How Insurance Works

America’s Climate Change Future – Session 1: Coastal properties and climate change

[MUSIC PLAYING] So let’s bring up
the first panel. And bring up your
little name tents. And people can sit across
here, if you don’t mind. It’s in your folder. But don’t worry about it. [INAUDIBLE] So Lint, you’re first. Right? I’m lost. You’re lost? So Curt, do you want
to start us off? [INAUDIBLE] Yeah. I think everybody knows me. It doesn’t really matter. Yeah, you guys sit here. I’ll do a quick
introduction of the topic. And then we’ll go. How do I do this? Where’s Stephie? Oh, there she is. Full screen mode. There we go. Well, hi everyone. My name’s Kurt Spalding. I’m a professor of the
practice, as Timmons introduced me, which
is something I’m starting to get my head around. But I’m very much enjoying
my time with the Institute. And I want to thank Amanda
for giving me this opportunity to be with all of you, and
to be with all of you today. So what I will do quickly is try
to put this panel in a context, a context I know pretty well. Because as regional
administrator of EPA, that means running EPA’s
operations across New England. I got to know these issues
at a very local level, what happens in communities. That was the charge
President Obama gave us in the President’s Climate
Action Plan announced in 2013. And I took it very seriously. So a lot of us know that most of
the media about climate change has been about how
hot it will get, how high the water will get,
how severe the storm will be. And that really
hasn’t connected, because people tend to be
psychologically risk adverse. They don’t embrace risks. And psychologists can
talk a lot about that. But the bottom line is, they
don’t connect to it till it actually happens to them. So here in New England it’s
been especially difficult, because as the paper
reported the other day, that the president
referred to, we have not had a shockingly
catastrophic climate connected event like
the fires in California or the floods in Houston. It hasn’t come home yet. Although, I will say
when Hurricane Irene or tropical storm Irene
went up through Vermont and basically destroyed much
of Vermont’s communities, it’s a different case up there. They are connected. But without these events,
we’re sort of stuck. How does this really translate
to communities, to action, to local thinking? And that’s what our
panel is about today, because what we’re
starting to see is climate change affecting
coastal property values. They’re not appreciating as
fast, as a recent study showed. And that starts to really
have cascading effects in communities. As most of you may
know, home ownership is the financial foundation
of most families. It’s that basic. They don’t save much. They don’t have a
lot in the bank. But they do have their home. So what happens when that
foundation starts to erode? That gets very serious at a
very personal level for people. Now, in New England we
depend on the property tax. We are completely
dependent on property tax to sustain our communities. More so than any other
place in the country. Now, compound that with
the fact that most cities and towns in New England
have larger dependence on public debt. So you look at the
foundation of what’s supposed to pay that debt. If you look at
our tax situation, and then you look at what our
panels are going to talk about, then you start to put
the pieces together. A connects to B, connects
to C, and suddenly we have a real climate connected
challenge in this region, and especially here
in Rhode Island. Let’s take it one step further. Schools, roads, parks. These are what people care
about in their daily lives. And that is what’s at
risk, and what we’re going to talk about today. Now, as RA I tried to
amplify this point, and did a tour of New
England, or especially coastal southern New England
to dramatize these connections. I got to see the
national seashore, and see erosion, and see
managed retreat out on Cape Cod. If you want to see the
front line of climate change in New England, you
must go to Cape Cod and see how they’re
wrestling with that. I went to Scituate,
Massachusetts, a community that has the largest
number of flood insurance claims in the Commonwealth. And I actually heard
a strategy, which was very scary, about broadening
the base of the flood insurance program to continue
to subsidize what is essentially unsustainable
in Scituate, Massachusetts. And that was before all
the northeasters hit there last spring. And then finally, I got to
go to Wickford, Rhode Island, stand in downtown, and watch
the geyser of water come up through the public parking
lot in downtown Scituate. So these connections
are starting to be made. And that’s what our
panel is about today. Today we turn the social
science of economics. We get to hear from
three, and I think you’ll appreciate, young– and I’m going to call them
pioneering economists that are striving to help us
understand what is starting to emerge as a slow,
chronic crisis that will have profound effects
in our communities. So I’m going to move
to our introductions and start with Dr.
Lint Barrage who is assistant
professor of economics and environmental studies,
and a faculty research fellow here at Brown in the
Institute, and in economics. She has here PhD from Yale. Lint’s research informs us on
the dimensions or difference of what she calls
heterogeneity about people living in the coastal zone. Understanding, I guess,
as we all come to stand, economics is not really the
rational science anymore. It’s a behavioral science. And Lint embraces that. And what I really love
about what she did, and I’ll let her
talk about this, is she actually got
out in the field and confirmed what her
research is talking about. She actually talked to real
people about their perceptions about their risk. So at this point I’d
like to turn it over to Lint, who will
speak on these topics. [APPLAUSE] The podium, can it be lowered? Oh. [INAUDIBLE] Yes, perfect. All right. Well, thank you so much. I’m so happy everyone’s here. And I’d also like to start
by thanking Amanda, Ibis, and Brown University for
giving me the opportunity to be here and conduct the work
I’ll be talking about today. So the work I
wanted to talk about is motivated by the question
of how climate risks will affect coastal housing markets. Coastal areas may only be
a small share of the land area of the United
States, but they’re home to 40% of the US population. In addition, by some
estimates, $1.4 trillion of real estate value are
located within an eighth of a mile of the shoreline. This is according to
estimates by Reuters, using property values from 2014. Going forward, a
lot of these assets are at significant risk
from sea level rise. 3.3 feet of sea level
rise, a plausible scenario by end of century,
could inundate, by some estimates, 13,000 square
miles of the United States. This is about the combined land
areas of Connecticut, Rhode Island, and Massachusetts. [INAUDIBLE] might also
increase flood zones by 40%. So clearly, there
is a lot at stake. Now, in a textbook
economic world where everyone has
perfectly informed beliefs and understanding of both
current and future flood risk, we would expect these
risks to already be accounted for in
coastal property markets. So, for example, if
we had two homes that are otherwise identical,
same school district, same view of the water, same
everything, except, say, one is at slightly
higher elevation, and thus less exposed to sea
level rise than the other, we would expect the safer
home to fetch a safety premium, and the
more exposed home to be subject to a risk penalty. And as predictions of
sea level rise get worse, we would expect the
more exposed home to have a bigger and bigger
risk penalty in anticipation of these impacts. Now, these losses
in property values are a real economic costs. They’re one of the fundamental
economic impacts of sea level rise. At the same time,
those price declines also serve as an important
price signal to reward safety. So, for example, if we think
of new housing investment and construction, then at least
if the prices reflect risks, new housing investors would have
an incentive, all else equal, to favor safer areas
and structures. Now, so that’s efficient
risk pricing in theory. The reality of how US
coastal housing markets have dealt with flooding
climate risks has been a lot more checkered. So broadly in the empirical
literature and the date, we commonly fail to detect this
kind of risk capitalization in vulnerable
coastal properties. Now, I should say
the record is mixed. In some areas, market
segments, and time periods we do see this risk
capitalization. So as Matthew will talk
about, for example, after flood events with
[INAUDIBLE] salience, we see a bigger
risk capitalization. But broadly, we
don’t yet quite see in the data what we might expect
from a theoretical perspective. We also see arguably somewhat
shockingly low uptake of flood insurance policies. So depending on the estimate,
in FEMA high risk flood zones only 30% to 50% of properties
have flood insurance policies on them. And this is in spite of the
fact that some of these policies come with a subsidy,
and also there is a mandate for federally
insured mortgages to have flood insurance
policies on them. So in work that I’ve
been doing with Laura Bakkensen at the
University of Arizona, we’ve been seeking to understand
the role that heterogeneity in people’s beliefs about
flood risk and climate risk plays in explaining these
housing market patterns. Now, the focus on beliefs
is from standard logic of asset pricing. The price of any asset, be
it commodities, gold, stocks, depends fundamentally
on people’s beliefs. If beliefs are
reasonably accurate, markets are efficient and do
this wonderful thing they do of us, which is to allocate
resources to their best use. But if people become
excessively optimistic about the future
value of an asset, there is a potential
for mispricing, bubbles, overinvestment, and
additional economic risks. So we’ve been
seeking to understand to what extent climate
believe heterogeneity and skepticism may
expose US coastal housing markets to similar risks. We’ve approached
this in three steps. So first, we’ve been
looking at housing price data and the
literature, which appear potentially consistent
with excessive optimism about flood risk. But we cannot say for sure. That data are not conclusive. And this is because there
are many other reasons why we might not see full risk
capitalization into housing prices. People might expect
seawalls to be built or FEMA assistance in
case of a flood event. So to get more evidence
we do something that’s a little
shocking for economists. We went into the field, went
door to door in Rhode Island, and talked to people. As economists we’re
normally trained to look at how people
spend their money and not even listen
to what they say. But in this case, we
felt we could learn more by actually talking to people. So we talked to
people, both those who do and do not choose
to live in high risk housing about their
beliefs, perceptions, and also how much
they value living by the waterfront, which is
another critical component of this. And then, finally, we
developed a quantitative model of the coastal housing
market in Bristol county to simulate coastal
home price trajectories across different risk
belief and policy scenarios. So I don’t have time to show
you all the survey results. But to me, the most
striking thing that we found is that not only is there a lot
of heterogeneity in people’s perceptions of flood risk. But we find that people who live
in a high risk FEMA flood zone actually are significantly
less concerned about flooding than their neighbors who live
just slightly outside the flood zone in those same communities. So on this graph we have a
histogram of what people tell us on a 10 point
worry scale, 1 being I’m not at all worried about
flooding affecting my home, 10 being I’m very worried. The pink bars are people who
live in a FEMA high risk flood zone. So in our sample,
40% of respondents who live in a FEMA
high risk flood zone tell us they are not at
all worried about flooding affecting their home. In contrast, the plurality
of their neighbors who live slightly
further inland told us they would be very worried
about flooding if they lived by the waterfront. That being said,
that’s, of course, not the only determinant
of selection. We also find people who live by
the water value it a lot more. So there’s a lot
going into this. But we do see data
consistent with sorting based on disproportionately
low flood risk beliefs. We also fail to detect
significant differences in people’s expectations of,
say, FEMA assistance in case of a flood event. So we don’t see
evidence that that’s what’s driving the sorting. We also ask people to
tell us numerically what they think the risk
of flooding to their home is, and compare that to
inundation model data. So for each home that we
visit, we have elevation data, and can combine this with
the storm tools inundation model to look what’s
that house’s flood probability in the model. And compared to the model
results, 70% of our respondents who live by the
water underestimate their home’s flood risk. Now, that’s not
true for everyone. Some people are very worried. But the people who
are very worried tell us they are
disproportionately likely to intend on
selling their home and leaving this housing market
over the next five years. And finally, as
you might expect, people who are less concerned
about flooding today also are less worried
about flood risk increases in the future. So the reason all this matters
is that markets cannot price risks efficiently if people
don’t believe in them. And why would we want
efficient risk pricing? At least two reasons. So the first is, if
risks are not already reflected in coastal
property values, that means we have a
lot further to go in the future in price declines
than we might otherwise expect. So we simulate Bristol
county coastal housing prices over the next 25 years. This is a one foot sea level
rise scenario we focus on. And we assume that by 2043
beliefs will have converged, perhaps because people agree,
or because of some policy reform that actually enforces a
flood insurance mandate. But we assume within
25 years we’ll agree. Well, in a world of
[INAUDIBLE] and everyone agrees 0% is optimistic,
everyone just adopts the official
forecast, the price declines that are yet
to come, depending on how much flood
risk increases, range from minus 3% to minus 1%. Now, let me stress,
this is not a prediction for housing prices. This is just the
additional correction holding all those constant
incomes, population, et cetera, for the extra flood risk. But the key message is that if
currently 35% of the population is excessively optimistic
about flood risk, we would predict
the remaining price declines to be much higher. 13%, 28%, 3%. It depends a lot
on what actually happens with flood risk. But the point is, it’s
a lot bigger what’s yet to come if
people are currently excessively optimistic. And from a housing
price perspective, a 13% correction is economically
extremely significant. Great recession peak to trough
housing price decline was 19%. The second reason why efficient
risk pricing is important is because our
beliefs about risks affect our investment decisions. So in a second project that I’ve
been doing with Jacob Furst– he is a phenomenal Brown
University undergrad. He was a sophomore when we
started working on this. He’s a junior. It’s now been accepted
for publication. So yay, Brown undergrads. We looked across the US eastern
sea shore and Gulf states at how new US housing
construction of single family homes varies with both sea
level rise exposure and climate change beliefs. So we obtained data on
new housing construction from the US Census Bureau. We combined that with NOAA
inundation layers of sea level rise exposure in each
of these counties, and with the Yale Project on
climate change communication, provides estimates
of people’s climate change beliefs in each county. We combined that with
standard controls for housing construction,
employment dynamics, housing construction costs,
demographics, et cetera. But then once we control for
these standard factors, what we see is that at
the county level, sea level rise
exposure is associated with significantly lower
new housing construction, but only in counties
where sufficiently many people believe that
climate change is happening. So now in our data that
the cutoff is at 72%, we have to be careful. This is an association. Sea level rise
exposure is associated with other things such
as flat topography, and view of the ocean, which
also affect new housing construction. But what we see is that the
relationship between exposure and new construction becomes
significantly smaller and eventually negative
as more people are concerned about climate change. Conversely, as fewer people are
concerned about climate change, we see the association being
bigger and more and more positive. So from this work,
I think what I’ve come to believe is that
understanding climate risk beliefs is very important for us
to both understand and forecast how housing markets will
respond to climate change. There is a possibility that
asset prices do not yet reflect risk and that climate
skepticism may be delaying adaptation in housing markets. So from a policy
perspective, this highlights the critical
importance of accurate and up to date flood
and climate risk information for the
efficiency and stability of coastal housing markets. So FEMA provides 100
year flood plain maps for much of the United States. This is a vital starting point. But these maps have a
number of shortcomings. For example, they’re
often out of date. So we checked the
community book last year. One in six maps was
over 20 years old. And as Matthew
will tell you, this was an issue with Hurricane
Sandy in New York. But in addition, we don’t
just need up to date maps. We need forward looking
flood risk information. So FEMA flood maps
are backward looking. But for a homebuyer
or investor, we want to understand what
are these risks going to look like in the future
over the course of my 30 year mortgage? And some communities that are
leading the way are New York. And as Kurt mentioned,
and Timmons mentioned, now also Rhode Island. So this was mentioned before. The Coastal Resource
Management Counsel in URI, they’ve created this coastal
environment risk index. So this is a simulation in
Warren a hundred year storm. Hurricane Carol, if
it happened again. You can see the homes
that are affected. And the color coding is for
what fraction of the home value would be destroyed. And so this is with
no sea level rise. This is with five feet
of sea level rise. But these kinds of
visualizations, this is new. I think it’s an open question
how it will affect people, and their beliefs, and their
decision making and property values. But my guess is that these
kind of visualizations will be very helpful for people
to internalize these risks, and for property markets. The least we can do
is adapt and make the best of the
situation going forward. Thank you. [APPLAUSE] Questions at the end. Correct. Save your questions. Right. Well, as you can see, I’ve
got a ton of questions. But I’m going to hold them off. So you can hold them off so we
can move to the next speaker. But thank you. It was very, very
important work. And I do want to acknowledge
the work you saw at the end is really pathbreaking
work that it speaks to some of the great
accomplishments of our friends down in Kingston at URI
and on the coastal campus. The next speaker is Dr. Matthew
Gibson, assistant professor of economics at
Williams College, with expertise in economics
and labor economics, or environmental economics
and labor economics. Fascinating in reading his
paper, because I lived it. When Hurricane Sandy hit
this region, or not exactly New England, immediately to
the west, all of us at EPA were drawn into the big
disaster that that represented. So his paper speaks to that. What does it mean to
have these things happen? And how do markets react? Namely, flood insurance
reform, or the attempt at it. I can let the senator
speak to that. Hurricane Sandy, which is a
really tremendous event that’s changed so much
down in that region. And, of course, the
infamous FEMA flood plain maps, which I can
tell you in this region, the revision of them was
largely a disastrous event for all of us, cause it created
all kinds of community angst and concern as we went forward. So now I’d like to turn the
program over to Matthew. Thank you very much
for the opportunity to talk about this
work with you today. First project on the docket here
has to do with, as Lint said, with the residential property
market in New York City. In particular, we’ll be
looking at single family homes. And this is work with Jamie
Mullins at UMass Amherst, and a former student
of mine, Alison Hill. We’re interested when you shock
a residential property market, like New York, a consequential
coastal market with more than $60 billion in its
1% annual risk floodplain, how do market
participants respond? And, in particular, we’ll be
talking about these three risk signals. The first is– and I do
hope that the co-sponsors of this bill were
chosen strategically– the Biggert-Waters Flood
Insurance Reform Act of 2012. What did this do? In brief, it set
subsidized NFIT premiums, National Flood Insurance
Program Premiums to increase at an annual
rate of up to 25% per year, in total, reaching
actuarially fair levels. Meaning levels at which
people’s payments, their premiums
reflect the flood risk to the property
that’s being covered. And also it was
supposed to do away with grandfathering
of risk ratings. So under the old
regime in the NFIT, if FEMA redrew the map for
your community, and said, well, we thought the annual
risk to your property was 0.5%, it turns out it’s 2%, you
got to keep your old premium. Biggert-Waters attempted
to do away with that. A little bit later
in the same year we get Hurricane Sandy
hitting the New York area. It’s not, of course, a hurricane
by the time it makes landfall. But this was a large,
slow moving storm. It piled up a great big wall
of water in front of it, and it generated a
catastrophic storm surge. Finally, the risk signal
in which we’re perhaps most interested, which might
have the greatest policy relevance, is the release of the
redrawn FEMA flood plain maps, ABFE’s advisory based
flood elevation. These are still the subject
of litigation and controversy. But the first release
of those updated maps was in 2013, received a lot
of press in “The Wall Street Journal,” “The New
York Times,” et cetera. If you look at Google
search activity, you can see it spiking quite
a bit just in this month. Why are these maps important? At the time Sandy came
ashore, the maps, in effect, have last been meaningfully
redrawn in 1983. But they were terribly outdated. Between 1983 and the release
of the new maps in 2013, the New York area experienced
about 3 and 1/2 inches of sea level rise. New York is getting
hit worse than average, because the plate
on which it sits is pivoting downward at the
same time the ocean is rising. And as a result, the
redrawn floodplain is substantially larger in
scope than it was previously. This is the southern part
of Brooklyn, Coney Island. And the old floodplain
is represented by these areas in orange. The new floodplain
includes the orange areas, but also the area’s
colored in yellow. By the way, each one
of the little dots you see on this slide reflects
one of the single family home transactions we
observe in our data. You can pick out the
parks and streets without even plotting them
explicitly, which is nice. But a lot of people
are plausibly getting some new information
in this scenario about the risk to their property. We’d like to know
how they react. You can do this
analysis with pictures. So if we’re interested in the
effect of the Biggert-Waters Flood Insurance Reform
Act, this rise in premiums, we can divide our
sample of properties into those that were
outside the 1983 floodplain, and thus unaffected by
Biggert-Waters largely, and those in the 1983
floodplain who were plausibly affected by Biggert-Waters. We see a relatively large
peak to trough drop here. But I do want to caution you. Many of the same homes that
experience premium increases under Biggert-Waters
also get hit by Sandy. So that peak to trough
reflects both phenomena. When we put this into a
richer statistical model and estimate the effect of
Biggert-Waters in isolation, it’s about a 2% fall in
price for the affected homes. In this picture, we’re looking
at the effect of Sandy itself, the experience of being flooded. This is, again,
plausibly informative. It’s unpleasant in addition
to being informative. But we’ve divided
the sample here into three groups, properties
unflooded by Sandy, properties in the old floodplain, the 1983
floodplain who then experience Sandy flooding. Sandy’s perhaps less of a
surprise for the properties in that group. And finally, in our
third group, properties that were flooded but were
outside the 1983 floodplain. This was the group for whom
the flooding is plausibly more surprising. We see market drops
in transaction prices in both of these groups. Now, ex ante we thought
that the surprise group would exhibit a bigger decline. You don’t see that here. And that’s because the
surprise properties got flooded less deeply. The depth of the flood
waters was less high. Once you adjust for the
depth of inundation, if we look at two minimally
flooded properties, one that was in the 1983 floodplain
at the time Sandy hit, and one that was not, we
see suggestive evidence that there’s more of a price
response for the property that was surprised, that was
not in the 1983 floodplain at the time Sandy hit. The average effect, by the
way, for those who are curious, of being hit by Sandy
at average inundation is about a 6% price decline. So bigger than the response
to the flood insurance premium increases, but smaller, as you
will see, than the response to the maps. And it’s this last
group that’s telling us something about the response
to the updated floodplain maps. Again, we’ve divided the
homes into three groups. Properties outside the
expanded 2013 floodplain, properties that are included
in that new 2013 floodplain, but had previously
been flooded by Sandy. You might imagine the response
you would have gotten had you gone to the door of a New
Yorker in 2014, somebody whose home was flooded by
Sandy and said, by the way, the new map says your home
is at risk of flooding. Our last group is
our surprise group. These are folks who are
located outside the boundary of the new flood plain, but
escaped flooding in Sandy. And for these people, the map’s
plausibly more informative. Interestingly, by the
way, in this final group, you might notice an upward
wiggle in the graph just here. That corresponds with the
passage of HFIA, the Homeowner Flood Insurance
Affordability Act, which partially walked back some
of the Biggert-Waters reforms. Importantly, though,
for our purposes, for new purchasers of
homes in a floodplain, there’s really no
advantage from HFIA. It slowed the rate
of premium increases that was laid out
under Biggert-Waters. It restored grandfathering
for incumbent owners. But for a person newly
purchasing a property in a floodplain, HFIA really
provides no long term benefit in terms of premiums. And you see that once
HFIA is actually passed, the burst of optimism goes away. And we see quite a large
price decline in this group. How large? It’s about an 18% price
decline for properties that escaped Sandy
flooding, and were then included in the 1% floodplain
under the redrawn 2013 maps. You may notice, by the way, that
I keep saying 1% floodplain. There’s been a
tendency in journalism and sometimes from
FEMA officials to refer to a 100
year floodplain. And that tends to lead people
into a species of the gambler’s fallacy, where they say,
I’ve been flooded once. I’m safe for 99 years. It’s correct, as most
of you in this room know to think instead,
you have a 1% chance of getting hit every year. And that chance is
independent from year to year. Now, we didn’t want
to stop with simply looking at the effect
on transaction prices. From a certain point
of view, saying that a property is more
risky and the price goes down should be unsurprising. We’d like to infer,
if we can, something about how people’s
beliefs are changing. Because efficient
decision making by people on the supply
side of the housing market, or the
demand side, depends on having accurate beliefs. So we impose some
assumptions about how people are making decisions. I am not going to
belabor those here. This is our attempt
to see what exactly is going on under the hood. And we’re taking advantage
here of an interesting feature of the National Flood
Insurance Program. This is estimating the
effect of the new maps in bins of structure value. Why is this informative? The National Flood Insurance
Program caps structure coverage at $250,000. That’s not indexed to location. It’s not indexed over time. This is a hard cap that’s
going to remain in place unless Congress changes it. Why is that advantageous for us? These are the non
surprise properties that get hit by the new maps. And you can see, most
of these estimates are clustered very
close to zero, no matter which bin of
structure value we look at. If we look over here,
these are estimates for the surprise
properties, properties that escaped flooding in
Sandy, but were then included in the 2013 floodplain maps. For cheap structures the effect
is indistinguishable from zero. This is consistent with the
risk being fully insurable for these properties. If my structure
is worth $200,000, I can buy NFIP coverage
that diversifies away all of the risk for me. Diversify is the
wrong word there. Covers all of the risk for me. For properties with structure
value above that cap, though, the map is telling
me that risk is higher than I thought. And I can’t insure it away under
the National Flood Insurance Program. And it’s precisely
for these properties that we see the
largest responses. These are the homes
that are driving our 18% average decline in price
for this group of properties. If we impose some structure
on how people are updating their beliefs, we can back
out what the implied change in the agents’– agents. Sorry. Jargony. –home buyers’ subjective
belief over flood risk is. For the Biggert-Waters
Flood Insurance Reform Act, we get pretty close to a zero. That is consistent with
people viewing a higher flood insurance premium
as an irritation, as perhaps a reason to curse
the federal government. There is no evidence that
they’re changing their belief about the riskiness
of their property in response to this
insurance price signal. If we do the same thing
for Hurricane Sandy, we see about a 0.2
percentage point increase in people’s implied beliefs. Is that small or large? Well, if this is a
1% annual floodplain, in proportional terms, that’s a
20% increase in perceived risk. So when people get hit
with a Hurricane Harvey, a Hurricane Sandy, when
it comes home, then potentially we’re seeing
some big belief updating. But this is not so
policy relevant. Right? It’s not a good idea for us to
engineer climate catastrophes so people will
update their beliefs. If we do this for
updated floodplain maps, we see our largest implied
belief updating of all. It’s 0.43 percentage points,
nearly half a percent increase in subjective belief. I can’t tell you whether people
are moving closer to or farther away from the truth. It is possible there is
some overreaction here. But it suggests that
at least in New York, where the Yale climate
polls will show you that most people believe
climate change is anthropogenic and happening, that there is
room for a pure information policy intervention to
move people’s beliefs, and therefore move
market outcomes. I’m going to say
something really quick about city survival. In a certain sense,
talking about the response of home prices can feel like
Nero fiddling while Rome burns. Broadly speaking,
climate leads us to ask the question of
how long our cities are going to be here? Especially for coastal
cities, but not only for coastal cities. And based on Laplace– Laplace is the old gentleman
you saw just here– provide us some way of
forming a coherent belief about the existential
risk to our cities. In particular, this
result due to Laplace is if I want the probability
of city survival, I can take the number of
past years it has survived, plug it into this
very simple formula, add 1 in the numerator,
add 2 in the denominator, and that’s my coherent
probability of the city surviving next year. What’s this say essentially? Cities that have been
around a long time have revealed themselves to be
in relatively safe locations. Cities that are young
are plausibly less safe, less good bets. American cities
are mostly young. I’m going to skip where
this formula comes from. But given your
belief about risk you can project survival
probability at any given point in the future. And we’ve done this
here for four US cities. Now, Providence is a
relatively old US city. It has about a 75% chance of
surviving another 200 years. It has about a 25% chance of
surviving another 1,000 years. But many American
cities are much younger. If we look instead at Tampa, it
has a 25% chance of surviving another 500 or so years. We can also do this
exercise within cities. And it’s perhaps here where
the connection to climate risk is most important. Census tracts, or plots
of lands within cities aren’t the same age and don’t
have the same population history. I’m showing you here San
Francisco in 1930 and in 2010. You’ll notice the marina
and parts of the Embarcadero did not exist even in 1930. A lot of this is landfill. We don’t have a
lot of experience with people taking the risk
of living in these locations. And that suggests that
our coherent belief over the annihilation risk
or the existential risk in those sites is,
in relative terms, much higher than it is for
someplace where people have been living for a long time. That’s all I have for today. Thank you very
much for your time. [APPLAUSE] Again, great questions
are in my mind. Last week we held a
forum in Providence to discuss that very topic,
the future Providence relative to its resilience
and surviving into the future. So don’t mind if I call
you back at some point and help us with that project. It’s now my pleasure to
introduce Dr. Stephie Fried, who I think has traveled
all the way from Arizona. She has. She is now at Arizona State
University in the Carey School of Business. She has her PhD from University
of California San Diego. Now, fascinatingly,
her research focuses on the environmental
impacts and implications for the macro economy. But put this in a
context for us today. She gives us insight about the
value of climate adaptation, this fascinating question of
what are the impacts as we try to figure out how to adapt
and look at the actual FEMA programs. FEMA has two programs. They have an assistance
program after a disaster hits. But they, importantly, have a
mitigation planning program, which is supposed to help
communities be stronger, or be prepared, or be more
resilient to that accident. So, again, it’s
my great pleasure to introduce Dr. [INAUDIBLE]. [APPLAUSE] Thank you very
much for having me and for putting together
this mini conference. So I want to start by defining
what I mean by adaptation for the context of this paper. So this is going to refer to
any capital whose specific purpose is to reduce the
damage from extreme weather. And by extreme weather what I
really have in mind are storms. So this picture on
the left is part of the system of flood
barriers and seawall surrounding New Orleans. So this could be a very big,
kind of large scale capital investment that we
count as adaptation. It could also be a much
smaller scale capital investment done by a house. So this is a house that’s
been raised up on stilts to prevent flood damage. There the adaptation is
not the entire house, but just that extra
capital necessary to put it on stilts to reduce the
risk of flood damage. So what I’m interested
in are two questions. First, I want to understand
how US federal disaster policy affects adaptation incentives. And so you can think of
federal disaster policy as having two prongs. So first, we provide
aid for disaster relief. And that’s primarily
through FEMA. And then second, we
provide subsidies for investment in adaptation. And those subsidies come
from several sources. Mainly through FEMA and the
US Army Corps of Engineers. Now, this FEMA aid
for disaster relief reduces the kind of
net cost of a disaster to a particular area, because
the government compensates them for some of their losses. And so what that means is
that could then, in turn, reduce that area’s incentives to
make investments in adaptation. So there’s the sort of what we
think of as a moral hazard type effect, or potential for
a moral hazard type effect from FEMA aid. So our government knows this.
or they seem to know this. So they also introduced
these subsidies, which are designed to counteract
that moral hazard effect, because they reduce the
relative price of adaptation. So if I want to put
my house up on stilts, maybe FEMA will pay
75% of the costs. I’ll only have to
pay 25% of the costs. They make it cheaper for
me, and so they increase the amount of adaptation I do. So what I’m interested
in here is, first, how big are these moral
hazard effects from FEMA aid? And then how effective is
the subsidy at offsetting that moral hazard? And then the second question
is to think about adaptation in the context of
climate change. And here I just want to
focus on one specific type of predicted outcome of
climate change, which is an increase in the severity
of extreme weather, of storms. And I want to understand,
first, how much does adaptation respond to
this type of climate change. And then how does
that adaptive response affect the damage
and the welfare costs from the climate change? So to do this, I develop a
general equilibrium macro style model of adaptation investment. I’m going to show you
the model in a picture. Then I’ll give you just
a tiny bit of the math. And then we’ll go
to the results. So what you think about
is dividing the US into N different regions, where
these regions are different based on their distributions
of extreme weather. Again, these are storms. Then inside each
region we’re going to think about there
being a continuum of heterogeneous localities. So think of these
as counties, lots of counties within a region. And then all of these
counties or localities are going to be
ex ante identical. But they experience
different sequences of extreme weather shocks. So in some periods,
some are hit by a storm. In other periods, others
are hit by a storm. And so on. These counties are all
run by local governments or local social planners that
make decisions to maximize the welfare of the
representative household living in that county. Now, that planner
or local government can choose three things. First, she can decide
the county’s level of productive capital. This is what we normally think
of as capital, your buildings, your factories, your machines. It’s what’s used
to produce output in the particular county. Second, the planner
can also decide that level of adaptive capital. These are your
seawalls, your stilts, your storm drains
that are reducing the damage that
county experiences if she’s hit by a storm. And then third, the planners
can purchase insurance, things like homeowner’s
insurance or flood insurance that reduce kind of the
risk that the county faces when given that there are
these extreme weather shocks. And then presiding
over all of this is the federal US
government, which taxes all of these
localities, and then uses the revenue basically to
finance US disaster policy. So to provide that FEMA
aid for disaster relief and the subsidies
for adaptation. OK. So just a small bit of the
math, the way this is set up is each period at the start of
the period the extreme weather shock realizes. So if epsilon equals 0, there
is no storm in your county. If epsilon equals 1,
you’re hit by a storm. And p is the probability that
you’ll get hit by a storm. And this probability
is allowed to vary across those different
regions in the US. Then if you are hit
by a storm, this destroys some of your
productive capital stock. So think about a hurricane
coming through and destroying your buildings, your
factories, and so on. So k superscript p here
is the productive capital in a particular locality.
kd is the capital that was damaged by the storm. And the subscripts
here, i is your region, and then j is the locality
within that region. And if we walk
through the pieces a little bit of
this equation, omega is telling you the severity
of the extreme weather in that region. So what fraction of your
productive capital stock is destroyed if you
get hit by a storm? And this is also going to be
allowed to vary across regions. So those regions of the US,
again, are differentiated based on the probability
that they get hit by a storm, and then by how bad the
storm is if it does occur. Ka is the locality’s stock
of adaptation capital. So how many seawalls, and
stilts, and so on it has. And h of ka is a
function that translates that stock of adaptation capital
into reductions in damage. So importantly, this function
is decreasing in the level of adaptation capital. So as I add more
seawalls and more stilts, this reduces the damage I
get if I am hit by a storm. Another important
parameter that we’re not going to have time to talk
about is this parameter theta, which is basically
determining the effectiveness of the adaptation. And this is going
to be very important for the quantitative results. And so I just want
you to know that this does come from, at least,
empirical evidence on the data on disasters in the US. OK. So turning next to our results. So we’re going to run two
experiments in the model, one for each of those questions. So first experiment is
to look at the effects of federal policy. And so I’m going to compare
three different equilibriums in the model. The first is what we’ll think
of as our baseline equilibrium. This you should view as kind
of the US economy today. And we have all the elements
of federal disaster policy that we have in place. So with the FEMA aid and with
the subsidies for adaptation. Then we’ll look at two
counterfactual equilibrium. In one of those equilibrium
I’m going to get rid of all federal disaster policy. So there’ll be no FEMA
aid and no subsidies. And then in the
other equilibrium we’ll have the FEMA aid, but
there will be no subsidy. And then comparing these
different equilbria will let us quantify the moral
hazard effects from FEMA aid, and the effectiveness
of the subsidy offsetting those
moral hazard effects. And so what we see
here is this graph is showing you
adaptation capital as a percent of the
total US capital stock in the counterfactual
equilibrium in which there is no federal disaster policy. So this says if we get rid of
the FEMA aid and the subsidies for adaptation, then
adaptation capital would be 0.04% of the US capital stock. Now, I want to
compare this value to what happens when we
introduce the FEMA aid but we don’t bring
in the subsidy. Then that moral
hazard channel says, FEMA aid means the
areas are basically reducing the cost of
extreme weather to an area. That reduces their incentives
to make adaptation investments. And so we should see
adaptation capital fall. And so we do. And in this case it goes
all the way to zero. It didn’t start that way. And now we say, OK,
well, this is not what the federal
government’s done. They provide this aid
for disaster relief. But they also have
this subsidy, which reduces that relative price
of adaptation investment, makes it cheaper. So it should offset some of
this moral hazard effect. So when we add back
in the subsidy, we should see
adaptation go higher than the aid only equilibrium. If that subsidy perfectly
offsets the moral hazard, then we would see adaptation
capital equal the value in that no policy equilibrium. Instead what we see is that
adaptation capital is actually a lot higher than in the
no policy equilibrium. And so what this means
then is that the net effect of US federal disaster
policy is actually to increase adaptation
capital in the US economy. Now, I want to
comment for a minute on the size of these numbers. So what this purple bar says
is that adaptation capital is 0.13% of the total US capital
stock, which seems small. But if you then compare this
to the amount of the US capital stock that’s destroyed by storms
each year, that’s about 0.23%. So what this says is
that adaptation capital is about over half of
the total capital that’s destroyed by storms, or
what it’s projecting. Oops. Wrong way. And then the second
experiment is to look at the effects
of climate change. And, again, focusing on just
one type of climate change, which is that increase
in storm severity. So we’ll model this as
a permanent increase in that severity parameter. And I want to think about
three different climate change scenarios. A 50% increase in
storm severity. 75% and 100% increase. And, again, I’m going to compare
several different equilbria or steady state. So we’ll have the same kind
of baseline equilibrium. You could think of this
as the US economy today, or the no climate
change equilibrium. And then we’ll calculate
two different climate change equilbria in which we
scale up that severity parameter by some factor
depending on the scenario. In one of those climate
change equilbria we will let adaptation adjust. So we’ll just resolve
the whole model with a higher value of
the severity parameter, and look at how
adaptation responds to that increase in severity. In the other climate
change equilibrium, we’re not going to
let adaptation adjust. We’re kind of going
to artificially fix the level of adaptation
capital at its value today in the US economy. And then we’re going
to resolve the model. And then basically comparing
those two equilbria will let us understand
how much adaptation effects the damage
and the welfare costs from climate change. So what this graph is
showing you on the x-axis is the scenario, so
the percentage increase in storm severity. A 50%, 75%, and 100%. And then what I’ve
plotted here is the percentage increase in
damage relative to our world today, to the baseline. So in the case where there’s no
adaptation, so we can’t adapt, adaptation capital
is fixed at its value today, if there is a 50%
increase in storm severity, this would lead to almost
a 50% increase in damage. It’s not a full
50% increase, cause there are some changes in
the productive capital stock. Now, what I want to
do is compare that to what happens if we do
allow for localities to adapt. And then we see that the 50%
increase in storm severity leads to about a 28%
increase in damage, instead of the full
47% increase in damage. So adaptation is considerably
reducing the increase in damage that we get by about 45%. And then what we kind
of care about is, well, what does this mean for welfare? And so to measure
welfare, we use something called the consumption
equivalent variation. So think about this as
your percentage increase in consumption you would
need in every period today in our kind
of baseline economy so that you’re indifferent
between living in our world today versus living in one of
these counterfactual worlds, which are our climate
change worlds. So negative numbers here
indicate that climate change makes you worse off. You would have to take
consumption away from people today in order to make them
indifferent between living in our world today
versus living in one of these counterfactuals. And so all these
numbers are negative. So climate change makes us
worse off, as we would expect. And they also get more
negative as the severity gets more severe, which
you’d also expect. I think the more interesting
lines of the table are comparing that no adaptation
and the adaptation row. And there you see
that adaptation is reducing the welfare
cost of climate change by about 15% to 20%. Now, that’s smaller than
that reduction in damage we saw before adaptation
reducing the damage by 45%. But what you have
to remember here is you have to pay for
this adaptation capital. It doesn’t come for free. So we’re going to get bigger
implications for damage than we’ll get for welfare. OK. So that’s it. Kind of what we’ve
done here is look at the implications of federal
disaster policy for adaptation, and also the effects of
adaptation on climate change. Thank you. [APPLAUSE] Awesome job, Timmons,
keeping us on time. And great job everybody
in bringing forth some really provocative,
thoughtful information about this whole
phenomena that I spoke to. And, again, to
re-emphasize, there are cascading effects that
could be very profound to just the way things are in New
England relative to the data that you were
presented here today. And so I want to
start this discussion. I’m only going to
ask one question. So the rest has
to come from you. So I give you a little
bit of a minute or two to sort of form questions. There’s an underpinning
of a presumption about the efficiency of
the markets and things that are essentially
warping those markets, or changing those markets. And you talked a little bit
about the FEMA information and its shortcomings. I’d like anyone really
to expound on that. I mean, what we saw,
and it was presented in Lint’s
presentation, an effort to make more transparent
to the coastal communities or coastal property
owners the actual issue. The presumption here is
with more information we would get more
rational decision making. And I guess I want to get your
insights as to what that means and where you see states– you
said New York and Rhode Island are moving forward on that– what that might
mean in our place, given we seem to be
a leader in that. Does anybody have an
orientation on, one, FEMA’s maps that are backward, and maybe
expound a little bit on what that means. And I could under
this special area. There’s something called a
Shoreline Change Special Area Management Plan in Rhode Island. And there’s something
called Chapter 5. And I could go into
those provisions. But I will after you
guys address that point. Better information. More efficiency. Do you see that as
being an opportunity to somehow over time
have a correction in what would seem to be
a warped market situation as we speak? So I’ll let anybody
spend a little more time with those issues. Do you want to take
a shot at that? Absolutely. Well, I think better
information is a necessary but not
sufficient condition is how we would think about it. Without better
information, there’s no way for markets
to be efficient. If we make information
accessible and understandable– Matthew can speak to this more– that does move markets in some
parts and for some people. Is it going to get
us all the way there? I personally don’t think so. But it’s an open
empirical question. Anything around New York? Those FEMA maps. What are they doing more so? Yeah, I think you have to get
closer to the mics to be heard. I’ll largely concur
with Lint here. We have some evidence
in New York City, which has experienced both
catastrophic flooding in the form of Sandy, and is
a relatively highly educated, highly informed
place where polling shows us people believe
in climate change, and information intervention
appears to be effective there. Even there, there may be limits. We’ve tried to
model, for example, whether there is an effect
of the proposed big U seawall around the
southern part of Manhattan. The sample of single family
homes affected is quite small. But we’re not able to discern
any positive effect on home prices from the big U. Also, NOAA and some
academic groups have drawn some forward
looking flood maps. So these are not legally
binding, don’t affect premiums, but are Googleable. And we’re not finding strong
price effects from those either. So targeted information
interventions under certain circumstances
do appear to work. They are, as Lint says,
necessary, but not sufficient. Do you have anything to add? I was going to add sort
of a political question to all of you. And I think it speaks to
all your presentations. And I’m going to ask you to get
out of your field a little bit and put on a political hat. Based on what we
heard this morning in the paper
legislation introduced to allow communities to give
tax breaks to homeowners who want to raise their
homes or build seawalls. What effect would that
have on this whole dynamic? That’s an adaptation strategy
to stilt, raise it up, or build a wall. If we start subsidizing that,
what would you see happens? Are we less efficient? Most probably, I would guess. I think the efficiency
question’s interesting. Because [INAUDIBLE]. So this would sort
of fall in that, I think, in a very
broad, general sense, a similar impact of the
subsidy to adaptation. So the subsidy coming
through the tax system instead of directly
qualifying for a grant. Then in terms of efficiency,
in some sense FEMA, there are lots of reasons for
welfare to introduce FEMA aid. And there are welfare
improvements from that. But in some sense,
it’s also introducing this distortion, where
we now invest less in adaptation capital. And then the subsidy’s designed
to counteract that distortion. So if that subsidy was
perfectly counteracting, then we would expect
adaptation capital to be the same as it
is without the FEMA. Since we actually see
adaptation capital being higher than without the FEMA, then
that suggests, in some sense, that we are introducing a new
distortion into the economy by creating kind of more
adaptation than would be there in the sort of
undistorted effort. I don’t know if that
was too jargony. Any other comments
on that, that idea? I just was surprised to
see it happening today, and this being introduced. Our public finance professors
would be very cross with us if we didn’t point out
that the efficiency consequence of a subsidy
like that depends not only to whom
the subsidy goes, how it’s targeted, but also
how the funds for the subsidy are raised. And in most actual
government settings, as opposed to academic models,
the funds for a subsidy are raised via some kind
of distortionary tax. So if we are distorting a market
and creating a big inefficiency in order to get money
and then hand it to somebody to raise
their house on stilts, it may or may not be efficient. Hello. We have about 10 minutes
left, and like to bring in the audience at this
point for any questions they’ve got before we have
our first coffee break. So who has a question? Well, I’ll just go to the
closest possible person. Great. Thank you. Thanks so much. Hi. Paul Roselli from the
Burrilville Land Trust and a bunch of other non-profit
environmental groups. So maybe you’ve
already answered it. And I apologize for asking it. But I want to hear it. So when does all of this
reach a critical point where it just doesn’t become
acceptable, either politically or financially,
emotionally, morally, to do the adaptation,
to do whatever, whether it be stilts or walls? Is there a point in any of your
calculations, your formulas, your whatever that says,
there’s no sum game here? It’s time to quit. Yeah. There’s your first unanswerable
question of the day. Go for that one. See what happens. First of many. Oh, are we answering this one? Yeah. Go ahead. I throw it open to anybody. There are so many
things in these models that can lead us to predict
when the change will happen. We see, for example, in
our work that expectations about long term FEMA policy
have a huge effect on what property values are today. So that’s from the
model’s perspective. But going door to
door and talking to people who have homes in the
flood zones, some of the people I spoke to were in tears when
they thought about floods maps being redrawn, because
as Timmons said, their retirement
savings are at risk. And you have people who it’s not
as simple as, well, the risk is coming so now I’m
going to leave, and we’re just going
to abandon this area. I think for a lot of people
their savings are at stake. If the price drops,
heaven forbid they’re underwater
on a mortgage, it becomes even harder to leave. So I think there
is a lot of factors that make it very
complicated for us to predict, let alone
say how we should get out of these risky areas. Yeah. Skip Hobbs. I’m representing the Council of
Scientific Society Presidents today. But I was a coastal homeowner. My family had a home in
Stonington Connecticut right at the entrance
to the harbor. And I can tell you the
efficiency of the FEMA maps on the market
price was overnight. When the maps were redone
our 150-year-old home that had survived the ’38 hurricane
was just put in the zone. It just touched it. I actually had it resurveyed. The house was for
sale at the time. The family decided we
were going to move on. House was for sale
for $1.7 million. When the map was
published, we got a notice. The price dropped to
$1.2 million in a week. And the reason for
that was in the zone if you do any
modifications to the house, we were 14 and 1/2 feet
above mean sea level. The people who ultimately
bought the house had to raise it to 16 feet
and put all the utilities out of the basement upstairs. So the market in Stonington
was extremely efficient. We and others experienced
the same thing. Our little yacht club had also
been put in the flood zone. And a poll was taken. Now, it’s a
well-educated community. The entire club
membership was polled. There was a 70% response to
that poll, which is outstanding. 90% of the respondents
agreed to form a climate task force on how to protect the
club from rising sea levels. So people are responding
in coastal communities. Though, I think if I may
potentially disagree, or just raise the question, I don’t
know if we would necessarily think that it’s
efficient for something to happen so drastically. Right? Because flood risk
isn’t changing the day the new flood maps are released. It’s just that people pay
attention and their information changes. So the least we can do is
for these price changes to occur more gradually so that
people like you and your family don’t have these sudden
wealth shock risks. That flood risk is
changing gradually. So should our information. Then things would
be smoother and not quite so sudden and severe. I don’t know if Matthew
had thoughts on that. Who has the mic? Hi. Thanks so much. My name’s Katie. I’m coming from Yale University. I’ve been really
interested in your work as sort of looking
at price effects as a proxy for people’s
perceptions of risk. And I’m curious whether any
of your work touches on, or whether economics
has any tools to offer us for assessing how
people are forming these risk perceptions in the first place,
and what sorts of institutions might be mediating how
they’re creating these risk perceptions. That’s a sociology question. Yeah. That’s more of a sociology
question, even a political. Timmons, you want to go for it? No. [INAUDIBLE] I’m asking the economists. Is that sort of outside
of the purview of what you’re interested in in this? No. That’s extremely
important and interesting, but I don’t know
anything about it. [LAUGHTER] How they form the risk. Yeah. Hi. I’m John. I’m a grad student
in sociology here. Hopefully this won’t be
outside of your wheelhouse. But what I’m hearing here is
information is really crucial. But what Kurt told us is
that property tax is also very important here. So have any of you seen
in your research any way of getting around
the disincentive that communities have not to
have their housing prices drop, and not just kick the
can down the road? It’s something I work
on in my research. And I’m wondering if you can
give me any sort of leads or ideas from your perspective. Come on. Speculate a bit. Well, that also gets into
the question of politics. Of course. The temptation to want to
maintain these property values for the sake of everyone
whose savings are at stake, for the sake of property taxes. There are so many
reasons why we would want to kick the can
further down the road. Again, how do we
get out of this? This is way above my
pay grade for sure. But I think a
smoother adjustment is perhaps the least
we can do to avoid this risk of sudden
devaluations of properties, and sudden loss of
property tax base, and then sudden loss of
revenue to fund adaptation and mitigation. So I think that’s the
best we can hope for. Well, I have just a clarifying–
when you were out surveying folks, some you
said who were away were more conscious of the risk,
or more accepting of the risk. Those who were closer who owned
the property were less so. Do you see that as a driver? Those who have already brought
the risk into their mindset, being very reluctant to see
we have to somehow subsidize that coastal property in
terms of credits or adaptation subsidies. Do you sense a
conflict there when you were talking to people? Were they, like those stupid
people live along the coast. They should get their
butt out of there. And I don’t want to
subsidize them being there. And we all know that. Did you sort of pick
up any political issues in those conversations? Cause you’re one
of the few people who’s gone out and
actually talked to people or had data on that. Or it’s just fine. They’re going to
lose their shirt. I don’t know if
IRB would permit me to share stories and anecdotes. But at the time we did
the first wave of surveys was in February, 2017, right
after President Trump’s inauguration. And there were a lot
of mixed communities. And there was so much palpable
tension about so many things. People who would tell
us that they stopped Christmas carolling
at each other’s homes, and people who would try
to pump us information and say, what does John
down the road think? I stopped talking to him. So there were a lot of tensions. And yes, some people
did have hard feelings. Also, some people who
lived by the shoreline said, I feel really bad. I don’t think I
should be living here. I don’t think my insurance
should be subsidized. So there was some
awareness of that. But as part of a much bigger
set of issues that’s going on. I wanted to say something
to this question of how we fix the political incentive. There’s an excellent book by
a journalist, Jeff Goodell, called “The Water Will Come.” And he’s got some anecdotes
about realtors in Miami who threatened to have his
head for just broaching the subject of climate change. So the incentives
of beneficiaries from a low information
regime are really a problem. How to fix that? I tend to want to absolve myself
of responsibility for that. It’s tempting to prescribe
higher information for voters. But there are a
lot of moving parts in between a voter’s information
set and the responsible politician. You might try
institutional fixes that would lengthen the
time horizon of politicians, longer terms. But more plausibly, I think we
need a political movement that actively seeks to get
politicians and realtors to face up to the long run
changes that are happening in coastal communities. This will have to
be the last question so we can stay on schedule. If we could fit one in. I’m Beth Fussell here at Brown. A lot of the focus of
disaster recovery policy is on homeowners. And, in fact, after
Hurricane Katrina there was a big study
by the GAO that came out saying that the Stafford Act
and other Federal emergency management policies
have really paid all their attention
to homeowners, and none to renters. And renters are really impacted
because they have the least amount of claim on
living in the place where they were affected, their home. And so what I’m interested
in finding out from you all is whether there’s
similar research as to what you’re doing that would address
the concerns of renters? And they’re an important segment
of the population because they are more disadvantaged. And so the consequences
of these events for the population,
the rental population, is going to be fairly
different than what we’re talking about now
when we’re talking about homeowners who are more
advantaged in terms of wealth. It’s a resilience question. Any responses to the
rent versus ownership, and what your models
may say about that? Behavioral wise. So I think to broaden the
question a little bit too, I think it’s also important
to look across just income generally, and to what
extent is FEMA aid more helpful to lower
income households, whether they be renters or
owners, or that type of thing. And I think that those
distributional concerns are really important. Thank you very much for
an excellent first panel. At this point we’ll cut for
coffee and come back at 11:00. Thank you. [APPLAUSE] [MUSIC PLAYING]

Reader Comments

  1. Wow, talk about not being aware of the subject they are discussing. These people aren't just rearranging deck chairs on the Titanic, they are discussing if it would be a good time (or not) to upgrade from 2nd class to 1st after the ice berg was hit. No wonder we are where we are.

  2. If you dont understand global warming… you dont get a vote . That's the fascist part …. the socialist part is what color do you want your government houses painted

  3. Ok . So thermal energy capacity is a function of mass . In space it is cold not because there is no radiation, plenty of radiation, it's cold because there is no mass to capture that radiation. Thermal mass is the thermal capacity of a given amount of mass . When we turn liquid and solid carbon into gas we are able to capture some of that energy and make it work, that change of state is a rapid expansion of matter… and the basis for all chemical energy. But now that mass is in a gaseous state and the mass of the atmosphere increases …. in turn increasing the capacity for our atmosphere to hold energy. I know you all consumed a tremendous amount of lead , so this may not be your fault. But you really need to understand nuclear energy asap . And start prioritizing your children. As far as liberal insanity, if you heat a human he gets lazy and stupid, again not their fault, they are mammals who need to maintain a constant body temp to survive. But shaming your sons into suicide and pimping your daughters to blacks as " reparations " well …
    It's just pathetic

Leave a Reply

Your email address will not be published. Required fields are marked *