WEBVTT 00:00:00.000 --> 00:00:02.240 Imagine standing right there, witnessing the 00:00:02.240 --> 00:00:05.320 raw power of a hurricane. The wind. The rain. 00:00:05.679 --> 00:00:08.019 It's terrifying. You brace. Maybe you even leave. 00:00:08.199 --> 00:00:11.199 Then it passes. And that roar turns into this 00:00:11.199 --> 00:00:15.660 eerie quiet. Just dripping water, creaking wood. 00:00:15.759 --> 00:00:17.579 You walk outside, see the splintered homes, the 00:00:17.579 --> 00:00:20.160 trees ripped up. Nature's force. Undeniable. 00:00:21.059 --> 00:00:23.359 But underneath all that visible destruction, 00:00:23.899 --> 00:00:26.420 something else starts. A different kind of disaster. 00:00:26.760 --> 00:00:29.910 The human disaster. And it's insidious, you know, 00:00:30.269 --> 00:00:32.329 it exposes these deep cracks in society, the 00:00:32.329 --> 00:00:35.229 inequalities that were already there. Welcome 00:00:35.229 --> 00:00:37.250 to Meteorology Matters. This is the podcast where 00:00:37.250 --> 00:00:39.390 we really dig into how major weather events shape 00:00:39.390 --> 00:00:41.509 our lives, our communities. Today we're taking 00:00:41.509 --> 00:00:43.229 a hard look at hurricanes, how these natural 00:00:43.229 --> 00:00:46.520 events act like... giant magnifying glasses. 00:00:46.880 --> 00:00:48.759 They bring these hidden fault lines, these social 00:00:48.759 --> 00:00:51.000 and economic divides, right into sharp focus. 00:00:51.500 --> 00:00:53.060 We'll be looking specifically at some big storms 00:00:53.060 --> 00:00:55.299 in Florida and Louisiana and honestly uncovering 00:00:55.299 --> 00:00:57.420 patterns that might really surprise you, might 00:00:57.420 --> 00:00:59.320 challenge how you think about disaster recovery. 00:00:59.479 --> 00:01:02.060 I'm genuinely curious to unpack all the research 00:01:02.060 --> 00:01:05.480 we've got today, hoping we can give you a truly 00:01:05.480 --> 00:01:08.920 well -informed perspective. And we really do 00:01:08.920 --> 00:01:11.439 have some incredibly compelling research findings 00:01:11.439 --> 00:01:13.219 to get into. It's fascinating stuff. We'll touch 00:01:13.219 --> 00:01:16.659 on everything from the subtle shifts in population 00:01:16.659 --> 00:01:19.959 after a storm to the really stark differences 00:01:19.959 --> 00:01:22.420 in how quickly different groups bounce back, 00:01:22.420 --> 00:01:24.459 and even the psychology behind it all, how people 00:01:24.459 --> 00:01:27.359 perceive risk, or maybe more accurately, how 00:01:27.359 --> 00:01:29.590 they often underestimate it. even facing these 00:01:29.590 --> 00:01:31.969 huge forces. It's a pretty comprehensive look, 00:01:32.010 --> 00:01:34.230 trying to connect the dots between the weather 00:01:34.230 --> 00:01:36.010 itself and the bigger social structures that 00:01:36.010 --> 00:01:38.430 determine how resilient we actually are. Absolutely. 00:01:38.510 --> 00:01:40.569 It's where meteorology meets society, right? 00:01:40.569 --> 00:01:42.909 That intersection. So definitely stay with us 00:01:42.909 --> 00:01:45.310 for this important discussion. And before we 00:01:45.310 --> 00:01:46.810 dive in, just a quick reminder, please like, 00:01:46.870 --> 00:01:48.750 follow, comment, and rate Meteorology Matters 00:01:48.750 --> 00:01:50.310 wherever you get your podcasts. It really does 00:01:50.310 --> 00:01:54.230 help us reach more people. OK, so let's start 00:01:54.230 --> 00:01:57.140 unpacking this by looking back. Hurricane Andrew 00:01:57.140 --> 00:02:00.319 1992, a really powerful storm that just tore 00:02:00.319 --> 00:02:02.620 through South Dade County in Florida. A lot of 00:02:02.620 --> 00:02:04.560 people remember Andrew for its sheer force, right? 00:02:04.579 --> 00:02:06.900 A category five monster, flattened neighborhoods, 00:02:07.219 --> 00:02:09.460 changed the landscape forever. But the research 00:02:09.460 --> 00:02:11.419 from back then, it paints this vivid picture. 00:02:11.780 --> 00:02:14.240 The aftermath revealed this deep, underlying 00:02:14.240 --> 00:02:17.280 human disaster, one rooted right in the social 00:02:17.280 --> 00:02:19.860 fabric of South Florida. Oh, absolutely. Andrew 00:02:19.860 --> 00:02:23.080 was a beast. Meteorologically speaking, landfall 00:02:23.080 --> 00:02:25.849 with winds, what, 165 miles per hour. one of 00:02:25.849 --> 00:02:28.409 the strongest ever to hit the U .S. And the immediate 00:02:28.409 --> 00:02:32.590 aftermath, just picture it, pure chaos. Neighborhoods 00:02:32.590 --> 00:02:35.789 just gone, rubble everywhere, debris for miles, 00:02:36.110 --> 00:02:38.849 and the basics of power, water, phones, completely 00:02:38.849 --> 00:02:41.289 wiped out. The scale of it was just overwhelming. 00:02:41.789 --> 00:02:43.770 And faced with that, many residents felt this 00:02:43.770 --> 00:02:46.250 profound frustration, and crucially, this feeling 00:02:46.250 --> 00:02:48.530 of being treated differently by the government 00:02:48.530 --> 00:02:51.349 response. They genuinely believed that the federal 00:02:51.349 --> 00:02:53.969 help, the relief efforts, the attention, it was 00:02:53.969 --> 00:02:56.740 slower for them. For a largely minority working 00:02:56.740 --> 00:02:58.719 class area, slower than it would have been for, 00:02:58.719 --> 00:03:01.199 say, wealthier or wider communities elsewhere. 00:03:01.560 --> 00:03:03.400 And that feeling, whether it was perfectly accurate 00:03:03.400 --> 00:03:05.419 or not, it was real. It drove a lot of anger. 00:03:05.840 --> 00:03:07.520 That feeling, that sense of being overlooked, 00:03:07.680 --> 00:03:10.039 it's so key to understanding the long game here. 00:03:10.539 --> 00:03:12.199 And what's really striking is how the storm, 00:03:12.199 --> 00:03:14.099 yeah, it wiped out buildings, but it didn't wipe 00:03:14.099 --> 00:03:16.919 out the old problems, the political fights, the 00:03:16.919 --> 00:03:19.479 economic struggles, the social division. No, 00:03:19.479 --> 00:03:21.419 not at all. In fact, it's kind of highlighted 00:03:21.419 --> 00:03:26.069 them. Maybe you've made them worse. Andrew created 00:03:26.069 --> 00:03:28.810 this. You could call it a clean slate, physically. 00:03:29.169 --> 00:03:31.629 Almost everything needed rebuilding. But those 00:03:31.629 --> 00:03:34.150 underlying issues that South Dade had before 00:03:34.150 --> 00:03:37.310 Andrew, they just continued. Mostly behind closed 00:03:37.310 --> 00:03:38.930 doors, you know, the decisions about who gets 00:03:38.930 --> 00:03:41.030 what, how things get rebuilt. Like a few people 00:03:41.030 --> 00:03:43.409 were asking out loud, did folks have enough insurance 00:03:43.409 --> 00:03:46.409 to rebuild? Or maybe more telling. Why could 00:03:46.409 --> 00:03:48.909 some people just pick up and move north to Broward 00:03:48.909 --> 00:03:52.189 or Palm Beach, while others just couldn't? They 00:03:52.189 --> 00:03:54.370 were basically trapped in the disaster zone. 00:03:54.439 --> 00:03:56.740 These were critical questions that just weren't 00:03:56.740 --> 00:03:58.840 really asked in the mainstream recovery talk. 00:03:59.280 --> 00:04:01.740 And that silence says a lot. And that really 00:04:01.740 --> 00:04:04.520 forces us to look at Miami's incredibly complex 00:04:04.520 --> 00:04:07.000 social makeup. You have to understand that to 00:04:07.000 --> 00:04:10.300 grasp Andrew's unequal hit, Miami's demographics, 00:04:10.360 --> 00:04:12.340 they're really unique, very different from the 00:04:12.340 --> 00:04:14.780 usual sort of black -white binary you see in 00:04:14.780 --> 00:04:17.540 a lot of the U .S. So many residents are first 00:04:17.540 --> 00:04:20.279 or second generation immigrants. Each group has 00:04:20.279 --> 00:04:22.779 its own history, its own economic standing, its 00:04:22.779 --> 00:04:25.680 own culture, and getting these distinctions right 00:04:25.680 --> 00:04:27.879 is vital for understanding the research. For 00:04:27.879 --> 00:04:30.800 instance, Cubans in the research, they often 00:04:30.800 --> 00:04:33.339 identify by nationality, not just broadly as 00:04:33.339 --> 00:04:36.660 Latino or Hispanic. It's tied to their specific 00:04:36.660 --> 00:04:39.430 migration story, political exile. OK. Then you 00:04:39.430 --> 00:04:42.170 have Anglos. That term refers to the non -Latino, 00:04:42.230 --> 00:04:44.170 non -black population. And it's distinct because 00:04:44.170 --> 00:04:47.730 many Cubans identify as racially white, but ethnically 00:04:47.730 --> 00:04:51.410 separate from Anglos. Then Haitians, specifically 00:04:51.410 --> 00:04:53.670 immigrants from Haiti, often facing unique language 00:04:53.670 --> 00:04:56.529 issues, discrimination, and African -Americans, 00:04:56.709 --> 00:04:58.810 native -born people of color with that long history 00:04:58.810 --> 00:05:01.110 of civil rights struggles here. So when the research 00:05:01.110 --> 00:05:03.329 uses the umbrella term black, it's usually covering 00:05:03.329 --> 00:05:05.370 African -Americans, Haitians, and maybe other 00:05:05.370 --> 00:05:07.370 people of color from the Caribbean. Gotcha. These 00:05:07.370 --> 00:05:10.069 aren't just labels. They matter because historical 00:05:10.069 --> 00:05:13.029 tensions, patterns of discrimination, they ran 00:05:13.029 --> 00:05:15.649 right along these lines. And that deeply affected 00:05:15.649 --> 00:05:18.649 how people experienced Andrew and how they recovered. 00:05:19.649 --> 00:05:21.930 That detailed breakdown is so important. It's 00:05:21.930 --> 00:05:25.490 not a monolith. And to really drive home these 00:05:25.490 --> 00:05:27.810 divisions, we probably need to talk about Nelson 00:05:27.810 --> 00:05:30.910 Mandela's visit to Miami. Controversial visit, 00:05:31.490 --> 00:05:35.050 1990, just two years before Andrew hit. Oh yeah, 00:05:35.689 --> 00:05:37.410 oh something. It wasn't just politics, it was 00:05:37.410 --> 00:05:40.050 like a social stress test for the city. Mandela 00:05:40.050 --> 00:05:41.910 was welcomed almost everywhere else in the U 00:05:41.910 --> 00:05:45.430 .S., right? Celebrated, icon of freedom, but 00:05:45.430 --> 00:05:48.949 Miami. these deep ethnic tensions just exploded 00:05:48.949 --> 00:05:51.569 into public view. You had Cubans, largely the 00:05:51.569 --> 00:05:53.930 conservative exile community, protesting really 00:05:53.930 --> 00:05:56.430 strongly because of his perceived ties to Fidel 00:05:56.430 --> 00:05:58.930 Castro. Signs saying things like, no friend of 00:05:58.930 --> 00:06:00.870 Castro is welcome here. Right, I remember that. 00:06:01.000 --> 00:06:03.139 Meanwhile, African -American, Miamians, Haitian 00:06:03.139 --> 00:06:05.000 immigrants, they rallied for him, saw him as 00:06:05.000 --> 00:06:07.660 this powerful symbol of black pride, you know, 00:06:07.759 --> 00:06:09.800 hope against oppression. Total upsets. Yeah. 00:06:10.139 --> 00:06:11.939 And one African -American leader said something 00:06:11.939 --> 00:06:14.819 powerful like Miami might go down as the only 00:06:14.819 --> 00:06:17.800 city in America that denounced and threw its 00:06:17.800 --> 00:06:20.970 welcome mat in the face of Nelson Mandela. This 00:06:20.970 --> 00:06:23.290 isn't just some historical footnote. It shows 00:06:23.290 --> 00:06:26.230 that Miami was actually the most residentially 00:06:26.230 --> 00:06:28.410 segregated major city in the U .S. through the 00:06:28.410 --> 00:06:31.910 80s. In a social landscape that fractured disasters, 00:06:32.829 --> 00:06:35.129 historically often made things worse. They divided 00:06:35.129 --> 00:06:37.610 South Floridians even more, didn't really unite 00:06:37.610 --> 00:06:40.240 them for recovery. And this brings us to a really 00:06:40.240 --> 00:06:42.019 useful way to think about these differences. 00:06:42.279 --> 00:06:45.300 Pierre Bourdieu's idea of capital. It's a sociological 00:06:45.300 --> 00:06:47.240 concept. And it's not just about money, right? 00:06:47.240 --> 00:06:49.620 He talks about three kinds. Economic capital, 00:06:49.620 --> 00:06:51.500 that's the straightforward one. Control over 00:06:51.500 --> 00:06:54.259 money, resources. Then social capital, that's 00:06:54.259 --> 00:06:56.699 your networks, your relationships, who you know, 00:06:56.779 --> 00:06:59.959 the favors you can call in. And cultural capital, 00:06:59.959 --> 00:07:02.160 this is more subtle. It's your knowledge, your 00:07:02.160 --> 00:07:04.300 skills, education, how you talk, how you navigate 00:07:04.300 --> 00:07:07.100 society, understand institutions, wield influence. 00:07:08.240 --> 00:07:10.839 types of capital, they massively influenced how 00:07:10.839 --> 00:07:13.399 different groups weathered the storm and maybe 00:07:13.399 --> 00:07:15.899 more importantly, how they navigated the really 00:07:15.899 --> 00:07:18.540 complex aftermath of Andrew. That's where it 00:07:18.540 --> 00:07:20.959 gets really clear applying that framework. You 00:07:20.959 --> 00:07:23.740 see this stark difference in South Florida, those 00:07:23.740 --> 00:07:26.000 early Cuban immigrants, the ones often called 00:07:26.000 --> 00:07:29.750 the Golden Exiles. They arrived, generally speaking, 00:07:29.889 --> 00:07:32.009 with pretty high levels of economic and social 00:07:32.009 --> 00:07:34.649 capital. Relatively speaking, yes. Many were 00:07:34.649 --> 00:07:37.189 educated professionals, business owners back 00:07:37.189 --> 00:07:40.769 in Cuba, and they quickly built this powerful, 00:07:41.230 --> 00:07:43.850 kind of self -sufficient enclave in Miami, their 00:07:43.850 --> 00:07:46.949 own businesses, jobs, strong internal networks. 00:07:47.230 --> 00:07:48.930 Mm -hmm. Little Havana being the most famous 00:07:48.930 --> 00:07:51.329 example. Right. And this let them handle economic 00:07:51.329 --> 00:07:54.110 bumps, create support systems, even challenge 00:07:54.110 --> 00:07:56.689 the established angle power structure over time. 00:07:57.370 --> 00:08:00.089 And crucially, this capital translated directly 00:08:00.089 --> 00:08:02.129 into practical advantages for something like 00:08:02.129 --> 00:08:04.620 Andrew. They could afford better housing. more 00:08:04.620 --> 00:08:07.259 resilient housing. They had access to good insurance, 00:08:07.779 --> 00:08:10.399 often top -tier companies. That's a level of 00:08:10.399 --> 00:08:13.160 preparedness and post -disaster leverage that 00:08:13.160 --> 00:08:15.779 others just didn't have. And you contrast that 00:08:15.779 --> 00:08:18.259 sharply with African -American and Haitian communities. 00:08:18.639 --> 00:08:20.939 They were largely pushed into, what the research 00:08:20.939 --> 00:08:23.740 terms, communities of fate. Immunities of fate, 00:08:24.040 --> 00:08:26.259 meaning? Meaning groups kind of bound together 00:08:26.259 --> 00:08:28.800 by shared vulnerability and, frankly, limited 00:08:28.800 --> 00:08:31.800 options. Often because of a long, painful history 00:08:31.800 --> 00:08:35.639 of systemic discrimination, redlining, deep residential 00:08:35.639 --> 00:08:38.320 segregation, they were systematically denied 00:08:38.320 --> 00:08:41.100 access to capital, financial, social, you name 00:08:41.100 --> 00:08:43.279 it. So it wasn't just bad luck where they live? 00:08:43.360 --> 00:08:45.840 No, not at all. It meant they lacked the money, 00:08:46.080 --> 00:08:48.379 including enough homeowners insurance, to move 00:08:48.379 --> 00:08:50.659 to safer areas, higher ground. It wasn't just 00:08:50.659 --> 00:08:53.279 personal choice. Historical discrimination was 00:08:53.279 --> 00:08:55.879 baked in. Research clearly shows real estate 00:08:55.879 --> 00:08:58.620 agents would actively steer black residents away 00:08:58.620 --> 00:09:00.600 from white neighborhoods. Treating segregated 00:09:00.600 --> 00:09:03.539 areas. Exactly, de facto segregation. And in 00:09:03.539 --> 00:09:06.059 those areas, property values were lower and, 00:09:06.059 --> 00:09:08.100 critically, housing quality was often worse. 00:09:08.399 --> 00:09:10.840 So the market itself, through these biased practices, 00:09:10.830 --> 00:09:13.529 and historical wrongs, it systematically made 00:09:13.529 --> 00:09:15.669 them more vulnerable long before Andrew even 00:09:15.669 --> 00:09:18.169 spun up. And the housing differences that resulted 00:09:18.169 --> 00:09:21.169 from that segregation were huge. African Americans 00:09:21.169 --> 00:09:23.370 generally lived in smaller homes, older homes. 00:09:23.970 --> 00:09:25.950 Many just weren't built to codes that could handle 00:09:25.950 --> 00:09:29.009 a cat five storm. Right. Lacking modern standards, 00:09:29.210 --> 00:09:31.309 maybe less well maintained, inherently weaker. 00:09:31.549 --> 00:09:33.909 And compounding that, nearly a third of homes 00:09:33.909 --> 00:09:36.730 in South Dade were rentals. and that number jumped 00:09:36.730 --> 00:09:39.470 to over 40 % in the predominantly black communities 00:09:39.470 --> 00:09:42.129 further south. And rentals often mean? Poorer 00:09:42.129 --> 00:09:45.029 quality. Compared to owner -occupied homes, especially 00:09:45.029 --> 00:09:48.190 in richer areas, landlords maybe didn't have 00:09:48.190 --> 00:09:50.570 the same incentive to invest in hurricane proofing. 00:09:50.750 --> 00:09:53.070 Less skin in the game, perhaps. Leaving renters 00:09:53.070 --> 00:09:55.029 in a much more dangerous spot when Andrew hit. 00:09:55.629 --> 00:09:58.309 When their homes were destroyed, renters often 00:09:58.309 --> 00:10:01.429 lost absolutely everything. No equity, no insurance 00:10:01.429 --> 00:10:03.889 claim, very little help to rebuild their lives 00:10:03.889 --> 00:10:05.889 right there. So you combine that older, weaker 00:10:05.889 --> 00:10:08.330 housing with the historical segregation, and 00:10:08.330 --> 00:10:10.549 yeah, you inevitably get a massive insurance 00:10:10.549 --> 00:10:13.250 gap. The stats the research uncovered are really 00:10:13.250 --> 00:10:15.549 quite shocking. They scream systemic failure. 00:10:15.750 --> 00:10:19.830 Only about, what, 2 .7 % to 4 .1 % of Anglo and 00:10:19.830 --> 00:10:22.210 Cuban homeowners didn't have insurance? Relatively 00:10:22.210 --> 00:10:26.600 low. Okay. Compare that to 8 .7 % of black homeowners. 00:10:27.639 --> 00:10:30.769 Completely uninsured. Wow. Double or more. And 00:10:30.769 --> 00:10:33.129 specifically in South Dade, black homeowners 00:10:33.129 --> 00:10:35.649 were four times as likely as Anglo homeowners 00:10:35.649 --> 00:10:38.889 to have no homeowners insurance at all. Four 00:10:38.889 --> 00:10:41.409 times. Think about that financial vulnerability. 00:10:41.750 --> 00:10:44.929 A whole segment of the population with zero safety 00:10:44.929 --> 00:10:47.570 net against a total catastrophe. And it gets 00:10:47.570 --> 00:10:49.730 worse, right? It wasn't just about having insurance. 00:10:50.049 --> 00:10:51.590 It was the kind of insurance they had access 00:10:51.590 --> 00:10:54.240 to. Exactly. That's another crucial layer the 00:10:54.240 --> 00:10:57.639 research found. Black neighborhoods were disproportionately 00:10:57.639 --> 00:11:00.340 insured by smaller, less well -known companies. 00:11:00.639 --> 00:11:02.840 And many of those smaller firms actually failed 00:11:02.840 --> 00:11:05.159 after Andrew. They just couldn't handle the sheer 00:11:05.159 --> 00:11:07.580 volume of claims. They went bankrupt. Leaving 00:11:07.580 --> 00:11:10.779 their policyholders completely stranded. Totally 00:11:10.779 --> 00:11:14.019 stranded, already devastated, now with no payout, 00:11:14.179 --> 00:11:16.899 no way forward. People insured by those smaller 00:11:16.899 --> 00:11:19.279 companies were three times more likely to say 00:11:19.279 --> 00:11:21.620 their settlement wasn't enough. They got pennies 00:11:21.620 --> 00:11:23.840 on the dollar. for what they needed. Unbelievable. 00:11:24.259 --> 00:11:26.759 And even when you control for income, so it's 00:11:26.759 --> 00:11:29.620 not just about being poor, the odds of a black 00:11:29.620 --> 00:11:32.460 household having insurance with a top tier stable 00:11:32.460 --> 00:11:35.259 company were roughly half that of an Anglo household. 00:11:35.419 --> 00:11:37.320 Half the chance, even at the same income level. 00:11:37.399 --> 00:11:40.379 Yeah, so it meant huge financial hardship for 00:11:40.379 --> 00:11:43.580 black homeowners. Unlike Anglos with their political 00:11:43.580 --> 00:11:46.980 and economic clout, or Cubans with those strong 00:11:46.980 --> 00:11:49.700 enclave networks, black homeowners were often 00:11:49.700 --> 00:11:52.700 just left to fend for themselves. There were 00:11:52.700 --> 00:11:55.259 heartbreaking stories, like that one homeowner, 00:11:55.399 --> 00:11:57.639 Jimmy Williams, whose adjuster apparently told 00:11:57.639 --> 00:11:59.340 him he couldn't make a profit from the damage 00:11:59.340 --> 00:12:01.059 when he just wanted to pay off his mortgage and 00:12:01.059 --> 00:12:04.139 start over. The whole system, housing, insurance, 00:12:04.600 --> 00:12:06.740 it was stacked against them. And the damage patterns 00:12:06.740 --> 00:12:09.100 themselves, they just mirrored the segregation 00:12:09.100 --> 00:12:11.580 and these disparities. They did. Research shows 00:12:11.580 --> 00:12:14.659 very clearly black households face significantly 00:12:14.659 --> 00:12:17.679 greater damage than Anglo households. But here's 00:12:17.679 --> 00:12:20.279 the clincher. That difference in damage levels, 00:12:20.299 --> 00:12:22.740 it mostly vanished once research was controlled 00:12:22.740 --> 00:12:24.399 for whether someone lived in a predominantly 00:12:24.399 --> 00:12:27.960 black neighborhood. Ah, so it directly links 00:12:27.960 --> 00:12:30.559 the higher damage to living in those segregated 00:12:30.559 --> 00:12:33.559 areas, those communities of fate. Precisely. 00:12:33.769 --> 00:12:36.210 where vulnerabilities were concentrated. But 00:12:36.210 --> 00:12:38.450 there was a surprising twist involving Cuban 00:12:38.450 --> 00:12:40.610 households. Right. You mentioned they also had 00:12:40.610 --> 00:12:43.350 high damage levels almost as high as black households, 00:12:44.049 --> 00:12:46.210 higher than the broader Hispanic group. Yeah, 00:12:46.230 --> 00:12:48.250 which seems counterintuitive given what we said 00:12:48.250 --> 00:12:51.669 about their capital. So why? Why would Cuban 00:12:51.669 --> 00:12:54.250 households with more capital, better networks, 00:12:54.730 --> 00:12:57.230 still see such high damage? What did the research 00:12:57.230 --> 00:12:59.789 suggest there? It's a really interesting interaction. 00:13:00.110 --> 00:13:02.070 The research points towards the housing boom 00:13:02.070 --> 00:13:04.470 that happened as the Cuban enclave grew rapidly 00:13:04.470 --> 00:13:07.809 in the 80s and early 90s. A lot of new affordable 00:13:07.809 --> 00:13:10.649 homes were built then. But South Florida hadn't 00:13:10.649 --> 00:13:13.149 had a major hurricane hit in decades. There was 00:13:13.149 --> 00:13:16.330 this maybe complacency. Complacency, yeah. And 00:13:16.330 --> 00:13:17.909 government authorities had actually relapsed 00:13:17.909 --> 00:13:20.250 some building codes during that period to encourage 00:13:20.250 --> 00:13:23.149 faster development, keep housing affordable for 00:13:23.149 --> 00:13:25.789 that growing population. Weaker codes during 00:13:25.789 --> 00:13:28.600 the boom. Exactly. So while many Anglos lived 00:13:28.600 --> 00:13:31.519 in older, often wealthier homes built under stricter, 00:13:31.600 --> 00:13:34.559 earlier codes, these newer homes in the expanding 00:13:34.559 --> 00:13:37.100 Cuban areas, even though they represented success 00:13:37.100 --> 00:13:39.740 and mobility, were structurally more vulnerable 00:13:39.740 --> 00:13:42.320 to Andrew's incredible wins. So it wasn't lack 00:13:42.320 --> 00:13:44.360 of insurance for them, but the buildings themselves. 00:13:44.460 --> 00:13:47.080 Right. It was a systemic vulnerability tied to 00:13:47.080 --> 00:13:49.799 rapid, maybe less regulated, development cycles. 00:13:50.100 --> 00:13:52.379 It's a fascinating kind of unexpected finding. 00:13:52.639 --> 00:13:54.919 Shows how different paths segregation versus 00:13:54.919 --> 00:13:57.590 maybe code relaxation can still lead to similar 00:13:57.590 --> 00:14:00.230 vulnerability in a monster storm. That just hammers 00:14:00.230 --> 00:14:03.009 home how complex these layers are. Vulnerability 00:14:03.009 --> 00:14:04.750 isn't simple. The core takeaway from all this 00:14:04.750 --> 00:14:08.559 Andrew research is just... profound natural disasters. 00:14:09.220 --> 00:14:11.620 They're rarely equal opportunity events. They 00:14:11.620 --> 00:14:13.980 ruthlessly find and amplify the pre -existing 00:14:13.980 --> 00:14:17.240 cracks in society. It's less about the storm's 00:14:17.240 --> 00:14:19.580 random path and much more about those existing 00:14:19.580 --> 00:14:21.700 paths of inequality that decide who gets hurt 00:14:21.700 --> 00:14:25.159 the most. It really does reshape the whole story 00:14:25.159 --> 00:14:28.279 of Hurricane Andrew. Moves it from just a weather 00:14:28.279 --> 00:14:31.500 event to this stark revelation of human -made 00:14:31.500 --> 00:14:35.100 weaknesses woven deep into the social, the economic 00:14:35.100 --> 00:14:37.740 fabric of a place. It wasn't just where Andrew 00:14:37.740 --> 00:14:40.580 hit, but who lived there and under what conditions 00:14:40.580 --> 00:14:43.759 that truly mattered. Okay, so Andrew clearly 00:14:43.759 --> 00:14:46.039 showed us those human -made vulnerabilities in 00:14:46.039 --> 00:14:48.019 South Florida's structure. But was that just, 00:14:48.019 --> 00:14:50.159 you know, a one -off? Or do we see these patterns 00:14:50.159 --> 00:14:52.519 repeating, maybe changing elsewhere in Florida 00:14:52.519 --> 00:14:55.860 later on? Let's broaden out now to the 2004 hurricane 00:14:55.860 --> 00:14:58.340 season. That hit Florida with a very different 00:14:58.340 --> 00:15:01.500 kind of impact, much more widespread. Yeah, 2004 00:15:01.500 --> 00:15:03.840 was truly remarkable for Florida and not a good 00:15:03.840 --> 00:15:06.360 way at all. Yeah. You had four major storms. 00:15:06.600 --> 00:15:09.000 Charlie, Francis, Ivan and Gene slammed the state 00:15:09.000 --> 00:15:11.279 in just six weeks. Four majors in six weeks. 00:15:11.340 --> 00:15:14.200 Four. So unlike Andrew, which was like this concentrated 00:15:14.200 --> 00:15:17.440 gut punch to South Dade, these 2004 storms caused 00:15:17.440 --> 00:15:19.899 disruption everywhere, maybe less catastrophic 00:15:19.899 --> 00:15:21.980 individually in one spot. But the cumulative 00:15:21.980 --> 00:15:23.919 effect across the state was immense. We're talking 00:15:23.919 --> 00:15:25.879 huge numbers, something like one point seven 00:15:25.879 --> 00:15:28.220 million people had to leave their homes temporarily. 00:15:28.659 --> 00:15:30.360 Two point six million homes. had at least some 00:15:30.360 --> 00:15:34.120 damage. Wow. It stretched resources, then really 00:15:34.120 --> 00:15:36.799 tested communities from the panhandle down to 00:15:36.799 --> 00:15:39.600 the keys. And the research really dug into why 00:15:39.600 --> 00:15:42.519 people left their homes during that crazy 2004 00:15:42.519 --> 00:15:44.519 season. It wasn't always because their house 00:15:44.519 --> 00:15:46.460 fell down, was it? No, interestingly enough, 00:15:47.379 --> 00:15:49.539 the reasons are quite revealing about modern 00:15:49.539 --> 00:15:52.700 life, really. Most people, about 72 % of those 00:15:52.700 --> 00:15:55.240 displaced, they left mainly because they lost 00:15:55.240 --> 00:15:59.480 basic utilities, power, water. gas, phone service. 00:15:59.539 --> 00:16:01.720 Ah, the infrastructure went down. Exactly. It 00:16:01.720 --> 00:16:04.019 wasn't necessarily direct structural damage forcing 00:16:04.019 --> 00:16:07.019 them out. Only about 14 % said structural damage 00:16:07.019 --> 00:16:09.840 was the main reason they left. It just highlights 00:16:09.840 --> 00:16:12.100 how incredibly dependent we are on that basic 00:16:12.100 --> 00:16:14.700 infrastructure and how quickly losing it can 00:16:14.700 --> 00:16:17.679 trigger mass temporary evacuations. Where did 00:16:17.679 --> 00:16:19.919 they go? Mostly stayed close within their networks. 00:16:19.980 --> 00:16:22.659 About 73 % moved in with family or friends for 00:16:22.659 --> 00:16:24.659 short periods, typically less than two weeks. 00:16:24.860 --> 00:16:27.000 So temporary moves mostly. Suggest temporary 00:16:27.000 --> 00:16:29.259 displacements, yeah. People sought refuge nearby, 00:16:29.480 --> 00:16:31.320 probably eager to get back home as soon as the 00:16:31.320 --> 00:16:33.460 lights came back on, basically. And certain types 00:16:33.460 --> 00:16:35.500 of housing, they continue to be more vulnerable, 00:16:35.679 --> 00:16:38.080 right? Like mobile homes. Oh, definitely. Mobile 00:16:38.080 --> 00:16:40.620 homes, structurally, they're just less resistant 00:16:40.620 --> 00:16:43.559 to high winds. Their damage rates are historically 00:16:43.559 --> 00:16:46.360 much higher, though it is worth mentioning those 00:16:46.360 --> 00:16:48.840 updated construction standards that came in after 00:16:48.840 --> 00:16:52.580 Andrew in 94. They did seem to have some positive 00:16:52.580 --> 00:16:55.379 impact. Mobile home damage rates were maybe a 00:16:55.379 --> 00:16:57.539 bit lower in 2004 than they might have been otherwise. 00:16:57.860 --> 00:17:00.720 So some lessons learned, at least on the building 00:17:00.720 --> 00:17:04.180 side. Some learning, yeah. But the vulnerability 00:17:04.180 --> 00:17:06.859 compared to traditional homes, still significant. 00:17:07.319 --> 00:17:11.519 Okay, so despite this huge impact in 2004, millions 00:17:11.519 --> 00:17:14.619 affected. The research suggests Florida as a 00:17:14.619 --> 00:17:17.380 whole didn't see huge long -term population shifts. 00:17:17.660 --> 00:17:20.420 People came back. Largely, yes. Most of those 00:17:20.420 --> 00:17:22.700 temporary losses were recovered relatively quickly. 00:17:23.099 --> 00:17:25.380 And this points to this really persistent, powerful 00:17:25.380 --> 00:17:28.099 trend in Florida. Which is? Despite the very 00:17:28.099 --> 00:17:31.099 real, undeniable, repeating threat of hurricanes. 00:17:31.599 --> 00:17:33.980 Florida just has this bundle of attractive qualities. 00:17:34.339 --> 00:17:36.400 The warm winters, the beaches, the low taxes, 00:17:36.599 --> 00:17:39.180 the job growth. The lifestyle appeal. Exactly. 00:17:39.400 --> 00:17:41.799 And for many people, that package deal seems 00:17:41.799 --> 00:17:44.759 to outweigh the known long -term risk of storms. 00:17:45.440 --> 00:17:47.900 People keep coming. Maybe there's some optimism 00:17:47.900 --> 00:17:49.940 bias. Maybe it's just a rational calculation 00:17:49.940 --> 00:17:53.160 for some. But the state remains incredibly appealing. 00:17:53.460 --> 00:17:55.200 So the market bounces back. The market bounces 00:17:55.200 --> 00:17:57.539 back because the demand stays high, which can 00:17:57.539 --> 00:17:59.759 sometimes mask those underlying vulnerabilities 00:17:59.759 --> 00:18:02.380 that haven't really gone away. It's a fascinating 00:18:02.380 --> 00:18:04.619 dynamic in human geography and how we assess 00:18:04.619 --> 00:18:07.140 risks. That powerful pull of the sunshine state. 00:18:07.799 --> 00:18:10.569 Yeah. It's real. But let's jump forward again, 00:18:10.589 --> 00:18:13.670 much more recently, to Hurricane Ian in 2022, 00:18:14.210 --> 00:18:16.630 another incredibly destructive, costly storm. 00:18:17.069 --> 00:18:19.390 Made landfall in southwest Florida that September, 00:18:19.910 --> 00:18:22.809 caused, what, an estimated $67 billion in damage? 00:18:23.349 --> 00:18:25.210 Significant loss of life, too. It's a terrible 00:18:25.210 --> 00:18:27.430 storm. Yeah. But what's really interesting about 00:18:27.430 --> 00:18:29.769 Ian and where the research gets fascinating is 00:18:29.769 --> 00:18:32.630 the mindset before the storm hit. Southwest Florida, 00:18:32.809 --> 00:18:35.190 pre -Ian, was a red hot housing market, right? 00:18:35.319 --> 00:18:38.480 Absolutely booming. And studies really dug into 00:18:38.480 --> 00:18:41.140 what homeowners and even real estate agents actually 00:18:41.140 --> 00:18:43.839 believed about flood risk in that very vulnerable 00:18:43.839 --> 00:18:46.799 coastal area. And what they found is this remarkable 00:18:46.799 --> 00:18:50.099 paradox. This disconnect in risk perception, 00:18:50.240 --> 00:18:53.400 even when the information is right there. Studies 00:18:53.400 --> 00:18:56.000 found that even when you showed homeowners localized 00:18:56.000 --> 00:18:59.079 flood risk maps, like for their specific zip 00:18:59.079 --> 00:19:03.309 code, even their property had Almost no effect. 00:19:03.430 --> 00:19:06.230 No effect on what? On their belief that their 00:19:06.230 --> 00:19:09.369 own home was susceptible to sea level rise, or 00:19:09.369 --> 00:19:11.309 their belief that future flood risk would hurt 00:19:11.309 --> 00:19:13.710 their property's value. None. Wow. They just 00:19:13.710 --> 00:19:15.710 didn't apply it to themselves. Seems like it. 00:19:15.710 --> 00:19:17.970 It points to that classic optimism bias, right? 00:19:17.970 --> 00:19:20.150 The feeling that, yeah, bad things happen, but 00:19:20.150 --> 00:19:22.509 they happen to other people, not me. So even 00:19:22.509 --> 00:19:26.029 with tailored local info, people weren't internalizing 00:19:26.029 --> 00:19:27.890 the risk for their own place. That's kind of 00:19:27.890 --> 00:19:29.809 scary. Yeah. And I think political affiliation 00:19:29.809 --> 00:19:32.980 came into play too. Yes. The research showed 00:19:32.980 --> 00:19:35.619 a strong correlation between a homeowner's political 00:19:35.619 --> 00:19:38.220 party and how they saw risk. Conservative Republicans 00:19:38.220 --> 00:19:41.019 were significantly less likely than, say, liberal 00:19:41.019 --> 00:19:43.359 Democrats or independents to believe climate 00:19:43.359 --> 00:19:45.799 change was happening, or that it was responsible 00:19:45.799 --> 00:19:48.700 for making storms more intense, or that sea level 00:19:48.700 --> 00:19:51.220 rise would affect their own home or its value 00:19:51.220 --> 00:19:53.980 down the line. So climate change belief acted 00:19:53.980 --> 00:19:56.480 as a filter for storm risk perception. It seems 00:19:56.480 --> 00:19:59.309 so. It suggests that climate change, and maybe 00:19:59.309 --> 00:20:02.089 her gain risk by extension, has become tangled 00:20:02.089 --> 00:20:04.829 up with political identity, which creates this 00:20:04.829 --> 00:20:07.250 filter for how scientific information gets processed, 00:20:07.309 --> 00:20:10.750 or maybe even just dismissed. It's a really complex 00:20:10.750 --> 00:20:13.470 mix of science and social politics. And despite 00:20:13.470 --> 00:20:16.599 living right there? in flood prone coastal zones. 00:20:16.920 --> 00:20:19.000 What about flood insurance? Did people have it? 00:20:19.019 --> 00:20:21.519 Shockingly low uptake. Only 38 percent of the 00:20:21.519 --> 00:20:23.599 homeowners surveyed had flood insurance on top 00:20:23.599 --> 00:20:26.720 of their regular homeowners policy. Only 38 percent 00:20:26.720 --> 00:20:28.980 in known flood zones. Yeah, leaving this huge 00:20:28.980 --> 00:20:31.839 majority financially exposed if or when the flooding 00:20:31.839 --> 00:20:35.400 came. That is genuinely alarming. So, OK, homeowners 00:20:35.400 --> 00:20:37.519 might have blind spots, but what about the professionals, 00:20:37.640 --> 00:20:39.440 the real estate agents? Their job is to know 00:20:39.440 --> 00:20:41.839 the market, know the risks. What did they perceive? 00:20:42.000 --> 00:20:45.140 Well, their perspective adds another really crucial 00:20:45.140 --> 00:20:48.660 and maybe equally surprising layer here. Agents 00:20:48.660 --> 00:20:51.380 overwhelmingly told researchers that basically 00:20:51.380 --> 00:20:53.839 buyers were not shying away from low elevation 00:20:53.839 --> 00:20:56.819 coastal properties. Not at all. Demand was still 00:20:56.819 --> 00:20:59.380 strong, even for risky areas. Incredibly strong, 00:20:59.420 --> 00:21:01.920 they said, because the lure, the pull of that 00:21:01.920 --> 00:21:04.160 coastal lifestyle, loving the water, the boating, 00:21:04.299 --> 00:21:07.000 the sports, the sunsets, it just outweighed the 00:21:07.000 --> 00:21:09.720 prospect of flooding for most buyers. The dream 00:21:09.720 --> 00:21:11.990 trumped the danger. It seems that way. It's an 00:21:11.990 --> 00:21:15.410 emotional aspirational thing that for many just 00:21:15.410 --> 00:21:18.230 overrides a logical risk calculation. So this 00:21:18.230 --> 00:21:20.730 powerful desire for the lifestyle just neutralized 00:21:20.730 --> 00:21:23.450 the risk concerns. Did that show up in prices? 00:21:23.809 --> 00:21:26.549 Were risky properties cheaper? Nope. Agents overwhelmingly 00:21:26.549 --> 00:21:28.930 said they did not see prices falling or even 00:21:28.930 --> 00:21:32.150 rising slower for flood prone properties. Yeah. 00:21:32.329 --> 00:21:35.730 A striking 84 % said they rarely or never saw 00:21:35.730 --> 00:21:39.529 that happen. Their take was that Wealthy buyers, 00:21:39.670 --> 00:21:41.670 especially, would just keep paying top dollar 00:21:41.670 --> 00:21:44.250 for coastal living. They'd just factor in the 00:21:44.250 --> 00:21:46.630 extra costs, more insurance, maybe repairs down 00:21:46.630 --> 00:21:49.210 the road, building the house up higher as the 00:21:49.210 --> 00:21:51.650 cost of admission for that dream location. Wow. 00:21:51.930 --> 00:21:54.170 And what about the banks? The appraisers? Were 00:21:54.170 --> 00:21:57.250 they flagging the risk? Mostly not, according 00:21:57.250 --> 00:22:00.230 to the agents. Most said they rarely or never 00:22:00.230 --> 00:22:02.569 saw mortgage lenders turn down loans or charge 00:22:02.569 --> 00:22:05.750 higher rates for low -lying, flood -prone areas. 00:22:06.250 --> 00:22:08.690 Nor did appraisers typically factor in elevation 00:22:08.690 --> 00:22:11.410 or flood likelihood into their property valuations. 00:22:11.589 --> 00:22:13.980 So the whole system seemed kind of... unconcerned. 00:22:13.980 --> 00:22:17.299 It points to a systemic blind spot maybe or perhaps 00:22:17.299 --> 00:22:20.660 just a collective market driven denial that permeated 00:22:20.660 --> 00:22:23.079 the whole real estate world there. Even when 00:22:23.079 --> 00:22:25.380 detailed digital flood maps from groups like 00:22:25.380 --> 00:22:27.240 the First Street Foundation were put right into 00:22:27.240 --> 00:22:29.779 online real estate listings. Yeah. Did that make 00:22:29.779 --> 00:22:33.039 a difference? Agents said nope. Those maps had 00:22:33.039 --> 00:22:36.880 no discernible effect on homebuyers. That desire 00:22:36.880 --> 00:22:39.500 for the coastal life just seems to be this incredibly 00:22:39.500 --> 00:22:43.700 powerful, almost immovable force overriding readily 00:22:43.700 --> 00:22:47.359 available critical risk information. That is 00:22:47.359 --> 00:22:49.180 truly baffling. People see the maps, they know 00:22:49.180 --> 00:22:52.759 the risk exists, but their behavior doesn't change. 00:22:53.269 --> 00:22:56.369 What does the research suggest is going on psychologically 00:22:56.369 --> 00:22:58.950 there? Is it just denial, or is it more complicated? 00:22:59.069 --> 00:23:00.710 Oh, it's definitely more complicated than just 00:23:00.710 --> 00:23:02.750 simple denial, though. That's part of it. Optimism 00:23:02.750 --> 00:23:04.769 bias, as we said. But there's also something 00:23:04.769 --> 00:23:07.289 called temporal discounting. That's where we 00:23:07.289 --> 00:23:09.970 value immediate rewards, like an amazing ocean 00:23:09.970 --> 00:23:12.309 view right now, much more highly than potential 00:23:12.309 --> 00:23:14.869 future costs, like a hurricane maybe 10 years 00:23:14.869 --> 00:23:17.390 away. The future feels abstract. OK. Discount 00:23:17.390 --> 00:23:18.950 the future risk. Right. And then there's the 00:23:18.950 --> 00:23:20.710 social proof angle. If you see everyone else 00:23:20.710 --> 00:23:22.490 buying property there and prices are going up. 00:23:22.349 --> 00:23:25.910 it makes the risk feel normal, manageable, accepted. 00:23:25.970 --> 00:23:28.369 Heard mentality almost. Kind of. There can also 00:23:28.369 --> 00:23:31.509 be a bit of learned helplessness or even fatalism 00:23:31.509 --> 00:23:33.730 for some. A feeling like, well, if a big storm 00:23:33.730 --> 00:23:35.890 comes, it comes. Nothing much I can do beyond 00:23:35.890 --> 00:23:38.470 boarding up. Right. And as we discussed, that 00:23:38.470 --> 00:23:41.029 political identity filter seems really strong. 00:23:41.430 --> 00:23:44.230 It shapes how people interpret or even accept 00:23:44.230 --> 00:23:47.049 the science about climate change impacting storms 00:23:47.049 --> 00:23:49.490 and sea levels. So it's this potent mix, really, 00:23:49.829 --> 00:23:52.210 of cognitive biases, market pressures, social 00:23:52.210 --> 00:23:54.390 influences, and they all kind of work together 00:23:54.390 --> 00:23:56.670 to dampen the effect of objective risk data. 00:23:56.849 --> 00:24:00.410 So knowing all that. this huge gap between the 00:24:00.410 --> 00:24:02.750 available information and how people actually 00:24:02.750 --> 00:24:05.829 behave, influenced by politics, psychology. How 00:24:05.829 --> 00:24:07.789 does that make you think about preparing coastal 00:24:07.789 --> 00:24:10.690 communities, about building resilience? It definitely 00:24:10.690 --> 00:24:12.930 makes me wonder if just throwing more data at 00:24:12.930 --> 00:24:14.750 people is the answer. It seems like we need something 00:24:14.750 --> 00:24:18.490 that Florida data is. It paints a picture of 00:24:18.490 --> 00:24:20.309 fascinating, sometimes really alarming human 00:24:20.309 --> 00:24:22.910 behavior around risk. Now let's shift gears, 00:24:23.049 --> 00:24:26.819 head west a bit to Louisiana. to a storm with 00:24:26.819 --> 00:24:28.740 a very different story, but one that echoes some 00:24:28.740 --> 00:24:31.420 incredibly familiar themes. Hurricane Katrina. 00:24:32.019 --> 00:24:34.019 Katrina's narrative felt different from the start, 00:24:34.180 --> 00:24:35.900 didn't it? More immediately stark in its social 00:24:35.900 --> 00:24:38.799 impact. Unlike Andrew, where maybe the racial 00:24:38.799 --> 00:24:40.839 angle was downplayed initially, maybe out of 00:24:40.839 --> 00:24:43.940 some desire for a unified America under attack 00:24:43.940 --> 00:24:47.400 feel, post -Katrina coverage was explicitly undeniably 00:24:47.400 --> 00:24:49.980 racialized. Right from day one. Absolutely. That 00:24:49.980 --> 00:24:52.500 explicit racialization is what really set Katrina 00:24:52.500 --> 00:24:55.160 apart in the public mind and in policy debates, 00:24:55.420 --> 00:24:58.529 too. You think about those images. people seeking 00:24:58.529 --> 00:25:00.609 shelter in the New Orleans Superdome, almost 00:25:00.609 --> 00:25:03.769 exclusively black faces, portraying this trapped, 00:25:03.769 --> 00:25:06.049 vulnerable population. Yeah, those images are 00:25:06.049 --> 00:25:08.210 burned into memory. And you contrast that with 00:25:08.210 --> 00:25:11.589 stories and some images of white residents who 00:25:11.589 --> 00:25:14.650 often had the means, the cars, the money, the 00:25:14.650 --> 00:25:16.890 family connections elsewhere to evacuate. They 00:25:16.890 --> 00:25:19.809 went to hotels, family and other cities, avoiding 00:25:19.809 --> 00:25:22.190 those terrible shelter conditions. A stark difference 00:25:22.190 --> 00:25:24.950 in options. Exactly. So the disaster was framed 00:25:24.950 --> 00:25:27.130 in racial terms by the media, by politicians. 00:25:26.920 --> 00:25:29.599 by everyone, really. It made it impossible to 00:25:29.599 --> 00:25:32.160 ignore that this was both a devastating natural 00:25:32.160 --> 00:25:35.240 event and a profound human disaster, deeply tangled 00:25:35.240 --> 00:25:38.140 up with generations of racial inequality, economic 00:25:38.140 --> 00:25:40.980 disparity. A sobering difference in how it was 00:25:40.980 --> 00:25:43.839 perceived immediately. And the research using 00:25:43.839 --> 00:25:46.299 internal revenue service data looking at county 00:25:46.299 --> 00:25:48.920 to county migration flows gives us this really 00:25:48.920 --> 00:25:51.400 detailed, compelling picture of how people moved. 00:25:52.000 --> 00:25:53.940 Specifically for Orleans Parish, that's what 00:25:53.940 --> 00:25:56.460 Louisiana calls its counties, parishes before 00:25:56.460 --> 00:26:00.000 and after Katrina and Rita hit in 2005. What 00:26:00.000 --> 00:26:02.339 does that granular data reveal about New Orleans' 00:26:02.680 --> 00:26:04.809 unique recovery path? What's really fascinating 00:26:04.809 --> 00:26:07.569 there is this pattern of concentration and intensification 00:26:07.569 --> 00:26:11.069 of migration ties. In the recovery period, say 00:26:11.069 --> 00:26:14.789 2007 to 2009, Orleans Parish actually saw an 00:26:14.789 --> 00:26:16.849 increase in the number of migration connections 00:26:16.849 --> 00:26:19.490 and larger flows of people moving in from nearby 00:26:19.490 --> 00:26:22.130 counties, specifically those in the Gulf of Mexico 00:26:22.130 --> 00:26:24.829 coastal region. So people came back mainly from 00:26:24.829 --> 00:26:27.170 close tie. It suggests exactly that, that the 00:26:27.170 --> 00:26:29.069 migration system helping New Orleans rebuild, 00:26:29.369 --> 00:26:32.069 it relied heavily on its strongest, closest existing 00:26:32.069 --> 00:26:34.849 social and geographical links. It was like the 00:26:34.849 --> 00:26:36.970 city was pulling people back mainly from its 00:26:36.970 --> 00:26:39.230 immediate orbit, concentrating the in -migration 00:26:39.230 --> 00:26:41.490 from nearby, while interestingly out -migration 00:26:41.490 --> 00:26:43.930 actually seemed to contract somewhat. So recovery 00:26:43.930 --> 00:26:46.309 was a very local affair, population -wise. It 00:26:46.309 --> 00:26:48.769 really suggests that. New Orleans' recovery in 00:26:48.769 --> 00:26:51.869 terms of repopulation seemed very much an internal 00:26:51.869 --> 00:26:55.190 localized effort, relying heavily on those immediate 00:26:55.190 --> 00:26:58.029 regional networks. It wasn't pulling large numbers 00:26:58.029 --> 00:27:00.730 back from across the U .S., it was rebuilding 00:27:00.730 --> 00:27:04.390 more from within its close a sphere, which speaks 00:27:04.390 --> 00:27:07.650 volumes perhaps about the strength of those local 00:27:07.650 --> 00:27:11.369 community bonds, even under such extreme stress. 00:27:12.109 --> 00:27:14.130 So the city drew from its immediate neighbors, 00:27:14.470 --> 00:27:16.789 reinforcing those regional ties, rather than 00:27:16.789 --> 00:27:19.369 pulling from further away. Precisely. The research 00:27:19.369 --> 00:27:21.710 also showed that the Orleans Parish migration 00:27:21.710 --> 00:27:24.950 system, both before and after, mostly involved 00:27:24.950 --> 00:27:27.769 other urban counties. Which makes sense, reflecting 00:27:27.769 --> 00:27:30.250 New Orleans' role as a major city. That basic 00:27:30.250 --> 00:27:33.019 pattern didn't change much. But what did change 00:27:33.019 --> 00:27:34.920 was the relative importance of different types 00:27:34.920 --> 00:27:37.559 of counties. Nearby counties, especially those 00:27:37.559 --> 00:27:39.640 that already had strong migration ties before 00:27:39.640 --> 00:27:42.079 Katrina, became significantly more important 00:27:42.079 --> 00:27:44.559 as places people came back from during the recovery. 00:27:45.079 --> 00:27:47.779 Conversely, counties far away, and maybe more 00:27:47.779 --> 00:27:50.500 significantly, those nearby coastal parishes 00:27:50.500 --> 00:27:53.279 that got absolutely hammered by Katrina's storm 00:27:53.279 --> 00:27:55.960 surge, places like St. Bernard, plaque mines, 00:27:56.180 --> 00:27:59.079 Harrison, over in Mississippi, they became less 00:27:59.079 --> 00:28:01.450 important as sources of return migration. because 00:28:01.450 --> 00:28:03.829 they were too devastated themselves. Exactly. 00:28:03.970 --> 00:28:05.829 Their own populations were just too disrupted, 00:28:06.049 --> 00:28:09.250 too displaced to contribute much to repopulating 00:28:09.250 --> 00:28:11.869 New Orleans right away. It really paints this 00:28:11.869 --> 00:28:14.569 picture of a very localized, network -driven 00:28:14.569 --> 00:28:16.829 recovery in terms of who came back and from where. 00:28:16.950 --> 00:28:19.250 This connects to a broader idea, something that 00:28:19.250 --> 00:28:22.109 newer research is exploring, measuring economic 00:28:22.109 --> 00:28:25.200 losses via migration. If you think about what 00:28:25.200 --> 00:28:27.900 the National Oceanic and Atmospheric Administration, 00:28:27.900 --> 00:28:30.420 NOAA, calls billion -dollar weather and climate 00:28:30.420 --> 00:28:33.500 disasters, you know, those extreme events causing 00:28:33.500 --> 00:28:35.559 at least a billion in losses. Yeah, we hear that 00:28:35.559 --> 00:28:37.779 term a lot now. Right, and the costs have just 00:28:37.779 --> 00:28:40.559 skyrocketed in recent decades, way past old records, 00:28:40.779 --> 00:28:43.940 like 2017 set a record then, something like $322 00:28:43.940 --> 00:28:46.759 billion in losses, far exceeding the previous 00:28:46.759 --> 00:28:49.319 record from 2005, which was Katrina's year, at 00:28:49.319 --> 00:28:52.740 around $228 billion. Those numbers are just... 00:28:52.640 --> 00:28:54.980 astronomical, hard to even wrap your head around. 00:28:55.299 --> 00:28:57.660 But how does people moving actually factor into 00:28:57.660 --> 00:29:00.240 those huge economic losses? Well, the research 00:29:00.240 --> 00:29:02.880 frames migration not just as people relocating, 00:29:02.900 --> 00:29:06.700 but as a vector, like a carrier, almost, for 00:29:06.700 --> 00:29:09.000 economic losses. OK, how does that work? Think 00:29:09.000 --> 00:29:11.000 about it. When people leave a community after 00:29:11.000 --> 00:29:12.700 a disaster, they don't just take themselves. 00:29:12.980 --> 00:29:14.940 They take their accumulated financial activity. 00:29:15.359 --> 00:29:17.799 Their debt balance, that's a key metric used 00:29:17.799 --> 00:29:20.220 in some studies, reflects things like mortgages, 00:29:20.559 --> 00:29:24.299 car loans, credit card debt, essentially their 00:29:24.299 --> 00:29:27.079 economic footprint. Using non -public data, like 00:29:27.079 --> 00:29:29.299 from the Federal Reserve Bank of New York EchoFax 00:29:29.299 --> 00:29:31.740 credit panel, researchers can estimate the total 00:29:31.740 --> 00:29:34.240 debt balance of everyone who moves out. As a 00:29:34.240 --> 00:29:36.460 proxy for the resources leaving. Exactly. It's 00:29:36.460 --> 00:29:38.839 a proxy for the economic resources, the spending 00:29:38.839 --> 00:29:41.240 power, the investment, the tax base, that are 00:29:41.240 --> 00:29:43.079 literally walking out the door with the departing 00:29:43.079 --> 00:29:45.940 population. It's a way to quantify the economic 00:29:45.940 --> 00:29:48.900 drain. So a measurable drain of economic vitality, 00:29:49.019 --> 00:29:50.900 not just a headcount change. And what did these 00:29:50.900 --> 00:29:54.299 studies find? Did losses spike after disasters 00:29:54.299 --> 00:29:57.299 like Katrina? Generally, yes, the studies found 00:29:57.299 --> 00:30:00.240 that these economic losses via migration tended 00:30:00.240 --> 00:30:03.079 to increase significantly in the year of a major 00:30:03.079 --> 00:30:05.539 disaster and the year immediately following. 00:30:06.059 --> 00:30:08.299 And for most of the disaster areas they looked 00:30:08.299 --> 00:30:11.480 at, including Katrina's impact zone, these losses 00:30:11.480 --> 00:30:14.380 were primarily driven by changes in out migration, 00:30:14.640 --> 00:30:17.180 meaning simply more people were leaving. So it 00:30:17.180 --> 00:30:19.940 was the volume of people departing? taking their 00:30:19.940 --> 00:30:22.559 economic activity with them? Mostly, yeah. Rather 00:30:22.559 --> 00:30:25.500 than, say, the average economic resources per 00:30:25.500 --> 00:30:28.079 person changing dramatically, it was the sheer 00:30:28.079 --> 00:30:30.599 number of departures driving the economic loss 00:30:30.599 --> 00:30:32.759 figure. Although interestingly, the research 00:30:32.759 --> 00:30:35.240 noted that these losses can sometimes cause a 00:30:35.240 --> 00:30:38.180 temporary disruption to the status quo by briefly 00:30:38.180 --> 00:30:40.839 reducing spatial inequality within the affected 00:30:40.839 --> 00:30:43.660 area. How so? Well, if higher income individuals 00:30:43.660 --> 00:30:45.859 who often have more resources and options to 00:30:45.859 --> 00:30:48.299 leave also depart in large numbers, they take 00:30:48.299 --> 00:30:50.900 their large debt balances representing more economic 00:30:50.900 --> 00:30:54.019 activity with them, which can momentarily narrow 00:30:54.019 --> 00:30:56.019 the economic gap between those who remain and 00:30:56.019 --> 00:30:59.299 those who left. Ah, okay. But probably only temporary. 00:30:59.579 --> 00:31:02.559 Usually a short -term effect, yeah. Those underlying 00:31:02.559 --> 00:31:04.799 inequalities tend to reassert themselves during 00:31:04.799 --> 00:31:07.819 the longer recovery process. Now let's focus 00:31:07.819 --> 00:31:11.900 specifically on race, socioeconomic status, and 00:31:11.900 --> 00:31:14.839 who actually returned to New Orleans after Katrina. 00:31:15.400 --> 00:31:18.140 The research paints a really stark picture here. 00:31:18.559 --> 00:31:21.279 It clearly indicates black residents came back 00:31:21.279 --> 00:31:24.519 much, much slower than white residents. A huge 00:31:24.519 --> 00:31:26.880 difference. Yeah, like a quarter of white residents 00:31:26.880 --> 00:31:29.279 were back within just two months, which seems 00:31:29.279 --> 00:31:31.160 pretty quick, all things considered. Very quick. 00:31:31.259 --> 00:31:34.180 But it took black residents an extra month, three 00:31:34.180 --> 00:31:37.140 months total, just to hit that same 25 % return 00:31:37.140 --> 00:31:40.440 mark. And even more telling, after 14 months, 00:31:40.559 --> 00:31:43.160 more than a year later, fewer than half of black 00:31:43.160 --> 00:31:45.470 residents were back. compared to half of white 00:31:45.470 --> 00:31:47.329 residents being back within only three months. 00:31:47.430 --> 00:31:50.109 It's a massive disparity in the timeline of recovery. 00:31:50.309 --> 00:31:52.170 It really underscores these deep differences 00:31:52.170 --> 00:31:54.410 in the ability or perhaps the opportunity to 00:31:54.410 --> 00:31:57.009 return home. Which brings us right to the crucial 00:31:57.009 --> 00:31:59.170 question the research tackled. What was really 00:31:59.170 --> 00:32:01.890 driving that huge racial gap in return rates? 00:32:02.529 --> 00:32:05.369 Was it race itself or something else? And the 00:32:05.369 --> 00:32:07.950 critical finding, the main takeaway here, is 00:32:07.950 --> 00:32:10.410 that the racial disparity largely disappears 00:32:10.410 --> 00:32:12.880 once you account for housing damage. Okay, explain 00:32:12.880 --> 00:32:15.579 that. Controlling for housing damage makes the 00:32:15.579 --> 00:32:18.880 race gap vanish. Pretty much. It means black 00:32:18.880 --> 00:32:21.279 residents were less likely to return quickly, 00:32:21.460 --> 00:32:23.630 not primarily because of their race directly, 00:32:23.970 --> 00:32:25.970 but because they disproportionately lived in 00:32:25.970 --> 00:32:28.750 the lower lying parts of New Orleans. The areas 00:32:28.750 --> 00:32:31.690 that got absolutely swamped by flooding suffered 00:32:31.690 --> 00:32:34.950 far more severe housing damage. So it links back 00:32:34.950 --> 00:32:37.470 to where people lived. Directly back to historical 00:32:37.470 --> 00:32:40.029 patterns, land development choices, decades of 00:32:40.029 --> 00:32:43.190 residential segregation in New Orleans that systematically 00:32:43.190 --> 00:32:45.769 pushed black residents into these more vulnerable 00:32:45.769 --> 00:32:48.769 flood prone areas, often land reclaimed from 00:32:48.769 --> 00:32:52.059 swamps lower elevation. exposure to the worst 00:32:52.059 --> 00:32:55.700 damage wasn't random. It was a legacy of inequality. 00:32:56.259 --> 00:32:58.539 A direct legacy of how the city was built. Yeah. 00:32:58.660 --> 00:33:01.119 Generations of urban planning decisions and social 00:33:01.119 --> 00:33:03.319 inequalities made them more vulnerable to begin 00:33:03.319 --> 00:33:06.160 with. So it wasn't about a difference in wanting 00:33:06.160 --> 00:33:08.720 to return based on race, but a difference in 00:33:08.720 --> 00:33:11.099 being able to return because their homes, their 00:33:11.099 --> 00:33:14.140 neighborhoods were simply hit much harder due 00:33:14.140 --> 00:33:17.119 to where history placed them. The research also 00:33:17.119 --> 00:33:19.740 found this really interesting and kind of heartbreaking 00:33:19.740 --> 00:33:22.680 difference between homes classified as destroyed 00:33:22.680 --> 00:33:25.920 versus just uninhabitable that affected return 00:33:25.920 --> 00:33:28.849 rates too. Yes, that's a really vital distinction 00:33:28.849 --> 00:33:31.710 the data revealed. Only about 30 % of residents 00:33:31.710 --> 00:33:34.109 whose homes were deemed completely destroyed, 00:33:34.490 --> 00:33:36.869 basically. Total loss, needing demolition, rebuilding 00:33:36.869 --> 00:33:39.049 from the ground up. Only 30 % of them returned. 00:33:39.150 --> 00:33:41.609 Makes sense. Nothing to return to. Right. But 00:33:41.609 --> 00:33:44.809 a significantly higher number, 54 % of those 00:33:44.809 --> 00:33:46.950 whose homes were classified as uninhabitable, 00:33:47.230 --> 00:33:49.690 meaning severely damaged but potentially fixable, 00:33:50.130 --> 00:33:52.849 54 % of them did manage to return. Why the big 00:33:52.849 --> 00:33:54.970 difference? What enabled the uninhabitable group? 00:33:55.279 --> 00:33:57.839 A key factor seemed to be temporary housing. 00:33:58.460 --> 00:34:01.480 That group could often get, say, a FEMA trailer 00:34:01.480 --> 00:34:04.200 or some other temporary shelter placed right 00:34:04.200 --> 00:34:06.099 there on their own property while they worked 00:34:06.099 --> 00:34:09.079 on rebuilding. That trailer became a crucial 00:34:09.079 --> 00:34:12.119 anchor, a base of operations, a sense of still 00:34:12.119 --> 00:34:14.300 being connected to their land, their neighborhood, 00:34:14.579 --> 00:34:18.019 overseeing repairs. The foothold. Exactly. But 00:34:18.019 --> 00:34:20.940 that luxury, if you can call it that, just wasn't 00:34:20.940 --> 00:34:23.400 an option for people whose homes were in obliterated 00:34:23.400 --> 00:34:26.219 areas, where entire blocks were wiped clean, 00:34:26.440 --> 00:34:28.840 infrastructure gone. They had no property left 00:34:28.840 --> 00:34:31.019 to put a trailer on, no temporary base, often 00:34:31.019 --> 00:34:33.719 no functioning neighborhood to return to, making 00:34:33.719 --> 00:34:36.119 permanent relocation often the only realistic 00:34:36.119 --> 00:34:38.719 choice. It really underscores how deep these 00:34:38.719 --> 00:34:40.820 vulnerabilities run. It's not just the storm 00:34:40.820 --> 00:34:42.719 hit, it's everything that comes after the whole 00:34:42.719 --> 00:34:45.539 long, difficult recovery process. And what feels 00:34:45.539 --> 00:34:47.340 particularly troubling about the post -Katrina 00:34:47.340 --> 00:34:49.119 story is what the research started to hint at 00:34:49.119 --> 00:34:52.500 early on. This idea that New Orleans might see 00:34:52.500 --> 00:34:55.199 an opposite pattern to what happened after Andrew. 00:34:55.369 --> 00:34:57.750 Yeah, it's a very concerning possibility that 00:34:57.750 --> 00:35:01.369 was raised. Remember, after Andrew, African -American 00:35:01.369 --> 00:35:04.010 and Haitian residents were often kind of trapped, 00:35:04.510 --> 00:35:06.309 forced to stay and try to rebuild in devastated 00:35:06.309 --> 00:35:08.570 zones because they lacked the resources, the 00:35:08.570 --> 00:35:10.670 options to move elsewhere? Right, they couldn't 00:35:10.670 --> 00:35:13.250 leave. But with Katrina, the narrative shifted. 00:35:13.710 --> 00:35:15.650 There were discussions and some actions taken 00:35:15.650 --> 00:35:18.010 that led researchers and community members to 00:35:18.010 --> 00:35:20.449 worry that the disaster was being seen by some 00:35:20.449 --> 00:35:23.289 as an opportunity. An opportunity for what? An 00:35:23.289 --> 00:35:26.110 opportunity to potentially remove black residents, 00:35:26.469 --> 00:35:29.309 particularly from desirable areas, and rebuild 00:35:29.309 --> 00:35:33.550 a different kind of city, a gentrified, revitalized, 00:35:33.630 --> 00:35:36.369 and some would explicitly say whiter New Orleans. 00:35:36.469 --> 00:35:39.269 Wow, using the disaster to reshape demographics. 00:35:39.769 --> 00:35:41.630 That was the fear, and some evidence pointed 00:35:41.630 --> 00:35:44.630 that way. It raises these really serious questions 00:35:44.630 --> 00:35:46.329 about the long -term social engineering that 00:35:46.329 --> 00:35:49.389 can happen after a major disaster, where rebuilding 00:35:49.389 --> 00:35:51.389 isn't just about fixing buildings, but actively 00:35:51.389 --> 00:35:53.730 changing who lives there, who benefits from the 00:35:53.730 --> 00:35:56.190 recovery investment. It's a powerful reminder 00:35:56.190 --> 00:35:58.489 that recovery can mean vastly different things 00:35:58.489 --> 00:36:01.090 depending on who you are and critically, who 00:36:01.090 --> 00:36:03.469 holds the power to define what that future city 00:36:03.469 --> 00:36:05.329 looks like. Okay, let's try to pull all these 00:36:05.329 --> 00:36:08.150 powerful threads together now. We've looked at 00:36:08.150 --> 00:36:11.530 Hurricane Andrew, Florida's intense 2004 season, 00:36:11.869 --> 00:36:15.349 Hurricane Ian, Hurricane Katrina. Diverse storms 00:36:15.349 --> 00:36:17.989 different places but across all of them this 00:36:17.989 --> 00:36:20.550 consistent and frankly deeply concerning pattern 00:36:20.550 --> 00:36:23.369 keeps showing up Natural disasters as we call 00:36:23.369 --> 00:36:26.090 them. They are rarely if ever equal opportunity 00:36:26.090 --> 00:36:28.610 events. The research just makes it crystal clear. 00:36:28.630 --> 00:36:31.699 They hit racial ethnic, socioeconomic minority 00:36:31.699 --> 00:36:34.619 groups disproportionately hard. Yeah, the vulnerability 00:36:34.619 --> 00:36:38.320 is in so many ways fundamentally baked in. Long 00:36:38.320 --> 00:36:40.960 before the storm even forms, those pre -existing 00:36:40.960 --> 00:36:43.360 conditions we talked about, the legacy of residential 00:36:43.360 --> 00:36:46.539 segregation, the quality and age of housing people 00:36:46.539 --> 00:36:48.900 live in, the unequal access to good insurance, 00:36:49.079 --> 00:36:51.400 the different levels of economic, social, cultural 00:36:51.400 --> 00:36:55.179 capital, these factors basically predetermine 00:36:55.179 --> 00:36:57.809 to a large extent. Who suffers the most damage 00:36:57.809 --> 00:37:00.409 and who faces the longest, hardest road to recovery? 00:37:00.650 --> 00:37:02.909 We saw it clearly in South Florida with Andrew 00:37:02.909 --> 00:37:06.530 and Ian. Where economic status and housing quality 00:37:06.530 --> 00:37:09.110 shaped outcomes. And just as clearly in New Orleans 00:37:09.110 --> 00:37:12.510 with Katrina, where historical segregation literally 00:37:12.510 --> 00:37:14.389 put people in the path of the worst flooding. 00:37:14.710 --> 00:37:16.769 Exactly. These aren't random outcomes. They are 00:37:16.769 --> 00:37:19.250 the direct result of deep -seated societal structures, 00:37:19.690 --> 00:37:21.869 historical decisions, and ongoing inequalities. 00:37:22.130 --> 00:37:25.130 And the way people move? or can't move, after 00:37:25.130 --> 00:37:27.369 a hurricane, those migration patterns act like 00:37:27.369 --> 00:37:29.670 this powerful mirror, reflecting these underlying 00:37:29.670 --> 00:37:32.670 structures. Who stays, who leaves, why they leave 00:37:32.670 --> 00:37:35.199 or stay. It tells us so much about power, about 00:37:35.199 --> 00:37:37.679 access, about agency. Or lack of agency. Right. 00:37:38.039 --> 00:37:40.519 Whether it's the flight of more affluent Anglos 00:37:40.519 --> 00:37:43.780 after Andrew, or the much slower return of black 00:37:43.780 --> 00:37:46.500 residents after Katrina, or tracking those economic 00:37:46.500 --> 00:37:49.360 losses as people are forced to migrate, these 00:37:49.360 --> 00:37:52.039 movements reveal who has choices and who doesn't 00:37:52.039 --> 00:37:54.760 in the face of disaster. For some, moving is 00:37:54.760 --> 00:37:58.679 an option. For others, staying put, even in devastation, 00:37:58.980 --> 00:38:01.070 is the only reality. It makes you think about 00:38:01.070 --> 00:38:03.530 Mark Granavetter's sociological work on weak 00:38:03.530 --> 00:38:06.510 ties. It's a classic idea. He argued that while 00:38:06.510 --> 00:38:08.929 your strong ties, close family, best friends 00:38:08.929 --> 00:38:11.250 are vital for emotional support, immediate help. 00:38:11.250 --> 00:38:14.090 Yeah. It's often your weak ties, acquaintances, 00:38:14.389 --> 00:38:15.969 colleagues, people in your wider network that 00:38:15.969 --> 00:38:18.130 are more crucial for getting ahead. For social 00:38:18.130 --> 00:38:20.570 mobility, accessing new information about jobs, 00:38:20.690 --> 00:38:23.010 housing, aid programs. Ah, broader connections. 00:38:23.389 --> 00:38:25.530 Exactly. And in those communities of fate we 00:38:25.530 --> 00:38:28.230 discussed, which often have fewer diverse external 00:38:28.230 --> 00:38:30.800 connections due to historical isolation or segregation, 00:38:31.599 --> 00:38:33.940 the absence of these weak ties can be incredibly 00:38:33.940 --> 00:38:36.579 damaging after a disaster. It can effectively 00:38:36.579 --> 00:38:39.679 trap residents. They lack those broader pathways 00:38:39.679 --> 00:38:42.760 to find resources, information, alternative options 00:38:42.760 --> 00:38:45.280 needed for real recovery from moving beyond just 00:38:45.280 --> 00:38:48.400 immediate survival. And then overlaying all of 00:38:48.400 --> 00:38:50.719 this is that persistent challenge of risk perception. 00:38:50.960 --> 00:38:54.260 which seemed especially stark in Florida, that 00:38:54.260 --> 00:38:56.840 enduring optimism, the underestimation of flood 00:38:56.840 --> 00:38:59.599 risk in Southwest Florida, even after experiencing 00:38:59.599 --> 00:39:02.340 storms like Ian. Yeah, almost defiant optimism 00:39:02.340 --> 00:39:04.519 sometimes. And how it seems that powerful draw 00:39:04.519 --> 00:39:07.440 of the coastal lifestyle can just override clear 00:39:07.440 --> 00:39:10.800 scientific warnings, detailed information, sometimes 00:39:10.800 --> 00:39:12.400 filtered through those political affiliations 00:39:12.400 --> 00:39:15.239 we've discussed. It really poses a profound puzzle, 00:39:15.420 --> 00:39:17.179 doesn't it? How do you effectively communicate 00:39:17.179 --> 00:39:19.820 risk and courage preparedness when all these 00:39:19.820 --> 00:39:22.400 cognitive biases, emotional pulls, and political 00:39:22.400 --> 00:39:25.010 filters are scrambling the signal? It raises 00:39:25.010 --> 00:39:27.050 that huge question. We have the maps. We have 00:39:27.050 --> 00:39:29.050 the data. We have increasingly sophisticated 00:39:29.050 --> 00:39:33.130 models. Why do some communities still seem unconcerned 00:39:33.130 --> 00:39:35.409 or even actively push back against acknowledging 00:39:35.409 --> 00:39:38.190 the real threats? It's clearly not just about 00:39:38.190 --> 00:39:40.250 giving people more facts. It's about understanding 00:39:40.250 --> 00:39:43.500 how that information gets processed or ignored 00:39:43.500 --> 00:39:45.800 through all those filters, political identity, 00:39:46.159 --> 00:39:49.039 optimism bias, that powerful desire for a certain 00:39:49.039 --> 00:39:51.960 way of life. Figuring out how to overcome that 00:39:51.960 --> 00:39:54.420 communication barrier is honestly probably one 00:39:54.420 --> 00:39:56.619 of the biggest hurdles for building genuine resilience 00:39:56.619 --> 00:40:00.659 moving forward. So looking ahead, it seems obvious 00:40:00.659 --> 00:40:02.760 that just rebuilding the damaged buildings, the 00:40:02.760 --> 00:40:05.420 roads, the power lines, that's only scratching 00:40:05.420 --> 00:40:07.719 the surface, isn't it? Absolutely. That's the 00:40:07.719 --> 00:40:10.659 necessary but insufficient part. The deeper questions, 00:40:10.820 --> 00:40:12.440 the ones that often get glossed over in the rough 00:40:12.440 --> 00:40:14.920 to get back to normal, are about who gets to 00:40:14.920 --> 00:40:17.159 live in those rebuilt communities. Who actually 00:40:17.159 --> 00:40:19.719 has the resources, the capital, support networks 00:40:19.719 --> 00:40:22.719 to truly recover and maybe even improve their 00:40:22.719 --> 00:40:26.559 situation. And crucially, Who gets left behind? 00:40:27.019 --> 00:40:30.300 Who remains stuck in vulnerable situations facing 00:40:30.300 --> 00:40:33.400 the same systemic disadvantages when the next 00:40:33.400 --> 00:40:36.159 storm inevitably comes? These questions often 00:40:36.159 --> 00:40:39.280 just don't get asked loudly enough. We're answered 00:40:39.280 --> 00:40:41.559 honestly. Right. So if we want to build genuinely 00:40:41.559 --> 00:40:43.719 resilient communities, not just resilient structures, 00:40:44.079 --> 00:40:46.260 we have to move past the surface. We have to 00:40:46.260 --> 00:40:49.159 confront these root causes of unequal vulnerability 00:40:49.159 --> 00:40:51.860 that the research lays bare. We have to learn 00:40:51.860 --> 00:40:53.679 from these historical patterns. Couldn't agree 00:40:53.679 --> 00:40:56.559 more, because until those fundamental societal 00:40:56.559 --> 00:40:59.000 structures are addressed, until we really grapple 00:40:59.000 --> 00:41:01.800 with the legacies of segregation, unequal access 00:41:01.800 --> 00:41:04.440 to good jobs and education, disparities and insurance, 00:41:04.579 --> 00:41:06.719 all of it, it's highly likely, almost inevitable 00:41:06.719 --> 00:41:09.119 really, that African Americans, Haitians, other 00:41:09.119 --> 00:41:11.460 marginalized groups, they will continue to bear 00:41:11.460 --> 00:41:13.420 the disproportionate burden of the human side 00:41:13.420 --> 00:41:16.340 of future hurricanes. That cycle of unequal impact, 00:41:16.559 --> 00:41:19.239 unequal recovery, sadly, it will just keep repeating. 00:41:20.329 --> 00:41:22.750 outro form. So as we wrap up what's been a really 00:41:22.750 --> 00:41:25.090 crucial exploration today, I want to leave you 00:41:25.090 --> 00:41:27.010 our listener with a final thought to ponder. 00:41:27.159 --> 00:41:29.340 How do you think your own community might fare 00:41:29.340 --> 00:41:32.360 if a major meteorological event hit? Think beyond 00:41:32.360 --> 00:41:34.360 just the physical stuff, the buildings and roads. 00:41:34.539 --> 00:41:37.019 What are the unseen underlying factors at play? 00:41:37.400 --> 00:41:39.440 The things we've talked about today, maybe historical 00:41:39.440 --> 00:41:41.420 settlement patterns, access to different kinds 00:41:41.420 --> 00:41:44.260 of capital, insurance deserts, even the local 00:41:44.260 --> 00:41:46.840 politics of risk perception. How could those 00:41:46.840 --> 00:41:49.159 factors profoundly shape the outcome for your 00:41:49.159 --> 00:41:51.219 neighbors, your friends, maybe even your own 00:41:51.219 --> 00:41:53.730 family? because understanding these deep patterns 00:41:53.730 --> 00:41:56.050 from our past is absolutely essential if we want 00:41:56.050 --> 00:41:58.469 to build a more equitable and truly more resilient 00:41:58.469 --> 00:42:00.969 future for everyone, even when facing nature's 00:42:00.969 --> 00:42:03.230 incredible power. Thank you so much for joining 00:42:03.230 --> 00:42:05.349 us on Meteorology Matters for this important 00:42:05.349 --> 00:42:08.190 and I hope insightful conversation. Your time 00:42:08.190 --> 00:42:10.590 and attention really do make a difference. Please 00:42:10.590 --> 00:42:13.030 do remember to like, follow, comment, and rate 00:42:13.030 --> 00:42:14.920 the podcast wherever you're listening. It helps 00:42:14.920 --> 00:42:17.119 others find the show. And if you want to dive 00:42:17.119 --> 00:42:19.619 even deeper into meteorology, especially hurricanes, 00:42:20.139 --> 00:42:22.800 make sure to follow meteorologist Rob Jones on 00:42:22.800 --> 00:42:25.780 Instagram. He's simply meteorologist on TikTok. 00:42:25.840 --> 00:42:28.880 You can find him at TV Meteorologist and on YouTube. 00:42:29.039 --> 00:42:31.800 Follow his channel Rob Jones Hurricane and bonus. 00:42:31.880 --> 00:42:33.539 You can find a playlist of all our Meteorology 00:42:33.539 --> 00:42:35.840 Matters episodes right there too. Until next 00:42:35.840 --> 00:42:37.699 time, please stay safe, stay informed and keep 00:42:37.699 --> 00:42:38.199 looking up.