prior decade) in forecasting

accuracy. Respondents are then asked to answer yes or no to a randomly-generated annual

tax increase meant to pay for these forecast improvements. To decrease ambiguity, we spec-

ify that the tax increase takes place at the household level and lasts for ten years. These

random tax increases follow a uniform distribution between $1 to $50.

A follow-up yes or no question with an increased or decreased tax, relative to the original

tax, is then presented to the respondent. As required in a dichotomous choice design, if the

respondent answered yes to the initial tax increase, then the following tax is 20% greater.

On the other hand, if the respondent’s initial answer is no, then the follow-up tax is reduced

by 20%. This random process combining a forecast attribute and rate of improvement is

repeated three times. Each survey participant observes all three of the forecast attributes

combined with a unique rate change.

The survey concludes with a sequence of questions asking respondents to explain their

11

own decision making process. Respondents are finally asked to identify their level of belief

that public officials will use the survey information to guide policy implementation.

5 Empirics

We seek to elicit the preferences of the respondents using a double bounded dichotomous

choice design (Hanemann et al., 1991). In our design, each respondent is presented with

a randomly selected hurricane forecast attribute and a randomly selected potential rate of

improvement. For each of these dimensions and their respective rates of improvement, each

individual i is presented with a first bid, b1i ∼ U[1, 50]. Depending on her answer, she would

be presented with a followup bid, b2i . If the answer in the first round is positive (i.e., she

accepts the tax burden on her household), the bid is then increased by 20%. If the answer

in the first round is negative, the bid is decreased by 20% instead.

For each forecast attribute, a respondent would then fall into one of four possible sce-

narios. Let Y ji ∈ {0, 1} denote the individual response for bid j = {1, 2}, and Y i = [Y 1

i , Y2i ]

the tuple representing her response to both questions for a given forecast attribute. Further,

suppose that for individual i, the willingness to pay for a certain rate of improvement is

given by:

WTPi(xi) = x′iβ + εi (3)

with xi as the vector including the order in which the forecast attribute is shown to the

respondent and the rate of improvement, along with all other individual observables. Fur-

thermore, let εi ∼ N(0, σ2). The four possible scenarios, as a function of the individual

survey responses, are then given by:

Pr(Y i = [1, 0]|xi) = Φ

(x′iβ − b1

i

σ

)− Φ

(x′iβ − b2

i

σ

)(4)

Similarly:

Pr(Y i = [1, 1]|xi) = Pr(x′iβ + εi > b1i ∧ x′iβ + εi > b2

i )

Because b2i > b1

i and Pr(xiβ + εi > b1i |xiβ + εi ≥ b2

i ) = 1, we can invoke Bayes rule, which

implies:

Pr(Y i = [1, 1]|xi) = Φ

(x′iβ − b2

i

σ

)(5)

12

The third scenario is then given by:

Pr(Y i = [0, 1]|xi) = Φ

(x′iβ − b2

i

σ

)− Φ

(x′iβ − b1

i

σ

)(6)

Accordingly, if both bids are rejected, we have the fourth scenario as:

Pr(Y i = [0, 0]|xi) = 1− Φ

(b2i − x′iβσ

)(7)

Finally, the log-likelihood function for parameters β and σ, would be then given by:

L(β, σ|xi) = 1Yi=[1,0] ln

[Φ

(x′iβ − b1

i

σ

)− Φ

(x′iβ − b2

i

σ

)]+ 1Yi=[1,1] ln

[Φ

(x′iβ − b2

i

σ

)]+ 1Yi=[0,1] ln

[Φ

(x′iβ − b2

i

σ

)− Φ

(x′iβ − b1

i

σ

)]+ 1Yi=[0,0] ln

[1− Φ

(b2i − x′iβσ

)](8)

The estimates for the parameters of interest, β and σ, maximize equation 8. Recalling from

equation 3 that E[WTPi|xi] = x′iβ, the estimate for the average willingness to pay is then

given by:

WTP = x′β (9)

with x as the vector of mean values for the order in which the forecast attribute is presented,

the rate of improvement, and all other observables for a given respondent. Our strategy is

then to perform this analysis for each individual hurricane forecast attribute (i.e., track, wind

speed and precipitation). Our main specification includes order and hypothetical rate of im-

provement, controls for income, dummy variables for female respondents as well as for having

experienced and evacuated during Florence or Michael, perceptions of how the respondents’

answers will be considered as well as to influence a policy change, and the respondent’s be-

liefs of a hurricane affecting them in the long-term. The results of implementing this model

are presented in the next section.

6 Results

In this section, we present the results of deploying the survey, econometrically analyzing

the responses, and the extrapolation benefits (willingness to pay) to susceptible populations.

In addition, we complement this section with a series of tables that are included in the

13

Appendix.

6.1 Summary statistics

Our elicitation device is a web-based survey. Following the design specified in Section 4,

the survey is distributed and collected using the Qualtrics platform. Our sample targets

two sub-populations: zip-codes affected by Hurricane Florence, and zip-codes affected by

Hurricane Michael. From here onwards, we refer to these two samples as Florence and

Michael, respectively. The summary statistics for the survey are shown in Table 1. In the

table, “Answers” are binary (0/1) and take values of 0 or 1 for negative and positive answers,

respectively.

Control Set 1 includes the average individual income per year by zip code (thousands of

2018 USD), binary indicators if the respondent self identifies as a female, if the respondent

self-identifies her household as having experienced the storm, as well as if the respondent

evacuated in the face of the storm. In addition, we include a categorical value from 1

to 5 representing the attitude respondents have that public authorities will take the survey

responses into consideration (Voice), and the attitude that survey answers will affect internal

changes in Department of Commerce (Action). Finally, this set includes a self-assessed

probability of the respondent being affected by a hurricane in the long-term (time horizon

of 10 years and range 10−4 − 1).

On the other hand, Control Set 2 includes the remaining observables of the survey with a

continuous age variable (#), a binary variable if the respondent declares herself as the owner

of their current residence, and a continuous variable for how long she may have inhabited

her current residence. In addition, we include variables for the self-assessed probability of

the respondent being affected by a hurricane in the short-term (5 years and range between

10−4−1). This set also includes a categorial value for the level of awareness of the respondent

regarding hurricane insurance, the Federal Emergency Management Agency (FEMA), and

the National Flood Insurance Program (NFIP). The level of awareness ranges from 0 to

4 representing no awareness to highly familiar, respectively (0-4). Finally, the remaining

variables include a binary indicator if the respondent declares to have suffered damages due

to the storm, and a continuous variable indicating how many individuals reside in her current

residence.

Table 1 illustrates several patterns. First, there are no statistically significant differences

between the unconditional mean for the referendum answers across samples. In other words,

without controlling for observables, the differences in answers of these two samples would

be statistically indistinguishable from zero. Nonetheless, these two groups are different in

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Table 1: Summary Statistics for Survey Responses

Florence Michael DifferenceMean Std.Dev. Mean Std.Dev. Mean Diff. Std. Error

Track

Track Answer 1 (0/1) 0.617 0.486 0.622 0.485 -0.005 0.015Track Answer 2 (0/1) 0.590 0.492 0.581 0.494 0.009 0.015

Wind Speed

Wind Speed Answer 1 (0/1) 0.565 0.496 0.571 0.495 -0.006 0.016Wind Speed Answer 2(0/1) 0.550 0.498 0.548 0.498 0.002 0.016

Precipitation

Precipitation Answer 1 (0/1) 0.562 0.496 0.549 0.498 0.012 0.016Precipitation Answer 2 (0/1) 0.537 0.499 0.543 0.498 -0.006 0.016

Control Set 1

Income ($ ×103) 68.681 19.507 61.672 19.252 7.009∗∗∗ 0.610Female (0/1) 0.697 0.460 0.752 0.432 -0.055∗∗∗ 0.014Experience (0/1) 0.817 0.387 0.911 0.284 -0.095∗∗∗ 0.011Evacuated (0/1) 0.187 0.390 0.175 0.380 0.012 0.012Voice (1− 5) 2.708 1.273 2.871 1.277 -0.163∗∗∗ 0.040Action (1− 5) 2.597 1.218 2.792 1.236 -0.195∗∗∗ 0.038Long-Term Risk (10−4 − 1) 0.203 0.349 0.141 0.287 0.062∗∗∗ 0.010

Control Set 2

Age (#) 46.129 17.582 40.258 15.998 5.871∗∗∗ 0.541Owner (0/1) 0.583 0.493 0.489 0.500 0.094∗∗∗ 0.016Household Tenure (#) 15.458 113.085 17.540 155.076 -2.082 4.020Short-Term Risk (10−4 − 1) 0.150 0.294 0.105 0.242 0.045∗∗∗ 0.009Hurricane Insurance Awareness (0− 4) 2.900 1.081 2.981 1.085 -0.080∗ 0.034FEMA Program Awareness (0− 4) 2.067 1.224 2.081 1.280 -0.014 0.039NFIP Insurance awareness (0− 4) 1.540 1.289 1.492 1.329 0.048 0.041Damage (0/1) 0.242 0.428 0.466 0.499 -0.224∗∗∗ 0.014Household Size (#) 2.873 1.880 3.343 3.302 -0.470∗∗∗ 0.076+ p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Notes: The unit of of observation is an individual respondent. The table shows the summary statistics for all of the variablesincluded in the analysis. Bids, order and rate are not included as they, by design, are uniformly distributed. The table is dividedas a function of the geographical sample, and then the differences between the two samples. The number of observations is 3,150for Florence and 1,500 for Michael. For each variable, the respective units are presented in parenthesis. Monetary variables arein 2018 dollar values. Statistical significance based on a two-sided t-test for difference in population means.

many ways: Florence has a higher income, 5% more participants that self-identify as female,

and about 10% less participants that experienced the storm. Respondents for Florence are

also less confident that their responses will be considered or lead to actual policy changes.

Individuals in Florence also have a higher average long-term risk perception. All of these

differences are significant at the 0.01% (two-sided t-test). Out of Control Set 1, however, it

15

appears that respondents evacuated at a similar rate (about 18% for both samples).

In addition, respondents in Florence are on average 5 years older, show a higher rate of

ownership, report higher average short-term (5-year) risk perception, and a higher average

hurricane insurance awareness. Florence also has a lower fraction of respondents reporting

having experienced damages due to the storm, and report a lower household size in average.

All of these differences are statistically significant at least at the 95% confidence level (two-

sided t-test). There are no detectable differences between the samples in terms of awareness

of FEMA and NFIP.

To put some of these figures in perspective, it is useful to map how the respondents

were affected by both storms. In Figure 5, we presents the spatial patterns for the rate

of evacuation, and the mode of the self-reported losses in the sample. Panel a) shows that

evacuations were more prevalent in coastal areas. However, inland counties in the path of the

storms experienced partial evacuation as well. In terms of losses, panel b in Figure 5 shows

that self-reported capital losses are widely distributed across the sample, but total losses

are more prevalent for coastal counties hit by Michael in the panhandle. In fact, comparing

the unconditional mean reported losses suggest that they were larger for the Michael sample

(p < 0.001 for a two-sided t-test).

Figure 5: Loss and Evacuation Maps. This map displays participant responses to evacuationand total loss questions in the survey. Map (a) depicts the percent of respondents who evacuated withina county (percent represents the survey sample). Map (b) displays total losses (categorized) for surveyrespondents that directly experienced either of the hurricanes. Those who did not directly experience thestorm were not presented with a loss question.

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Building on these cross-sectional data, we now turn to the econometric analysis of survey

responses. The goal is to derive the willingness to pay (WTP) for improvements to hurricane

forecast accuracy across each of the three dimensions of forecasting (i.e., track, wind speed

and precipitation).

6.2 Contigent Valuation

This section reports the results for the contingent valuation method implemented to elicit the

WTP for improvements along different attributes of a hurricane forecast. We split this section

by forecast attribute. Table 2 reports the results for the maximum likelihood estimation in

the case of improvements in track forecast. The results for the full sample indicate a positive

and statistically significant unconditional mean WTP for an improved track forecast of $42.53

(Constant). Nonetheless, WTP significantly decreases if the attribute is shown later in the

survey (Order). The point estimate is a $4.78 penalty for the attribute being shown second,

and a penalty of $9.56 when it is show third, respectively. The point estimate for the rate of

improvement is positive, although small in magnitude and not statistically significant (Rate).

Including Control Set 1 in the estimation gives us some further insights regarding track

forecast improvements (Table 2). First, the effect of Order and Rate remain robust to

this specification. Second, WTP increases for respondents with higher income, for female

respondents, by having experienced the storm, and by having evacuated during the storm.

All of these estimates are significant, at least at the 95 percent confidence level. Second, WTP

increases if the respondent believes that the survey will be considered by authorities (Voice),

and if they believe it will effectively lead to policy changes (Action). Finally, WTP also

increases if the respondent has a higher long-term risk perception (likelihood of experiencing

a hurricane in the next ten years, Long-term Risk). These last three estimates are all

relatively high in magnitude and highly statistically significant.

Finally, Table 2 shows the estimates for including both Control Set 1 and Control Set 2

in the estimation. The result shows that this additional battery of controls does not alter

the relative magnitude and significance of the previous estimates. The only exception is the

estimate for long-term risk perception, which increases from $6.70 to $10.34. In other words,

it seems like Control Set 1 captures much of the variation observed in the survey.

When examining both samples separately, however, some differences arise. In the case

of Florence, Table 2 shows that while most estimates remain relatively stable and robust

when compared to the full sample, there is a loss of precision for the effect of being female

and having evacuated during the storm. In the case of Michael, it shows a further loss in

precision in the estimates for Control Set 1, and only evacuation, action, and long-term risk

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Table 2: Regression Results for Improvements in Track Forecast

Full Sample Florence Michael

Order -4.78∗∗∗ -4.79∗∗∗ -4.88∗∗∗ -5.08∗∗∗ -5.12∗∗∗ -5.25∗∗∗ -4.13∗∗ -4.02∗∗ -3.96∗∗

(0.73) (0.72) (0.72) (0.87) (0.85) (0.86) (1.34) (1.33) (1.33)

Rate 0.65 0.65 0.82 0.70 0.49 0.50 0.54 1.14 1.71(0.73) (0.72) (0.72) (0.87) (0.86) (0.87) (1.32) (1.32) (1.32)

Control Set 1

Income 0.11∗∗∗ 0.09∗∗ 0.11∗∗ 0.09∗ 0.10 0.08(0.03) (0.03) (0.04) (0.04) (0.06) (0.06)

Female 3.32∗∗ 3.40∗ 2.80 3.11∗ 4.63 4.04(1.29) (1.32) (1.50) (1.55) (2.49) (2.55)

Experience 5.18∗∗ 4.00∗ 5.42∗∗ 4.88∗ 5.79 2.92(1.65) (1.73) (1.84) (1.92) (3.80) (3.97)

Evacuated 3.70∗ 3.21∗ 2.21 1.99 7.05∗ 6.08∗

(1.57) (1.61) (1.85) (1.92) (2.98) (3.00)

Voice 2.71∗∗∗ 2.87∗∗∗ 3.10∗∗∗ 3.26∗∗∗ 1.91 1.96(0.62) (0.63) (0.75) (0.76) (1.12) (1.12)

Action 4.46∗∗∗ 4.50∗∗∗ 4.23∗∗∗ 4.40∗∗∗ 5.01∗∗∗ 4.92∗∗∗

(0.65) (0.66) (0.79) (0.80) (1.17) (1.16)

Long-term Risk 6.70∗∗∗ 10.34∗∗∗ 5.95∗∗ 10.06∗∗ 9.23∗ 11.56∗

(1.81) (2.70) (2.04) (3.14) (3.94) (5.32)

Constant 42.53∗∗∗ 7.35 6.25 42.94∗∗∗ 8.57 5.77 41.68∗∗∗ 3.55 4.89(2.16) (3.77) (4.41) (2.58) (4.48) (5.31) (3.97) (7.19) (8.14)

Observations 4650 4644 4581 3150 3148 3117 1500 1496 1464Log-Likelihood -5264.57 -5131.73 -5045.45 -3571.41 -3478.03 -3437.84 -1692.15 -1650.17 -1600.06χ2 43.55 249.74 263.46 34.71 181.48 188.66 9.65 72.89 84.84Control Set 2 X X X

Robust standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Notes: The unit of of observation is an individual respondent. The table is split into three panels. Each panel works with adifferent sample: all respondents (Full Sample), only those residing in areas affected by Florence (Florence), and only thoseaffected by Michael (Michael). Each column is a different estimation. Order represents the order in which the track attributewas shown to the respondent and takes values from 1 to 3. Rate is the rate at which the errors of the forecast product arereduced in the next decade. Income is the average yearly income in the respondent’s zip code. Female is a binary indicator ifthe respondent self-identifies as a female. Experience and evacuated are binary variables indicating if the respondent declared tohave experienced the storm and/or is she evacuated, respectively. Voice and Action are categorical variables taking values from1 to 5 depending on their beliefs that the survey will be considered by the authorities and if the results will lead to actual policychange, respectively. Long-tern risk is the perceived change that the respondent will experience a hurricane in the next fiveyears. Control Set 2 includes controls for age, house ownership, length of residence in current household, short-term hurricanerisk perception, awareness of insurance programs, evacuation decisions, damages experienced, and household size.

remain significant under the different specifications. These disparities are further evidence

that there are underlying differences across samples. In other words, the characteristics of a

given storm appear to have a strong influence when it comes to what is considered valuable

information ex-post. The full report of coefficient estimates is available in Table A.1 in the

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Appendix.

We now turn to examine the results for the contingent evaluation for improvements in

wind speed forecast, which are shown in Table 3. Overall, the results suggest a similar

pattern as with improvements in track: large unconditional valuation for improvements, a

penalization for the attribute being shown later in the survey, and no measurable effect of

the rate of improvement. This pattern is consistent for both the full and the individual

samples.

Including the sets of controls, however, results in a loss of precision for the effect of female

respondents and their long-term risk perceptions in the full sample (Table 3). Despite this

problem, statistically significant estimates remain consistent and intuitive when compared

to the analysis on track forecast improvement. That is, the WTP increases for respondents

with higher income, by having experienced the storm, by having evacuated during the storm,

and by believing the survey will be considered and implemented in future policy changes.

Long-term risk beliefs are only significant in the fully specified model model, but are still

consistent with the idea that respondents who think they are more likely be hit by a hurricane

in the next ten years are also willing to pay more for a more accurate wind speed forecast.

Breaking down these estimates by sample suggest a similar pattern. The main difference

however, is that having experienced the storm has a higher valuation in the Michael sample

(Table A.2). This finding could be related to that fact that most of the impacts from Michael

were due to strong winds, but also due to the physical geography of the Florida panhandle

facilitating evacuation, since driving an hour due north improves safety in the face of the

storm. Arguably, in the case of Michael, the forecast entered into evacuation decisions more

easily. In the case of Florence, even if an individual knew that they would experience the

storm, the best decision might still be to stay home rather than risk a flash flood encounter.

In addition, both samples’ WTP increases as the respondents believe the survey will be

considered and cause policy changes by the agency. The full report of coefficient estimates

is available in Table A.2, which is also included in the Appendix.

Finally, we cover the results of the maximum likelihood estimation for improvements

in the precipitation forecast, which are shown in Table 4. Much like the two previous

sets of results, there is also a somewhat large unconditional valuation for improvements,

a penalization for the attribute being shown later in the survey, and no effect measurable

effect of the rate of improvement. This pattern is again consistent for both full and individual

samples.

Focusing on the full sample, it is important to highlight that there is a consistent decrease

in the magnitude of the estimates, when compared to the WTP for better wind speed

forecasts. Moreover, both income and long-term risk perception are no longer statistically

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Table 3: Regression Results for Improvements in Wind Speed Forecast

Full Sample Florence Michael

Order -4.24∗∗∗ -4.11∗∗∗ -3.93∗∗∗ -4.99∗∗∗ -4.86∗∗∗ -4.64∗∗∗ -2.78∗ -2.73∗ -2.66∗

(0.76) (0.74) (0.75) (0.96) (0.93) (0.94) (1.27) (1.25) (1.26)

Rate 0.46 0.32 0.37 0.40 0.24 0.24 0.52 0.38 0.33(0.76) (0.74) (0.75) (0.95) (0.92) (0.93) (1.26) (1.23) (1.25)

Control Set 1

Income 0.10∗∗ 0.10∗∗ 0.11∗∗ 0.12∗∗ 0.06 0.04(0.03) (0.03) (0.04) (0.04) (0.05) (0.06)

Female 1.37 1.46 1.33 1.43 1.66 1.25(1.35) (1.39) (1.64) (1.69) (2.38) (2.46)

Experience 6.23∗∗∗ 5.47∗∗ 5.22∗∗ 5.19∗ 10.38∗∗ 8.12∗

(1.73) (1.82) (2.02) (2.11) (3.63) (3.83)

Evacuated 5.96∗∗∗ 5.48∗∗ 6.17∗∗ 5.74∗∗ 5.26 4.82(1.64) (1.69) (2.03) (2.11) (2.78) (2.85)

Voice 4.26∗∗∗ 4.21∗∗∗ 4.87∗∗∗ 4.71∗∗∗ 3.20∗∗ 3.15∗∗

(0.66) (0.66) (0.83) (0.84) (1.06) (1.08)

Action 4.24∗∗∗ 4.38∗∗∗ 4.53∗∗∗ 4.77∗∗∗ 3.62∗∗∗ 3.64∗∗

(0.68) (0.69) (0.87) (0.88) (1.10) (1.11)

Long-term Risk 3.03 6.33∗ 3.33 6.96∗ 2.73 4.51(1.87) (2.84) (2.21) (3.41) (3.63) (5.22)

Constant 38.30∗∗∗ 0.65 -0.71 39.91∗∗∗ 0.25 -3.64 35.35∗∗∗ 0.22 2.05(2.27) (3.98) (4.61) (2.87) (4.97) (5.89) (3.68) (6.74) (7.65)

Observations 4650 4644 4581 3150 3148 3117 1500 1496 1464Log-Likelihood -5315.78 -5163.08 -5071.72 -3534.25 -3416.75 -3365.09 -1780.16 -1741.95 -1698.36χ2 31.21 278.22 283.58 27.60 210.96 214.35 4.89 69.62 76.89Control Set 2 X X X

Robust standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Notes: The unit of of observation is the voting decision made by an individual respondent. The table is split into three panels.Each panel works with a different sample: all respondents (Full Sample), only those residing in areas affected by Florence(Florence), and only those affected by Michael (Michael). Each column is a different estimation. Order represents the order inwhich the track attribute was shown to the respondent and takes values from 1 to 3. Rate is the rate at which the errors ofthe forecast product are reduced in the next decade. Income is the average yearly income in the respondent’s zip code. Femaleis a binary indicator if the respondent self-identifies as a female. Experience and Evacuated are binary variables indicating ifthe respondent declared to have experienced the storm and/or is she evacuated, respectively. Voice and Action are categoricalvariables taking values from 1 to 5 depending on their beliefs that the survey will be considered by the authorities and if theresults will lead to actual policy change, respectively. Long-tern risk is the perceived change that the respondent will experiencea hurricane in the next five years. Control Set 2 includes controls for age, house ownership, length of residence in currenthousehold, short-term hurricane risk perception, awareness of insurance programs, evacuation decisions, damages experienced,and household size.

significant. Breaking down the analysis by sample is consistent with this story as well.

The only coefficients that remain significant across the spectrum, are voice and action,

respectively (TableA.2). In other words, the most consistent controls associated with higher

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Table 4: Regression Results for Improvements in Precipitation Forecast

Full Sample Florence Michael

Order -5.09∗∗∗ -5.21∗∗∗ -5.26∗∗∗ -4.81∗∗∗ -4.85∗∗∗ -4.95∗∗∗ -5.71∗∗∗ -5.96∗∗∗ -5.75∗∗∗

(0.82) (0.81) (0.81) (1.00) (0.98) (0.99) (1.44) (1.43) (1.44)

Rate 0.27 0.44 0.63 0.04 0.00 0.11 0.75 1.27 1.43(0.81) (0.79) (0.80) (0.99) (0.96) (0.97) (1.41) (1.40) (1.41)

Control Set 1

Income 0.06 0.06 0.06 0.07 0.03 0.01(0.03) (0.03) (0.04) (0.04) (0.06) (0.06)

Female 4.10∗∗ 3.68∗ 5.29∗∗ 4.73∗∗ 1.60 0.76(1.44) (1.48) (1.72) (1.77) (2.65) (2.74)

Experience 4.09∗ 4.09∗ 4.15∗ 3.99 5.90 5.86(1.84) (1.93) (2.10) (2.18) (4.07) (4.31)

Evacuated 4.22∗ 3.92∗ 3.38 2.78 5.61 5.83(1.74) (1.79) (2.10) (2.18) (3.09) (3.17)

Voice 4.25∗∗∗ 4.34∗∗∗ 5.07∗∗∗ 5.18∗∗∗ 2.74∗ 2.75∗

(0.70) (0.71) (0.87) (0.88) (1.18) (1.20)

Action 4.83∗∗∗ 4.70∗∗∗ 4.72∗∗∗ 4.51∗∗∗ 5.03∗∗∗ 4.84∗∗∗

(0.73) (0.74) (0.90) (0.92) (1.24) (1.24)

Long-term Risk 1.15 4.70 1.05 5.17 1.10 3.65(1.99) (3.01) (2.31) (3.54) (4.07) (5.80)

Constant 39.31∗∗∗ 3.55 4.90 39.35∗∗∗ 1.45 5.33 39.24∗∗∗ 7.62 4.98(2.41) (4.22) (4.90) (2.96) (5.16) (6.14) (4.15) (7.50) (8.47)

Observations 4650 4644 4581 3150 3148 3117 1500 1496 1464Log-Likelihood -5495.70 -5343.76 -5261.78 -3710.59 -3597.80 -3547.07 -1784.84 -1741.40 -1705.47χ2 38.63 265.24 272.82 23.27 197.87 204.69 15.80 72.66 79.58Control Set 1 X X X X X XControl Set 2 X X X

Robust standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Notes: The unit of of observation is the voting decision made by an individual respondent. The table is split into three panels.Each panel works with a different sample: all respondents (Full Sample), only those residing in areas affected by Florence(Florence), and only those affected by Michael (Michael). Each column is a different estimation. Order represents the order inwhich the track attribute was shown to the respondent and takes values from 1 to 3. Rate is the rate at which the errors ofthe forecast product are reduced in the next decade. Income is the average yearly income in the respondent’s zip code. Femaleis a binary indicator if the respondent self-identifies as a female. Experience and Evacuated are binary variables indicating ifthe respondent declared to have experienced the storm and/or is she evacuated, respectively. Voice and Action are categoricalvariables taking values from 1 to 5 depending on their beliefs that the survey will be considered by the authorities and if theresults will lead to actual policy change, respectively. Long-tern risk is the perceived change that the respondent will experiencea hurricane in the next five years. Control Set 2 includes controls for age, house ownership, length of residence in currenthousehold, short-term hurricane risk perception, awareness of insurance programs, evacuation decisions, damages experienced,and household size. Sample sizes differ due to missing responses.

WTP are related to the trust that the agency will consider the survey and will trigger some

policy change. The full set of estimates is also shown in the Appendix in Table A.3.

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Overall, these results present us with several insights. First, all rates of improvement for

all attributes are valued by the respondents. Second, respondents are insensitive (in terms of

statistical significance) to changes in the rates tested. In other words, there is a demand for

improving track, wind speed and precipitation forecast. Third, while the effect of the different

controls mostly goes in the expected direction, trusting that the respondents’ opinion will

lead to actual policy changes is the only factor that is robust across all different attributes

and samples. This pattern suggest that credible public feedback is strongly associated with

a higher WTP for forecast products. Finally, it is also worth noting that there are obvious

differences across all different attributes and samples. Again, these differences indicate that

the experiences of the respondents are likely to have a major impact on their perceived value

of different forecast products.

Accordingly, we now turn to estimate the average WTP for different forecast products

and across samples. To do so, we implement equation 9 limited to statistically significant

coefficients in the previous estimations. The average progress is circumscribed to the 72-hour

prior landfall time window, and assumes a 40% error reduction in storm track, a 29% error

reduction in wind speed, and a 42% error reduction in precipitation forecast, respectively.

These results are shown in Table 5.

Table 5: Average Willingness to Pay for Hurricane Forecast Improvement

Full Sample Florence Michael

WTP (Track) 33.01∗∗∗ 25.74∗∗∗ 26.10∗∗∗ 32.82∗∗∗ 22.55∗∗∗ 24.67∗∗∗ 33.44∗∗∗ 8.31∗ 11.39∗∗

(1.57) (3.48) (3.56) (1.88) (3.98) (4.24) (2.89) (4.18) (4.38)

WTP (Wind speed) 29.77∗∗∗ 27.87∗∗∗ 28.91∗∗∗ 29.87∗∗∗ 28.74∗∗∗ 30.86∗∗∗ 29.75∗∗∗ 21.78∗∗∗ 22.90∗∗∗

(1.64) (3.51) (3.61) (2.06) (4.37) (4.50) (2.71) (4.71) (5.02)

WTP (Precipitation) 29.17∗∗∗ 21.36∗∗∗ 16.59∗∗∗ 29.77∗∗∗ 24.18∗∗∗ 19.80∗∗∗ 27.87∗∗∗ 9.05∗ 8.97∗

(1.77) (2.88) (3.55) (2.16) (3.50) (3.09) (3.08) (3.93) (4.01)Observations 4650 4644 4581 3150 3148 3117 1500 1496 1464Control Set 1 X X X X X XControl Set 2 X X X

Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Notes: The unit of of observation is an individual respondent. The table has three panels of three columns each. Each panelworks with a different sample: all respondents (Full Sample), only those residing in zip codes affected by Florence (Florence),and only those affected by Michael (Michael). Control Set 1 includes control for income, female respondents, having experiencedthe storm, having evacuated for the storm, the belief that the survey findings will be considered by the relevant agencies, thebelief that the survey will lead to actual policy adjustments, and the self-assessed chance of experiencing a hurricane in thenext ten years. Control Set 2 includes controls for age, house ownership, length of residence in current household, short-termhurricane risk perception, awareness of insurance programs, evacuation decisions, damages experienced, and household size.Sample sizes differ due to missing responses.

Table 5 shows a non-zero valuation for improvements across all attributes and samples.

This result is robust in all specifications. Consider the fully specified estimation in the full

sample. Respondents are, in average, willing to pay $26.10, $28.91, and $16.59 in additional

22

household taxes per year for the next ten years for improvements in track, wind speed

and precipitation forecasts, respectively. All three estimates are significant at the 1% level.

Importantly, the attribute with the biggest WTP across the sweep of results, is wind speed.

Breaking down the estimates by sample, however, reveals that the average WTPs differs

across the two regions in the sample. In particular, Florence has a higher average WTP for

every attribute evaluated.

These differences should come as no surprise. We previously found that there are system-

atic differences in the characteristics of both samples, particularly for income levels. These

differences were present in the coefficient, and thus manifest in the WTP as well. For in-

stance, differences in coefficient for track and wind speed explain a difference of about $8

across both samples. Differences in the WTP for precipitation, however, are not explained

by income differences, but the female composition of the sample and individual responses.

Further, when comparing intra-samples and using wind speed as the reference, track and pre-

cipitation WTPs are relatively much more valued in Florence (0.79 and 0.64, respectively)

than in Michael (0.49 and 0.39, respectively).

Intuitively, this suggests an anchoring effect following each storm, which would have

elevated WTP for improved rainfall prediction for those who experienced Florence, and

elevated WTP for wind speed forecast improvements by those who experienced Michael. Our

results are consistent with a hypothesis of anchoring. To address this issue, we evaluated the

effect of including a storm fixed effect in the full sample, and found no major or significant

changes in the estimates. Below, we provide a framework to interpret these results in a

national context. Sample sizes differ due to missing responses.

6.3 Extrapolation

In the previous section we derived the average willingness to pay for improvements to the

hurricane forecast along three of its dimensions. Extrapolating these results to out-of-sample

individuals, however, requires a mechanism to account for the fact that our sample targets

communities affected by either Florence or Michael. Our approach is to perform this exercise

at the household level.

In particular, we take the fully specified WTP for an attribute in the full sample and

assume the average respondent is representative of households in the sampled regions. Im-

plicitly, we are also assuming that in average, the forecast keeps improving by ±20% relative

to the rate of improvement occurred between 2008-2018. This assumption is plausible, based

on the previous result that any and all improvements are strictly positively valued, regardless

of the actual rate of improvement.

23

As an illustration, we extrapolate the WTP for improvements in wind speed forecast.

Using the census data we then scale the WTP of $28.91 by the number of occupied household

in the counties present in the sample. This exercise is shown in panel a) of Figure 6.

Aggregating these by-county WTPs suggest a total WTP of about $67 million per year for

the surveyed region. Further, we can extend this exercise to all counties having experienced

wind speeds of at least 30 miles per hour due to a hurricane between 2006-2018 in the

continental US. Spatially, this exercise is shown in panel b) of Figure 6. The total WTP

considering these hurricane exposed counties is about $1.18 billion.

Figure 6: Sample and Extrapolated Willingness to Pay for Wind Speed Forecast.The figure shows the aggregated willingness to pay (WTP) for wind speed forecast improvement. Panel a)extrapolates the household WTP for counties present in the survey, while panel b) extrapolates WTP toevery county that has experienced at least 30 miles per hour wind due to a hurricane between 2006-2018.WTP is log-adjusted to control for counties with disproportionally large number of occupied households.

On a per-capita basis, these numbers are equivalent to a WTP of $13.96 per adult per

year in additional taxes. Extending this payment over ten years, as stated in the survey, and

discounting at 5% gives a total net present WTP of $113 per adult. Similar calculations for

the other two forecast attributes are included in Table 6. These calculations indicate that

the net present value WTP for track and precipitation forecast improvements are $102.15

and $64.94, respectively.

The projection of the WTP to the vulnerable population is straight forward. Nevertheless,

coastal and inland regions are not equally exposed to intensity and frequency of hurricanes,

which in turn implies the decision on the wind speed threshold will have sizable implications

24

Table 6: Selected Willingness to Pay for Hurricane Forecast Improvement

Average Estimate Counties in Survey Vulnerable Counties Per-capita NPV(USD/hh/year) (USD/year) (USD/year) (USD/year/person) (USD/person)

Storm Track $26.10 $60.9×106 $1,031×106 $12.60 $102.15Wind Speed $28.91 $67.4×106 $1,142×106 $13.96 $113.18Precipitation $16.59 $38.7×106 $655×106 $8.01 $64.94

Notes: All monetary units are in 2018 USD. Extrapolation based on the number of households (hh) occupied as reported in the2018 census. Vulnerable counties are those that have experienced at least 30 miles per hour wind due to a hurricane between2006 and 2018. Per-capita values only consider individuals over 18 years old as per the 2018 census, and future values arediscounted at 5% per year.

in the extrapolated total WTP. Consequently, we report these calculations for thresholds

at 20, 30, 40 and 50 miles per hour, respectively. Spatially, the WTP for improved wind

forecast in vulnerable counties under these different thresholds is shown in Figure 7. As

expected, the lower the wind speed threshold, the greater the area identified as vulnerable.

Bearing this implications in mind, the relevant WTP calculations for all of these thresholds

are shown in Table 7.

Table 7: Extrapolation of Willingness to Pay for Hurricane Forecast Improvement

> 50 mph > 40 mph > 30 mph > 20 mph

Total WTP (USD×106/year)

Storm Track $409 $617 $1,031 $1,566Wind Speed $453 $684 $1,142 $1,735Precipitation $260 $392 $655 $995

Per-capita WTP (USD/year/person)

Storm Track $12.33 $12.50 $12.60 $12.71Wind Speed $13.65 $13.85 $13.96 $14.08Precipitation $7.83 $7.95 $8.01 $8.08

NPV (USD/person)

Storm Track $99.96 $101.35 $102.16 $103.05Wind Speed $110.67 $112.29 $113.19 $114.16Precipitation $63.48 $64.46 $64.94 $65.51

Notes: All monetary units are in 2018 USD. Extrapolation based on the number of households occupied as reported in the 2018census. Vulnerable counties are those that have experienced at least 20, 30, 40, and 50 miles per hour wind due to a hurricanebetween 2006 and 2018, respectively. Per-capita values only consider individuals over 18 years old as per the 2018 census, andfuture values are discounted at 5% per year.

It is important to note, however, that although the three forecast attributes are treated

separately and ranked differently, all three of them are intertwined in reality. For example,

if hurricane research looking at internal processes, air-sea interactions, or sensitivity to wind

shear leads to an improvement to track forecasts, it should also lead to better intensity

25

Figure 7: Extrapolated Willingness to Pay for Wind Speed Forecast. The figureshows the aggregated willingness to pay (WTP) for forecast improvement. Panels a), b), c), and d) showthe extrapolated WTP to every county that has experienced at least 20, 30, 40, and 50 miles per hourwinds due to a hurricane between 2016-2018, respectively. WTP is log-adjusted to control for counties withdisproportionally large number of occupied households.

and rainfall forecasts. This suggest that joint improvements are likely to follow traditional

research efforts, and thus compound the value of improvements across different attributes.

The problem, however, is that these compounded values will not be additively separable.

Nonetheless, the purpose of this study is to understand preferences for these components as

primary attributes. Moreover, eliciting responses for each attribute separately makes it more

tractable to the respondents and is still useful when considering funding advances in these

areas. Therefore, and bearing in mind these potential confounding sources of valuation, the

26

extrapolation exercise Table 7 suggest the more conservative lower bounds to the willingness

to pay for forecast improvement are $453 million per year for wind speed, $409 million

per year for storm track, and $260 million a year for precipitation. Any efforts resulting

improvements across multiple dimensions are likely to be even more valuable than these

individual estimates.

7 Discussion

We analyze the perceived valuation of forecast improvement across space, time and forecast

attributes. We document the plausible policy argument for why decision makers may assign

value to such improvements, and then test for the existence of that value in two regions

recently affected by hurricanes. We find that improvements in forecast accuracy are strictly

positively valued, but insensitive to the rate of improvement. Among all three attributes,

improvements in wind speed forecasts are the most valued. In aggregate, our main results

calculate that maintaining the prior decade’s rate of wind speed forecast error reduction is

valued at about $1.14 billion per year in all hurricane-prone counties in the US. Storm track

and precipitation forecast improvements are valued at about $1.03 billion and $655 million

per year, respectively.

We recognize that improvements in track, intensity and rain forecasts have interlinkages

at the operational levels but the purpose of this study is to understand public references

for these components as separate key primary attributes. The hurricane risk information is

a complex product which is not exchanged frequently in a regular market set-up to allow

consumers have a well-defined preference structure. While understanding the complexities

and interlinkages are important factors to consider, given the unique nature of this product

we had to draw a balance to present these attributes in a manageable way and elicit corre-

sponding values that will be relevant for decision-makers. Because of these interlinkages, our

separate estimates are likely lower bounds for research efforts that result in a wide array of

forecast improvements. These bounds are useful for future cost-benefit analyses seeking to

establish the societal benefit of scientific and operational efforts to further improve hurricane

modeling and forecasting research.

To achieve these results, we integrate hurricane forecast error statistics with a contingent

valuation method. By targeting areas in which respondents are already familiar with the

National Hurricane Center forecast products, they are able to give a more reasoned reply

when presented with the hypothetical improvement scenarios from the atmospheric models.

Economic theory suggests that if better information allows them to take better decisions in

terms of adaptive behavior in the face of a hurricane, then there would be an associated value

27

assigned to having an improved forecast. This argument, however, requires that forecasts

actually influence adaptive behavior, such as evacuation and the purchase of emergency

supplies. Our results provide quantitative evidence that this relationship exists.

In particular, the magnitudes of our WTP estimates (roughly $16-$26 per household per

year) exceed those of Lazo and Waldman (2011), $13 per household per year or $15 after

adjusting for inflation, which may stem from our different methodological approaches. In

fact, a strength of this study is that the analysis is based on a large representative sample size.

Earlier studies focused on this topic are usually based on a smaller and non-representative

sample. For instance, the stated preference study by Lazo and Waldman (2011) values

improved hurricane forecasts in the US using a sample size of only 80 respondents from

the Miami-Fort Lauderdale Metropolitan area. Similarly, Ahsan et al. (2020) estimates

household’s willingness to pay for improved cyclone warning services in Bangladesh based

on a sample 490 households, while Nguyen et al. (2013) do so in Vietnam using responses

from 1014 households. Our approach is a large-scale randomized, double-bounded contingent

evaluation. Our sample of 4650, drawn from two regions, is both more granular and allows

us to control for several observables. Thus, the difference is not surprising.

Our analysis provides evidence of a value of improving hurricane forecasts, but several

caveats remain. First, we use observed forecast errors of track and wind speed during the

prior decade to inform what forecast errors could be a decade in the future. In particular,

a linear trend line through errors from 2008 to 2018 yields an average percent improvement

during that decade. We project that same percentage forward to 2028 as the “status quo”

scenario and then build our relative scenarios of improvement around that baseline. The

implicit assumption is that forecasts made in 2028 will not only be more accurate than those

made in 2018, but also have less uncertainty surrounding them. While the choice of a specific

decade as the baseline is arbitrary, we assert that all three scenarios are plausible for both

track and intensity, and that the results would not differ noticeably if some other length of

time was used to construct the error reduction values.

The precipitation model is more complicated and involves many more assumptions, ac-

cordingly. Track and intensity metrics can be adjusted independent of each other; however,

rainfall is inherently tied to both of them. Further, the National Hurricane Center does not

make forecasts of rainfall swaths for hurricanes as part of its six-hourly advisory packages,

and therefore, there are no official annual average errors of rainfall forecasts. To create

self-consistent rainfall forecasts from the two hurricanes (Florence and Michael) and four

scenarios (2018 and three for 2028), we use a parametric precipitation model that accounts

for the hurricane’s intensity, size, and track as well as the environmental vertical wind shear

and topographic effects (Lonfat et al., 2007). Of course, a parametric model is imperfect

28

because it is built on empirical relationships and is not a full-physics four-dimensional nu-

merical weather prediction model, but it has the advantage of treating each hurricane and

its limited set of attributes in an identical fashion.

Rather than just nudging the parametric precipitation model with slightly different track

and intensity values for each of the scenarios, we explore probabilistic forecasts using the

National Hurricane Center’s 1000-member Monte-Carlo ensemble. The spread of realizations

within the ensemble takes into consideration the observed error characteristics of intensity,

track, and size. In addition to the real-time ensembles created in 2018, we also acquire

ensembles that used the track and intensity values from the hypothetical future scenarios.

This approach allows us to run the parametric rainfall model on each member of each of the

ensembles, calculate probabilistic information, and evaluate the likelihood of over- or under-

forecasting rainfall by a specified amount relative to what a “deterministic” operational

rainfall forecast would have been. While these preparations are indeed modeling and time

intensive, we deem them necessary to approach the actual forecast products that individuals

would see under different rates of improvement.

In addition and since contingent valuation relies on stated values for what people would

pay instead of estimating how much they actually do pay, it can be biased. One possibility is

strategic bias, which occurs when people deliberately misstate their WTP in order to improve

their net benefits from any anticipated policy change. To control for strategic bias, we

include questions in our survey about whether respondents believed their replies might have

any influence on policy. Another possibility is embedding, which occurs when respondents

state that they are willing to pay the same amount for goods that differ in quality or different

amounts for the same good (Carson et al., 1995). To control for this bias, we randomized

the order in which the attributes were shown to the respondents, and we find sufficient

variation in our WTP estimates across attributes to dismiss concerns about embedding. A

third concern is hypothetical bias, which occurs when respondents are unfamiliar with the

good or service being valued. Because our sample targeted those who had recently come

under a tropical storm warning, our respondents are familiar with hurricane forecasts, thus

minimizing the chance for hypothetical bias.

Despite these caveats, the results in the analysis are clear and consistent, with noticeable

implications for policy making. Namely, the public values further improvement, even after

the remarkable progress observed since the start of the Hurricane Forecast Improvement

Project in 2007. This important result is encouraging, and highlights the relevance of the

ongoing efforts to make the forecast even more accurate. Nonetheless, our results also raise

a question for the adequacy of the mandated standards that focus on track and wind speed.

While justifiable in political discourse, it is unclear if such goals would pass a cost-benefit

29

analysis for optimal allocation of public resources. This analysis sheds a light on this problem

and provides figures that can be implemented in such evaluation.

Amongst all three attributes, improvements in wind speed forecasts are consistently and

robustly valued higher. This result is perhaps related to the way in which respondents process

different sources of forecast information. On the one hand, wind speed is directly related

to the well-known Saffir-Simpson category of a storm, which is both single-dimensional and

directly related to the damages associated with a given hurricane. Arguably, this measure

allows an individual to rapidly assess the potential danger of a storm and engage in adaptive

behavior accordingly. If true, the decision maker’s relative ease in thinking about wind

speed is an availability effect and is important even if wind speed is not objectively the

most threatening hurricane attribute to human life. Moreover, categories are often used in

the media as well, so individuals are more familiar with that index. Track and precipitation

forecast on the other hand, are not as effective in describing the potential damage of a storm.

In addition, individuals may not even understand how to interpret these forecasts in the first

place. Our results provide partial evidence for this potential linkage.

Another noteworthy result is the attitude respondents display when it comes to their

voices reaching policy makers. The survey being considered by the agency, as well as the

likelihood of actual policy adjustments had a positive and a statistically robust effect on vir-

tually all of our analyses. In our context, this is plausible evidence of a “signal mechanism.”

In other words, the more credible the communication vehicle, the more the incentive to com-

municate a signal to encourage agencies to provide the improvement without an actual tax

burden on their end. This result would be consistent with previous evidence in contingent

valuation studies (Carson et al., 1995). Unfortunately, we are unable to completely resolve

this caveat with this, or any survey mechanism, and advise to consider this pattern carefully

when interpreting our results.

Finally, we believe one of the main contributions of this paper is the demonstration

of the potential for further interdisciplinary collaborations in hurricane research, but in

other fields as well. Our study differentiates itself from previous attempts to measure the

value of improving hurricane forecasts by integrating the key insights of both atmospheric

science and the stated preferences literature. To our knowledge, this is the first time such

effort has been implemented for a large-scale survey. The robust findings gives us enough

confidence that the estimated values can be worth considering in analyzing policy options

for future hurricane research fund allocation. We are confident that this multi-disciplinary

(i.e., atmospheric science - economics) approach was necessary for this study, but other

policy-relevant question will greatly benefit from other integrative approaches.

30

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33

A Estimation Tables

This section reports the full estimation tables for the results reported in Section 6.

Table A.1: Full Regression Results for Improvements in Track Forecast

Full Sample Florence Michael

BetaOrder -4.78∗∗∗ -4.79∗∗∗ -4.88∗∗∗ -5.08∗∗∗ -5.12∗∗∗ -5.25∗∗∗ -4.13∗∗ -4.02∗∗ -3.96∗∗

(0.73) (0.72) (0.72) (0.87) (0.85) (0.86) (1.34) (1.33) (1.33)

Rate 0.65 0.65 0.82 0.70 0.49 0.50 0.54 1.14 1.71(0.73) (0.72) (0.72) (0.87) (0.86) (0.87) (1.32) (1.32) (1.32)

Income 0.11∗∗∗ 0.09∗∗ 0.11∗∗ 0.09∗ 0.10 0.08(0.03) (0.03) (0.04) (0.04) (0.06) (0.06)

Female 3.32∗∗ 3.40∗ 2.80 3.11∗ 4.63 4.04(1.29) (1.32) (1.50) (1.55) (2.49) (2.55)

Experience 5.18∗∗ 4.00∗ 5.42∗∗ 4.88∗ 5.79 2.92(1.65) (1.73) (1.84) (1.92) (3.80) (3.97)

Evacuated 3.70∗ 3.21∗ 2.21 1.99 7.05∗ 6.08∗

(1.57) (1.61) (1.85) (1.92) (2.98) (3.00)

Voice 2.71∗∗∗ 2.87∗∗∗ 3.10∗∗∗ 3.26∗∗∗ 1.91 1.96(0.62) (0.63) (0.75) (0.76) (1.12) (1.12)

Action 4.46∗∗∗ 4.50∗∗∗ 4.23∗∗∗ 4.40∗∗∗ 5.01∗∗∗ 4.92∗∗∗

(0.65) (0.66) (0.79) (0.80) (1.17) (1.16)

Long-term Risk 6.70∗∗∗ 10.34∗∗∗ 5.95∗∗ 10.06∗∗ 9.23∗ 11.56∗

Perception (1.81) (2.70) (2.04) (3.14) (3.94) (5.32)

Age 0.01 0.06 -0.08(0.04) (0.05) (0.07)

Owner 2.78∗ 1.43 5.37∗

(1.30) (1.57) (2.35)

Tenure -0.01∗ -0.01 -0.01(0.00) (0.01) (0.01)

Short-risk -5.61 -6.32 -2.28Perception (3.15) (3.63) (6.32)

Hurricane Ins. -0.28 -0.42 0.13Awareness (0.64) (0.77) (1.16)

FEMA 1.01 0.73 1.56Awareness (0.68) (0.80) (1.25)

NFIP -0.32 -0.45 0.01Awareness (0.61) (0.73) (1.13)

Damaged 1.76 0.94 3.70(1.35) (1.75) (2.31)

Household -0.24 0.09 -0.73Size (0.29) (0.38) (0.52)

Constant 42.53∗∗∗ 7.35 6.25 42.94∗∗∗ 8.57 5.77 41.68∗∗∗ 3.55 4.89(2.16) (3.77) (4.41) (2.58) (4.48) (5.31) (3.97) (7.19) (8.14)

SigmaConstant 33.07∗∗∗ 32.16∗∗∗ 32.04∗∗∗ 32.36∗∗∗ 31.35∗∗∗ 31.38∗∗∗ 34.62∗∗∗ 33.89∗∗∗ 33.22∗∗∗

(0.88) (0.85) (0.85) (1.03) (0.99) (1.00) (1.66) (1.63) (1.60)Observations 4650 4644 4581 3150 3148 3117 1500 1496 1464Log-Likelihood -5264.57 -5131.73 -5045.45 -3571.41 -3478.03 -3437.84 -1692.15 -1650.17 -1600.06χ2 43.55 249.74 263.46 34.71 181.48 188.66 9.65 72.89 84.84

Standard errors in parentheses+ p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Notes: The unit of observation is the voting decision made by an individual respondent. The table is split into three panels. Each panel workswith a different sample: all respondents (Full Sample), only those residing in areas affected by Florence (Florence), and only those affected by

Michael (Michael). Each panel reports all elements of β and σ. Sample sizes differ due to missing responses.

34

Table A.2: Full Regression Results for Improvements in Wind Speed Forecast

Full Sample Florence MichaelOrder -4.24∗∗∗ -4.11∗∗∗ -3.93∗∗∗ -4.99∗∗∗ -4.86∗∗∗ -4.64∗∗∗ -2.78∗ -2.73∗ -2.66∗

(0.76) (0.74) (0.75) (0.96) (0.93) (0.94) (1.27) (1.25) (1.26)

Rate 0.46 0.32 0.37 0.40 0.24 0.24 0.52 0.38 0.33(0.76) (0.74) (0.75) (0.95) (0.92) (0.93) (1.26) (1.23) (1.25)

Income 0.10∗∗ 0.10∗∗ 0.11∗∗ 0.12∗∗ 0.06 0.04(0.03) (0.03) (0.04) (0.04) (0.05) (0.06)

Female 1.37 1.46 1.33 1.43 1.66 1.25(1.35) (1.39) (1.64) (1.69) (2.38) (2.46)

Experience 6.23∗∗∗ 5.47∗∗ 5.22∗∗ 5.19∗ 10.38∗∗ 8.12∗

(1.73) (1.82) (2.02) (2.11) (3.63) (3.83)

Evacuated 5.96∗∗∗ 5.48∗∗ 6.17∗∗ 5.74∗∗ 5.26 4.82(1.64) (1.69) (2.03) (2.11) (2.78) (2.85)

Voice 4.26∗∗∗ 4.21∗∗∗ 4.87∗∗∗ 4.71∗∗∗ 3.20∗∗ 3.15∗∗

(0.66) (0.66) (0.83) (0.84) (1.06) (1.08)

Action 4.24∗∗∗ 4.38∗∗∗ 4.53∗∗∗ 4.77∗∗∗ 3.62∗∗∗ 3.64∗∗

(0.68) (0.69) (0.87) (0.88) (1.10) (1.11)

Long-term Risk 3.03 6.33∗ 3.33 6.96∗ 2.73 4.51Perception (1.87) (2.84) (2.21) (3.41) (3.63) (5.22)

Age -0.00 0.04 -0.05(0.04) (0.05) (0.07)

Owner 1.55 -0.60 5.43∗

(1.37) (1.73) (2.26)

Tenure -0.01 0.00 -0.01(0.00) (0.01) (0.01)

Short-term Risk -4.98 -5.58 -2.61Perception (3.32) (3.99) (6.10)

Hurricane Ins. -0.84 -0.86 -0.53Awareness (0.67) (0.84) (1.12)

FEMA 0.83 0.94 0.39Awareness (0.71) (0.88) (1.19)

NFIP 0.17 -0.18 0.92Awareness (0.64) (0.80) (1.08)

Damaged 2.28 1.97 2.57(1.42) (1.91) (2.21)

Household 0.15 0.67 -0.05Size (0.24) (0.43) (0.30)

Constant 38.30∗∗∗ 0.65 -0.71 39.91∗∗∗ 0.25 -3.64 35.35∗∗∗ 0.22 2.05(2.27) (3.98) (4.61) (2.87) (4.97) (5.89) (3.68) (6.74) (7.65)

SigmaConstant 35.07∗∗∗ 33.72∗∗∗ 33.75∗∗∗ 35.80∗∗∗ 34.22∗∗∗ 34.33∗∗∗ 33.71∗∗∗ 32.67∗∗∗ 32.50∗∗∗

(0.96) (0.92) (0.93) (1.23) (1.16) (1.18) (1.55) (1.50) (1.51)Observations 4650 4644 4581 3150 3148 3117 1500 1496 1464Log-Likelihood -5315.78 -5163.08 -5071.72 -3534.25 -3416.75 -3365.09 -1780.16 -1741.95 -1698.36χ2 31.21 278.22 283.58 27.60 210.96 214.35 4.89 69.62 76.89

Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Notes: The unit of observation is the voting decision made by an individual respondent. The table is split into three panels. Each panel workswith a different sample: all respondents (Full Sample), only those residing in areas affected by Florence (Florence), and only those affected by

Michael (Michael). Each panel reports all elements of β and σ. Sample sizes differ due to missing responses.

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Table A.3: Full Regression Results for Improvements in Precipitation Forecast

Full Sample Florence Michael

BetaOrder -5.09∗∗∗ -5.21∗∗∗ -5.26∗∗∗ -4.81∗∗∗ -4.85∗∗∗ -4.95∗∗∗ -5.71∗∗∗ -5.96∗∗∗ -5.75∗∗∗

(0.82) (0.81) (0.81) (1.00) (0.98) (0.99) (1.44) (1.43) (1.44)

Rate 0.27 0.44 0.63 0.04 0.00 0.11 0.75 1.27 1.43(0.81) (0.79) (0.80) (0.99) (0.96) (0.97) (1.41) (1.40) (1.41)

Income 0.06 0.06 0.06 0.07 0.03 0.01(0.03) (0.03) (0.04) (0.04) (0.06) (0.06)

Female 4.10∗∗ 3.68∗ 5.29∗∗ 4.73∗∗ 1.60 0.76(1.44) (1.48) (1.72) (1.77) (2.65) (2.74)

Experience 4.09∗ 4.09∗ 4.15∗ 3.99 5.90 5.86(1.84) (1.93) (2.10) (2.18) (4.07) (4.31)

Evacuated 4.22∗ 3.92∗ 3.38 2.78 5.61 5.83(1.74) (1.79) (2.10) (2.18) (3.09) (3.17)

Voice 4.25∗∗∗ 4.34∗∗∗ 5.07∗∗∗ 5.18∗∗∗ 2.74∗ 2.75∗

(0.70) (0.71) (0.87) (0.88) (1.18) (1.20)

Action 4.83∗∗∗ 4.70∗∗∗ 4.72∗∗∗ 4.51∗∗∗ 5.03∗∗∗ 4.84∗∗∗

(0.73) (0.74) (0.90) (0.92) (1.24) (1.24)

Long-term Risk 1.15 4.70 1.05 5.17 1.10 3.65Perception (1.99) (3.01) (2.31) (3.54) (4.07) (5.80)

Age -0.02 -0.05 0.06(0.04) (0.05) (0.08)

Owner -0.35 -0.73 0.67(1.45) (1.79) (2.50)

Tenure -0.00 0.00 -0.01(0.01) (0.01) (0.01)

Short-term risk -5.13 -6.97 -1.07Preception (3.54) (4.15) (6.79)

Hurricane Ins. -1.44∗ -1.15 -1.46Awareness (0.72) (0.89) (1.25)

FEMA 1.36 1.44 0.95Awareness (0.76) (0.92) (1.34)

NFIP 0.16 -0.59 1.74Awareness (0.69) (0.83) (1.21)

Damaged 1.03 2.79 -0.47(1.51) (1.99) (2.46)

Household 0.25 0.10 0.43Size (0.26) (0.43) (0.34)

Constant 39.31∗∗∗ 3.55 4.90 39.35∗∗∗ 1.45 5.33 39.24∗∗∗ 7.62 4.98(2.41) (4.22) (4.90) (2.96) (5.16) (6.14) (4.15) (7.50) (8.47)

SigmaConstant 37.38∗∗∗ 36.19∗∗∗ 36.21∗∗∗ 37.43∗∗∗ 36.02∗∗∗ 36.07∗∗∗ 37.24∗∗∗ 36.40∗∗∗ 36.22∗∗∗

(1.04) (1.01) (1.01) (1.28) (1.22) (1.23) (1.81) (1.77) (1.77)Observations 4650 4644 4581 3150 3148 3117 1500 1496 1464Log-Likelihood -5495.70 -5343.76 -5261.78 -3710.59 -3597.80 -3547.07 -1784.84 -1741.40 -1705.47χ2 38.63 265.24 272.82 23.27 197.87 204.69 15.80 72.66 79.58

Standard errors in parentheses+ p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Notes: The unit of observation is the voting decision made by an individual respondent. The table is split into three panels. Each panel workswith a different sample: all respondents (Full Sample), only those residing in areas affected by Florence (Florence), and only those affected by

Michael (Michael). Each panel reports all elements of β and σ. Sample sizes differ due to missing responses.

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B Survey Sample

In this section we provide examples of the survey questions used to elicit participants’ willing-ness to pay for proposed hurricane forecast improvements. We use the survey for HurricaneFlorence as demonstration; however, the process is exactly the same for Hurricane Michael.As described in the Survey Implementation section, the unique forecast-improvement com-binations are randomly generated for each participant. In addition, the order of forecastspresentation is randomized for each participant. Each of the following subsections representsone of the three forecast attributes that are used in the survey.

The valuation section is preceded with the following statement, “Please consider theadvisory referendum below. The expected cost to your household may seem high but considerthat hurricane research is costly, as it has to fund both human and physical capital. Whenconsidering how to vote, please bear in mind that there may be other things that you wouldrather spend your money on. Think about your monthly budget and how much, if anything,you are willing to pay before casting your vote. Click the box labeled “I vote Yes” if you arein favor of covering the proposed cost; otherwise, vote “I vote No” if you are against.”

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B.1 Track Forecast

This section describes the combinations for the track forecast attribute. Each participant isfirst provided with a description and image of the current track forecast:

“This image describes the progress in hurricane track forecast. Consider Hurricane Flo-rence. The dotted line describes what would have been the 72-hour predicted landfall regionif we had the same accuracy as in 2008. Over the last decade, forecasts have improved andreduced the track error by about 4.9% annually, and thus allowed us to narrow down thepotential landfall region to the solid line circle. The landfall regions are circles because ofprediction error: while we say the storm will be at a specific location in 72 hours (predictedlandfall), it is equally likely that the storm will be anywhere else in the circle.

Figure B.1: Hurricane Track Forecast

These improvements also mean that the time window for landfall for the 72-hour forecast,using Florence’s actual forward speed as a reference, went form 170 hours in 2008, to about103 hours in 2018. This is equivalent to an improvement of 40% in error reduction overten years.”

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Survey respondents are then presented with one of the three following rate improvementscenarios; status quo, 20% increase, or 20% decrease. The description of these changes is asfollows:

“Proposed changes are expected to reduce the error of track forecast even further. Thisrate of improvement would mean that the 72-hour predicted landfall for Florence, would looklike the figure:

Figure B.2: Hurricane Track Forecast Improvements

1) This level of progress means that the time window for landfall for the 72-hour forecast, us-ing Florence’s actual forward speed as a reference, would go from about 103 hours in 2018,to about [62 hours, 54 hours, 80 hours] in 2028. This is equivalent to an improvementof about [40%, 48%, 32%] in error reduction over the next ten years.

The proposed changes would provide a service to all the U.S. population susceptible to hur-ricanes, and it will increase the taxes your household currently pays. Knowing that it wouldcost your household an extra $(random bid value) each year in additional taxes, howwould you vote?”

The survey respondents answer yes/no to the first bid. Then depending on their re-sponse they are asked a follow up question that either increases or decreases the bid valueby 20%.

2) “If the changes instead cost your household an extra (1.2/0.8) * bid each year in addi-tional taxes, how would you vote?”

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B.2 Wind Speed Forecast

This section describes the combinations for the wind speed forecast attribute. Each partici-pant is first provided with a description and image of the current wind speed forecast:

“This image describes the progress in error reduction for hurricane wind speed forecastover the last decade for a 72-hour forecast. The X-axis represents how far into the futurethe forecast predicts wind speed, while the Y-axis shows the average error associated withthat prediction. The dotted line represents the accuracy in 2008, while the solid linerepresents the accuracy in 2018. Note that that errors increase because predictions fartherinto the future are less precise; in other words, they have larger errors. The closer the linesare to zero, the better the forecast.”

Figure B.3: Hurricane Wind Speed Forecast

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Survey respondents are then presented with one of the three following rate improvementscenarios; status quo, 20% increase, or 20% decrease. The description of these changes is asfollows:

“Proposed changes are expected to reduce the wind speed forecast error even further. Thisrate of improvement would mean that the 72-hour predicted wind speed would have the fol-lowing margins of error:

Figure B.4: Hurricane Wind Speed Forecast Improvements

1) This level of progress means that the error margin for the 72-hour forecast, using Florenceas a reference, would go from about +/- 13 mph in 2018, to about [+/- 9.5, +/- 8.7,+/- 10.4] in 2028. This is equivalent to an improvement of about [29%, 35%, 24%] inerror reduction over the next ten years.

The proposed changes would provide a service to all the U.S. population susceptible to hur-ricanes, and it will increase the taxes your household currently pays. Knowing that it wouldcost your household an extra $(random bid value) each year in additional taxes, howwould you vote?”

The survey respondents answer yes/no to the first bid. Then depending on their re-sponse they are asked a follow up question that either increases or decreases the bid valueby 20%.

2) “If the changes instead cost your household an extra (1.2/0.8) * bid each year in addi-tional taxes, how would you vote?”

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B.3 Rainfall Forecast

This section describes the combinations for the rainfall forecast attribute. Each participantis first provided with a description and image of current rainfall forecasts:

“This image describes the accuracy of a rainfall forecast. Consider Hurricane Florence.The shaded area describes the area susceptible to an underforecast by at least one inch ofrain. In other words, the area that could receive one inch or more of rain than what themodel would predict 72 hours before landfall. Over the last decade, forecasts have improvedand reduced the rain forecast error by about 4.9% annually, and thus allowed us to narrowdown the actual rain significantly. This is equivalent to an improvement of about 40% inerror reduction over ten years.”

Figure B.5: Hurricane Rainfall Forecast

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Survey respondents are then presented with one of the three following rate improvementscenarios; status quo, 20% increase, or 20% decrease. The description of these changes is asfollows:

“Proposed changes are expected to reduce the error in rain forecast even further. Thisimprovement would mean that the 72-hour area susceptible to underforecast for Florence,would look like the figure:”

Figure B.6: Hurricane Rainfall Forecast Improvements

1) “This level of progress means that the area receiving at least one inch of rain over whatwas expected, using Florence as a reference, would go from about 50,300 squared milesin 2018, to about [41,200, 38,100, 43,900 squared miles in 2028. This is equivalent toan improvement of about [18%, 25%, 12% in error reduction over the next ten years.

The proposed changes would provide a service to all the U.S. population susceptible to hur-ricanes, and it will increase the taxes your household currently pays. Knowing that it wouldcost your household an extra $(random bid value) each year in additional taxes, howwould you vote?”

The survey respondents answer yes/no to the first bid. Then depending on their re-sponse they are asked a follow up question that either increases or decreases the bid valueby 20%.

2) “If the changes instead cost your household an extra (1.2/0.8) * bid each year in addi-tional taxes, how would you vote?”

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