Climate Emigration: Evidence from the United States from2011-2021
Akshay Yeddanapudi
School of Global Policy and Strategy, UC San Diego

April 9, 2024

Abstract
I study the effects of climate-related shocks on emigration within the US. I develop a theoretical framework under which a Bayesian hypothetical migrant will update their expectation
about the number of events they will experience in a given location and will choose to emigrate
as a function of these changes. I empirically show that, in a destination ambivalent setting, there
is a statistically significant and positive relationship between the number of emigrant households
and the number of climate shocks, of any type, in the origin county. In a dyadic setting I findthat a similar statistically significant and positive relationship exists between the number ofhouseholds emigrating to a given destination and the number of climate shocks to the origin
county. Curiously, I also find a positive, increasing and statistically significant relationship between the number of destination county climate shocks, prior to emigration, and the number of
emigrant households. I subsequently utilize migration as an instrument by which climate shocks
to the origin county can influence mean annual wages in the destination county, in the year
post emigration, and find that the results are inconclusive. The inclusion of origin-destination
fixed effects eliminating any statistically significant relationship while the omission of such fixed
effects lead the relationship to be statistically significant and positive.

1

Introduction

Recent literature has demonstrated that climate change has altered the frequency and intensityof precipitation patterns as well as the frequency and intensity of tropical storms and cyclones(Xi, Lin, and Gori 2023, Knutson et al. 2021). Other work has highlighted the effects of climatechange on the increasing risks of wildfires (Westerling 2016, Turco et al. 2023) and on changes tosnow cover patterns (Mitterwallner et al. 2024). Concurrently, there exists an extensive literature
analyzing the effects of climate shocks on domestic and interntional migration. The majority ofthese studies utilize temperature or precipitation fluctuations to proxy for climate related shocks
before then examining the effect on migration (Cattaneo and Peri 2015). Recent work focused on the
United States has shown that emigration is responsive prolonged variations in temperature (Mullins
and Bharadwaj 2021). Given the reliance of temperature or precipitation fluctuations to proxy forclimate related shocks, the rational question of whether aggregate storm or weather event shocks,
1disparate from precipitation or temperature measurements, can explain climate change induced
migration. Moreover migration, in response to climate change, has also been shown to be damped,
the US, in response to wage feedback loops (Fan, Fisher-Vanden, and Klaiber 2018). This raises a
secondary question of whether climate-shock induced migration alters this feedback systematicallyover time, by altering wages in the destination county via an expanding labor pool in counties
experiencing net immigration. I tackle both these problems.
I first construct a theoretical process detailing how a Bayesian individual might update theirbeliefs about the expected number of climate-related shocks of type m they would experience ina finite time period in a given location. I then define a condition on the rationality of migrationand link the individual’s perception of the expected number of climate-related shocks to the place
based utility that they derive from a given location. Under the assumptions of the propositions
I put forth, decreases in place based utility should increase the set of "acceptable" locations towhich an individual could migrate to, thus making emigration more likely. Conversely increases inplace based utility lead to decreased likelihoods of emigration. To empirically test this claim andto answer the first of the 2 questions raised earlier, I construct both a destination ambivalent anddyadic, gravity-style model of migration. I consider only domestic emigration within the US tominimize dampening effects of temporal budget constraints on migration patterns. Unlike much of
the existing literature, I utilize National Oceanic and Atmospheric Data on the occurrence of storm
and other weather events as an indicator of a climate-related shock and map migration using the
IRS US Population Migration Dataset. Under the destination ambivalent framework, I specific apooled, binned and type disaggregated set of models to test for the effects of origin county shocks
on total emigration. I then utilize a similarly defined pooled and binned model in the dyadic setting
to test for the effects of origin and destination county shocks on dyadic emigration from the origin
to destination county. To answer the second question raised - namely the effect of origin county
climate shocks on destination county wages - I utilize migration as an instrument and estimate a
2 stage least squares model. As I consider year on year changes, the time horizons are sufficiently
short to suppose that labor has enough time to update beliefs about the place based utility andemigrate while firms do not due to their relatively higher fixed costs. Moreover, a considerationof year on year changes, as opposed to a shorter time horizons, allows for the "averaging-out" offluctuations in wages due to damages from climate-related shocks. Working at the year on year time
horizon, I thus avoid a maximal amount of bias introduced by both hyper-short run fluctuations
and long run trends, thus providing the best possible environment for an estimation of the causal
effect of climate-related shocks on destination county wages.
In the broader economic context, I make 2 contributions to literature. First, I show how, ina partial equilibrium setting, Bayesian updating with regards to the expected number of climate
related shocks can alter place based utility and thus alter the rationality of migration between any
pair of counties. Under this setting, I demonstrate that the probability of emigration, from any given
origin county, is decreasing with regards to the expected number of climate related shocks whicharise from individual level Bayesian updating. Second, I provide evidence that is not solely related
to temperature or precipitation fluctuations, that climate shocks have a statistically significant andmostly positive effect on emigration such that a county which experiences a greater number of2

climate shocks experiences a greater outflow of migrants. This work most closely follows the pathset by Mullins and Bharadwaj 2021 as I restrict the focus of this analysis to be domestic emigration.
This allows me to minimize biases arising from varied barriers to migration, such as national borders,
capital constraints, institutions, labor considerations etc. I also contribute to existing literature in
the vein of Catteneo and Perri 2015 and Chen, Oliva and Zhang 2017 who respectively show theeffects of temperature and air pollution shocks on migration in a international context (Catteneo
and Perri 2015, Chen, Oliva and Zhang 2017).
The remainder of this paper is structured as follows. Section 2 provides a brief discourse about
the rationality of migration given a Bayesian potential migrant who updates their beliefs about the
expected number of climate shocks they will experience over a fixed interval in a given location.
Section 3 introduces my data and details all data cleaning, the methodology for the constructionof the dyadic and destination ambivalent working data sets and provides brief descriptive statistics. Section 4 specifies the models I use to empirically test the effect of climate-related shocks on
migration and the effect on destination county wages though an emigration instrument. Section 5
discusses the results in the destination ambivalent setting. Section 6 does the same in the dyadic
setting. Section 7 details the results of robustness checks for heterogeneous treatment effects andfalse detection rate corrections. Section 8 concludes.

2

Emigration Given Updated Climate Beliefs

I begin by constructing a theoretical processes detailing how an individual, given prior beliefs about
the number of climate-related shocks they will experience in a given location, "learns" as they experience new climate-related shocks, thus updating their belief about the number of climate-related
events they will experience, in the same location in the future. I show mathematically that undera model of migration wherein the individual only migrates if there is at least null change in utility,
this affects the locations to which they would rationally choose to migrate to.

2.1

Framework

Suppose that a representative individual l, in location i at time t has a prior belief about the
distribution of the frequency λm with which location i experiences a climate-related shock of typem such that λm ∼ Gamma(sm , rm ). Suppose also that individual l remains in location i oversome discrete time interval [t, t + 1) which can be decomposed into n disjoint sub-intervals overwhich individual l observes following set of climate shocks {ym,1 , ym,2 , . . . , ym,n } such that ym,k isthe number of climate-related shocks of type m that occurs during the k-th sub-interval. at timet + 1, individual l updates their prior belief about the distribution of λm such that their posterior
distribution on λm , at t + 1 is thenf1 (λm |{ym,1 , ym,2 , . . . , ym,n }) ∝ g({ym,1 , ym,2 , . . . , ym,n }|λm )f0 (λm )3where each of the ym,k ’s are i.i.d draws such that ∀1 ≤ k ≤ n, ym,k |λm ∼ P oisson(λm ) and wheref0 (λm ) is the corresponding probability density function to the Gamma distribution. Note thenthat over the interval [t + 1, t + 2), f1 (λm |{ym,1 , ym,2 , . . . , ym,n }) is individual l’s prior.
Proposition 1: Let τ > t + 1 and take λm ∼ Gamma(sm , rm ) where rm and sm are endogenous
hyperparameters on the distribution of λm . Then the total number of events, of all types, that
individual l expects at location i between [t + 1, τ ] is denoted Ni,t+1 such that
Ni,t+1 = (τ − (t + 1))P
p X
sm + nk=1 ym,k

m=1



rm + n(1)

Proof: See Appendix A.1
Proposition 1 then illustrates how exposure to types of climate events m influence the belief
that individual l has about the number of events of type m that will strike location i in [t + 1, τ ] aswell as the total number of events which will occur. As each event bears a cost on the individual, it
is rational to suppose the existence of a place-based utility function for individual l, denoted ut (i)which is implicitly a function of the expected number of events that will occur in a given location,i. Take ut (i) to define l’s utility of living in location i at time t such thatut (i) = At (i)ct (i)zt (i)where At (i) denotes the amenity value of location i at time t, ct (i) denotes the utility of consumption and zt (i) denotes wages. Let At (i) be linearly decomposed into a climate and non-climate
component, such that
At (i) = Act (i) + Anct (i)where Act (i) denotes the climate influenced component of the amenity value and Anc
t (i) denotes the
non-climate component. In expectation, changes in Ni,t+1 that then arise due to the individual updating their beliefs should then alter ut (i) only through changes in the climate-related amenity value
Act (i). Given this construction of ut (i), Proposition 2 then links the change in l’s expected number of
events to a change in the number of possible locations to which l will rationally choose to migrate to.
Proposition 2: Take Ni,t as defined in proposition 1 and suppose that individual l’s perception ofclimate-related amenity values of location i evolve such that
Act+1 (i) = Act (i) · h(Ni,t , Ni,t−1 )where h : R2 → R is any continuous and differentiable function with the following properties:h(Ni,t = k, Ni,t−1 = j) ≥ 0h(Ni,t = Ni,t−1 = k) = 1

4

(2)

∂h
<0
∂Ni,t
∀j, k ∈ R. Let L denote the set of all locations to which l can migrate to. Suppose that at each timeinterval t + k for k ∈ Z+ , individual l randomly samples location j ∈ L and chooses to migrate if
ut (i) ≤ ut (j) − C(i, j)(3)where C(i, j) is the constant cost of moving from i to j. As At (i), ct (i), zt (i) > 0 and assuming
constant consumption and wages in location i between t and t + 1, the set of locations to which it isrational to for individual l to migrate to at time t + 1, Fi,t+1 ⊂ L, will decrease ifh(Ni,t , Ni,t−1 ) > 1(4)h(Ni,t , Ni,t−1 ) < 1(5)

Conversely, Fi,t+1 ⊂ L, will increase if

Proof: See Appendix A.2
The assumption of constant consumption and wages in location i, between t and t + 1, whilestrong, is reasonable in the context of developed countries given the existence of robust social safety
nets. Likewise, so is the assumption of relatively constant costs of migration between counties. A
direct prediction of Proposition 2 is then the idea that as shocks, in location i will lead individual
l to update Ni,t−1 at time t + 1 to be Ni,t and that if h(Ni,t , Ni,t−1 ) > 1, the individual will be
have a fewer number of "acceptable" locations to move to. Moreover, note that in the frictionlesssetting, C(i, j) = 0 and thus individuals will always move to the utility maximizing location. The
introduction of non-negative frictions C(i, j) implies that some individuals may not utility maximize
since, at at a given time t′ , ut′ (j) − ut′ (i) ≤ C(i, j) thus making migration irrational.
Proposition 2, while discussing the conditions surrounding the rationality of migration, does notdescribe the probability with which a representative agent will emigrate. I turn now to this problem.
To model the migration decision, suppose that individual l chooses some j ∈ L with probability
P(j), at each time t + k k ∈ Z+ , subject to the constraint that ut+k (j) > ut+k (m) ⇒ P(j) ≥ P(m).
Define the sets S1,t+k , S2,t+k , ..., S|L|,t+k such that
S1,t+k := arg max ut+k (j)j∈LS2,t+k := arg max ut+k (j)j∈L−S1,t+k..
.
S|L|,t+k := arg min ut+k (j)j∈Lwhere P(s ∈ S1,t+k ) = p1 , P(s ∈ S2,t+k ) = p2 , ..., P(s ∈ S|L|,t+k ) = p|L| are constant and sum to 1.
5This construction implies that while the probability of any fixed location, j ∈ L, being randomly
selected as the migration destination can change discontinuously over integer time intervals, themagnitude ordered probabilities remain the same across time. As such P(s ∈ S1,t+k ) = p1 , P(s ∈S2,t+k ) = p2 , ... are time invariant with only the elements of the sets S1,t+k , S2,t+k , ... change asa function of time. Moreover note that the constraint that ut+k (j) > ut+k (m) ⇒ P(j) ≥ P(m)implies that individual l is more likely to select locations with higher utilities. This does notpreclude locations with a utility ut+1 (j) < ut+1 (i), for j ∈ L, from being selected by individual l.
Rather, allowing for some j ∈ L, for which ut+1 (j) < ut+1 (i), to be selected with some probability
P(j) > 0 allows for uncertainty in the information that individual l has about the place-based utilityof each location.
Now take i ∈ L to be origin location for individual l and suppose that h(Ni,t , Ni,t−1 ) > 1. Sinceh(Ni,t , Ni,t−1 ) > 1 ⇒ |Fi,t | ≥ |Fi,t+1 | and Fi,t+1 ⊆ Fi,t ⊂ L, note that if h(Ni,t , Ni,t−1 ) > 1 thenX
j∈Fi,t+1

Pi (j) ≤X

Pi (j) ≤X

Pi (j) = 1

j∈Lj∈Fi,tFor simplicity of notation, let
X

Pi (Fi,t+k ) =Pi (j)j∈Fi,t+k

for all k ∈ Z+ . Then Pi (Fi,t+1 ) denotes the probability of individual l selecting any location toemigrate to for which migration is rational, as defined in Proposition 2. Thus h(Ni,t , Ni,t−1 ) >1 ⇒ Pi (Fi,t+1 ) ≤ Pi (Fi,t ) and thus the individual is less likely to migrate from location i at timet + 1. Since the statement is symmetric for all k ∈ Z+ , a similar statement holds true for all times
t + k. Alternatively, suppose that h(Ni,t , Ni,t−1 ) < 1. Then by Proposition 2, h(Ni,t , Ni,t−1 ) < 1 ⇒|Fi,t | ≤ |Fi,t+1 | and thus Fi,t ⊆ Fi,t+1 ⊂ L. Then, given h(Ni,t , Ni,t−1 ) < 1, note that
Pi (Fi,t ) ≤ Pi (Fi,t+1 ) ≤X

Pi (j) = 1

j∈L

and thus the individual is more likely to migrate from location i at time t + 1, relative to at timet. Consequently, it follows that changes in the expected number of climate shocks then alters theprobability of an individual emigrating from a given location wherein an increase in the number ofshocks expected imply a greater probability of migration while a decrease in the number of shocks
expected implies the converse. This constitutes the major theoretical prediction of the the model
presented here. Note that this is independent of any functional specification of the the probability
measure P, on the probability space (L, G, P), beyond the assumption that the probability measureP(j) is monotonically increasing with respect to ut (j).
The above claim can be taken further to consider changes in the expected number of emigrants
from the initial fixed location i. In particular, since individual l is assumed to be a representative
agent, suppose that there exist Mi,t other similar individuals at location i and at time t who also
make a similar migration decision given similar preferences and having undergone a similar update
to Ni,t . As the migration decision across Mi,t similar individuals can then be modeled using a
Binomial (Mi,t , Pi (Fi,t+1 )) distribution the expected number of individuals who choose to emigrate6

from location i at time t + 1 is Mi,t (Pi (Fi,t+1 )). Proposition 3 then discusses 2 special cases where
the time evolution of the population, in given location i, is monotonic.
Proposition 3: Let i ∈ L be an arbitrary fixed location. The following claims hold true.
1. If Mi,0 ≥ Mi,1 ≥ ... ≥ Mi,t , where Mi,0 is exogenously given, and if h(Ni,t , Ni,t−1 ) > 1 for allt > 1 then the number of expected emigrants from location i is weakly decreasing.
2. If Mi,0 ≤ Mi,1 ≤ ... ≤ Mi,t , where Mi,0 is again exogenously given, and if h(Ni,t , Ni,t−1 ) < 1
for all t > 1, then the number of expected emigrants from location i is weakly increasing.
Proof: See Appendix A.3
Proposition 3 then claims that conditioned on a representative individual l having a decreasing
expected number of shocks to location i and conditional on the size of population in location ibeing bounded above by the initial population, the number of emigrants should, in expectation,
monotonically decrease. Conversely, if the expected number of shocks is increasing over time andthe population at any time t > 0 is lower bounded by its initial condition, then there will be anincrease in the expected number of emigrants. Note that this is concordant with expectations of
aggregate behavior as it is rational to expect that locations where the climate related amenity value
increases, all else fixed, should experience a decrease in net emigrants. Likewise locations where
the climate related amenity value decreases, all else fixed, should experience an increase in net
emigrants.

3

Data

To empirically test the effect of climate-related shocks on migration and wages, I compile my main
environmental data from the National Oceanic and Atmospheric Administration’s (NOAA 2019)
National Center for Environmental Information and migration data from the IRS’s US Population
Migration Dataset. Additional data is sourced from the IRS County, Metropolitan and Micropolitan
SOI Tax Dataset, the USDA Rural-Urban Continuum Codes and the Bureau of Economic Analysis’Economic Profiles by County. I discuss these sources here and detail the construction of the compiled
data products used in my analysis.

3.1

US Population Migration DataI filter the IRS US Population Migration dataset to include only the years in 2011-2021. Outflow
data is dyadic and is recorded at the county level with source and destination counties by year.
The data records both the number of households and the approximate number of individuals which
migrate from county i to county j. An emigrant household is recorded as residing in county i in year
t and in county j in year t+1 for t ∈ {2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020}. The
migration dataset which I utilize is constructed from personally identifiable information submitted
to the IRS via a Tax Form 1040. Form 1040 submissions are aggregated by the IRS, using the7

filing address of an individual as an identifier, to generate non-personally identifiable information
on household migration between counties in adjacent year pairs. The IRS Population Migration
Data does not record instances of origin-destination migration where the number of households
emigrating from a given origin county, i, to a destination county, j, were less than 20 due to privacy
concerns. The approximate number of individuals is constructed aggregated Form 1040 submissions
by considering the number of dependents per migrant household. The data further contains statelevel aggregates and county-to-foreign migration measures, at both the household and approximate
individual level. As I focus on domestic inter-county emigration, I exclude all non-county-to-county
migration flows from the data. I likewise exclude all non-migrants from the dataset by eliminatingflows where the origin and destination counties are the same. This reduces my sample to consistof 604,073 dyadic county-county emigration observations between 2011 and 2021. The dyadic data
only contains information on county-county pairs where individuals migrate from the former to thelatter and does not record cases of 0 county-to-county migration flows.

3.2

NOAA Storm Events Data

The NOAA Storm Events Database denotes unusual weather phenomenon in a given geographic
location. Events are deemed unusual, and thus included, if the event is defined as (1) having
sufficient intensity to cause significant damage or other major harms; (2) being rare in a given
location relative to the expected type of event; or (3) representing a local extrema in the typeof event (NOAA 2019). Typical or expected events are therefore omitted. As such, the NOAA
Storm Events Database represents an underestimate of the total number of events that occurred ina given geographical location in a given year. The individual weather and climatic events (i.e. Hail,
Wildfires, Tropical Storms, Rain, etc.) are recorded at county, county-equivalently, zone and marinelevels. To match the county-year level unit of analysis used throughout this paper, I consider only
county and county-equivalent data points and discard any marine or zone level events. Between 2011
and 2021 there were 515,200 observed events across 3,245 unique counties and county equivalentlevels in the US. Each event satisfies at least 1 of the 3 aforementioned necessary conditions to
be recorded in the dataset. Additional descriptors by event in the NOAA Storm Events Databaseinclude (1) the monetary magnitude of crop and property damage, (2) start time, (3) end time,(4) start date, (5) end date, (6) magnitude (for selected events), (7) reporting agency, and (8) ashort description of the event. To aggregate the event to the year level, I discard all minute, hour,
day and month information from the start and end times. I define an event to have occurred in agiven year on the basis of the start time. The end time of the event is not considered. I construct
event counts per county-year with regards to both the total number of climate-related events andthe number of events grouped by type to serve as county-level measures of exposure to a climate
shock. Data is aggregated to the county-year level.

3.3

Tax Data and Economic Covariates

Additional economic covariates, which may influence the migration decision of households are
sourced from IRS County, Metropolitan and Micropolitan SOI tax data. As was the case with
the NOAA storm data, this is filtered down to the years 2011-2021. The raw data is decomposed8

by county-year and adjusted gross income level. I aggregate it up to the county-year level to matchthe coarseness of the IRS US Population Migration Data. The dataset is filtered down to only
keep variables describing (1) the number of single returns, (2) number of joint returns, (3) the total
adjusted gross income in the county and in a given year, (4) the number of returns with a positive
income, (5) the total income by county and year, (6) the number of returns with business income,(7) the amount of net business income in a county and year, (8) the number of farm returns, (9)the number of returns with unemployment compensation and (10) the amount of unemployment
compensation in the county in a given year.
Rural-Urban Continuum Codes are sourced from the USDA Economic Research Service. These
codes classify each county on a discrete scale, from 1 to 9, by the degree by which they are consideredmetropolitan. A county with a continuum code of 1 is classified as the most metropolitan while acounty with a continuum code of 9 is the most non-metropolitan county. This classification is done
every 10 years thus prompting me to use the 2013 iteration of the classification as it lies within thesample time frame of 2011-2021. For those counties which did not exist prior to 2013, and thus
were not included in the 2013 Rural-Urban Continuum Codes, I use the updated 2023 version. This
allows me to classify all 3,245 unique counties on a scale of metropolitan to non-metropolitan.
The Bureau of Economic Analysis’ Economic Profile by County provides wage information bycounty-year. The data exists for all years between 1969 and 2022 and provides information on (1)total personal income, (2) net earnings by place of residence, (3) personal current transfer receipts,(4) per capita personal income, (5) earnings by place of work, (6) proprietor’s income and (7) total
employment. As the aim is to understand the effect of climate-related shocks on wages in destination
counties, through a migration pathway, I keep only data on per capita personal income. This data isfurther filtered to only consider the years between 2011 and 2021 so as to match the IRS Population
Migration Dataset.

3.4

Destination Ambivalence and the Dyadic Settings

I consider specifications involving both a dyadic and a destination ambivalent form of the effect ofclimate-shocks on emigration and wages. Using the aforementioned data sources, the dyadic data isconstructed by joining the 2011-2021 filtered NOAA Storm Events and Tax and Economic Covariates
datasets to the 2011-2021 filtered IRS Population Migration dataset detailing domestic emigration.
This is done by joint origin-year and joint destination-year. I further construct origin-destination,
joint origin-year and joint destination-year indicators to be used in the modified gravity style estimation model detailed in Section 4.2. The result is a working data set which contains 604,703
variables, across 105 climate-shock related and economic variables by origin-joint destination-year
and with covariates pertaining to both the joint origin-year and joint destination-year counties.
Destination ambivalent data is constructed by the aggregating dyadic data to the joint originyear level. Let ni,j,t denote the number of households who migrate such that the household is locatedin county i at time t and in county j at time t + 1. Then define ñi,t
ñi,t :=

Xj9ni,j,tto denote the total emigration from county i at time t. Joint origin-year economic and climaterelated covariates are consistent across a given joint origin-year county and thus are collapsed toa single observation. The result is working data set, termed destination ambivalent, of 28,736
variables across 40 variables. Aggregation to the origin-year level drops all variation introduced bydestination-year and origin-destination variables from the working data set however preserves any
origin-year variation.

3.5

Descriptive Statistics

Table 9 provides a visualization of the number of events by type. As noted in Section 3.1, thisrepresents an under counting of the total number of events which occurred between 2011 and 2021
as, for an event to have been included in the NOAA Storm Events Database it must have met at least
1 of the 3 aforementioned conditions. From Table 9, Thunderstorm Wind, Hail and Flash Flood
events are most frequently recorded "unusual" events between 2011 and 2021 while Drought, Dense
Fog, Excessive Heat and Wildfires only have 1 observation each between 2011 and 2021. Given theconstruction of Proposition 1, I note that the inclusion of only "unusual" events, as termed by the
NOAA Storm Events Database, should not significantly bias the empirically estimated results awayfrom theory. This follows as if only expected events of type m occurred, the individual’s updated
distribution on λm would be similar to their prior. Table 10 provides a summary of the number oforigin counties with (1) < 20 events, (2) 20 − 70 events, (3) 70 − 120 events and (4) > 120 eventsfor each year between 2011 and 2020. 2021 is not included in the table since the dyadic structureof the data implies that data on 2021 is only included for destination counties. Note that the bulkof counties experience < 20 events in a given year with only a marginal subset of origin counties
experiencing > 120 events in any given year.
Table 11 provides summary statistics on the number of emigrants, from all counties, per year.
Note that the the 25th percentile, 50th percentile, 75th percentile, mean, standard deviation and total
number of emigrating households all follow the same trend with a local minima in the number of
emigrants in 2014 and a local maxima in 2016. The 25th percentile and median number of households
emigrating domestically decreased in 2020, relative to 2011 levels, while the 75th percentile, mean,
standard deviation and total number of households all increased in 2020, relative to 2011 levels.
Figure 1 disaggregates emigration by county Rural-Urban Continuum Code and plots the mean
emigration by year. Note that while the mean number of emigrants differs by county classification,
under the Rural-Urban Continuum Codes, the general trend in emigration reflects the national
level averages seen in Table 11. Urban counties appear to track this trend more closely, relative
to their rural counter parts, however this can be attributed to number of emigrants in both cases,
with the greater number of urban emigrants dominating the trend observed in Table 11. Figure 2
provides a visualization of the distribution of county level wages per capita for even years between
2011 and 2021. Figure 2 demonstrates that for the selected even years between 2011 and 2021, thedistribution of wages has remained relatively constant with year on year increases in the mean ofthe distribution.

104Model Specification

Given the ability of the data to be aggregated into both a dyadic and a destination ambivalent framework, I estimate the effects of origin climate-related shocks in both settings. In both cases, I note theorigin specific climate-related shocks have the capacity to act as push factors, with increases in thenumber of events leading individuals to emigrate from a given origin county under the frameworkof Proposition 2. Estimation in the dyadic setting adds a consideration of destination county-year
counts of climate-related events which may act as a pull factor when the statement of Equation 3 ismet. In contrast estimation in the destination ambivalent framework, while conceptually simpler,
does not account for such pull factors. If destination county-year counts of climate-related shocks
are not a significant factor in the consideration of a representative migrant, it would then followthat the dyadic estimation should collapse to the destination ambivalent formulation. I begin witha discussion of the destination ambivalent model specifications.

4.1

Destination Ambivalent Specification

Aggregation of the number of emigrant households, ñi,t , at the level of the origin county, i, allows
for a OLS-style estimation of the effect of climate shocks on emigration. Given the differences in
how I treat the climate-related shock, these models can broadly be described as "Pooled," "Binned"
and "Type Dis-aggregated" and are defined as follows.
A. "Pooled" Destination Ambivalent Model
Equation 6 specifies the pooled model in the destination ambivalent setting.
−ñi,t = α · Ei,t + →κ · Xi,t + ηi,t + ϵi,t(6)ñi,t is defined as in Section 3.4 and denotes the total number of emigrants from county i at time t.
Ei,t denotes the total number of events, of all types, in county i at time t. Xi,t is a vector of origin,
i, specific economic covariates at time t. ηi,t denotes the set of joint origin-year specific fixed effects
and ϵi,t denotes the error term.
B. "Binned" Destination Ambivalent Model
Equation 7 specifies the binned model in the destination ambivalent setting. Events are binned
by decile to average out noise arising from the relatively few number of counties with high countsof climate-related shocks which may bias estimation.
→−−−ñi,t = →α · F i,t + →κ · Xi,t + ηi,t + ϵi,t

(7)

→
−
F i,t denotes a vector of indicators for the decile of events in the origin county, i, at time t.
ñi,t , Ei,t , Xi,t , ηi,t and ϵi,t are defined as in Equation 6.
C. "Type Dis-aggregated" Destination Ambivalent Model
As was the case in the dyadic setting, I note that the estimation of a type dis-aggregated model11

is motivated by heterogeneities in the effective "treatment" generated by disparate climate-related
shock types. Equation 8 specifies the event type dis-aggregated model in the destination ambivalentsetting.
−−ñi,t = →α · Typei,t + →κ · Xit + ηi,t + ϵi,t
(8)


(1)
(m)
(p) T
.
As was the case in the dyadic setting, Typei,t is a vector such that Typeit = Ti,t , ..., Ti,t , ..., Ti,t(m)

Ti,t is defined to be the type of climate-related shock (i.e. tropical storm, hail, tornado, etc.) andeach 1 ≤ m ≤ p denotes a unique type of climate-related shock. ñi,t , Ei,t , Xi,t , ηi,t and ϵi,tare defined as in Equation 6. This model is estimated in Section 7 towards a consideration ofheterogeneous treatment effects in the destination ambivalent setting.

4.2

Dyadic Specification

A. "Pooled" Dyadic Model
Let i denote the origin county and j denote the destination county. The dyadic "gravity-style"migration regression is given by Equation 9.
−−
ni,j,t = α · Ei,t + β · Ej,t + θ · ln(di,j,t ) + →κ · Xit + →γ · Yj,t + ηi,t + δj,t + ρi,j + ϵi,j,t(9)

ni,j,t denotes the number of emigrant households, as measured by the IRS Population Migration
data, that were in county i at time t and moved to county j at time t + 1. As the data is filtered
to only consider emigrants, ni,j,t > 0 and likewise, i ̸= j. Ei,t and Ej,t denote the number of events
at time t in the origin, i and destination j respectively. di,j,t denotes the distance between county
i and county j at time t and is taken from the NBER county-distance database. Xi,t is a vectorof origin county covariates at time t that describe the mean economic conditions in origin county iwhich may drive out-migration through push factors. Likewise Yj,t is a vector of destination county
covariates at time t which describe the mean economic conditions in the destination j and may
affect emigration from the origin, i, through pull factors. ηi,t denotes the set of origin county-year
fixed effects and δj,t denotes the set of destination county-year fixed effects. ρi,j denotes origindestination fixed effects. ϵi,j,t denotes the error term.
B. "Binned" Dyadic Model
To understand cumulative effects across deciles of events, I re-estimate a binned version of Equation 9. This allows me to average out noise arising from the relatively few number of observations ofcounties with high numbers of climate-related shocks which may bias the estimation. Equation 10
specifies the "binned" model.
→
− →
→
−
−
−
−
−
ni,j,t = →α · F i,t + β · F j,t + θ · ln(di,j,t ) + →κ · Xit + →γ · Yj,t + ηi,t + δj,t + ρi,j + ϵi,j,t

(10)

→
−
F i,t denotes a vector of indicators for the decile of events in the origin county, i, at time t and→−F j,t denotes a vector of indicators for the decile of events in the destination county, j, at time t.
di,j,t , Xi,t , Yj,t , ηi,t , δj,t , ρi,j and ϵi,j,t are defined as in Equation 9.

12

C. Second Stage Model and Reduced Form
Given the dyadic structure, the conceptual effect of migration on wages is then self-evident.
For counties experiencing net emigration the labor pool decreases in size so as to shift the labor
supply curve inwards. This generates a supply side effect on the market which theoretically raises
wages assuming constant labor demand. Likewise counties experiencing net immigration would
see their labor pool increase such that the labor supply curve shifts outwards, thus generating an
theoretical decrease in wages. In particular, the short-run assumption of constant labor demandis rational given high fixed costs associated with the relocation of firm production from an origin
county i to destination county j. Therefore, it is reasonable to suppose that the relative stickinessof firm demand for labor relative to labor mobility between US counties generates an approximately
constant demand for labor in counties across the short-run. As I consider year on year changes, thetime horizon are sufficiently short enough to suppose that labor has enough time to update beliefs
about the place based utility and emigrate while firms do not due to their relatively higher fixed
costs. Moreover, a consideration of year on year changes, as opposed to a shorter time horizons,
allows for the "averaging-out" of fluctuations in wages due to damages from climate-related shocks.
As such, at the time horizon of my data, both hyper-short run fluctuations and long run trends
should be minimized providing the best possible environment for an estimation of the causal effect ofclimate-related shocks on destination county wages. Insofar as climate shocks theoretically generate
county-to-county migration, as suggested by Proposition 2, this motivates an estimation in the
dyadic setting of the effect of climate-related shocks on destination wages via a migration instrument.
Equation 9 is implemented as the first stage model. Equation 11 then describes the second
stage model which seeks to estimate the effect of climate-related shocks on wages to the destination
county.
−wj,t+1 = α · n̂i,j,t + →γ · Yj,t+1 + δj,t+1 + νi,j,t+1(11)
wj,t+1 denotes the wage in destination county j at time t+1, which is after emigration has occurred.
n̂i,j,t denotes the fitted number of households migrating from origin county i to destination county
j at time t and is estimated as described in Equation 9. Yj,t+1 denotes a vector of destination
specific economic covariates at time t + 1 which is after emigration. δj,t+1 is a set of destination j
fixed effects at time t + 1. νi,j,t+1 denotes the error term. In addition, I consider the reduced form
expression given by Equation 12.
−−
wj,t+1 = α · Ei,t + β · Ej,t + θ · ln(di,j,t ) + →κ · Xit + →γ (1) · Yj,t
(1)
(2)
−
+→
γ (2) · Y+η +δ +δ
+ρ +ϵ
j,t+1

i,t

j,t

j,t+1

i,j

(12)

i,j,t+1

Each of the terms in Equation 12 are defined as in a corresponding manner to Equations 9 and 11.

5

Destination Ambivalent Results

I note that amongst the 3,245 counties in the destination ambivalent dataset, 34 never experiencea climate-related shock, as defined by NOAA. To determine whether migration outcomes follow
county-specific trends, I test for unit specific trends on these never-treated counties. Table 12

13

provides a summary of the results of these tests. Model (1) performs a simple bivariate regressionof the County × Y ear interaction on the number of emigrant households. Model (2) adds in county
fixed effects. Model (3) adds in a set of origin county economic covariates to the statement of Model(2) and is the main specification of the unit specific trends test. Model (4) adds in fixed effects for
Rural-Urban Continuum Codes. I note that while the inclusion of economic covariates and county
fixed effects lead coefficients on most county × Y ear terms to not be statistically different from0, there still exist 4 counties which display statistically significant unit specific trends at a 95%
confidence level. This is robust to the inclusion of Rural-Urban Continuum Code Fixed effects as
in Model (4) of Table 12. The relatively small sample size of 220 observations across 34 counties
suggests that the lack of statistical significance amongst on county-year interaction coefficients may
be due to the regressions being under-powered. I thus interpret the results, while partially indicativeof unit specific trends, as being inconclusive on a whole. In subsequent models, I thus omit theinclusion of any unit-specific trends however include joint origin-year fixed effects, as specified in
Equations 6, 7 and 8, to account for any time-variant trends to ensure that subsequent models
are unbiased. I turn now to a discussion of the empirical results given by the Pooled and Binned
Destination Ambivalent models from Section 4.1. I return to the Type Disaggregated Model in
Section 7’s consideration of heterogeneity in treatment.

5.1

Pooled Destination Ambivalent Models

Table 1 provides a summary of the results in the case of the pooled model specified by Equation 6 inSection 4.1. Model (1) in Table 1 is a simple bivariate model estimating the relationship between thenumber of climate-related shocks that a given county experiences and the total number of emigranthouseholds. Model (2) adds in origin state-year fixed effects to the specification of Model (1) toaccount for time-varying heterogeneities which arise at the state level. County-year fixed effects,
while the ideal solution given the ambiguous nature of the unit specific trends problem highlighted
earlier, are collinear with the number of climate-related shocks that a county experiences in a given
year and thus are not included in any of the specifications in Table 1. Model (3) adds Rural-Urban
Continuum Code fixed effects to the specification of Model (2) to account for heterogeneities inthe degree to which a county is metropolitan. The inclusion of such fixed effects are motivated by
Figure 1 which demonstrates that while, in mean, counties appear to follow a similar trend withregards to emigration, urban counties dominate in terms of the volume of total migrants. Model(4) adds a set of economic covariates to the specification of Model (3). County Adjusted Gross
Income is added to control for total wealth in a county, at the time of the shock, as higher levelsof total wealth in a county may increase amenity values in a location via the increased provisionof desirable public or club goods. Origin Wages control for per capita mean annual wage in theorigin county, at the time of the shock, and are included to control for heterogeneities in initialaverage wage of an emigrant, by county, as increased wages may decrease the perceived costs ofemigration by relaxing budget constraints in the period prior to said emigration. Business Net
Income is included to control for employment related pull factors in the origin county which may
discourage emigration. The Amount of Taxable Pensions is included as a proxy for the extent towhich a county is composed of retirees in a given year as pensions denote fixed incomes which may
14

Dependent Variable:
Model:
Variables(Intercept)
Number of Events

Number of Emigrant Households
(1)
(2)
(3)
(4)
492.5∗∗∗(70.93)134.0∗∗∗(6.424)

153.4∗∗∗(7.318)121.8∗∗∗(6.727)113.4∗∗∗(7.431)-71.29(240.9)0.1130∗∗∗(0.0118)-113.2∗∗(44.97)-0.6134∗(0.3447)-0.0079(0.0078)

Yes

Yes
Yes

Yes
Yes

28,7360.18701
0.07898

28,736
0.31338
0.05626

25,4380.33733
0.08471

County Adjusted Gross Income†
Origin Wages
Business Net Income†Amount of Taxable Pensions†
Number of Farm Returns†
Fixed-effects
State-year
Rural-Urban Continuum CodeFit statistics
Observations
R2
Within R2

28,736
0.06537

Clustered (State-year) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1
Table 1: Destination ambivalent estimation of the effect of climate-related shocks in the origin county on
emigration. Number of emigrant households are measured in unit steps. Note that († ) denotes a variable
whose value is rooted to its 2011 value such that it is then expressed as a real-valued multiple of the 2011
value. Standard errors are clustered at the joint origin-year level.

inhibit emigration due to cost of living considerations. Finally, Model (4) includes the Number ofFarm Returns to control for the existence of farming communities in a county as potential migrants
who own farms, and thus file farm returns, face greater fixed costs when emigrating which may thenact as a pull factor keeping them in the same county across years. Models (1) - (4) in Table 1 utilize
joint origin-year clustered standard errors.
Across all four models in Table 1, I note that the coefficient on the Number of Events is positiveand statistically significant at a 95% confidence level. Moreover, the magnitude of the coefficientis shown to be robust to the inclusion of joint origin-year fixed effects as well as a set of economic
covariates at the joint origin-year level. The results of Model (4) indicate an increase of 113.4
emigrant households for every unit increase in the number of climate-related shocks in a given
county. As the climate-related events considered in Models (1)-(4) are denoted as "unusual" storm
events, the occurrence these events would lead a Bayesian potential migrant to update their belief
about the rates λm with which climate related shocks occur thus increasing Ni,t . This generates
15

a decrease in Aci,t+1 and under the prediction of Proposition 2, should generate emigration. The
results of Model (4) support this hypothesis insofar as emigration is increasing the number of events
experienced in the origin county in a given year. Under the specification of Model (4), I note that
only Origin Wages and Business Net Income are statistically significant at a 95% confidence levelwith increased origin wages yielding a net positive effect and business net income yielding a net
negative effect on emigration from the origin county.

5.2

Binned Destination Ambivalent Models

Table 2 provides a summary of the results in the case of binned events model specified by Equation 7
in Section 4.1. Model (5) estimates the relationship between the binned number of climate-related
shocks and the number of emigrant households by county with state-year and Rural-Urban Continuum Code fixed effects. As was the case in Table 1, Models (1) - (4), I note that county-year fixed
effects are collinear with the binned number of events and thus render it impossible to estimate theeffect of said climate-related shocks on emigration if included. I therefore again turn to the secondbest solution of applying state-year fixed effects to control for time-variant heterogeneity at the statelevel which would otherwise bias my estimate of the the number of households emigrating from acounty in a given year. Rural-Urban Continuum Code fixed effects are again added to account forheterogeneities in the degree to which a county is metropolitan given the trends shown in Figure 1.
Model (6) adds a series of joint origin-year economic covariates to the specification of Model (5).
These joint origin-year covariates are identical to those added in the case of Model (4) in Table 1
and likewise are similarly motivated in their addition. Standard errors are clustered by county-year.
Note that across Table 2 the binned events appear to generate a positive and statistically significant
effect, at a 95% confidence level on the number of emigrant households. The exception to thistrend is the coefficient on the bin of 130-140 events. Examining the data, I note that there exist
only 28 observations where a county in a given year experience between 130-140 climate-related
shocks. Contrasting this to the case where a county experienced < 10, wherein there were 15,962
observations, I conclude that the lack of statistical significance on this coefficient may be due toa lack of statistical power at higher numbers of events per county-year observation. Despite this,I note that, from Model (6), a county which experiences < 10 events expects 832.9 households toemigrate over the course of a year while a county with 50 − 60 such climate-related events expects
6, 685.5 households to emigrate over the same time period. From Table 2, I thus conclude that theeffect of the binned number of climate-related shocks is weakly increasing as the number of binned
shocks increases. In aggregate, I thus conclude that models (5) and (6) in Table 2 corroborate thenarrative presented in Table 1. More specifically, Models (5) and (6) similarly suggest that increases
in the number of binned climate-related shocks generates an increase in the number of emigrants
from a county in a given year and that this relationship is robust to the inclusion of state-year
and Rural-Urban Continuum Code fixed effects and joint origin-year economic covariates. Of thecovariates, I note the only Origin Wages, Business Net Income and the Amount of Taxable Pensions
are statistically significant at a 95% confidence level.

16

6

Dyadic Results

I now turn to a discussion of the results in the dyadic case. Throughout the models discussed
in this section, I apply joint origin-year fixed effects to control for heterogeneities arising fromtime-variant trends in the origin county and similarly apply joint destination-year fixed effects
to control for heterogeneities arising from time-variant trends in the destination county. Origindestination pair fixed effects are also frequently implemented in the models in this section to control
for heterogeneities that arise between pairs of origin and destination counties. In addition to an
examination of the models specified by Equations 9 and 10, I here also exploit the dyadic structureof the data to consider the effect of climate-related shocks on destination county wages through an
migration instrument, as specified in Equations 11 and 12.

6.1

Pooled Dyadic Models

Table 3 provides a summary of the results in the case of the Pooled Dyadic model specified by Equation 9. Model (7) applies the framework of a trade gravity-style model to the case of emigration andregresses the number of origin climate-related shocks, the number of destination climate-related
shocks and the distance between the origin and destination counties on the number of emigranthouseholds. Moreover Model (7) applies destination state-year and origin state-year fixed effects
to control for time-variant heterogeneities in the origin and destination counties. Similarly, origin
Rural-Urban Continuum Code and destination Rural-Urban Continuum Code fixed effects are included to control for heterogeneities in the degree to which the origin and destination county is
metropolitan, given the trends demonstrated in Figure 1. Model (8) adds origin and destination
economic covariates to Model (7). These take the form the form of covariates describing Origin
County Adjusted Gross Income, Destination County Adjusted Gross Income, Origin Wages, Destination Wages, Origin Number of Farm Returns and Destination Number of Farm Returns. Model(9) adds origin and destination economic covariates, in the form of Origin Taxable Pension Amountand Destination Taxable Pension Amount, to the specification of Model (8). The motivations between the inclusion of the adjusted gross income, wages, number of farm returns and taxable pension
amount remain unchanged, in the dyadic setting, from the discussion in Section 5.1 and thus is notrepeated here. Model (10) adds covariates for Origin Unemployment Compensation and Destination
Unemployment Compensation to Model (9). The inclusion of origin and destination controls for unemployment are used to proxy for unemployment rates by county and are motivated the fact that,like a pension, unemployment can introduce liquidity constraints which inhibits emigration due tocost of living considerations and a fixed budget constraint. Model (11) adds origin-destination pair
fixed effects to the estimation of Model (10). Model (12) re-estimates Model (11) using a Poisson
Pseudo-Maximum Likelihood (PPML) estimator. The use of a PPML estimator in this setting is
motivated by the discrete count structure of the number of emigrant households as the outcome
variable wherein ni,j,t > 0 as defined in Equation 9. Moreover, the missing-ness of migration datain the case where the number of emigrant households from a given origin to a given destination
county is less than 20 is akin to the missing data problem faced by the trade literature and which
has been partially resolved by the use of such a PPML estimator.

17

I note that across Models (7) - (10), in Table 3, the effect of both the number of origin climaterelated shocks and number of destination-climate related shocks is positive and statistically significant at the 95% confidence level. Moreover, I note that the coefficients on both origin anddestination counts of climate-related shocks is robust to the inclusion of economic covariates, inboth the origin and destination counties, and to the use of the aforementioned set of fixed effects.
Models (7) - (10) notably lend further evidence in support of Proposition 2 as the number of emigrant households is shown to be increasing in the number of origin events in Table 3 with 0.9401
households emigrating for every unit increase in climate-related shocks in the origin county in Model(10). However, the effect on the number of emigrant households also appears to be increasing in
the number of destination events as 0.8848 additional households emigrate from the origin to thedestination for every unit increase in the number of destination climate-related events. In this way,
the number of events in the destination appears to act counter to a hypothetical pull factor. Table 3
does not provide any information on the relative number of events in the origin and destination andthus it is impossible to determine whether the positive coefficient on the number of destination
climate-related shocks is due to relative differences between the origin and destination numbers or
not. Of the origin and destination economic covariates included in Model (10), I note that wages,
and unemployment compensation, in both the origin and destination, are shown to be statisticallysignificant at the 95% confidence level. Specifically, the number of emigrant households is seen to
be increasing in both origin and destination wages and decreasing in origin and destination unemployment compensation. Model (10) further demonstrates that the number of emigrant households
is decreasing in the taxable pension amount in the destination and is increasing in destination adjusted gross income. Put together, these results suggest that beyond the effect of the climate-related
shock, increases overall income facilitates emigration while fixed budget constraints impede it.
As Model (11) re-estimates Model (10) with the inclusion of origin-destination pair fixed effects,
I note that the addition of origin-destination pair fixed effects renders it impossible to identify astatistically significant effect of the number of origin and destination climate-related shocks on thenumber of emigrant households. Given the similarities of Model (11) to the gravity model, I thus
re-estimate Model (11) using a PPML estimator to determine whether the loss of significance is aconsequence of the structure of OLS model or whether it reflects an actual sensitivity to controllingfor heterogeneities between origin and destination county pairs. Using a PPML estimator, Model(12) demonstrates that the loss of statistical significance on the coefficients of the number of origin
and destination climate-related shocks is most likely a consequence of the inherent structure ofan OLS estimator relative to the PPML estimator. with Model (12), I show that the number of
emigrant households is indeed increasing in the number of origin and destination climate-related
shocks. Moreover, Table 3 demonstrates that this relationship is statistically significant at a 95%
confidence interval and is robust to the inclusion of origin-destination pair fixed effects when aPoisson Pseudo-Maximum Likelihood Estimator is used. More concretely, I note that from Model(12) a unit increase in the number of origin climate related shocks generates a 0.03% increase in thenumber of emigrant households to the destination county. Given the coefficient on the number ofdestination county climate-related shocks, I similarly conclude that a unit increase in the number ofdestination climate-related shocks generates a 0.03% increase in the number of emigrant households

18

to the destination county.

6.2

Binned Dyadic Models

Table 4 summarises the results in the case of the binned dyadic models given by Equation 10.
Model (13) estimates the effect of replaces the a series of indicators for the binned deciles of climate
related shocks in the origin county and a series of indicators for the binned deciles of climate related
shocks in the destination county on the number of emigrant households from the origin county.
Model (13) also includes a covariate to control for the distance between the origin and destination
counties. Unlike the case of the binned destination ambivalent model in Section 5.2, I note that
only the coefficients on binned origin indicators for > 150 events, 100 − 110 events, 130 − 140 events,
50 − 60 events and 70 − 80 events are statistically significant at a 95% confidence level. Moreover
only the coefficient on a binned destination indicator for 140 − 150 events is statistically significant
at a 95% level. I further note that wherein the parity of coefficients was continuously positivein the prior specifications in Tables 1, 2 and 3, Model (13) illustrates significant variability in theparity of the coefficients on binned origin and destination decile indicators of climate-related shocks.
Model (14) adds in the same set of origin and destination economic covariates for wages, adjusted
gross income, number of farm returns, taxable pension amount and unemployment compensation
to the specification of Model (13). I note that the statistically significant coefficients estimated inModel (13) are robust to the inclusion of such economic origin and destination county covariates.
Despite this, only the coefficient on the indicator for 60 − 70 binned events in the origin shifts
from not being statistically significant in Model (13) to being statistically significant in Model (14).
Moreover, of the covariates included, on origin wages, destination unemployment compensation andadjusted gross income and taxable pension amount, in both the origin and destination counties, arestatistically significant at a 95% confidence interval. To test whether the lack of statistical significant
throughout Models (13) and (14) is a consequence of the specification of an OLS style estimator,
I use Model (15) to re-estimate the specification of Model (14) using a PPML estimator. Doingso increases the number of origin and destination coefficients that are found to have a statisticallysignificant effect on the number of emigrant households. I note that changing the specification of
the estimator does not alter the observed variation in parity thus suggesting that such observed
variation in coefficient parity is an accurate descriptor of the effect of each decile in the Model as it
is shown to be robust to fixed effects, origin and destination economic covariates and changes to thefunctional form of the estimator. I note that amongst the origin decile indicators in Model (14), acounty which experiences > 150 climate-related shocks will then experience a 3.987% increase in thenumber of emigrant households while a county which experiences 130 − 140 climate related-shocks
will experience a −6.452% increase in the number of emigrant households.

6.3

Climate Shock Effects on Wages

Having estimated the empirical forms of Equations 6, 7, 9 and 10, I now turn to a considerationof the effect of climate-related shocks on destination county wages via a migration instrument. As
discussed in Section 4.2, I note that the year on year time horizon of my data allows me to minimize
both hyper-short run fluctuations arising, from the recovery process from a climate-related shock,19

and the long run trends, which generate multiple causal pathways for climate events to affect wages.
As such the year on year time horizon of my data enables me to study the effect of an origin climate
shock on destination wages given the theoretical justification presented in Section 4.2. Table 5
provides a summary of the 2 stage least squares regression as well as the reduced form model.
Model (16), in Table 5 denotes the reduced form expression given by Equation 12. In particular,
Model (16) estimates the effect of climate-related shocks, in the origin and destination county, on
the number of emigrant households given controls for origin and descrination economic covariates;
joint origin-year and joint destination-year fixed effects and origin-destination fixed effects. Notethat Model (16) indicates a −0.01% decrease in mean annual destination income per capita per unit
increase in the number of origin climate-related shocks and a 0.03% increase in mean annual income
per capita per unit increase in the number of destination climate-related shocks. Both results are
statistically significant at a 95% confidence level. As such, the reduced form expression of Model(16) would suggest that an increase in the number of climate related shocks to an origin county
does indeed have a negative effect on wages to the destination county. This lends support to thenotion put forwards in Section 4.2 that the re-allocation of labor, due to migration induced fromclimate-related shocks, should have a negative affect on wages in destination counties are labor flows
in and thus expands the potential labor pool relative to an approximately constant demand. Notethat the t + 1 destination county economic covariate given by the Adjusted Gross Income is dropped
due to collinearity. Consequently mean annual income in the destination county is recorded at timet + 1 while all remaining covariates in Model (16) are recorded at time t. At a 95% confidence level,
I note that Model (16) also demonstrates that the mean annual wage in the destination county is
increasing in the distance between the origin and destination counties, the adjusted gross incomein the destination county in the prior year, the number of destination county farm returns and indestination county unemployment compensation. Conversely, Model (16) indicates that at a 95%
confidence level, mean annual income in the destination county is decreasing in taxable pension
amounts in the origin and destination counties.
In Table 5, Model (17) utilizes the 2 stage least squares approach specified in Equations 9 and
11 to estimate the effect of climate-related shocks on mean annual income in the destination county,
via an emigration instrument. Note that all covariates and fixed effects applied in Model (16) areapplied in Model (17). Model (18) re-estimates Model (17) with the addition of origin-destination
pair fixed effects. As the coefficient on the Fitted Number of Emigrant Households is statisticallysignificant at a 95% confidence level in Model (17) but fails to be statistically different from 0,
at the same level in Model (18), I note that the fitted effect of climate-related shocks on meanannual wages in the destination county is not robust to the inclusion of origin-destination pair fixed
effects. As noted in the comparison of Models (11) and (12) in Table 3, this may be due to themethod of OLS estimation as Model (12) indicates that when the number of emigrant households isestimated using a PPML estimator, the results are robust to the inclusion of origin-destination pair
fixed effects. Given the results of Table 5, I thus conclude that while there may exist a statisticallysignificant and robust second stage relationship between the fitted number of emigrant households
and mean annual wages in the destination county, I do not find strong evidence in favor of such a
relationship here.

20

7

Robustness

Thus far I treat disparate types of climate shocks as having the same effect on the potential migrant.
Given the differential frequency and perceived potential harms of disparate shocks, this may fail tohold and thus may bias results. I thus test for heterogeneous treatment effects, in the Destination
Ambivalent setting, using Equation 8. Moreover, given the high number of simultaneous hypothesis
tests run in the type disaggregated and binned models, I apply a False Detection Rate correction.

7.1

Heterogeneity in Treatment

Table 6 provides a summary of the results in the case of the Type Disaggregated model specified
by Equation 8. Model (19) re-estimates Model (3) in the setting where the "treatment" effect of
disparate climate-related shocks cannot be considered to be identical. Model (20) adds in a seriesof origin county economic covariates to Model (19). Note that this practically is a re-estimationof Model (4) under the assumption that disparate event types cannot be treated identically due toheterogeneous treatment effects. Model (19) demonstrates that of the 16 types of climate-related
shocks, for which a coefficient can be estimated, a statistically significant relationship, at the 95%
confidence level, exists for only Flash Flood, Flood, Hail, Thunderstorm Wind, Funnel Cloud, Heavy
Rain, Dust Devil, Lightning, Fog and Strong Wind events. Moreover, I note that disaggregation
by type reveals heterogeneities in the parity of the effect on the number of emigrant households as,of the events with statistically significant coefficients, only Heavy Rain is shown to have a negative
effect on the number of emigrant households. Model (20) indicates that the coefficient estimates
for the effects of Heavy Rain and Fog on the number of emigrant households are not robust to
the inclusion of origin county-year economic covariates. In particular, Model (20) indicates thatof each of the 16 types of climate-related shocks, for which a coefficient can be estimated, Dust
Devils appear to have the greatest effect on the number of emigrant households with a predicted
3,025.6 households emigrating for a unit increase in the number of Dust Devils. Putting the resultsof Models (19) and (20) together, I thus conclude that, at a minimum, Flash Floods, Floods, Hail,
Thunderstorm Wind, Funnel Clouds, Dust Devils and Lightning have a positive and statisticallysignificant effect on the number of emigrant households from a given origin county.

7.2

False Detection Rate Corrections

Considering the results of Tables 2 and Table 6, I note the large number of simultaneous hypothesis
tests being performed. To correct for the expected number of false positives amongst parameter
estimates in these models, I employ a false detection rate correction for Models (6) and (20). Table 7
provides the resultant p-values, by coefficient in Model (6), with and without the false detection
rate correction. From Table 7, I note that the application of the false detection rate correction does
not alter the statistical significance of any climate-shock related coefficients at a 95% confidence
interval thus implying that even with a correction for potential type I error, the conclusions reachedin Section 5.2 remain valid. Table 8 summarises the p-values, with and without the false detection
rate correction, for the type disaggregated results given in Table 6 Model (20). As was the case with
Table 7, I note that applying a correction for the false discovery rate did not alter the statistical21

significance, at the 95% level, of terms which were already significant at or above the 95% confidence
level. Thus, I conclude that the conclusions drawn in Section 7.1, from Model (20) remain valid.

8

Conclusions

Dealing with the increased risk of climate shocks in the modern world is a foregone conclusion
given anthropogenic climate change. In a destination ambivalent framework, I show that when suchshocks are measured by the total of number "usual" events in a given county, the effect of sucha shock is to increase the number emigrant households from the origin county by 113.4 per unit
increase in the number of shocks. Using a decile binned setting, I further show that the effect on
the number of emigrant households is weakly increasing in accordance with the number of shocks.
Moreover, when shocks are disaggregated by type, I demonstrate that only Flash Floods, Floods,
Hail, Thunderstorm Wind, Funnel Clouds, Dust Devils and Lightning have a statistically significant
impact on the number of emigrant households. I note that these results may be a feature of the data
and that the lack of significance on events such as observations of "unusual" Wildfires, Tornadoes
and Tropical Storms, which generate greater damages, are limited.
In a dyadic setting, I show that that the effect on the number of emigrant households, of anincrease in the number of origin climate-related shocks, is positive and statistically significant. In
an OLS setting without origin-destination fixed effects, I estimate that for every addition climate
related shock in the origin county, 0.9401 households emigrate from the origin to the destination.
Surprisingly, I find a positive effect that arises when the number of shocks in the destination county
increase. In the same OLS setting without origin-destination fixed effects, I estimate that every
addition climate related shock in the destination county increases emigration from the origin to
the destination by 0.8843 households. I show that given the similarity to gravity-style models a
Poisson Pseudo-Maximum Likelihood estimator is well suited to estimate the effect on the numberof emigrant households when origin-destination pair fixed effects are included. I estimate an increase
0.03%, with respect to the number of emigrant households, when for a unit increase in the numberof events in either the origin county or the destination county. I find similar results in a binned
setting however note that relative. I finally show that while there is a positive and statisticallysignificant, post emigration, increase of 0.00807% in mean annual destination county wages per unit
increase in origin county climate shock, this result is not robust to the inclusion of origin-destination
fixed effects. In contrast I demonstrate that my migration regressions, in the destination ambivalentsetting, are robust to the correction for heterogeneous treatments arising from disparate event types
and are similarly robust to a false detection rate correction.
In aggregate, my results are broadly supportive of the major theoretical implications of Proposition 2 and indicate that climate shocks to the origin county, measured not by temperature fluctuations or changes in precipitation but rather by event counts, do indeed have a statistically significant
and positive relationship with migration. In effect, such shocks appear to act as push factors, potentially altering the place based utility for the hypothetical migrant and leading them to relocate
to a new county within the US. Future work might look at why destination county climate shocks
appear to have a pull effect that is positive, as opposed to negative, such that emigration to the22destination county is increasing in the number of shocks to the destination. Moreover, future work
might look more closely at the robustness of 2 stage least squares relationship between climate
shocks in the origin county and wages, post emigration, in the destination given that the results
here were statistically significant under certain specifications. Future work might also look at the effects of international migration using event type data, as opposed to the more tradition temperatureand precipitation fluctuation data, as the treatment. Finally, future work may approach the casewhere rm and sm are not fixed but rather have an underlying distribution of their own such thatthe aggregate population is constructed from a distribution of individuals with differential priors
on the distribution of the rate of climate shocks of type m in location i. Such an approach would
shift away from a representative migrant to instead consider how distributional changes in priors
impact migration outcomes and is particularly relevant given the existing literature which focuseson individual learning given climate risks.

23

References[1] Bureau of Economic Analysis. 2024. “Regional Economic Accounts: CAINC30: Economic Profile
by County.” Apps.bea.gov. 2024. https://apps.bea.gov/regional/downloadzip.cfm.
[2] Cattaneo, Cristina, and Giovanni Peri. 2015. “The Migration Response to Increasing Temperatures.” NBER, October. https://doi.org/10.3386/w21622.
[3] Chen, Shuai, Paulina Oliva, and Peng Zhang. 2017. “The Effect of Air Pollution
on Migration: Evidence from China.” Social Science Research Network, November.
https://doi.org/10.3386/w24036.
[4] Fan, Qin, Karen Fisher-Vanden, and H. Allen Klaiber. 2018. “Climate Change, Migration, andRegional Economic Impacts in the United States.” Journal of the Association of Environmental
and Resource Economists 5 (3): 643–71. https://doi.org/10.1086/697168.
[5] Internal Revenue Service. 2024a. “SOI Tax Stats - US Population Migration Data .” www.irs.gov.
February 7, 2024. https://www.irs.gov/statistics/soi-tax-stats-migration-data.
[6] Internal Revenue Service. 2024b. “SOI Tax Stats - County, Metropolitan and Micropolitan Data.”
www.irs.gov. February 29, 2024. https://www.irs.gov/statistics/soi-tax-stats-county-data.
[7] Knutson, Thomas, Maya Chung, Gabriel Vecchi, Jingru Sun, Tsung-Lin Hsieh, and Adam
Smith. 2021. “Climate Change Is Probably Increasing the Intensity of Tropical Cyclones.”
https://sciencebrief.org/uploads/reviews/ScienceBrief_Review_CYCLONES_Mar2021.pdf.
[8] Leah Platt Boustan, Price V Fishback, and Shawn Kantor. 2007. “The Effect of Internal Migration on Local Labor Markets: American Cities during the Great Depression.”
Library Union Catalog of Bavaria, Berlin and Brandenburg (B3Kat Repository), July.
https://doi.org/10.3386/w13276.
[9] Mitterwallner, Veronika, Manuel Steinbauer, Gregor Mathes, and Anna Walentowitz. 2024.
“Global Reduction of Snow Cover in Ski Areas under Climate Change.” PLOS ONE 19 (3):e0299735–35. https://doi.org/10.1371/journal.pone.0299735.
[10] Mullins, Jamie T, and Prashant Bharadwaj. 2021. “Weather, Climate, and Migration in the
United States.” NBER, March. https://doi.org/10.3386/w28614.
[11] National Bureau of Economic Research. n.d. “County Distance Database.” NBER. Accessed
March 20, 2024. https://www.nber.org/research/data/county-distance-database.
[12] National Oceanic and Atmospheric Administration NCEI. 2019. “Storm Events
Database | National Centers for Environmental Information.” NOAA.gov. 2019.
https://www.ncdc.noaa.gov/stormevents/.
[13] Turco, Marco, John T Abatzoglou, Sixto Herrera, Yizhou Zhuang, Sonia Jerez, Donald D Lucas, Amir AghaKouchak, and Ivana Cvijanovic. 2023. “Anthropogenic Climate Change Impacts
24

Exacerbate Summer Forest Fires in California.” EARTH, ATMOSPHERIC, and PLANETARYSCIENCES 120 (25). https://doi.org/10.1073/pnas.2213815120.
[14] USDA Economic Research Service. 2024. “USDA Economic Research Service Rural-Urban
Continuum Codes.” www.ers.usda.gov. January 22, 2024. https://www.ers.usda.gov/dataproducts/rural-urban-continuum-codes/.
[15] Westerling, A.L. 2016. Increasing western U.S. forest wildfire activity: Sensitivity to changes
in the timing of spring. Phil. Trans. R. Soc. B. 371:20150178.
[16] Xi, Dazhi, Ning Lin, and Avantika Gori. 2023. “Increasing Sequential Tropical Cyclone
Hazards along the US East and Gulf Coasts.” Nature Climate Change, February, 1–8.
https://doi.org/10.1038/s41558-023-01595-7.

25

A

ProofsA.1

Proof of Proposition 1

Given thatλm ∼ Gamma(sm , rm ) ⇒ f0 (λm ) =smrm
λsm −1 e−rm λm
Γ(sm ) mfor λm > 0 and with hyperparameters sm , rm that describe the shape of the distribution. Then overthe interval [t, t + 1) with n disjoint subintervals and observations of {ym,1 , ym,2 , . . . , ym,n } shocks
in each corresponding sub-interval, l’s posterior distribution on λm at time t + 1 is then given by
f1 (λm |{ym,1 , ym,2 , . . . , ym,n }) ∝ g({ym,1 , ym,2 , . . . , ym,n }|λm )f0 (λm )where g({ym,1 , ym,2 , . . . , ym,n }|λm ) denotes the likelihood function such that ym,k |λm ∼ P oisson(λm ) ∀ 1 ≤k ≤ n. Since on the interval [t, t + 1), l has not yet updated the distribution f0 , consider each ym,kto be an i.i.d draw such thatg({ym,1 , ym,2 , . . . , ym,n }|λm ) =ynYe−λm λmm,k
k=1(ym,k !)

Then, note thate−nλm λ⇒ f1 (λm |{ym,1 , ym,2 , . . . , ym,n }) ∝ Qn

P
m

ym,k

k=1 (ym,k !)

·

sm
rm
λsm −1 e−rm λm
Γ(sm ) m

where the ym,k ’s are assumed to be independent. Considering only terms in r, s and noting thatλm ∼ Gamma(sm , rm ) is the conjugate prior to P oisson(λm ),P−1+sm + ym,kf1 (λm |{ym,1 , ym,2 , . . . , ym,n }) ∝ e−(rm +n)λm λm

Thus i’s belief about the distribution of the frequency of shocks of type m at time t + 1 is thengiven by!
nX
λm ∼ Gamma sm +ym,k , rm + n
k=1

where n denotes the number of time steps between [t, t + 1).
Now take Nm (t) to define a Poisson Process with intensity λm . During [t, t + 1), individual lhas not updated their belief about the distribution of λm and thus believes that in expectation, fort′ ∈ [t, t + 1) and for some fixed τ > t + 1,
E0 ((Nm (τ ) − Nm (t′ ))) = E0 (E0 (Nm (τ ) − Nm (t′ )|λm = k))
Z ∞=E0 (Nm (τ ) − Nm (t′ )|λm = k)f0 (k)dk0

where E0 ((Nm (τ ) − Nm(t′ ))) defines the expectation during [t, t + 1).

26

By definition of a Poissonprocess, E0 (Nm (τ ) − Nm (t)|λm = k) = k(τ − t′ ) and thus′Z ∞E0 ((Nm (τ ) − Nm (t ))) =k(τ − t′ )f0 (k)dk = (τ − t′ )E0 (λm )0⇒ E0 ((Nm (τ ) − Nm (t′ ))) = (τ − t′ ) ·smrm

Similarly, for t + 1 ≤ t′′ < τ , note thatP
sm + nk=1 ym,kE1 ((Nm (τ ) − Nm (t ))) = (τ − t ) ·rm + n′′′′Then for fixed τP
sm + nk=1 ym,ksm>
⇒ lim E0 ((Nm (τ ) − Nm (t′ ))) <lim E1 ((Nm (τ ) − Nm (t′′ )))rm + nrmt′ →(t+1)−
t′′ →(t+1)+
thus implying that the expectation changes discontinuously at t+1. Then, E1 ((Nm (τ )−Nm (t+1)))
yields the number of events of type m that individual l expects between [t + 1, τ ] at location i.
Recognizing that m is arbitrary, note that this holds for all m and thus the individual l updates
the distribution of each λm over the interval [t, t + 1) such that for each m after updating,λm ∼ Gamma sm +nX!
ym,k , rm + n

k=1

holds. Superposition of Poisson Processes then implies that if N1 (t), N2 (t), ..., Nm (t), ..., Np (t) are
Poisson Processes describing the number of climate shocks of types 1, 2, ..., m, ..., p, then N (t) =N1 (t) + ... + Nm (t) + ... + Np (t) also denotes a Poisson Process describing the total number of shocks
such that ∀ t′ ∈ [t + 1, τ ]. Then
E1 (N (τ ) − N (t′ )) = E1 ((N1 (τ ) − N1 (t′ )) + ... + (Np (τ ) − Np (t′ )))= E1 (N1 (τ ) − N1 (t′ )) + ... + E1 (Np (τ ) − Np (t′ ))= E1 (E1 (N1 (τ ) − Nm (t′ )|λ1 = k)) + ... + E1 (E1 (Np (τ ) − Np (t′ )|λp = k))so the number of total climate shocks that individual l expects in location i is given by′

Ni,t+1 := E1 (N (τ ) − N (t + 1)) = (τ − t )Pp Xsm + nk=1 ym,k

m=1



rm + nThe above formulation of Ni,t+1 then allows for changes in the expectation of individual climate
shock types to alter the individual’s expectation of the total number of shocks that will be experiencein location i.

27

A.2

Proof of Proposition 2

Without a loss of generality, I demonstrate the claim for migration at time t + 1. All other cases
are similar since k ∈ Z+ . At time t + 1, individual l updates their belief about Ni,t , following
proposition 1, and thus updates ut (i) to be ut+1 (i). For each j ∈ L, the decision for individual lto migrate is rational if, for a cost, C(i, j), of migration from location i to location j at time t, thefollowing inequality is satisfied
ut (i) ≤ ut (j) − C(i, j)
Rearranging terms,
ut+1 (i) − ut (i) + ut (i) ≤ ut+1 (j) − C(i, j)⇒ ∆ut (i) + ut (i) ≤ ut+1 (j) − C(i, j)
By substitution,∆ut (i) = At+1 (i)ct+1 (i)zt+1 (i) − At (i)ct (i)zt (i)nc
By assumption of the proposition, Anc
t+1 = At (i), ct+1 = ct and zt+1 = zt , or equivalently that
non-climate amenity values, consumption and wages remain constant across short run time steps t
and t + 1,
∆ut (i) = (Act (i) · h(Ni,t , Ni,t−1 ) − Act (i)) ct zt⇒ (Act (i) · h(Ni,t , Ni,t−1 ) − Act (i)) ct zt + ut (i) ≤ ut+1 − C(i, j)
For completeness, note that by construction of the function h,h(N < N∂hi,ti,t−1 ) > 1< 0 ∧ h(Ni,t = k, Ni,t−1 = k) = 1 ⇒h(Ni,t > Ni,t−1 ) < 1∂Ni,t
and thus Ni,t < Ni,t−1 ⇒ Act (i) < Act+1 (i) so that a decrease in the expected number of climate
shocks over equal length time intervals leads to an increase in the amenity value of a given location,i. Now let Fi,t denote the set of locations to which it was rational for individual l to migrate to attime t such that
Fi,t := {j ∈ L : ∆ut (i) + ut (i) + C(i, j) ≤ ut+1 (j)}Similarly define Fi,t+1 to denote the set of locations for which it is rational for individual l to migrate
to at time t + 1. Note that since, At , ct , zt > 0, the definition of the function h(Ni,t , Ni,t−1 ) implies
that(h(Ni,t , Ni,t−1 ) > 1 ⇔ ∆ut (i) > 0) ⇒ |Fi,t | ≥ |Fi,t+1 |(h(Ni,t , Ni,t−1 ) < 1 ⇔ ∆ut (i) < 0) ⇒ |Fi,t | ≤ |Fi,t+1 |Thus the number of locations j ∈ L, for which it is rational for individual l to migrate from i, isincreasing between t and t + 1 if h(Ni,t , Ni,t−1 ) > 1 and is decreasing if h(Ni,t , Ni,t−1 ) < 1.

28

A.3

Proof of Proposition 3

I begin by demonstrating the first claim. For the base case, noteh(Ni,1 , Ni,0 ) > 1 ⇒ Pi (Fi,1 ) < Pi (Fi,0 )Since Mi,0 is exogenously given and Mi,0 ≥ Mi,1 by assumption, it immediately follows that
Mi,0 Pi (Fi,0 ) ≥ Mi,1 Pi (Fi,1 ) and thus there are a greater number of expected emigrants at time
t = 0 relative to time t = 1. Now suppose that Mi,0 ≥ Mi,1 ≥ ... ≥ Mi,t and h(Ni,t , Ni,t−1 ) > 1
for some arbitrary t > 1. By the inductive hypothesis, Mi,t−1 ≥ Mi,t and h(Ni,t , Ni,t−1 ) > 1 ⇒Pi (Fi,t−1 ) < Pi (Fi,t ). Thus, Mi,t−1 Pi (Fi,t−1 ) ≥ Mi,t Pi (Fi,t ) and by the claim
Mi,0 Pi (Fi,0 ) ≥ Mi,1 Pi (Fi,1 ) ≥ ... ≥ Mi,t−1 Pi (Fi,t−1 ) ≥ Mi,t Pi (Fi,t )Since for arbitrary t, Mi,t Pi (Fi,t ) denotes the expected number of emigrants, it follows that the
expected number of emigrants is weakly decreasing over time.
A proof of the second claim is similar. As the base case follows identical logic, I consider it obvious
and show only the inductive step. Suppose that Mi,0 ≤ Mi,1 ≤ ... ≤ Mi,t and h(Ni,t , Ni,t−1 ) < 1 forsome arbitrary t > 1. Then Mi,t−1 ≤ Mi,t and h(Ni,t , Ni,t−1 ) < 1 ⇒ Pi (Fi,t−1 ) > Pi (Fi,t ). Thus,
Mi,t−1 Pi (Fi,t−1 ) ≤ Mi,t Pi (Fi,t ) and by the claim
Mi,0 Pi (Fi,0 ) ≤ Mi,1 Pi (Fi,1 ) ≤ ... ≤ Mi,t−1 Pi (Fi,t−1 ) ≤ Mi,t Pi (Fi,t )Since for arbitrary t, Mi,t Pi (Fi,t ) denotes the expected number of emigrants, it follows that the
expected number of emigrants is weakly increasing over time.

29

Figure 1: Logarithm of the mean number of emigrants (2011 - 2020) disaggregated by rural-urban continuum
code. Note that a continuum code of 1 denotes the most metropolitan counties while a code of 9 denotes
the most rural counties.

B

Additional Tables and Figures

30

Figure 2: Density plot of log wages per capita, at the county-level, for 2012, 2014, 2016, 2018 and 202031

Dependent Variable:
Model:
Binned Events
10-20
20-30
30-40
40-50
50-60
60-70
70-80
80-90
90-100100-110110-120120-130130-140140-150>150

Number of Emigrant Households(5)(6)
940.6∗∗∗(82.83)
1,765.5∗∗∗(138.5)2,941.4∗∗∗(273.0)4,654.3∗∗∗(502.4)
7,157.5∗∗∗(782.1)7,564.6∗∗∗(1,143.4)
7,928.6∗∗∗(1,616.6)
13,735.9∗∗∗(3,171.3)
10,099.4∗∗∗(1,674.9)
10,765.1∗∗∗(2,556.6)
11,332.6∗∗∗(2,502.3)
8,758.7∗∗∗(2,984.2)
3,519.2(3,224.7)
40,119.0∗∗(19,128.3)
16,482.1∗∗∗(4,833.2)

832.9∗∗∗(87.79)1,574.1∗∗∗(144.4)2,702.1∗∗∗(283.4)
4,059.5∗∗∗(520.2)
6,685.5∗∗∗(842.9)6,843.6∗∗∗(1,263.6)
8,153.2∗∗∗(1,742.1)
14,809.9∗∗∗(3,477.4)
8,515.5∗∗∗(1,588.0)
11,618.7∗∗∗(2,951.3)
7,616.0∗∗∗(1,594.3)
8,754.5∗∗∗(3,086.7)-17.61(4,000.5)
39,393.7∗∗(18,952.9)
11,028.1∗∗∗(4,221.9)-95.42(241.1)0.1141∗∗∗(0.0118)-103.4∗∗(42.23)-0.7999∗∗(0.3372)-0.0084(0.0080)

Yes
Yes

Yes
Yes

28,736
0.31373
0.05673

25,4380.33966
0.08793

County Adjusted Gross Income†
Origin Wages
Business Net Income†Amount of Taxable Pensions†
Number of Farm Returns†
Fixed-effects
State-Year
Rural-Urban Continuum CodeFit statistics
Observations
R2
Within R2

Clustered (County-year) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Table 2: Destination ambivalent estimation of the effect of the binned number of climate-related shocks inthe origin county on emigration. Number of emigrant households are measured in unit steps. Note that († )denotes a variable whose value is rooted to its 2011 value such that it is then expressed as a real-valued
multiple of the 2011 value. Standard errors are clustered at the joint origin-year level.

32

Dependent Variable:
Model:
Variables
Number of Origin Events
Number of Destination Eventsln(distance)

Number of Emigrant Households
(9)
(10)
(11)
OLS
OLS
OLS

(7)
OLS

(8)
OLS

0.9682∗∗∗(0.0828)0.9124∗∗∗(0.0828)-125.9∗∗∗(4.422)0.9161∗∗∗(0.0847)0.8757∗∗∗(0.0865)-128.7∗∗∗(4.591)0.0013∗∗∗(0.0003)0.0012∗∗∗(0.0002)8.423(12.89)18.15∗(10.29)-0.0006(0.0007)-0.0003(0.0010)0.9400∗∗∗(0.0860)
0.8843∗∗∗(0.0880)-125.2∗∗∗(4.459)0.0014∗∗∗(0.0003)0.0011∗∗∗(0.0002)9.334(13.11)43.85∗∗∗(11.31)-0.0005(0.0007)-0.0003(0.0010)-0.0748(0.0602)-85.66∗∗∗(22.10)0.9401∗∗∗(0.0860)
0.8848∗∗∗(0.0880)-125.2∗∗∗(4.460)0.0014∗∗∗(0.0003)0.0011∗∗∗(0.0002)
9.272(13.11)43.85∗∗∗(11.31)-0.0005(0.0007)-0.0003(0.0010)0.0129(0.0630)-85.74∗∗∗(22.10)-0.0882∗∗∗(0.0194)-0.0004∗∗∗(2.02 × 10−5 )-0.0168(0.0149)
0.0051(0.0138)-80.99
(1,486,910.9)0.0011∗∗∗(0.0001)-0.0002(0.0001)
27.26∗∗∗(4.393)15.04∗∗∗(2.857)2.05 × 10−5
(5.14 × 10−5 )1.4 × 10−5
(7.69 × 10−5 )-15.74∗∗∗(5.162)43.37∗∗∗(5.311)0.0039(0.0033)
5.87 × 10−6∗∗(2.52 × 10−6 )0.0003∗∗∗(6.8 × 10−5 )0.0003∗∗∗(6.3 × 10−5 )-0.8138(31,256.8)
4.24 × 10−6∗∗∗(6.67 × 10−7 )−3.84 × 10−6∗∗∗(4.82 × 10−7 )0.2455∗∗∗(0.0255)
0.1230∗∗∗(0.0179)−1.33 × 10−7
(2.31 × 10−7 )7.35 × 10−9
(1.97 × 10−7 )-0.0235(0.0259)
0.3731∗∗∗(0.0253)-0.0002(0.0001)
1.25 × 10−7∗∗∗(1.93 × 10−8 )

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

572,6200.10751
0.00759
8,530,830.0

504,7280.11065
0.00783
7,509,813.7

410,372
0.10875
0.00771
6,086,230.2

410,372
0.108760.00772
6,086,250.9

410,372
0.96600
0.22660
6,096,751.9

410,372
0.99143
0.97330
4,452,506.2

Origin Wages
Destination Wages
Origin County Adjusted Gross Income†
Destination County Adjusted Gross Income†
Origin Number of Farm Returns†
Destination Number of Farm Returns†
Origin Taxable Pension Amount†
Destination Taxable Pension Amount†
Origin Unemployment Compensation†
Destination Unemployment Compensation†
Fixed-effects
Destination State-Year
Origin State-Year
Origin Rural-Urban Continuum Code
Destination Rural-Urban Continuum Code
Origin-Destination Pair
Fit statistics
Observations
Squared Correlation
Pseudo R2
BIC(12)PoissonClustered (Origin-Destination Pair) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Table 3: Dyadic estimation of the effect of the number of origin climate-related shocks on emigration.
Number of emigrant households are measured in unit steps. Note that († ) denotes a variable whose value isrooted to its 2011 value such that it is then expressed as a real-valued multiple of the 2011 value. Standard
errors are clustered at the origin-destination pair level. Note that Model (12) is a restimation of Model (11)using a Poisson Pseudo-Maximum Likelihood estimator.

33

Dependent Variable:
Model:
Origin>150
10-20
100-110110-120120-130130-140140-150
20-30
30-40
40-50
50-60
60-70
70-80
80-90
90-100Destination
>150
10-20
100-110110-120120-130130-140140-150
20-30

Number of Emigrant Households
(13)
(14)
(15)
OLS
OLS
Poisson-4.109∗∗(1.842)0.3788(0.4844)3.799∗∗(1.595)-0.4197(1.551)-0.0358(2.819)-6.445∗∗∗(2.243)3.499∗(1.838)0.8010(0.5018)-0.9543(0.6950)
1.803∗∗(0.8788)2.717∗∗(1.109)2.027∗(1.138)-4.870∗∗∗(1.786)1.670(2.077)-2.229(2.105)-6.015∗∗(2.570)-0.2467(0.5678)4.801∗∗(2.336)-2.091(2.141)-1.401(4.357)-15.39∗∗∗(2.561)-11.60∗∗(4.949)-1.069(0.6649)-2.678∗∗∗(0.9062)0.1099(1.063)3.499∗∗(1.656)3.477∗∗(1.545)-9.562∗∗∗(2.468)2.086(2.198)-3.263(3.140)0.0391∗∗∗(0.0107)0.0030(0.0021)0.0609∗∗∗(0.0093)0.0304∗∗∗(0.0109)
0.0070(0.0286)-0.0667∗∗∗(0.0214)-0.0076(0.0151)-0.0052∗(0.0030)-0.0097∗∗(0.0046)
0.0112∗∗(0.0045)0.0339∗∗∗(0.0078)0.0352∗∗∗(0.0070)-0.0228∗∗∗(0.0066)
0.0301∗∗∗(0.0085)0.0328∗∗∗(0.0104)

0.3763(1.878)-0.0491(0.3834)
1.293(1.393)0.6702(1.655)-2.749(2.405)-1.893(2.720)-4.327∗∗(2.004)-0.2693(0.5154)-3.495(2.459)-0.0900(0.5208)
0.7326(1.820)-3.372∗(1.744)-6.443∗(3.324)-3.785(3.789)6.637∗∗∗(2.391)-1.150∗(0.6746)

0.0420∗∗∗(0.0116)-0.0004(0.0026)
0.0129(0.0089)0.0150(0.0109)-0.0353∗∗(0.0153)-0.0268(0.0314)0.0500∗∗∗(0.0120)-0.0083∗∗(0.0039)34

30-40
40-50
50-60
60-70
70-80
80-90
90-100ln(distance)-0.0571(0.7439)-0.7120(0.8212)1.596(1.008)0.5606(1.086)0.0087(1.635)2.878∗(1.734)-4.611(3.035)-39.94(1,475,511.0)-0.6700(0.9320)-1.984∗(1.022)3.681∗∗(1.584)-0.0405(1.311)-0.4784(2.202)2.423(1.905)-3.095(3.993)-57.47(1,495,225.4)
0.0011∗∗∗(0.0002)-0.0002∗(0.0001)
27.39∗∗∗(4.387)15.38∗∗∗(2.906)
1.85 × 10−5
(5.15 × 10−5 )1.45 × 10−5
(7.69 × 10−5 )-16.26∗∗∗(5.128)42.55∗∗∗(5.305)0.0030(0.0027)
2.07 × 10−5∗∗∗(3.86 × 10−6 )0.0019(0.0050)-0.0079(0.0049)
0.0287∗∗∗(0.0063)-0.0014(0.0056)
0.0226∗∗∗(0.0087)
0.0245∗∗∗(0.0075)
0.0330∗∗∗(0.0102)
0.1564(30,569.3)
4.4 × 10−6∗∗∗(6.71 × 10−7 )−3.82 × 10−6∗∗∗(4.82 × 10−7 )0.2495∗∗∗(0.0256)
0.1242∗∗∗(0.0183)−1.5 × 10−7
(2.36 × 10−7 )1.21 × 10−8
(1.97 × 10−7 )-0.0234(0.0253)
0.3688∗∗∗(0.0257)-0.0002∗(0.0001)
2.03 × 10−7∗∗∗(2.27 × 10−8 )

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

572,6200.971930.23841
7,986,951.4

410,372
0.966020.22663
6,096,939.4

410,372
0.99155
0.97333
4,448,897.1

Origin Wages
Destination Wages
Origin County Adjusted Gross Income†
Destination County Adjusted Gross Income†
Origin Number of Farm Returns†
Destination Number of Farm Returns†
Origin Taxable Pension Amount†
Destination Taxable Pension Amount†
Origin Unemployment Compensation†
Destination Unemployment Compensation†
Fixed-effects
Destination State-Year
Origin State-Year
Origin Rural-Urban Continuum Code
Origin Rural-Urban Continuum Code
Origin-Destination Pair
Fit statistics
Observations
Squared Correlation
Pseudo R2
BIC

Clustered (Origin-Destination Pair) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Table 4: Dyadic estimation of the effect of the binned number of origin climate-related shocks on emigration.
Number of emigrant households are measured in unit steps. Note that († ) denotes a variable whose value isrooted to its 2011 value such that it is then expressed as a real-valued multiple of the 2011 value. Standard
errors are clustered at the origin-destination pair level. Note that Model (12) is a restimation of Model (11)using a Poisson Pseudo-Maximum Likelihood estimator.

35

Dependent Variable:
Model:
Variables
Number of Origin Events
Number of Destination Eventsln(distance)
Origin County Adjusted Gross Income†
Destination County Adjusted Gross Income†
Origin Number of Farm Returns†
Destination Number of Farm Returns†
Origin Taxable Pension Amount†
Destination Taxable Pension Amount†
Origin Unemployment Compensation†
Destination Unemployment Compensation†

Log Mean Annual Destination Income per capita
(16)
(17)
(18)
-0.0001∗∗∗(2.98 × 10−5 )0.0003∗∗∗(2.85 × 10−5 )0.0217∗∗∗(0.0008)0.0147∗(0.0084)
0.5693∗∗∗(0.0388)
8.09 × 10−8
(3.02 × 10−7 )1.28 × 10−6∗∗∗(1.63 × 10−7 )−7.82 × 10−5∗∗(3.06 × 10−5 )-0.2951∗∗∗(0.0241)
4.56 × 10−5
(3.05 × 10−5 )3.97 × 10−7∗∗∗(3.63 × 10−8 )0.0315∗∗∗(0.0027)0.0117(0.0085)
0.5621∗∗∗(0.0385)
1.29 × 10−7
(3.09 × 10−7 )1.3 × 10−6∗∗∗(1.85 × 10−7 )−8.14 × 10−5∗∗∗(3.1 × 10−5 )-0.2902∗∗∗(0.0242)
5.47 × 10−5∗(3.03 × 10−5 )4.15 × 10−7∗∗∗(3.55 × 10−8 )8.07 × 10−5∗∗∗(2.3 × 10−5 )0.6044(2,378.1)
0.0715(0.0546)0.1731∗∗∗(0.0185)
4.48 × 10−8
(8.7 × 10−8 )2.24 × 10−8
(1.13 × 10−7 )-0.0422(0.0293)0.0132(0.0557)−2.41 × 10−5∗(1.28 × 10−5 )2.09 × 10−7∗∗∗(3.3 × 10−8 )-0.0014(0.0012)

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

415,656
0.52198
0.10396415,656
0.513760.08854415,656
0.82688-19.846

Fitted Number of Emigrant Households
Fixed-effects
Destination State-Year
Origin State-Year
Origin Rural-Urban Continuum Code
Destination Rural-Urban Continuum Code
Origin-Destination Pair
Fit statistics
Observations
R2
Within R2
Clustered (pair) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Table 5: 2 Stage least squares and reduced form estimation of the effect of the number of origin anddestination climate-related shocks on wages in the destination county. Number of emigrant households aremeasured in unit steps. Note that († ) denotes a variable whose value is rooted to its 2011 value such that
it is then expressed as a real-valued multiple of the 2011 value. Standard errors are clustered at the origindestination pair level.

36

Dependent Variable:
Model:

Number of Emigrant Households
(19)
(20)

Variables
Flash Flood
Flood
Hail
Thunderstorm Wind
TornadoFunnel Cloud
Heavy Rain
Dust Devil
DebrisLightningHigh Wind
Fog
Heat
Dust Storm
Tropical Storm
Strong Wind

312.4∗∗∗(28.10)109.2∗∗∗(26.70)93.62∗∗∗(11.67)60.94∗∗∗(11.90)56.09(47.48)540.2∗∗∗(127.3)-79.11∗∗∗(25.38)2,823.9∗∗∗(695.8)79.77(213.9)1,385.8∗∗∗(148.3)-426.2(1,308.4)
2,591.1∗∗∗(335.2)-641.9(613.5)250.5(183.2)996.5(6,003.6)
1,159.5∗∗(550.1)308.1∗∗∗(31.08)97.46∗∗∗(32.40)93.89∗∗∗(12.56)49.12∗∗∗(12.79)56.90(51.45)486.7∗∗∗(125.0)-43.72(27.58)3,025.6∗∗∗(736.9)-134.9(189.8)1,265.8∗∗∗(158.0)-401.6(1,353.4)

Yes
Yes

Yes
Yes

27,1670.349310.08925

24,1170.36805
0.11113

County Adjusted Gross Income†
Origin Wages
Business Net Income†Amount of Taxable Pensions†
Number of Farm Returns†
Fixed-effects
state-year
Rural-Urban Continuum CodeFit statistics
Observations
R2
Within R2-633.5(513.1)635.2∗∗∗(193.9)1,373.3(6,010.2)
71.39(573.3)-79.27(253.5)0.1030∗∗∗(0.0115)-106.3∗∗∗(39.12)-0.6123∗(0.3474)-0.0047(0.0082)

Clustered (county-year) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Table 6: Destination ambivalent estimation of the effect of disparate types of climate-related shocks in theorigin county on emigration. Number of emigrant households are measured in unit steps. Note that († )denotes a variable whose value is rooted to its 2011 value such that it is then expressed as a real-valued
multiple of the 2011 value. Standard errors are clustered at the origin county-year level.

37

>150
10-20
100-110110-120120-130130-140140-150
20-30
30-40
40-50
50-60
60-70
70-80
80-90
90-100County Adjusted Gross Income
Origin Wages
Business Net Income
Amount of Taxable Pensions
Number of Farm Returns

p-values
0.00900
0.00000
0.00008
0.00000
0.00457
0.99649
0.037670.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.000020.00000
0.69232
0.00000
0.01433
0.017700.29638

FDR Correction
0.012860.00000
0.000140.00000
0.00703
0.996490.04432
0.00000
0.00000
0.00000
0.00000
0.00000
0.00001
0.00004
0.00000
0.728750.00000
0.019110.022130.32931

Significance
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗
∗

Table 7: p-values with and without the false detection rate correction corresponding to Model (6). Notethat those coefficients which are significant at a 95% level following the false detection rate correction are
denoted with a ∗ in the significance column.

38

Flash Flood
Flood
Hail
Thunderstorm Wind
TornadoFunnel Cloud
Heavy Rain
Dust Devil
DebrisLightningHigh Wind
Heat
Dust Storm
Tropical Storm
Strong WindCounty Adjusted Gross Income
Origin Wages
Business Net Income
Amount of Taxable Pensions
Number of Farm Returns

p-values
0.00000
0.002630.00000
0.000120.268810.000100.113020.00004
0.477160.00000
0.76665
0.216910.00106
0.819270.900900.754480.00000
0.00660
0.07797
0.56540

FDR Correction
0.00000
0.00584
0.00000
0.00035
0.38401
0.00033
0.188370.00016
0.636210.00000
0.85183
0.33371
0.002640.862390.900900.851830.00000
0.013200.14177
0.70675

Significance
∗
∗
∗
∗
∗
∗
∗

∗

∗
∗

Table 8: p-values with and without the false detection rate correction corresponding to Model (20). Notethat those coefficients which are significant at a 95% level following the false detection rate correction are
denoted with a ∗ in the significance column.

39Event TypeFlash Flood
Flood
Hail
Thunderstorm Wind
TornadoLightning
Funnel Cloud
Heavy Rain
Dust Devil
Debris Flow
High Wind
Drought
Dense Fog
Heat
Dust Storm
Tropical Storm
Excessive Heat
Strong Wind
Heavy Snow
Wildfire

Number of Occurrences
53099
37161
139901234542
19327
6111
4484
19018
123
1405
7
1
1
5
3
2
1
3
5
1

Table 9: Number of events by type between 2011 and 2021 in US counties. Data is aggregated from the
NOAA storm events database. Note that this does not include all weather events in counties in the US
between 2011 and 2021 but rather only includes those which are defined as (1) having sufficient intensity
to cause significant damage or other harms; (2) being rare in a given location; or (3) representing a local
extrema in the type of event according to NOAA.

40

Year
2011
2011
2011
2011
2012
2012
2012
2013
2013
2013
2014
2014
2014
2015
2015
2015
2016
2016
2016
2016
2017
2017
2017
2018
2018
2018
2018
2019
2019
2019
2019
2020
2020
2020
2020

Group
20-70
70-120
<20
>120
20-70
70-120<2020-7070-120<2020-7070-120<2020-7070-120<2020-7070-120
<20
>120
20-70
70-120<2020-7070-120
<20
>120
20-70
70-120
<20
>120
20-70
70-120<20>120

Number of Counties
876
31
2137
2
496
14
2529
429
14
2403
365
15
2379
425
20
2391
412
7
2467
2
484
16
2350
418
21
2381
2
528
30
2272
5
431
23
2356
2

Table 10: Number of climate-related shocks by year in origin counties. Note that group refers to the binningof county level observations of events into 1 of the following 3 groups: (1) < 20 events, (2) 20 − 70 events,(3) 70 − 120 events and (4) > 120 events

41

Year
2011
2012
2013,
2014
2015
2016
2017
2018
2019
2020

Number of Households
6213920.00
6278252.00
5397206.00
4077939.00
5635951.00
7720963.00
6028155.00
5903777.00
6462009.00
6731320.00

P25
152.00148.00128.00107.00128.00160.00131.00127.00140.00142.00P50
384.00381.00344.50281.00346.00440.00354.00348.50369.00372.00P75
1142.75
1157.00
1078.50
861.501103.25
1444.75
1143.75
1130.75
1199.50
1216.50

Standard Deviation
6518.476537.996040.314832.166410.498643.616909.30
6805.097671.38
8145.44

Mean
2040.032065.89
1896.421478.05
1987.29
2673.462115.142092.05
2279.372391.23

Table 11: Summary statistics of the number of households emigrating within the US (2011 - 2020). Notethat while the data covers 2021, it is not included in this table as only destination specific terms can be
determined for 2021 variables. Thus summary statistics on emigration in 2021 are not supported by thisdataset.

42

Dependent Variable:
Model:
Variables
Constant
02013 × Year
02016 × Year
02050 × Year02060 × Year02068 × Year02070 × Year02090 × Year
02100 × Year
02105 × Year
02110 × Year02122 × Year
02130 × Year
02150 × Year
02164 × Year02170 × Year02180 × Year02185 × Year02188 × Year02195 × Year02198 × Year02240 × Year02261 × Year02270 × Year02275 × Year02282 × Year02290 × Year
06003 × Year08065 × Year
16025 × Year46113 × Year
48211 × Year
49009 × Year
49033 × Year
51735 × Year

Number of Emigrant Households
(2)
(3)

(1)

22,182.4∗∗∗(6,370.8)-11.00∗∗∗(3.164)-10.98∗∗∗(3.161)-10.93∗∗∗(3.161)-11.02∗∗∗(3.168)-10.99∗∗∗(3.162)-10.99∗∗∗(3.161)-10.08∗∗∗(3.161)-11.02∗∗∗(3.167)-11.00∗∗∗(3.163)-10.87∗∗∗(3.161)-10.73∗∗∗(3.161)-10.97∗∗∗(3.161)-10.94∗∗∗(3.161)-11.00∗∗∗(3.162)-10.41∗∗∗(3.161)-10.97∗∗∗(3.161)-10.97∗∗∗(3.161)-10.97∗∗∗(3.161)-11.01∗∗∗(3.166)-11.00∗∗∗(3.161)-10.97∗∗∗(3.161)-10.94∗∗∗(3.163)-10.99∗∗∗(3.167)-11.02∗∗∗(3.169)-11.03∗∗∗(3.169)-10.96∗∗∗(3.161)-11.02∗∗∗(3.167)-10.97∗∗∗(3.161)-11.02∗∗∗(3.167)-10.98∗∗∗(3.167)-11.01∗∗∗(3.169)-11.02∗∗∗(3.167)-11.02∗∗∗(3.167)-10.91∗∗∗(3.161)-4.792∗∗∗(2.65 × 10−15 )-6.212∗∗∗(7.24 × 10−16 )-5.479∗∗∗(3.73 × 10−16 )-1.000∗∗∗(2.41 × 10−16 )-0.5471∗∗∗(7.02 × 10−17 )-1.630∗∗∗(2.33 × 10−16 )-66.53∗∗∗(5.6 × 10−15 )1.000∗∗∗(N aN × 10−Inf )0.1456∗∗∗(1.17 × 10−17 )-26.81∗∗∗(1.87 × 10−15 )-33.50∗∗∗(2.24 × 10−15 )-8.988∗∗∗(1.77 × 10−15 )-11.00∗∗∗(1.68 × 10−15 )0.7165∗∗∗(N aN × 10−Inf )-28.96∗∗∗(6.72 × 10−15 )-3.382∗∗∗(1.87 × 10−16 )-4.521∗∗∗(3.73 × 10−16 )-2.697∗∗∗(1.12 × 10−15 )-6.214∗∗∗(1.03 × 10−15 )-0.4059∗∗∗(1.53 × 10−16 )-6.103∗∗∗(6.54 × 10−16 )-11.19∗∗∗(1.47 × 10−15 )-15.50∗∗∗(3.85 × 10−15 )6.947(10.22)-2.473(6.642)-3.175(6.257)64.62(46.03)
13.27(10.12)1.900(6.559)-62.49∗∗∗(6.685)20.01(13.34)2.034(6.702)-22.46∗∗∗(6.937)-31.10∗∗∗(6.877)-6.005(6.731)-7.280(6.189)3.346(4.880)-26.97∗∗∗(6.872)0.7411(6.202)-12.35(9.096)
1.274(5.789)0.3817(14.05)5.016(6.935)-1.555(6.133)-9.294(7.261)

6.947(10.40)-2.473(6.759)-3.175(6.367)64.62(46.83)
13.27(10.29)1.900(6.674)-62.49∗∗∗(6.802)
20.01(13.57)2.034(6.819)-22.46∗∗∗(7.058)-31.10∗∗∗(6.998)-6.005(6.849)-7.280(6.297)3.346(4.965)-26.97∗∗∗(6.992)0.7411(6.311)-12.35(9.255)1.274(5.890)0.3817(14.29)5.016(7.056)-1.555(6.240)-9.294(7.388)-2.406∗∗∗(1.87 × 10−16 )2.172(5.506)

2.172(5.602)0.2441∗∗∗(8.67 × 10−16 )5.594(7.780)5.594(7.916)

13.86(42.66)-0.0003(0.0003)-0.0041(0.0029)

13.86(43.40)-0.0003(0.0003)-0.0041(0.0029)

Yes

Yes

Yes
Yes

220
0.943640.19679204
0.94395
0.20256204
0.94395
0.20256-2.000∗∗∗(4.81 × 10−16 )3.006∗∗∗(7.47 × 10−16 )County Adusted Gross Income†Number of Farm Returns†
Wages
Fixed-effects
CountyRural-Urban Continuum CodeFit statistics
Observations
R2
Within R2

220
0.93383(4)

Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Table 12: Test for unit specific trends amongst never shocked counties. Note that († ) denotes that a variable
is rooted to its 2011 value and thus is expressed as a real-valued multiple of said 2011 value. Numbers(XXXXX) × Y ear denote the county FIPS code.

43