Heterogeneous Evacuation Responses to Storm Forecast Attributes Daniel Petrolia* & Sanjoy Bhattacharjee, Department of Agricultural Economics – Mississippi State University Terrill Hanson, Dept. of Fisheries & Allied Aquacultures, Auburn University *Corresponding author:
[email protected] Abstract This paper investigates the variation in the effects of key storm forecast factors on hypothetical evacuation decisions collected from a mail survey using a random-effects probit model with heterogeneity. Results indicate that once heterogeneity is accounted for, wind speed and landfall time are the only two significant storm forecast attributes. Further, through the use of interaction terms between the forecast attributes and individual-specific indicators, the impact of the forecast factors were found to vary significantly across race, gender, the presence of disabled persons, and geography. Experimental Design and Data Introduction A contingent behavior mail survey was developed to collect the required data. We generated 15 hypothetical hurricane scenarios based on four characteristics (factors) of hurricane forecasts, namely Tropical storms are a constant threat to residents living in coastal regions, and with this threat comes the decision of whether to evacuate. Broadly speaking, an evacuation decision is an economic wind speed (85 mph, 121 mph, or 156 mph), storm trend (wind intensity likely to decrease, increase, or remain the same), estimated time to landfall (three or five days), and evacuation notice decision under uncertainty, and the forecast information plays an important role in mitigating those uncertainties. In this study, we investigate the links between four key attributes of a hurricane (mandatory or advisable) (see Table 1). Choice of these four factors was based partially on the findings of previous literature. Additionally, we included the trend factor; we are aware of no previous forecast (wind speed, storm trend, official notice, and time to landfall), individual characteristics, and evacuation choice. study accounting for the impact of variation in this factor on evacuation choice. Finally, we included the three-day versus five-day cone attribute for the reason detailed in the introduction. Key Contributions Realistic Scenarios: This study is based on evacuation choices made under a series of hypothetical forecasts that mimic, visually and content wise, the actual NOAA forecasts made public The experimental design was based on the fractional factorial design rationale (1999). Measures were taken to make the design efficient (D-efficiency) and finally, the design was generated using SAS during the hurricane season. Previous work on evacuation behavior typically has not used graphics to generate evacuation responses, and when they were used (e.g. Baker (1995), the visual aids software (Kuhfeld 2009). To mitigate respondent fatigue, we made three random blocks out of the original fifteen hypothetical hurricane scenarios and randomly assigned one of the three blocks to were not consistent with what people are presented with in reality. Furthermore, in Whitehead et al. (2000), each individual responded to one hypothetical hurricane of a particular category each individual. Table 1: Factor profiles of hypothetical scenarios and mean evacuation response, listed in order of mean evacuation response (last column). Figure 1. Sample Storm Forecast Scenario (randomly chosen from category 1 to category 5). In contrast, our study is based on an individual’s responses to five randomly-assigned scenarios. This design enabled us to gather more Wind information about an individual’s probable reactions to the variations in forecast attributes. Accounts for Individual Heterogeneity: This study explicitly accounts for individual heterogeneity by allowing for interaction between the forecast attributes and individual-specific indicators such as demographics and stated perceptions regarding storm risk and evacuation. In other words, we determine if individuals of different demographic and other strata respond differently to particular aspects of storm forecasts, and if so, how the response rate varies across the sampled population. From a statistical standpoint, failing to do so could result in biased estimates, and from a practical standpoint, in misleading conclusions.
Scenario
Sampling Block Type
14 6 3 15 2 7 11 8 12 1 5 9 13 4 10
3 2 1 3 1 2 3 2 3 1 1 2 3 1 2
NHC 5-Day Cone: The National Hurricane Center (NHC) recently developed a “5-day cone”, a map showing a storm’s forecasted path and forward speed five days prior to landfall. Visually, the 5-day cone is just an extension of the 3-day cone previously in use. Theoretically, a five-day pre-landfall forecast allows more time to prepare for the pending storm relative to a three-day prelandfall forecast. However, if an individual thinks that the longer-term forecast is more likely to deviate from the storm’s actual path than will a forecast of a shorter duration, or that five days is simply too early to make any decision, then producing those two extra days of forecast may be of little practical value. In a recent study, Letson et al. (2007) found that, in spite of the fact that a great amount of economic value is presumed to be attached to a hurricane forecast and in its improvement, very little work has been done to verify if this is indeed true. Thus, a secondary objective of this work is to ascertain the difference in evacuation behavior given a 5-day cone relative to a 3-day cone. Survey Sample The survey generated 531 responses (a 30% response rate after adjusting for undeliverables), out of which 314 returned after the first mailing and 218 returned after the replacement mailing. Surveys were sent to 2,000 residents randomly selected across all or part of four Gulf of Mexico States: Alabama, northwest Florida, southeast Louisiana, and Mississippi, with greater sampling weight given to coastal counties. In particular, 66% of the 2,000 households sampled were taken from the first two counties inland from the water’s edge for all four states. Surveys were mailed during the first week of August 2008. We planned to send the reminder letter two weeks after the first mailing; however, there was a two-week delay because of landfall of Hurricanes Gustav and Ike during that period. Incidentally, this event introduced another testable hypothesis into the analysis: whether hypothetical survey choices were affected by the simultaneous occurrence of actual storm events. Thus, a dummy variable “2nd Survey” was introduced to capture any effects of having responded to the survey after Hurricanes Gustav and Ike versus prior. Replacement surveys were sent during the last week of September and the first week of October. Table 2. Mean evacuation response (MER) across forecast
factors, and results of pairwise z-tests of proportions. Pairwise z-test of proportions Factor (X) MER # Obs Mean Evacuation Response (MER) Table 2 contains the results of unconditional mean evacuation responses (MER) under each forecast factor, and Landfall 3 days 0.39 1373 the results of pairwise z-tests of proportions. MER was ten percent higher for the three-day landfall scenario relative to the five-day, and this difference was found to be significant. MER was three percent higher under 5 days 0.29 1163 z = 5.33*** mandatory evacuation notice relative to an advisable notice, and this was significant at the p = 0.05 level. For Notice wind speed, fifty-one percent of respondents indicated that they would evacuate with 156 mph wind speed, which Advisable 0.33 1367 is twenty percent higher than that for 121 mph, and thirty-two percent higher than that for 85 mph wind speed. Mandatory 0.36 1169 z = -1.97** All three pairwise z-tests were significant at the p = 0.01 level. For storm trend, thirty-seven percent of Wind Speed respondents indicated that they would evacuate under a decreasing or increasing storm trend, which was eight 85 vs. 121 : z = -5.252*** 85 mph 0.19 654 percent higher than that when the storm trend was forecast to remain the same. The pairwise z-tests were 85 vs. 156: z = -12.564*** 121 mph 0.31 1023 significant for decreasing and increasing relative to remains same, respectively. As the far-right column of table 121 vs. 156: z = -8.797*** 156 mph 0.51 859 1 shows, wind speed was the dominant forecast factor: the five scenarios with the highest mean evacuation responses (MER) were those with the highest wind speed level. Among those top five, those with a mandatory Trend Decreasing vs. Remains same: evacuation notice had the higher MERs, and among those top five, those with a three-day landfall had higher z = 3.599*** Decreasing 0.37 714 MERs relative to those with a five-day landfall. There appears to be no clear relationship for the storm trend Decreasing vs. Increasing: factor. z = -0.136 Remains same 0.29 838 Increasing vs. Remains same: Table 3. Variable specifications, frequencies, means and standard deviations. z = -4.018*** Increasing 0.37 984 Variable Variable Type and Description Freq. Mean Dev ***, ** significant at p = 0.01 and 0.05, resp. Landfall
= 3 if 3-day landfall scenario; = 5 if 5-day landfall
2655
3.92
1.00
Notice Wind
=1 if mandatory evac notice scenario; = 0 if voluntary Scenario wind speed (85, 121, or 156 mph)
2655 2655
0.46 123.63
0.50 27.24
Trend
= -1 if storm trend is decreasing; = 0 if constant; = 1 if increasing Respondent age (in years) Yes=1, No=0 Physically disabled at home: Yes = 1, No = 0
2655
0.11
0.81
510 506 513
56.14 0.11 0.14
14.30 0.32 0.35
landfall distance (in miles) th th th < 9 grade = 1; 9 -12 grade, no diploma = 2; H.S. diploma = 3; some college = 4; associate’s deg. = 5; bachelor's deg. = 6; grad./prof. deg. = 7
531 520
76.01 4.67
54.16 1.58
Female = 1, Male = 0 Number of members of household
515 531
0.45 2.56
0.50 1.40
482
0.90
0.30
Age Black Disabled Distance Education
Female HH Size Job
Job allowed to leave if evacuation chosen: Yes = 1, No =0 Ln(Income) Natural Log of Reported Annual Income Category Mean Mobile Live in a mobile home: Yes = 1, No = 0
439
1.63
0.51
518
0.09
0.29
Order Past Evacs
531 531
3.00 0.98
1.41 1.35
Relative placement of scenario in order (1 - 5) Number of times individual evacuated for selected hurricanes (Camille, Elena, Georges, Ivan, Katrina, Rita, Gustav, Ike) Pets Pets owned: Yes = 1, No = 0 Place to Go Do they already have a specific place in mind to which they would evacuate for a storm : Yes = 1, No = 0 Protest = 1 if respondent indicated that survey did not capture most of their hurricane decision-making process; = 0 otherwise Rescued
2nd Survey
Confident that they would be rescued if needed be: Very confident = 3, Somewhat Confident = 2, Not At All Confident = 1 = 1 if replacement survey (post-Ike and Gustav); = 0 otherwise
517 530
0.60 0.60
0.49 0.49
508
0.15
0.36
501
2.07
0.74
531
0.40
0.49
Empirical Model of Evacuation We model an individual’s evacuation decision using Random Utility theory (McFadden 1973), where an individual chooses to evacuate under a given scenario if and only if the expected utility associated with evacuating is greater than that of not evacuating. Additionally, we assume that an individual’s evacuation decision is a function of a vector of independent variables, which includes forecast attributes, individual-specific indicators, and survey-design variables. The controlled (scenario-specific) storm forecast attributes were landfall time, type of official evacuation notice issued, storm wind speed, and storm trend. When a household has more than one member, individual will be considered as the decision maker for the entire household; thus the set of individual-specific indicators actually comprises two sets of variables: individual characteristics of the respondent such as age, race, education level, and gender; and household characteristics such as household income level, the presence of a disabled person in the household, household size, whether a member of the household has a job that does not allow time off to evacuate, whether the home is a mobile home, the presence of pets in the home, whether the household has a specific evacuation destination already identified, and confidence of the household in being rescued. Additionally, we include a continuous distance variable, which is the Euclidean distance between residents’ location (by zip code) and the landfall point (e.g., the point where the hypothetical track in Figure 1 touches the shoreline of the State of Mississippi). Respondents were asked to consider each storm scenario independently, regardless of their response to the other scenarios. However, because respondents were asked to evaluate five different scenarios in the same survey, it is likely that the errors across the individual choices were correlated. To address this possibility, we structured the data as a panel and adopted a random-effects probit model (Greene 2000). Table 3 summarizes all of the variables used to predict evacuation choice. In order to capture in some way the heterogeneous effects of the storm forecast attributes, we constructed interaction terms for each forecast attribute with each individual-specific indicator. To mitigate sample bias, we introduced a weight variable to the likelihood function. Race was chosen as the weighting variable because it was highly correlated with the other demographic variables, and because this variable had relatively more observations than the income variable (the other leading candidate). The practical result was that contributions to the likelihood function by observations from minorities were inflated and those of whites were deflated. Specifically, the likelihood function was weighted using weights of 2.47 for observations from minorities and 0.75 for observations from whites. All models were estimated using NLOGIT 4.0 .
Speed of impending storm (mph) 156 156 156 156 156 121 121 121 121 121 85 85 121 85 85
Storm Trend (change in wind intensity)
Landfall time (days)
Evacuation notice
Mean Evacuation Response
Decreasing Remains same Increasing Increasing Decreasing Increasing Decreasing Increasing Remains same Remains same Increasing Increasing Decreasing Remains same Remains same
3 3 3 5 5 3 3 5 3 5 3 5 5 3 5
Mandatory Mandatory Advisable Advisable Advisable Advisable Mandatory Mandatory Advisable Mandatory Mandatory Mandatory Advisable Advisable Advisable
0.553 0.552 0.549 0.464 0.414 0.405 0.344 0.329 0.309 0.290 0.273 0.192 0.173 0.149 0.136
After a common introduction explaining these four factors and their respective levels, each respondent was presented five scenarios to evaluate. Accompanying each scenario was a graphic map with a legend stating the hurricane conditions (see Figure 1 for a sample question). The purpose of the visual aid is to help the respondent assess the location of their home relative to the landfall point and storm path. The respondent was asked whether they would evacuate under each scenario. Additionally, we collected data on age, race, gender, education, household size, home type, income level, job flexibility (in case of evacuation), distance from the forecasted landfall point, presence of disabled persons or pets, household size (a proxy for the presence of children), whether the respondent has a particular destination already in mind should the need to evacuate arise, and previous evacuation experience. Additionally, we collected data indicating respondents’ confidence in being rescued Table 4. Random-Effects Probit estimates with and without hetergeneity. should they choose not to evacuate. Model without Heterogeneity Model with Heterogeneity Results Table 4 contains the results of the random-effects probit models for evacuation choice. Note that for Marg. Marg. both models, the rho coefficient is highly significant, indicating the presence of correlated errors across Variable Eff. ^ Eff. ^ S.E. P[|Z|>z] S.E. P[|Z|>z] Mean Coef. Coef. Landfall -0.340 *** 0.065 0.000 -0.135 -0.257 *** 0.047 0.000 -0.101 3.919 individuals, and thus justifying the use of the random-effects model. Under the non-heterogeneity model specification, all four of the storm forecast attributes are significant, with the marginal effects indicating that a 5-day landfall time reduces the probability of a decision to evacuate by 10% relative to a 3-day landfall time. A mandatory evacuation notice increases the probability of evacuation by 12%, a 50-mile increase in wind speed increases the probability of evacuation by 30% (multiply marginal effect of 0.006 by 50), and a change in storm trend from negative to constant (or constant to increasing) increases the probability of evacuation by 2%. Regarding individual-specific indicators, being African-American/Black increased likelihood of evacuation by 43%, the presence of a disabled person in the home increased probability of evacuation by 24%, and having an evacuation destination in mind increased probability by 20%. Other significant indicators included education level (negative effect), having already evacuated for past major storms (positive effect), being a “protest” respondent (negative effect), and responding to the replacement survey (negative effect). The heterogeneous model is an improvement based on McFadden’s Pseudo R2 and BIC criteria, although the AIC ranks the non-hetergeneous model slightly better. In this model specification, only two of the storm forecast attributes are significant: Landfall and Speed. Additionally, the Black, Disabled, and Past Evacs variables are no longer significant. However, the interaction terms associated with these variables are significant. The Landfall x Black interaction term is significant and positive, indicating that Blacks are 4% more likely to choose to evacuate under the 5-day notice relative to the 3-day notice than are non-Black households. Although the storm trend attribute is not significant in the heterogeneous model, the interaction term Trend x Disabled is highly significant, indicating that homes with disabled persons are 7% more likely to evacuate if the storm trend is to increase in strength. Thus, results indicate that the effect of changes in storm trend influence evacuation decisions of disabled households, but not necessarily non-disabled households. With regard to the Notice attribute, results indicate that when interaction terms are included, the significance that was initially attributed to Notice is shifted to the Notice x Female interaction term. Thus, results indicate that although the overall evacuation decisions of females are not significantly different from that of males (as indicated by the non-significance of the Female variable), the type of official notice (mandatory vs. voluntary) does in fact affects the decisions of females differently than males, increasing the probability of evacuation by a female under mandatory evacuation notice by 18%. Similar results hold for the Speed forecast indicator. The significance originally attributed to the wind speed forecast attribute alone is shifted partially to the four Speed interaction terms. Note also that with the inclusion of interaction terms, the Distance, Mobile, Past Evacs, and Pets variables become significant. These must be interpreted in tandem with the interaction terms. Results indicate that households with pets are 38% less likely to evacuate, but that the probability is mitigated somewhat as the storm’s wind speed increases (as indicated by a positive coefficient on the interaction term). The model without interaction terms predicted no significant relationship between pet ownership and evacuation choice. Regarding distance from the landfall point, results indicate that the probability of evacuation increases the further one lives from the coast. This may appear counter-intuitive, but keep in mind that this is controlling for all other factors. This may indicate simply that, all else constant, those living further from the coast and thus less-familiar with tropical storms and evacuation are more likely to vote in favor of evacuation under a hypothetical scenario. This is consistent with the negative sign on the Speed x Distance interaction term, indicating that the relatively lower probability of evacuation among respondents nearer to landfall is mitigated as wind speed increases. Results indicate that respondents living in mobile homes are 50% more likely to evacuate; however, this relative difference between mobile and non-mobile home respondents is mitigated somewhat by increases in wind speed (as indicated by the negative sign on the interaction term). Finally, the heterogeneous model indicates that having evacuated in the past is not a significant factor explaining the probability of evacuation, but that when interacted with wind speed, it is.
Notice 0.135 0.161 0.403 0.029 0.495 *** 0.106 Wind 0.031 *** 0.008 0.000 0.005 0.033 *** 0.002 Trend 0.015 0.146 0.916 0.003 0.140 * 0.075 Age 0.008 0.010 0.418 0.001 0.006 0.009 Black 0.467 0.886 0.598 0.122 1.315 *** 0.271 Disabled 0.291 0.576 0.614 0.071 0.788 ** 0.367 Distance 0.011 ** 0.004 0.015 0.002 -0.001 0.002 Education -0.252 *** 0.083 0.003 -0.041 -0.243 *** 0.080 Female -0.189 0.263 0.474 -0.041 0.138 0.217 HH Size 0.099 0.095 0.296 0.016 0.088 0.089 Job 0.159 0.403 0.693 0.032 0.226 0.376 Mobile 1.525 * 0.867 0.079 0.504 -0.253 0.365 Order 0.036 0.042 0.395 0.006 0.047 0.039 Past Evacs 0.067 0.232 0.772 0.011 0.460 *** 0.084 Pets -1.460 *** 0.541 0.007 -0.375 -0.301 0.238 Place to Go 1.057 *** 0.248 0.000 0.206 0.945 *** 0.231 Protest -0.776 ** 0.338 0.022 -0.122 -0.746 ** 0.317 Rescued -0.605 0.398 0.128 -0.098 -0.199 0.147 2nd Survey -0.435 * 0.240 0.070 -0.089 -0.434 * 0.223 Landfall x Black 0.189 * 0.115 0.099 0.042 Landfall x Disabled 0.119 0.110 0.283 0.026 Notice x Black 0.082 0.284 0.773 0.019 Notice x Female 0.678 *** 0.212 0.001 0.181 Wind x Black 0.001 0.005 0.756 0.000 Wind x Distance -0.003 *** 0.001 0.001 0.000 Wind x Mobile -0.014 ** 0.006 0.020 -0.002 Wind x Past Evacs 0.004 ** 0.002 0.036 0.001 Wind x Pets 0.009 ** 0.004 0.013 0.001 Wind x Rescued 0.003 0.003 0.314 0.000 Trend x Disabled 0.421 *** 0.162 0.009 0.068 Trend x Distance 0.027 0.035 0.445 0.004 Trend x Mobile -0.126 0.206 0.540 -0.020 Constant -3.586 ** 1.454 0.014 -4.506 *** 1.015 Rho 0.786 *** 0.025 0.000 0.765 *** 0.026 # Observations 2,136 2,136 # Individuals in Panel 433 433 Log-Likelihood -864.0 -892.5 0.227 0.2178 McFadden Pseudo R-sq AIC 0.842 0.856 BIC 0.935 0.9147 ***,**,* Significant at 1%, 5%, and 10% levels, resp. ^ MEs for binary variables calculated as ΔF(z) = F(β'x+αz | z = 1) - F(β'x+αz | z = 0).
0.000 0.000 0.064 0.486 0.000 0.032 0.487 0.002 0.527 0.325 0.547 0.489 0.232 0.000 0.206 0.000 0.019 0.177 0.052
0.117 0.006 0.024 0.001 0.434 0.235 0.000 -0.041 0.032 0.015 0.047 -0.052 0.008 0.078 -0.072 0.198 -0.128 -0.034 -0.095
0.000 -0.767 0.000
0.461 123.711 0.105 54.390 0.106 0.126 76.035 4.757 0.434 2.602 0.904 0.094 2.999 0.989 0.623 0.605 0.143 2.057 0.376 0.417 0.493 0.049 0.195 13.110 413.951 11.484 121.640 77.287 254.673 0.007 0.427 0.014 1.000
Caveats Our results are based on a sample of 2,136 observations collected from 433 individuals across the four Gulf of Mexico states, and our survey had a response rate of 30%; thus around two-thirds of the possible respondents did not contribute their opinions, and thus our results may not necessarily be representative of the general public. The demographic deviations in our sample relative to the population only confirm this weakness. Furthermore, our results are based on stated-choice data, i.e., on the expected choices of individuals under hypothetical conditions, which may not be representative of actual choices made during actual storm events, although there is evidence that stated and actual evacuation behavior are consistent (Whitehead 2005). Further, we readily admit that the use of demographic information captures only a fraction of the total heterogeneity present; however, given the topic of interest, storm evacuation behavior, these results may go a long way in improving the approaches taken in targeting hurricane forecast information to the public and in better influencing evacuation choices. References cited available from authors upon request.
