HIAS-E-36

Long-run Consequences of Exposure to Natural
Disasters
Krzysztof Karbownik
Institute for Policy Research, Northwestern University

Anthony Wray
Hitotsubashi Institute for Advanced Study, Hitotsubashi University

November 2016

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
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http://hias.ad.hit-u.ac.jp/
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Long-run Consequences of Exposure to Natural Disasters*
Krzysztof Karbownik†

Anthony Wray‡

November 5, 2016

Abstract
We utilize the individual-level World War I Draft Registration Cards matched to late-nineteenth century hurricane paths and the 1940 U.S. Census to explore whether fetal and early childhood exposure
to stress caused by hurricanes a¯ects human capital development and labor market outcomes in adulthood. Di¯erence-in-di¯erences estimates indicate that white males who were born in the South andexperienced a hurricane either in utero or as infants had lower income at ages 42 to 53. They arerobust to alternate specifications of either the treatment or outcome variables, as well as changes inthe tolerance for imperfectly matched historical data.
Keywords: Prenatal stress, natural disasters, labor market outcomes
JEL Codes: I10, J24, Q54* We thank David Dranove, Joe Ferrie, David Figlio, Joel Mokyr, Kjell Salvanes, and numerous seminar and conference participants at
Hitotsubashi University, Northwestern University, FAU Erlangen-Nurnberg, the 2014 Early Life Conference at the University of Michigan, the2014 Canadian Economic Association Annual Meeting, the 2016 SOLE Annual Meeting, and the 2016 Early Childhood Inequality Workshop
for valuable suggestions that helped to improve the paper. Karbownik and Wray acknowledge support from the Japan Society for the Promotionof Science KAKENHI Young Scientists B Grant Number J160100115 (PI: Wray) and the Hitotsubashi Institute for Advanced Study (HIAS) at
Hitotsubashi University, as well as the Northwestern University Economics Department’s Center for Economic History and Eisner Fund, andWray acknowledges support from the Canadian Economic Association.
†
Institute for Policy Research, Northwestern University. E-mail: krzysztof.karbownik@northwestern.edu‡
Hitotsubashi Institute for Advanced Study, Hitotsubashi University. E-mail: anthony.wray@r.hit-u.ac.jp

Introduction
There is a growing consensus that healthy fetal development has a positive relationship with the levelof subsequent health, educational, and labor market outcomes (Barker 1995; Black et al. 2007; Almondand Currie 2011; Figlio et al. 2014; Bharadwaj et al. 2016). Furthermore, a large body of evidence has
documented that sizable and severe in utero health insults, such as a global flu epidemic (Almond 2006;
Lin and Liu 2014) and nuclear fallout (Almond et al. 2009), can a¯ect exposed individuals over a long time
horizon. However, with the exception of recent studies that examine airborne pollution (Isen et al. 2016)
and seasonal influenza (Schwandt 2016), much less is known about the broad economic consequences ofmilder shocks caused by more typical events, such as low intensity hurricanes.
Yet, a number of factors make the long-run consequences of hurricanes a concern for policymakers.
First, the rise in the number of storms and storm-days in the North Atlantic basin over the past few
decades (Webster et al. 2005) suggests that the individual probability of exposure to storms has increased.
Second, a large fraction of children around the world now face the risk of prenatal exposure to theserecurring events. In the U.S. context, 9 to 14 percent of births from 1992 to 2010 occurred in coastal
shoreline or watershed counties along the southern Atlantic coast, which is vulnerable to hurricaneforce winds.1 Third, exposure to tropical cyclones has been shown to lower a country’s growth andproductivity in the long run (Hsiang and Jina 2014).
This paper investigates the short- and long-run e¯ects of in utero and early childhood exposure to
low intensity (Category 1 and 2) hurricanes that a¯ected the U.S. South during the late-nineteenth century. Our primary contribution to the literature is to provide evidence that prolonged maternal stress,
caused by relatively commonplace events that may occur during pregnancy, leads to long-term e¯ects
on individual productivity. Maternal stress during pregnancy can have harmful consequences for theneurodevelopment of a child, as several biology studies have hypothesized, based on evidence of raisedlevels of the stress hormone, cortisol (Glover et al. 2010). Evidence from siblings indicates that in utero
exposure to elevated cortisol is associated with a reduction in years of schooling through its e¯ect on achild’s IQ and health (Aizer et al. 2016). Hurricanes are stressful events not only for pregnant women,
but also for the population at large. Psychology studies document experiences of post traumatic stressdisorder (PTSD) and psychological distress (PD) among individuals a¯ected by natural disasters (Galeaet al. 2007; Paxson et al. 2012), and show that these conditions persist anywhere from 30 days to at least
2 years following the event, depending on the intensity (Galea et al. 2005; 2007).
When it comes to low intensity hurricanes, the quasi-experimental treatment will operate primarily through prolonged stress induced by uncertainty, loss of normalcy, or temporary displacement fromunanticipated and potentially life-threatening natural phenomena (Badakhsh et al. 2010), rather than major destruction to property and infrastructure, or long-lasting disruption to economic activity. Prenatal
stress induced by hurricanes (Currie and Rossin-Slater 2013) is distinct from environmental toxins (Isen1
A coastal shoreline county is defined as a county directly adjacent to the open ocean or major estuaries, while a county is
considered a coastal watershed county if at least 15 percent of the county overlaps a coastal watershed, or at least 15 percent ofa coastal watershed is contained within the county (Ache et al. 2015). Alternatively, if we consider hurricane-prone areas of theeastern U.S. as defined by the American Society of Civil Engineers (ASCE) based on the occurrence of winds in excess of 90
mph, then 15 to 17 percent of children were at risk of fetal exposure to hurricanes among births between 1992 and 2010.

1

et al. 2016) and infectious disease (Schwandt 2016) as a pathway linking the mild shock of recurring
events to fetal programming and neonatal health.2 Existing evidence on the persistent e¯ects of prenatal
stress is mixed, with Black et al. (2016) documenting no long-run consequences, and Persson and RossinSlater (2016) finding adverse e¯ects on mental health. However, since these studies are based on the acutetrauma of family bereavement, it is not clear whether the consequences of prolonged prenatal stress for
fetal programming translate into influences on adult well-being.
This paper links newly collected population-level war registry data to spatial data on hurricane tracks
and complete-count census data to study the long-run educational and labor market e¯ects of in utero
and early-life exposure to hurricanes. We follow individuals born between January 1887 and August
1897, and exposed to hurricanes between June 1885 and October 1899, until they were aged 42 to 53 at
the time of the 1940 U.S. Census. We use a di¯erence-in-di¯erences identification strategy in which thefirst di¯erence is a comparison of individuals exposed to a hurricane in utero to those born before or after
a storm, and the second is a comparison between those born in exposed and surrounding locations.
We find evidence that exposure to hurricanes while in utero and during infancy had negative e¯ects
on the labor market outcomes of white Southern males. In the preferred specification, we estimate adecrease in income of 7.5 percent for those exposed to hurricanes while in utero, and 5.1 percent for
those exposed during infancy before the age of 6 months. The magnitude of the in utero exposure e¯ect
on income is larger than Black et al. (2013) and Isen et al. (2016), comparable to Almond (2006), andsmaller than Schwandt (2016). The labor market deficiencies, albeit smaller, remain even after accounting
for educational attainment. When we examine hurricane exposure by trimester of pregnancy, we find ane¯ect of first trimester exposure on educational attainment and income, while third trimester exposurea¯ects income and full time employment. We find little evidence that the long-run e¯ects were mediatedby migration in response to the disasters. While data and institutional concerns necessitate the focus
on the white population for the long-run outcomes, we expect hurricanes to also have a negative e¯ecton the underprivileged black population. We document that in utero and age 0 to 6 month exposure
to hurricanes were associated with increased mortality for blacks but not for whites, when we use themale-to-female gender ratio in 1900 as a proxy for mortality from hurricane exposure.
This project also contributes to a growing literature that examines the consequences of in utero
and early life exposure to natural disasters for socioeconomic and health outcomes. Recent work has
shown the e¯ects of hurricanes on birth outcomes (Simeonova 2011; Currie and Rossin-Slater 2013;de Oliveira and Quintana-Domeque 2016), infant mortality (Antilla-Hughes and Hsiang 2013), earlyschooling outcomes (Fuller 2014; Deuchert and Felfe 2015), and health (Sotomayor 2013; Tseng 2016).
Similarly, earthquakes have been found to a¯ect infant mortality and birth outcomes (Torche 2011; Liuet al. 2015), in addition to completed education (Caruso and Miller 2015).3 Several of these studies havepointed to prenatal stress as a potential mechanism, but we are the first to provide evidence of persistent
2
Almond et al. (2015) also study a moderate and relatively common distruption to fetal health — nutritional deficiency due
to maternal fasting during the month of Ramadan — and examine e¯ects on academic performance at age 7.
3
Other environmental shocks experienced during pregnancy include rainfall that led to positive changes in income throughimproved agricultural output (Maccini and Yang 2009), Dust Bowl erosion that resulted in poor economic conditions (Cutler
et al. 2007; Arthi 2015), and wildfires that caused a deterioration in air quality (Jayachandran 2009). A parallel stream of theliterature focuses on the role of fetal exposure to famine (Lindeboom et al. 2010) and alcohol (Nilsson, 2016).

2

e¯ects on later-life labor market outcomes. Importantly, our data identify birth location at a muchfiner level of geography than some of the aforementioned papers, reducing measurement error in theassignment of exposure to a disaster. Finally, given that our data contain information on the exact dateof birth, our results provide a cleaner distinction between the e¯ects of shocks to fetal and infant health.
Our analysis also provides a counterpoint to studies that find short-run benefits for survivors ofnatural disasters and a¯ected communities when examining the contemporaneous implications of positive mortality selection (Ho et al. 2016) and labor market responses to natural disasters (Deryugina et
al. 2014; Groen et al. 2016). Similarly, potential medium-run gains can arise due to induced migration(Nakamura et al. 2016), and the transfer of wealth that occurs when disaster relief flows into a¯ected
regions (Deryugina 2016). These immediate e¯ects ignore the potential negative and long-lasting consequences of exposure to natural disasters for vulnerable populations such as developing fetuses and young
children. Evidence that prenatal stress induced by hurricanes is linked to adverse labor market outcomesmay explain in part the macroeconomic consequences of environmental disasters documented by Hsiangand Jina (2014). Thus, by failing to account for in utero and early childhood insults that persist into
adulthood, assessments of the overall consequences of environmental shocks may be misleading.

1

Data and descriptive statistics
In the absence of radar and satellite technology at the end of the nineteenth century, the hurricane

forecasting and warning system was largely ine¯ective. Large cities had limited advanced notice of an
incoming storm, while hurricanes often hit rural areas with no warning. Beginning in 1870, the U.S.
Army Signal Corps developed a network of meteorological reporting stations linked by telegraph inorder to collect meteorological information and provide storm warnings. However, the reliability of
the hurricane warning system was compromised by the frequent breakdown of telegraph lines when astorm made landfall (Barnes 1998, pp. 34, 37, 66-67). Moreover, ships recorded the position of storms
at sea, but without ship-to-shore telegraph technology, warnings usually could not be communicated to
the weather stations until ships docked at a port. Even though the forecasting and advanced warning ofstorms was often ine¯ective, the meteorological data was collected ex-post and preserved, allowing forthe reconstruction of storm paths over one hundred years later.

1.1

Principal data sources
Our paper brings together data on historical hurricane paths, the date and place of birth for the near

universe of males born in the U.S. during the late-nineteenth century from World War I draft records,
in addition to education and labor market outcomes from the complete-count records of the 1940 U.S.
Census of Population.
We reconstruct the paths of all Atlantic-basin hurricanes that made continental U.S. landfall between
June 1885 and October 1899 from the HURDAT2 database of “best track” estimates. Table O1 lists
the twenty-six hurricanes that occurred during this period, along with the landfall date, the maximum
category of strength and the maximum wind speed recorded over land, as well as the states exposed to3each storm. It also lists the four hurricanes that passed within 30 kilometers of the continental U.S.
during the sample period, which were close enough to a¯ect coastal areas. The HURDAT2 database
contains a standardized set of latitude and longitude coordinates for the location of a storm every six
hours, in addition to the wind speed, direction, and speed of the storm at each coordinate, which are
estimated from storm observations in historical sources (Fernández-Partagás and Diaz 1996; Landsea andFranklin 2013). Although the database contains coordinates for all storms that matched or exceeded theintensity of tropical depressions, we restrict attention to coordinates with a recorded wind speed of atleast 120 kilometers per hour, which is the minimum threshold for a hurricane.4 It is important to notethat our sample consists almost entirely of Category 1 and 2 hurricanes (approximately 60 and 40 percentof the sample, respectively), which are typically not strong enough to cause long-lasting destruction ofproperty and infrastructure. However, these storms are powerful enough to cause damage to rooftops,
and can lead to fatalities from flooding and collapsed trees.
We obtain the date of birth and detailed place of birth from the database of U.S. World War I Draft
Registration Cards.5 When the U.S. entered World War I in May 1917, all male residents between theages of 18 and 45 were required to register for the draft regardless of citizenship status. The First andSecond Registration waves contain the draft cards for the near universe of the June 1886 through August
1897 male birth cohorts. We are unable to make use of the Third Registration, which covered males born
between 11 September 1897 and 12 September 1900 as well as those born between 11 September 1872
and 5 June 1886, because it did not request individuals to report place of birth. We extract the records
of approximately two million individuals born between January 1887 and August 1897 in one of nineSouthern states: Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina,
Texas, and Virginia. None of the hurricanes in our sample passed over any other state as hurricanestrength storms, although some of them continued inland as tropical storms or depressions.6
We assign latitude and longitude coordinates to the places of birth in the WWI records by matchingplace name strings to the universe of U.S. physical and cultural geographic features in the Geographic
Names Information System (GNIS), a database developed by the U.S. Geological Survey (2014). Since
the GNIS database contains many non-unique feature names, some of which did not exist during thenineteenth century, we use residential locations from a sample linking WWI records to the 1900 census
to identify matches to features in the correct counties. We account for changes in county boundaries
over time by assigning all coordinates to the county in which they were located in 1880, the most recent
census year prior to the sample period.7 The main analysis sample restricts attention to individuals in the
WWI records with non-missing city or town of birth, and excludes those who reported only the county
4

See Appendix A.1 for more details on the construction of the hurricane tracks.
Historical population census records typically asked respondents to report their age in years on the day of the census, andthus are not useful in determining an individual’s in utero period. We are not aware of any other data source that reports theexact date of birth and within-state place of birth for the cohorts of interest.
6
Specifically, the records only include individuals born up to 24 August 1897 since younger individuals were not recorded
until later registrations. The Second Registration also covered individuals born in 1886 after 6 June, but we exclude the 1886birth cohort due to under-registration. See Appendix A.2 for more details on the timing and coverage of the registration waves
for the World War I draft.
7
See Table O2 for a summary of match rates and Appendix A.2 for more details on the procedure used to match the birth
locations reported in the WWI records to the GNIS file.
5

4

of birth. We also geocode birth locations with the coordinates of the county centroid, which allows for alarger sample at the cost of precision in the assignment of exposure. Our results remain significant whenhurricane exposure is defined at the county level, though they are smaller in magnitude (Table A1).
A map shown in Figure 1 displays the locations surrounding the paths of hurricanes that hit the Gulf
Coast and Eastern seaboard of the U.S. (from Texas to Florida and as far north as Virginia) between 1885
and 1899. The green circles denote individuals born in locations ever within 30 kilometers of any point
along a hurricane path during our sample period, and red circles denote birth locations ever within 30
to 100 kilometers of a sample period storm path, while the diameter of the circles represents the sample
size at each location. We follow the previous literature (Weatherford and Gray 1988; Currie and RossinSlater 2013) in choosing a 30-kilometer-radii band to represent the “eyewall,” which is the area, 30 to
60 kilometer in diameter around the eye of the storm, with the strongest winds and heaviest rainfall.
Given that many hurricane tracks overlap or pass in close proximity to a previous storm, 57 percent
of individuals in the main estimation sample were born in locations within 100 kilometers of multiple
hurricane paths during the sample period. Thus, a subset of individuals in the sample will be treated by
one storm and form part of the control group for individuals exposed to another storm.
We extract long run outcomes from the 1940 U.S. Census of Population complete count file forindividuals in our sample, who were ages 42 to 53 on the day of enumeration, 1 April. The 1940 census isvaluable since it is the first, and only publicly available, U.S. census to report (annual) individual wage andsalary income (for 1939), and years of completed education, but it also has limitations. The census reveals
very little about an individual’s business, farm, or self-employment income, given that respondents were
only asked to indicate “yes” or “no” in response to whether they had at least $50 in non-wage income.
This limitation is of particular concern for our sample, in which 76 percent of individuals employed inagriculture and 36 percent of those in the labor force do not report any wage income.
We take a number of approaches to remedy the missing data problem. In our primary analysis,
we construct a measure of total income that combines reported wage and salary income with a predictedvalue for self-employment (business and farm) income based on estimating 1950 self-employment income
as a function of age, completed years of education, as well as state, county, industry code, and occupation
code fixed e¯ects for white males aged 39 to 56. We use the IPUMS 1-percent sample of the 1950 U.S.
Census (Ruggles et al. 2015) and follow the procedure implemented by Feigenbaum (2015b), which we
describe in more detail in Appendix A.3. While our baseline sample from the WWI draft records contains
individuals who would be ages 42 to 53 when enumerated in the 1940 census, we extend the age range for
imputing income to account for misreporting of age in the 1940 census. In Section 4.1, we show that theresults are robust to alternative specifications of the dependent variable, such as restricting attention to
only the wage and salary income reported in the 1940 census, using occupational earnings and education
scores, and trimming the tails of the income distribution. We also take the number of weeks worked in1939 and construct an indicator for working at least fifty weeks, which represents a proxy for full-time
employment, to study e¯ects on the extensive margin of labor supply.
From the 1940 census we also use the years of completed education and an indicator for middle school
graduation as measures of educational attainment. The census question on education asked enumerators5

to report the highest grade successfully completed by an individual, but they were also directed to report
the number of years in school when the highest grade was unknown. Margo (1986) provides evidence
that blacks and whites interpreted the question di¯erently, particularly older blacks educated in ungradedschools. Since we study older cohorts we cannot escape this measurement issue, and choose to drop blacks
from the main analysis to minimize this and other potentially problematic factors outlined in Section 1.3.

1.2

Data linkage
We begin with the population of records from the January 1887 through August 1897 birth cohorts

in the WWI Draft Registration Cards database with non-missing first name, last name, state of birth,and date of birth, excluding records that only report a first or last initial. We also extract all males
aged 39 to 56 with non-missing first name, last name and state of birth from the 1940 U.S. Census ofPopulation complete count file. We follow the procedure in Parman (2015) and construct the Phonex
codes for the first and last names in each data set, which enables us to allow for di¯erences in the spellingof phonetically similar names across data sets that might arise from typographical errors and changes inthe spelling of names.8 Prior to the implementation of the matching algorithm, we perform a ”blocking”
step in which the two data sets are joined using four blocking variables: the Phonex code of the firstand last names, age in years when enumerated in the 1940 census, and state of birth (Christen 2012).
The matching algorithm draws on elements of the procedures pioneered by Ferrie (1996) and utilized by
Abramitzky et al. (2012), Feigenbaum (2015a), and Mill and Stein (2016). It proceeds as follows:
1. Drop all pairs of WWI-1940 observations that do not have matching Phonex codes or state of birth,while allowing discrepancies in the reported age of up to 3 years.
2. Compute the Jaro-Winkler score between the first names and last names in each pair of observations. Discard all pairs with a Jaro-Winkler score less than 0.7 for either the first or last name.9
3. For each record in the WWI data set, determine the maximum Jaro-Winkler score averaged overthe first and last names, and the minimum discrepancy in age among all 1940 records identified in
Step 2. Count the number of records in the 1940 census with a Jaro-Winkler score (Js ) satisfying(1 + 0.1) J s J, where J¯ is the Jaro-Winkler score of the best match, and having a reported age
within one year of the closest match.
4. Drop all pairs of WWI-1940 observations with a discrepancy in reported age greater than the minimum discrepancy across all 1940 records matched to a given WWI record.
5. Drop any remaining pairs of WWI-1940 records with a Jaro-Winkler score (Js ) satisfying (1 +0.1) J s < J, where J¯ is the Jaro-Winkler score of the best match.
8

See Nix and Qian (2015) and the supplemental materials to Parman (2015) for discussions of the Phonex algorithm.
The Jaro-Winkler score has become the preferred string distance measure among economic historians linking names acrosscensus databases because it places greater weight on characters that match at the beginning of a string (Feigenbaum 2015a; Milland Stein 2016). Jaro-Winkler scores range from 0 to 1, where a score of 0 indicates no common letters, while a score of 1
indicates a perfect match. For more details on the Jaro-Winkler method, and other string comparison algorithms, see Christen(2012).
96

6. Keep all pairs of WWI-1940 records with a Jaro-Winkler score greater than 0.9 averaged across thefirst and last name, that satisfy the following conditions: each WWI record has a unique match in
the 1940 census, and each 1940 census record has a unique match in the WWI records.
We perform similar analysis for matching the WWI records to the 1900 census to examine short-run
migration patterns in response to hurricanes, and other robustness exercises.
Table 1 presents the linkage rates from the WWI draft records to the 1900 and 1940 censuses (inpanels A and B, respectively) for all U.S.-born males, and separately for Southern and non-Southernstates, ever-treated locations (within 0 to 30 kilometer-radii bands around the storm tracks) and control
areas (30 to 100 kilometers away), and finally individuals exposed in utero to hurricanes. The overall
match rate to the 1940 census is about 33 percent, which is similar to rates that have been documented
by previous studies and are considered standard in the literature.10 When matching the WWI records to
the 1900 census, we observe a lower rate of multiple matches (10 percent in comparison to 28 percent
for the 1940 census), and a higher rate of failing to find any potential match (53 percent compared to 39
percent), which are consistent with the use of additional and more precise information on date of birthin the 1900 census.11
The match rate to the 1940 census is also higher outside the South than in the South (36 percent
versus 24 percent), but match rates are similar across Southern states in our sample, ranging from about
21 percent (Georgia and South Carolina) to about 28 percent (Florida). The match rates for individuals
exposed in utero to the hurricanes are very similar to individuals born in the same locations before or after
the storms, while those born in surrounding counties (30 to 100 kilometers away) are matched at slightly
higher rates. The match rates are based on samples that pool together blacks and whites, because the race
variable has not been transcribed in the electronic version of the WWI records (National Archives andRecords Administration, n.d.). The overall regional di¯erences in the likelihood of matching individuals
across censuses likely overstates di¯erences in the white population that is the focus of our analysis.
As shown in Figures A1a and A1b, the match rate exhibits seasonality, though the variation is small
compared to the di¯erence between the South and non-South. More crucially, we do not observe any
trend in the match rates by year of birth. Figures A1c and A1d indicate that the match rate has an inverse
U-shape relationship with commonness of name, as we are less likely to match the least common names– many of which will be typographical errors – and the most common names.
10
The linkage rates found by Abramitzky et al. (2012), Collins and Wanamaker (2014), Long and Ferrie (2013), and Milland Stein (2016) using similar matching algorithms are approximately 30 percent, 21 percent, 22 percent, and 11 to 34 percent,respectively. Other studies have found considerably higher match rates by manually linking data sets or by including multiple
matches. Feigenbaum (2015b) uses training data and a machine learning algorithm to achieve a match rate of 56 percent, while
Nix and Qian (2015) obtain match rates ranging from 61 to 67 percent by randomly selecting a record among multiple potential
matches to augment samples of unique matches. We are unable to apply the matching method of Feigenbaum (2015b) due to alack of the training data. While others have developed methods for incorporating multiple matches into the estimation sample
in cases where there are multiple potential outcome variables (Bugni et al. 2014; Nix and Qian 2015), or multiple potential
explanatory variables (Poirier and Ziebarth 2016), neither approach is suitable for the present application, in which the sample
of matches from the WWI records to the 1940 census contains non-uniqueness for both outcome and explanatory variables.
11
The 1900 census reports month and year of birth, whereas the 1940 census only reports the age in years on the day of thecensus. Give that the WWI records were created in 1917 and 1918, the database is linked back in time to the 1900 census, andmatched forward in time to the 1940 census.

7

1.3

Sample selection
Our main analysis deals exclusively with white males, given the presence of a racially segregatedlabor market in the U.S. in 1940 and concerns that census questions about educational attainment andlabor market outcomes may have been interpreted and enumerated di¯erently by race.12 Carruthers and
Wanamaker (2016) attribute the majority of racial wage inequality during this period to lower humancapital and the inferior quality of separate but unequal schools for blacks, which can be traced backto public sector discrimination and local control of schools. Regardless of the underlying cause of thedi¯erences, the data on education and income are ultimately not consistent between races.
After restricting attention to white males matched from the WWI records to the 1940 census, we
exclude a subset of observations for which we are unable to identify the latitude and longitude coordinatesof the birth city or town. In 7.9 percent of cases in the baseline matched sample, the birth place is only
known at the county level, while 14.7 percent of the baseline sample is excluded when a county has thesame name as a city or town in a di¯erent county of the same state. We also exclude an additional 13.7
percent of the sample when the place of birth cannot be distinguished between the name of a city and thecounty in which it is located.13 After we restrict attention to individuals with geocoded birth locations,
we are left with 82,024 observations. We exclude a small set of individuals born in Dade and Monroe
counties, at the southern tip of Florida, because there are no suitable control locations in the surroundingarea that were not exposed to storms.
The next set of sample restrictions relate to the characteristics observed in the 1940 census. Afterexcluding individuals with missing outcome variables (total income and weeks worked) and years ofeducation, about 27.6 percent of the remaining sample, we are left with 58,979 observations. We also
drop 128 individuals who were working in non-standard occupational categories (military and religious
orders), incarcerated or residing in institutional care, engaged in housework, or enrolled in school, leaving
us with 58,851 observations in the main analysis sample. We impute zero income for individuals who
were out of the labor force with zero wage and salary income, non-wage income below $50, and noreported occupational or industry category. All in all, due to missing data and sample restrictions, we
utilize 45.7 percent of the 128,821 white males linked from the WWI records to the 1940 census who
were born within 100 kilometers of a hurricane path during the sample period.
Table A2 examines, in a regression framework, whether exposure to hurricanes a¯ects the probabilityof successfully matching a WWI draft record to U.S. census records. In column 1 the dependent variable
is an indicator for a unique match from the WWI draft records to the 1900 census, in column 2 it isan indicator for more than one match, and in column 3 it takes the value one for the subset of uniquematches used in the estimation of migration outcomes (Table 7). Columns 4 and 5 show results forunique and multiple matches of the WWI records to the 1940 census, while column 6 shows estimates for
12

As we discussed in Section 1.1, data quality with respect to the highest grade completed variable is more of an issue forblacks, especially among the cohorts in our sample (ages 42 to 53). We are unable to study outcomes for women since the WWI
draft only registered men.
13
Table O2 reports match rates of birth locations separately for locations ever within 0 to 30 kilometers of the sample period
hurricane tracks, and locations 30 to 100 kilometers away (but never within 30 kilometers). For ease of exposition, we discussoverall match rates for the hurricane exposure zone.

8the probability of appearing in the main estimation sample (Table 4). In comparison to the descriptive
match rates presented in Table 1, the regression results are based on the sample of WWI draft records forwhich the birth location is known. In general, pre-conception, in utero, and early-childhood exposure tohurricanes have small and statistically insignificant associations with the probability of a unique match,or multiple matches, to either census. Coe˘cient sizes are at most 0.8 percentage points and are only
significant (at the 5 percent level) in two cases for early-childhood exposure when matched to the 1900
census: the probability of multiple matches and the probability of a unique match that appears in theanalysis sample. In combination with the descriptive match rates shown in Table 1, this finding suggeststhat the di¯erence in the match rates for individuals exposed to hurricanes, if any, is small enough that itshould not bias the main results.

1.4

Descriptive statistics
Table 2 provides some baseline characteristics at the time of the 1880 U.S. Census, the closest pre-period census available, for the population of interest in the nine Southern U.S. states that represent thefocus of the analysis. The first column presents averages based on the entire population of U.S.-born
males, while the subsequent two columns compare the nine Southern states to the remainder of the
U.S. Columns 4 and 5 separate the counties in these nine states into those that were within 30 to 100
kilometers of a storm at some point during the sample period, but never within 30 kilometers (column4), and those that were ever within 30 kilometers of a storm (column 5). Southern states in 1880 had
a larger black population, were slightly more rural, and were poorer that the reminder of the U.S.14
Further comparison between column 3, on one hand, and columns 4 and 5, on the other, reveals that the“hurricane zone” sample has, on average, an even higher share of blacks, while the mean occupational
status of the white population – the principal focus of this paper – is higher than in the South as a whole.
Although we do not observe much of a di¯erence in terms of occupational status or the urbanization
rate between counties in our sample that were ever treated and those that constitute a control group, there
is a di¯erence in average farm value and farm size between the two regions.15 In particular, the counties
that were ever within 30 kilometers of a hurricane between 1885 and 1899 had larger and less valuable
farms, on average. Overall, we view the populations of counties that constitute part of the treatment
and control groups as comparable in terms of observable characteristics in 1880, six years prior to thebeginning of our study period. Since we use a di¯erence-in-di¯erences strategy, level di¯erences acrosstreated and control counties will not bias our results as long as these are constant over time.
Table 3 presents summary statistics for the full population of U.S-born white males, who were aged 42
to 53 in the 1940 census (column 1), as well as for Southern and non-Southern states separately (columns
2 and 3). For the purposes of this table, we adopt a definition of Southern states limited to the nine states
14

Individuals are considered urban residents if they resided in cities or incorporated places with at least 2,500 inhabitants(Ruggles et al. 2015).
15
Average farm value represents the per acre fair market value of farms, including land, fences, and buildings, while averagefarm size is measured in acres. Both statistics are obtained from the 1880 Census of Agriculture (Walker and Seaton 1880). The
census schedules define a farm to include “all considerable nurseries, orchards, and market gardens, which are owned by separate
parties, which are cultivated for pecuniary profit, and employ as much as the labor of 1 able-bodied workman during the year.”
Farms smaller than three acres were not reported unless they sold more than $500 in produce.

9ever exposed to hurricanes in our sample. Based on these three samples, it is clear that the population in
the states that we investigate is di¯erent from the rest of the country, and that this pattern is in line withevidence based on the 1880 census provided in Table 2.16 In particular, even among whites, individuals in
the Southern states included in the analysis are less educated, poorer, and work in occupations with lower
status. The subsequent two columns focus on the characteristics of the South and non-South samples thatare successfully matched to the database of World War I Draft Registration Cards. As shown in Table
1, we are about 11 percentage points less likely to match individuals residing in the Southern states, but
in both sub-samples we match relatively better o¯ individuals. Consequently, our working sample ispositively selected from the population, in part due to di¯erential survival rates of individuals with lower
socioeconomic status (Ferrie and Rolf 2011).
Columns 6 to 8 focus on the estimation sample, which includes individuals in one of the nine Southern states who were born in locations that were within 100 kilometers of a hurricane path during thesample period. Column 6 presents characteristics of individuals born within a 30 kilometers band ofa hurricane path (our preferred distance threshold for exposure), while column 7 describes individuals
born in surrounding areas 30 to 100 kilometers away from the storm paths. In column 8, we present
descriptive statistics for a subset of individuals, drawn from the sample in column 6, who were exposedto the storm while in utero. The final estimation sample appears to be modestly positively selected incomparison to all counties within the Southern states. The comparison of columns 6 and 7 with column 8, as well as the kernel density plot of total income shown in Figure O1, preview the main results
documenting worse labor market outcomes among the individuals exposed in utero.

2

Empirical framework
We use a di¯erence-in-di¯erences framework to estimate the e¯ects of in utero and early childhood

exposure to hurricanes on long-run outcomes. The first di¯erence compares individuals exposed in utero
to those born before and after a hurricane struck a given location, while the second di¯erence compares
locations ever exposed to the hurricane path to the surrounding counties. In Figure 1 each circle represents a geographic location in the World War I Draft Registration Cards database, with green circles
denoting locations within 30 kilometers of the path of a hurricane-strength storm that occurred during
the sample period, and red circles denoting locations 30 to 100 kilometers away. The size of the circles
represents the number of observations at each location in the final estimation sample.17
Our baseline estimates are obtained from the following regression:
Yibyw = ↵0 + ↵1 (In-uteroibyw ) + Xibyw +c + y + ⇡w + "ibyw(1)

where i indexes individuals, b indexes birth location, c indexes birth county, y indexes conception years,
16
Note that there is overall improvement in a˙uence of the “hurricane zone” counties over the 60 years between censuses.
For example, the occupational income score improves by about 40 percent.
17
These locations are overlayed on a map of 1880 U.S. county boundaries. The sum of individuals represented by the green
circles corresponds to the sample size reported in column 6 of Table 3, while the sum of individuals represented by the redcircles corresponds to the sample size of column 7 in the same table. Figure O2 plots the birth locations and sample sizes of
only the individuals exposed in utero (column 8 of Table 3).

10and w indexes conception weeks. The vector Xi includes linkage quality measures, an indicator forbirth locations ever within 30 kilometers of a hurricane during the sample period, and state specificlinear trends. We also include a set of birth county ( ), as well as year ( ) and week (⇡) of conception
fixed e¯ects.18 In this regression model, the coe˘cient ↵1 measures the e¯ect of an in utero shock fromhurricane exposure on later-life outcomes. The treatment takes on a value of one for individuals who werein utero when a hurricane passed within 30 kilometers of their birth location. We assume that pregnant
mothers do not move between exposure and birth, and thus, in our setting, county of conception isequivalent to county of birth.19 In all specifications we cluster standard errors by county of birth.
In a second specification, we refine this model by adding indicators for post-storm conception andexposure during post-birth periods, and estimate the following regression:
Yibyw = ↵0 + ↵k

3
X(Periodkibyw ) + Xibyw +c + y + ⇡w + "ibyw

(2)

k= 1

where Periodk consists of five indicator variables for exposure to storms: up to one year pre-conception,
in utero, between birth and six months, between six and twelve months after birth, and between ages
one and two. We divide the first year of life into two six-month periods in order to understand whether
the biological e¯ect operates through the mother, child or both. Arguably, the in utero shock a¯ects the
fetus only through the mother’s experience, while exposure at ages six to twelve months (and age one totwo) operates directly through the child’s experience. Treatment in the first six months of life can operate
both indirectly through the mother (e.g. breastfeeding) and directly through the child’s experience.
The identifying assumption underlying the interpretation of the in utero and post-birth exposurecoe˘cients in these equations is that pregnant women, or mothers raising young children, with lower
unobserved human capital or propensity to earn income do not di¯erentially relocate to, or away from,areas struck by hurricanes. In other words, the natural disasters are as good as randomly assigned. Although the absence of any baseline characteristics in the WWI records prevents us from providing direct
evidence for the exogenous timing of hurricanes, previous research that has used high resolution data
has supported this assumption, even in settings with advanced warning and forecasting systems (Currie
and Rossin-Slater 2013; Fuller 2014; Deuchert and Felfe 2015). The case for the exogenous timing of
hurricanes at end of the nineteenth century is arguably even stronger, given the limited communications
and forecasting technology available at the time.
Since we do not observe place of residence, we proxy for it with place of birth when assigning
post-birth hurricane exposure status. While this assumption is reasonable during infancy, it becomesincreasingly untenable as children age, and thus we refrain from estimating e¯ects of hurricane exposure
at older ages. Finally, we interpret the post-storm conception coe˘cient as a reduced form e¯ect thatcombines fetal exposure to the potentially depressed economic conditions in the aftermath of a hurricane,18

The results remain unchanged if we include conception year-by-week fixed e¯ects.
While our baseline estimates utilize county fixed e¯ects, the results are qualitatively similar if we use location of birth
fixed e¯ects. These results remain significant, but are less precise, due to the fact that the WWI draft records do not containa su˘cient number of births at relatively small locations to generate meaningful variation. Nonetheless, in all regressions the
exposure is defined based on location of birth. We relax this assumption in Table A1 and show results for larger samples that
include individuals with birth locations defined at the county level.
1911and selection due to a di¯erential fertility response of parents or di¯erential migration into and out ofareas struck by hurricanes. Since the selection e¯ect could go in either direction, we remain agnostic
about the sign of the post-storm conception coe˘cient.

3

Main results
We begin with the main analysis of the causal e¯ect of in utero exposure to natural disasters, forwhite males, on years of completed education, income, and employment. Then, we extend the analysis
to include controls for post-birth exposure and post-storm conception, and di¯erentiate the e¯ects bytrimesters of pregnancy and category of the hurricane, before turning to the heterogeneity in the estimates. Next, we estimate the e¯ects on the gender ratio, separately by race, for individuals enumerated in
the 1900 U.S. Census, which we view as a proxy for survival to 1900. We also study potential mediatorsof the main estimates and report e¯ects on the probability of migrating to di¯erent destinations by 1900
or 1940.
Panel A of Table 4 presents estimates of equation 1 for the e¯ects of in utero exposure to hurricanes on long-run outcomes. The first two columns report e¯ects on years of completed education andthe probability of middle school graduation. The conditioning variables for these models include birthcounty fixed e¯ects, conception year and week fixed e¯ects, an indicator for individuals born in locations
that were within 30 kilometers of a hurricane during the sample period, linkage quality measures, andstate-specific linear trends.20 We include these controls in all our main estimates. The first column indicates that average years of completed education decreased by 0.13 years, while the second column shows
a 2.3 percentage point (pp) reduction in the probability of graduating from middle school (9th grade).
Both are statistically insignificant at conventional levels, but the e¯ect size for middle school graduationsuggests a decline in the probability of completion by 6.1 percent.
The third column presents our main result for the e¯ects of in utero exposure on the natural logarithm of total income. The estimate indicates that individuals born in locations within 30 kilometers ofa hurricane that occurred during the fetal period earn income in 1939 that is 7.3 percent lower, on average, in comparison to individuals born within 100 kilometers of the hurricane with no fetal exposure to astorm.21 This e¯ect size is comparable to the 5 to 9 percent e¯ect of in utero exposure to the 1918 flu pandemic on wages for men (Almond 2006), smaller than the 10 percent reduction in earnings from in utero
exposure to seasonal influenza (Schwandt 2016), and larger than the penalties for exposure to radiation(Black et al. 2013) and airborne pollution (Isen et al. 2016). The fourth column documents a statisticallyinsignificant 1.6 pp (2.2 percent) decline in the probability of working at least 50 weeks during the year, a
proxy for full-time employment.22 In columns 5 and 6 we show that the income and employment e¯ects
20
The linkage quality measures include flexible controls for the absolute deviation in the ages reported in the WWI records
and the 1940 census, as well as indicators for exact string matches of the first and last names between the two sources. We
also flag individuals for whom we impute self-employment income, and individuals whose self-reported wage or imputed selfemployment income are truncated at $5000 (1939 USD).
21
The average age of individuals in the estimation sample when enumerated in the 1940 census is 47.1 years, with ages ranging
from 42 to 53 years, based on the birth date reported in the WWI records.
22
We have also estimated the regression with total income per weeks worked as the dependent variable (Carruthers and12decrease only modestly after we condition on completed years of education. This is likely due to therelatively small and imprecise estimates for the e¯ects on human capital accumulation, and suggests thatthe decrease in long-run labor market outcomes operates primarily through productivity rather than theaccumulation of cognitive skills.23
In panel B of Table 4 we extend the analysis to include separate e¯ects of exposure during the firsttwo years after birth, and for individuals conceived up to one year post-storm. We do not find any
statistically significant e¯ects on the post-storm conception indicator variable, and the coe˘cient in thespecification with income as the dependent variable is small and positive. On the other hand, we findnegative estimates for in utero and early childhood exposure, with the largest e¯ects on income stemmingfrom the fetal period. In the case where the dependent variable is the log of total income, we can reject
the null hypothesis of equality of the coe˘cients across periods. The finding that e¯ects are larger for
exposure in utero and at ages 0 to 6 months also supports the hypothesis that mothers potentially playa crucial role in the transmission process. Similar to the results in panel A, we also find negative, but
statistically insignificant, coe˘cients on educational attainment.
Table 5 further expands the analysis with two extensions of the model from panel B of Table 4. First,
panel A documents that the e¯ects on income are primarily driven by exposure during the first and third
trimesters.24 In the regressions with employment as the dependent variable, the third trimester coe˘cientis the largest, while for education, the first trimester matters the most. Although we cannot reject theequality of the estimates across trimesters, the observed pattern of results is consistent with the fact thatneurons in the brain develop rapidly during first trimester, while the fetus grows the most during thethird trimester. Thus, if early brain development is more important for educational achievements andcognition, while overall health matters more for labor market productivity (Cunningham et al. 2010;Black et al. 2007), then we would expect to observe such a pattern of results. Similarly, when we control
for years of completed education in the income regression, the first trimester coe˘cient decreases while
the third trimester coe˘cient increases in magnitude, which is also consistent with the first trimestere¯ect operating through cognition and schooling more so than through productivity.
Second, Panel B examines whether the negative e¯ects of in utero exposure are stronger when thefetus is exposed to a more severe storm, where severity is proxied by hurricanes that reach Category 2
intensity or above.25 We do not find any statistically significant di¯erences in e¯ects by storm intensity,
which may be the case because wind speed is poorly measured, or that Category 1 and 2 hurricanes do
not di¯er substantially in their impact. On the other hand, Figure A4 shows evidence of heterogeneity
Wanamaker 2016), and find statistically significant results that are qualitatively similar to our preferred measures.
23
Since we find much larger e¯ects of hurricane exposure on the probability of middle school completion, we also included
the latter as a conditioning variable in place of the flexible years of education controls. We find that the estimated coe˘cient forincome is almost identical to the estimates from the specification with flexible education controls (-0.058 compared to -0.060).
24
The table does not show coe˘cients on the controls for post-storm conception, exposure at 0 to 6 months, 6 to 12 months,
and 1 to 2 years.
25
We group together Category 2 and 3 hurricanes since the sample only includes one Category 3 hurricane and no hurricanesof greater intensity. The regression also includes interactions of the indicator for hurricanes, that reach Category 2 intensity
or above, with each of the remaining hurricane exposure controls: post-storm conception, exposure at 0 to 6 months, 6 to 12
months, and 1 to 2 years. The e¯ects of exposure at ages 0 to 6 months are similar to those found in the main specifications(panel B of Table 4).

13

in severity of exposure within storms, and suggests that mothers living further away from the storm pathexperienced a milder shock. The figure plots coe˘cients on separate indicators for in utero exposure 0 to
10 kilometers, 10 to 20 kilometers, 20 to 30 kilometers, 30 to 40 kilometers, and 40 to 50 kilometers awayfrom the storm path, each from the same regression with log income as the dependent variable.26 The
indicator for exposure 10 to 20 kilometers away from the hurricane path has the largest negative point
estimate, which corresponds to a 10.8 percent reduction in income, while the point estimate is slightly
smaller (-9.4 percent) in the 20 to 30 kilometer band further away from the storm path. The coe˘cienton exposure in the 0 to 10 kilometer band is smaller and statistically insignificant, which reflects the factthat the eye of the storm is relatively calm. The coe˘cients on in utero exposure decrease monotonicallybeyond the 30 kilometer-radii band, which is consistent with finding the strongest winds in the “eyewall”
of the storm.
Due to data limitations, we cannot perform standard heterogeneity analysis that has typically been
explored in similar contexts, such as splitting results by gender, parental education, or race. However, we
are able to examine the heterogeneity in the e¯ects of in utero exposure to hurricanes by baseline countylevel urbanization rates and farm values measured in 1880. We estimate a variant of the specification inpanel B of Table 4, in which we include interactions of the birth county urbanization rate with each of
the hurricane exposure indicators. For parsimony, panel A of Table A3 only presents the level e¯ect ofin utero exposure and the interaction of the in utero exposure coe˘cient with the urbanization rate. Thee¯ect of in utero exposure on log income does not di¯er substantially across urban and rural counties,
which provides suggestive evidence that the main results are not driven by rural counties dependent
on agriculture.27 Panel B of Table A3 shows results from interacting the e¯ect of in utero exposure
to hurricanes with an indicator for whether the average farm value in the birth county was below themedian.28 We do not find any statistically significant interaction terms. While we cannot definitively
conclude that the main results for log income are driven by poorer or wealthier counties, the results are
consistent with poorer and more vulnerable populations fairing worse in the long-run.
Thus far, we have examined e¯ects of hurricane exposure on educational attainment and long-run
labor market outcomes for white Southern males. In Section 1.3 we pointed out the data quality issues andinstitutional di¯erences in the labor market by race that preclude the analysis of long-run outcomes forthe black population, but for completeness, we show parallel results for the main specifications in Table
O3. With the exception of e¯ects on the probability of full-time employment, we do not estimate any
significant or sizable e¯ects of exposure to hurricanes for blacks. However, it would be unexpected if theblack population did not su¯er the consequences of exposure to hurricanes, given that it is characterized26

The specification also includes indicators for whether a storm passed within each of the distance bands around a birth
location during the sample periods, as well as interactions of the distance bands with each of the exposure dummies: conception
up to one year post-storm, in utero, age 0 to 6 months, age 6 to 12 months, and age 1 to 2 years.
27
The mean urbanization rate is 17.9 percent and the median is 0, indicating that more than half of the individuals in thesample were born in counties that did not have any settlements with a population exceeding 2,500 in 1880. The interactionof the urbanization rate with in utero exposure is positive and significant in the middle school completion and weeks worked
regressions, with the e¯ect sizes for a one standard deviation change in the urbanization rate being 4.1 and 4.9 pp, respectively.
28
The specification also includes interactions of the indicator for below median farm values with each of the remaining
hurricane exposure indicators. In most cases, these interaction terms are generally imprecise and are not substantially di¯erent
across the two groups of farm values.

14

by a higher rate of poverty compared to the white population.
We examine e¯ects on mortality selection using the male-to-female gender ratio, which has been used
to test the hypothesis that male fetuses are more sensitive to external shocks (Trivers and Willard 1973;
Norberg 2004; Almond and Edlund 2007). We turn to the 1900 U.S. Census for data on population
counts by gender and race, but in the absence of data on birth county we use county of residence asa proxy. Although it is probable that some individuals migrated across county boundaries during theten-year span, perhaps selectively in response to disasters, if we assume that parents did not have strong
gender-specific preferences, then it is likely that the gender ratio is unrelated to storm exposure except
through the fetal shock channel.
For the purposes of the gender ratio analysis, we collapse data on the 1886 to 1896 conception cohorts,
by race, at the county of residence-by-month of conception level and define the dependent variable as theratio of males to females.29 The regression includes county of birth fixed e¯ects, year and month ofconception fixed e¯ects, state-specific linear trends, along with the standard set of exposure controls inequation 2. An observation in the regression is a county of birth-by-conception month cell. We assume
that, for all individuals, the conception month is nine months prior to the birth month reported in the1900 census, and that the county of conception, county of birth, and county of residence are equivalent.
We estimate the regression on the set of counties within 100 kilometers of storm paths in our main
analysis sample.
Table 6 presents the results beginning with estimates that pool births for blacks and whites in columns
1 and 2. In line with the hypothesized e¯ect, we find fewer males than females surviving until 1900,
among those exposed to stress from hurricanes while in utero, with estimates in column 1 and panel
B of Table 6 suggesting a 2.7 pp reduction in the gender ratio compared to the mean of 1.074. Thise¯ect corresponds to a 1.2 percent decrease in the probability that a child surviving until 1900 was a boy,and is six times the size of the di¯erence between married and unmarried mothers, and twice as large
as the di¯erence between mothers with a high school degree and mothers with some college education,
estimated by Almond and Edlund (2007).30 We also find evidence for a reduction in the gender ratio of
2.6 pp for those exposed up to 6 months of age.
In the second column, we estimate the same specification using individuals conceived in the latter halfof the sample period, between 1892 and 1896, to take advantage of the younger cohorts who were lesslikely to have moved at the time of enumeration in the 1900 census. For the e¯ect of in utero exposure
we find a 3.2 pp reduction in the gender ratio in comparison to a mean of 1.063, which corresponds to a
1.5 percent decrease in the probability that a male child survived until 1900.
In the subsequent columns of the table we present estimates separately by race and show that the29More specifically, we include individuals born between January 1887 and August 1897 to match the cohorts used in theanalysis of long-run labor market outcomes.
30
Almond and Edlund (2007) find that married mothers are 0.2 percent more likely to give birth to sons than unmarriedmothers, while mothers who did not graduate high school were 0.6 percent less likely to have a boy than mothers with somecollege education. At the mean gender ratio of 1.074, our sample contains 517.9 boys per 1,000 individuals surviving to 1900. If
in utero exposure reduces gender ratio by 2.7 pp to 1.047, then there will be 511.7 boys per 1,000 individuals alive in 1900. That
511.7
corresponds to a 1.2 percent ( 517.9· 100) reduction in the probability of having a surviving boy. The e¯ect of in utero
517.9
exposure to hurricanes on the gender ratio in 1900 also corresponds to 55 percent (2.75/4.84) of the e¯ect of the 9/11 terrorist
attacks on the gender ratio for children born in New York City (Catalano et al. 2006).

15

results for exposure in utero and up to 6 months in age are driven by the black population, reflecting
higher mortality risk for a more vulnerable individuals with lower socioeconomic status. For blacks,
in utero exposure is associated with a 4.8 pp reduction in the gender ratio in 1900 relative to a mean of
1.087, which corresponds to a 2.1 percent decrease in the male survival probability. The corresponding
estimates for whites are very small and statistically insignificant, suggesting that the estimates for long-run
outcomes are not biased by mortality selection.31
In Table 7, we examine whether migration in the aftermath of a hurricane represents a potential
mediator of the long-run e¯ects on income (Boustan et al. 2012). In columns 1 to 3, our analysis documents residential location decisions of parents whose children were exposed to hurricanes, with the
caveat that we do not observe instances when parents give birth to children after migrating in response
to experiencing a hurricane. Furthermore, one channel through which the negative e¯ects on income
and employment may arise is a decrease in geographic mobility, and in particular restricted possibilities
to migrate out of the South (Collins and Wanamaker 2014; Eichenlaub et al. 2010). Thus, in columns 4
to 6, we investigate migration decisions in adulthood. We consider three migration destinations outside
the county of birth: counties within the same state, di¯erent states, and states outside the South.32
We find small and statistically insignificant e¯ects on the probability of migration by 1900, whichindicates that short-run mobility in response to the disasters is not a significant mediator for the analysisof the long-run outcomes. Similarly, we generally do not find much evidence for e¯ects of exposure tohurricanes on changes in long-run migration patterns.33 The only significant coe˘cients suggest that individuals exposed in utero were about 2.8 pp (10.8 percent) less likely to move outside the state of birth,and that individuals exposed at age 1 to 2 were 1.5 pp (15.5 percent) more likely to move outside theSouth. The remaining estimates are not only insignificant, but also much smaller in magnitude. Overall,
the results in Table 7 suggest that in utero exposure to hurricanes was unlikely to have restricted the opportunities of individuals to migrate to locations with better employment and earnings opportunities.34
To further examine the potential influence of post-storm selective mortality and fertility, Table O5
shows results based on a sample that excludes individuals conceived after a hurricane, and restricts thecontrol group to individuals born before the storm. The advantage of this specification is that the composition of the control group is not a¯ected by selection due to a migration response or di¯erential fertility
in the aftermath of the hurricane. The results are very similar to the main specification in Table 4 within utero exposure estimated to reduce income by 7.8 percent in the full model of panel B, which indicatesthat the baseline results are not biased by comparisons to a selective group of individuals conceived in the31

We find a weakly significant negative e¯ect of post-storm conception and positive e¯ect of exposure at ages 7 to 12 months
on the gender ratio, but these estimates are not robust to di¯erent specifications of the range of conception years in the sample.
32
Given that we match the WWI draft records backwards in time to the 1900 census, appearing in the sample is conditional on
survival to 1917/18 and registering for the WWI draft. We assume that longevity and registration for the draft are not correlatedwith the decision to migrate by 1900. Elsewhere in the paper we have referred to the nine states exposed to hurricanes as the“South,” but here we utilize a more standard definition of Southern states. In addition to the nine hurricane states, they include
Arkansas, Delaware, Kentucky, Maryland, Oklahoma, Tennessee, and West Virginia.
33
The results are virtually unchanged when we include flexible controls for years of completed education.
34
Individuals in our sample who migrated outside their county of birth by 1940 have higher levels of education and income
than those who stayed behind. Those who migrated outside their state of birth and those who migrated outside the South haveeven higher educational attainment and income. When we control for migration status by including indicators for the threemigration categories, our results for in utero and age 0 to 6 month exposure are attenuated slightly, but remain significant.

16

year after a storm.
We have argued that prenatal stress is an important pathway through which in utero exposure tohurricanes can have long-term labor market consequences (Currie and Rossin-Slater 2013). Galea et al.
(2005) and Paxson et al. (2012) document increased PTSD and PD prevalence, in the aftermath of natural
disasters, that can persist for extended periods of time, while Badakhsh et al. (2010) provide qualitative
evidence on the psychological struggles that pregnant women faced in areas a¯ected by hurricane Katrina.
The hurricanes observed in our historical sample are not as extreme as the recent Category 4 and 5 hurricanes examined in the stress literature, but in comparison to today, people living in the late-nineteenth
century lacked the technology and relief aid to cope with a disaster. Importantly, the stress interpretation
does not preclude immediate but temporary disruptions to resource access and everyday routines, in addition to repairable damage to residential structures. However, an alternative channel to rule out, might bepoor nutrition and the loss of income due to more extensive destruction of property and infrastructurein the aftermath of the storm.
While we lack systematic data on the damage caused by hurricanes during the late-nineteenth century, we are able to proxy for the potential short-run impact on local economic activity using averagecounty-level farm values available every ten years from the U.S. Census of Agriculture. We present estimates for the relationship between hurricane exposure and farm values in Table O4. The coe˘cientscompare farm values in 1880 (column 1) and 1890 (column 2) to values in 1890 and 1900, respectively,in addition to a comparison of counties within 30 kilometers of a hurricane, during a two-year exposure
window, to counties 30 to 100 kilometers away. Although the coe˘cient of interest is similar to thedi¯erence-in-di¯erences estimator that we utilize in the main specification, we give it a more descriptive
interpretation because of measurement error in the farm value data and the strong assumption of timeinvariant unobservables across the 10-year spans. Nonetheless, hurricane exposure does not appear tocorrelate significantly with farm values. The estimates are relatively small in magnitude in comparison
to average farm values and inconsistently signed. We thus view this result as suggestive of the fact thatthe damage caused by relatively mild, Category 1 and 2 hurricanes, did not lead to severe declines in
economic activity. It also corroborates our assumption that the primary channel for the long-run e¯ects
is the PTSD-type experience of pregnant mothers exposed to hurricanes.

4

Testing the robustness of the results
Our empirical objective is to isolate the causal e¯ects of in utero and early childhood exposure tohurricanes on long-run outcomes such as completed education and income. In Section 3 we have documented that selective mortality and migration are unlikely to have had a major confounding e¯ect on themain outcomes of interest. We now consider a series of approaches to test the robustness of the labormarket results, which are the primary focus of the paper.35
In the following subsections we address concerns that a particular state or hurricane may have undue
influence on the income results (Figures A2 and A3). We then explore the influence of measurement error
35

For parsimony, we suppress the output for the educational attainment outcomes. Nonetheless, we perform the robustness
checks and find that the estimates are qualitatively similar.

17

in self-reported wage and salary income, and the imputation of self-employment income from historicalcensus records, and show that our results are robust to alternative specifications of the outcome variable.
In particular, we show that similar results are found when we restrict attention to self-reported wage
and salary income (Table A4) or consider e¯ects on occupational income scores (Table O6), and that
our results are not driven by the tails of the income distribution (Table O7). We also supplement our
main sample with individuals for whom we only observe the county of birth, or define exposure basedon distance from the hurricane path to the centroid of the birth county (Table A1), and test di¯erent
definitions of the treatment variable by modifying the radii of exposure (Table A6 and Figure A4). Next,
we perform sensitivity checks for match quality and accuracy that are standard when using historical data
sources (Figures A5, A6a, and A6b). Finally, we place restrictions on the range of conception years in thesample (O8), and collapse data at the birth-county-by-conception month level (Table A7).
Before turning to exercises that test the robustness of the income results to alternate specifications of
the dependent variable and the treatment, we check whether any particular state or hurricane is driving
the results. Figure A2 plots point estimates and 95-percent confidence intervals for the coe˘cient on inutero exposure from equation 2 and the specification shown in column 3 and panel B of Table 4, witheach line representing separate estimates based on samples that exclude each state one at a time. The point
estimates and confidence intervals are very stable, ranging from -5.7 to -8.4 pp compared to -7.5 pp in themain specification, which indicates that the income results are not driven by any particular state.36
Next, Figure A3 plots the results of performing the same exercise, but dropping individual storms
from the sample one by one. Given that 57.3 percent of individuals in our main estimation sample wereborn in locations that were within 100 kilometers of multiple storm paths during the sample period,
we take two approaches to dropping a single storm from the analysis. In both cases, only individuals
who were born within 100 kilometers of the excluded storm, but not within 100 kilometers of any
other storm during the sample period are dropped from the sample. For individuals born within 100
kilometers of multiple storms we assign exposure in two di¯erent ways. First, we ignore exposure to
the dropped storm when coding an individual’s exposure to the remaining storms in the sample (Figure
A3a). Next, we define exposure to hurricanes based on all storms in the sample, including exposure to
the excluded storm (Figure A3b). Regardless of the approach taken, we find that no particular storm
drives the result that in utero exposure leads to reduced income in adulthood, as the coe˘cients remain
stable and statistically significant across all samples.37

4.1

Alternate specifications of income
In Table A4 we present estimates from specifications that do not add predicted values for non-wage

income to total income, but rather define the dependent variable as the natural logarithm of self-reported
36

The coe˘cients on exposure at ages 0 to 6 months, when dropping states one by one, are similar in magnitude to thepreferred estimate of -5.1 pp, ranging from -2.7 to -6.2 pp, but are less precisely estimated in some cases. The point estimate
is only significant at the 10 percent level when North Carolina is dropped from the sample, and is no longer significant atconventional levels when Florida, Louisiana and South Carolina are (separately) dropped from the sample.
37
The coe˘cient on exposure at ages 0 to 6 months is also stable (ranging from -2.9 to -6.4 pp) and remains significant at the
10-percent level in 56 out of 60 samples across the two approaches (Figure O3).

18

wage and salary income from the 1940 census, and show results for three samples. First, we consider a“restricted sample” (columns 1 and 2), which only includes individuals who reported having between $0
and $50 of self-employment income. In these cases, wage and salary income provides a reasonably good
approximation of total income. Second, in a “relaxed sample” (columns 3 and 4), we add individuals withmore than $50 of self-employment income, but ignore the estimates of predicted non-wage income. In
both cases, we impute zero income only when other contextual variables make it clear that the individual
was out of the labor force. Specifically, we restrict attention to individuals out of the labor force, with zero
reported wage and salary income, non-wage income less than $50, no reported occupation or industry,and not in school or engaged in housework. And third, in the “full sample” we add all individuals
with no reported wage income, regardless of whether other variables suggest that the respondent earned
unreported wage income.38
The estimates based on the “restricted” and “relaxed” samples in panel A indicate that in utero exposure is associated with a 5.5 to 6.1 percent reduction in log wages, which is modestly smaller than
the 7.3 percent e¯ect on total income in our preferred specification. Larger e¯ect sizes around 7 percent
are found in the “full sample” which presumably includes poorer individuals reporting zero income, but
the estimates are insignificant, which reflects the noise from imputing zero incomes when wages are notreported. These estimates are generally larger when we expand the treatment to include early childhood
exposure, which is also in line with results in Table 4.39
In Table O6 we abstract from the actual income reported in the 1940 census, and instead focus on avariety of occupation and education based income and earnings scores (Ruggles et al. 2015). In particular,
we use the occupational income score, occupational earnings score, occupational education score, andthe Nam-Powers-Boyd occupational status score as alternative measures of income, which are describedin detail in the Online Appendix. For each outcome, the e¯ects are negative, but in one out of four
specifications we lose statistical significance for estimates of in utero exposure. Nonetheless, the in uteroe¯ect sizes suggest reductions in the outcome variables ranging from 1.8 to 6.5 percent, as compared tothe preferred estimate of negative 7.3 percent for the e¯ect on income.
A drawback of the IPUMS occupational score variables is that they are computed using data on
income for blacks and whites, whereas our sample includes only whites. Thus, we compute an alternative
measure of average income using data on only whites in the IPUMS 1-percent sample of the 1950 census.
Specifically, we calculate average 1950 income by occupational code, based on wages and salaries, as wellas business, farm, and self-employment income. This measure is similar to the occupational income scorecomputed by Carruthers and Wanamaker (2016), though we deviate by restricting the data to the age
range in our sample (ages 42 to 53 years old) when calculating average income by occupation code. The
results are reported in column 5 and indicate that in utero exposure to hurricanes is associated with a
2.5 percent decline in average occupational income. While the estimate is smaller in magnitude than thepreferred specification, it only captures e¯ects on sorting into di¯erent occupations and not productivity
38

In specifications where we perform a log transformation of wage and salary income, we assign a small value ($1) to observations with zero income.
39
Measurement error in earnings is of particular concern for farmers, many of whom do not report any wage income. Table
O9 shows that the main results for total income hold when individuals employed in agriculture are dropped from the sample.

19di¯erences within an occupation.
Table O7 explores the extent to which our results are driven by the tails of the income distribution.
In columns 1 to 4 we trim the top or bottom 5 to 10 percent of the income distribution, while in columns
5 and 6 we simultaneously trim both tails. The results are generally smaller in magnitude, especially when
we exclude relatively poorer people, but they remain statistically significant. This finding is in line withdi¯erential sensitivity of already impoverished people to negative shocks, and with the finding that thecoe˘cient for the e¯ect of in utero exposure on log wages increases in magnitude when estimated on the“full sample” that includes more individuals with zero income (columns 5 and 6 in Table A4).

4.2

Definition of exposure
In Table A1, we test whether the selection of birth cities and towns that are matched to geographic

coordinates impacts our results, by adding to the sample individuals for whom we only observe birthplace at the county level (columns 1 and 2). The downside of this approach is that the coordinatesof the county centroid provide a much noisier measure of distance to the storm, and it follows that theestimated e¯ects of in utero exposure on income are smaller than our preferred estimates. The coe˘cientsare further attenuated when exposure is defined relative to the county centroid for all observations, butthey remain statistically significant.
Next, we examine two sources of measurement error in the determination of hurricane exposure
status, which may bias our results. First, a limitation of historical data is that, unlike in contemporary
studies, gestational length is not observable, forcing us to assume that all pregnancies had a uniform
39-week gestational period, which may generate misassignment of fetal exposure status. Consequently,
some early-term infants will be coded as exposed in utero, when in fact they were conceived after thestorm, and vice versa. We address this issue in Table A5 by presenting a range of specifications that vary
the uniform gestational length of the sample between 37 and 41 weeks. In panel A, the estimates forthe e¯ects of in utero exposure on total income range between negative 6.7 and 7.3 percent, which aresimilar in magnitude to our preferred estimates. That the point estimates change minimally when wevary the assumed gestational age is also consistent with the absence of substantial e¯ects of in utero stresson gestational length (Currie and Rossin-Slater 2013).
Second, the 30 kilometers band around the storm paths may not accurately capture areas exposed tothe storms. Table A6 and Figure A4 explore the sensitivity of the main results to di¯erent assumptions
about the diameter of the area exposed to the storm, and di¯erent ways of specifying how the treatmente¯ect varies by distance from the storm. The first three columns of Table A6 change the indicatorvariable for exposure to include individuals living within 20 kilometers, 40 kilometers, or 50 kilometersof the hurricane path, respectively. Our results are fundamentally unchanged for the first two distancebands, but they become smaller when exposure is defined to include a 50 kilometers band around thestorm. The observed attenuation is consistent with introducing false positives into the treatment group,
as mothers living further away from the storm would have experienced a milder dosage of the treatment
or were not a¯ected at all. In columns 4 to 6 we replicate this exercise, but drop a 20 kilometers band
between the treated and control groups from the sample. For example, in column 4, treated individuals
20

are those exposed at birth locations within 20 kilometers of the hurricane path, while the control group
includes individuals born 40 to 100 kilometers away from the storm path. This specification, in particular,
excludes individuals in the control group most likely to have been exposed to the treatment. The resultsin the “donut hole” regressions are broadly similar to the specifications without these sample restrictions.

4.3

Match quality and accuracy
A consequence of using imperfectly linked historical data sources is that biased estimates can arise

from the fact that the analysis is conducted on a sample of uniquely matched individuals accounting for
only about 24 percent of the population (Column 3 in Table 1). One method of relaxing the restrictiveness of a sample that consists of uniquely matched records is to vary the threshold for the minimum
match quality required to enter the sample. Our results for total income are robust to decreasing theminimum Jaro-Winkler string similarity score by increments of 0.02 from 1.0 to 0.8 (Figure A5).
In the presence of misreported ages in the census, the probability of a false match is increasing in thenumber of records with similar names in neighboring birth cohorts. Figure A6 shows that the results arerobust to restricting the number of census records similar to the matched observation. In comparison to
the main specification, which places no restriction on the number of similar census records, the e¯ects
on working fifty or more weeks are more precisely estimated and significant at the 10 percent level.

4.4

Restrictions on range of conception years
An important concern with the WWI records is the extent to which the registration process wascomplete. Figure 2 shows estimated completeness rates by birth cohort for the Southern “hurricane zone”
states and the rest of the country, computed by comparing the number of individuals who registered
for the WWI draft to the number of individuals enumerated in the 1910 U.S. Census. Since mortality
between 1910 and 1918 would have reduced the surviving cohort sizes, the estimated completeness rates
should be considered as lower bounds. The near universe of draft-age males were registered outside theSouth, but there is some indication of under-registration in the South, and among the earlier cohorts inour sample. Part of the di¯erence between the South and non-South could be due to di¯erential mortality,
especially for the earlier cohorts. Individuals had to survive until at least June 1917, seven years after the1910 census, in order to be registered.40 The registration rate in the South ranged between 64 and 82
percent for the 1887 to 1889 birth cohorts, but rose above 90 percent for all subsequent cohorts, except in1892 when it fell to 82 percent.41 Under the assumption that the rate of under-registration did not di¯er
40

The estimated under-registration can also partly be attributed to the fact that individuals already serving in the military did
not register for the draft. However, with only 100,000 men serving in the U.S. Army in 1914, the size of the active militaryaccounted for less than 1 percent of the population eligible for the draft, and an even smaller percentage of the draft eligible
population in the U.S. South, since Southerns volunteered for the military at a lower rate than the rest of the country.
41
The birth cohort for year t includes births between 16 April in year t 1 and 15 April in year t. We adopt this convention
since the 1910 census took place on 15 April and asked respondents to report their age in years on the day of the census. While
the WWI records are only 64 percent complete for births between 16 April 1886 and 15 April 1887, the 1886 births are notincluded in the main estimation sample. In 3 percent of state-by-birth year groups, we find more individuals registered in the
WWI draft records than enumerated in the 1910 census. Although we assign a weight of 0.5 to observations with missing genderand a weight of N1 to observations for which the birth place may be located in one of N states, we may still under count thecohort sizes in the 1910 census due to missing data.

21

based on exposure to hurricanes, our results should not be a¯ected by the incomplete registration.
Table O8 shows that the main results for e¯ects on log income hold when restricting attention to thelater cohorts, for whom registration was e¯ectively complete (column 3). The estimates for the 1889 to1891 conception year cohorts (column 2), when there were few cases of in utero exposure to hurricanes,
are statistically insignificant, but the magnitudes are within the same ballpark as the main estimates. The
results are also robust to dropping the 1889 to 1891 conception year cohorts (column 4), as well as the1892 and 1896 to 1897 birth cohorts (columns 5 and 6, respectively), which were years subject to a greater
degree of under-registration in the WWI records.

4.5

Collapsed data
With many birth locations in the WWI draft records having few individuals in the estimation sample,

outlier observations may have undue influence on the regression results. Table A7 presents results fromcollapsing the individual-level data at the birth county-by-conception month level. The interpretationof these results is di¯erent from the main specification given that the dependent variable is the mean
educational or labor market outcome in the birth county-by-conception month cell, while the treatment
is an indicator variable equal to one if at least one individual in the cell was exposed in utero to a hurricane
within 30 kilometers of the birth location.
In comparison to the main results in panel A of Table 4, the e¯ects on educational outcomes areslightly larger and statistically significant, with in utero hurricane exposure leading to reductions in thehighest grade completed and the probability of middle school graduation of 3.3 and 9.4 percent (compared to 1.6 and 6.1 percent), respectively. When it comes to the labor market outcomes, the e¯ect on
log income is slightly larger without educational controls (8.1 versus 7.3 percent), and slightly smaller
once educational controls are added (5.3 versus 6.0 percent). The e¯ects on working fifty or more weeks
are now also statistically significant, and larger than the main estimates (3.7 vs 2.2 percent without educational controls). Overall, the results based on birth county-by-conception month averages are very
similar to the main results. Generally, the standard errors are larger in the main specification based on theindividual-level sample, which reflects the presence of measurement error from having a small number ofobservations at some birth locations.

5

Conclusions
A growing literature has documented negative e¯ects of exposure to natural disasters on fetal devel-

opment, but due to data limitations, the majority of this research has focused on either health at birthor intermediate schooling outcomes. If these shocks operate, at least in part, through prenatal maternal
stress and elevated levels of cortisol, then these mechanisms may explain the link between exposure tonatural disasters, child development, and long-lasting consequences. In addition to examining short-run
e¯ects on mortality, which is proxied by the gender ratio in childhood, this study extends the horizon
by analyzing the e¯ects of prenatal maternal stress on long-run outcomes: completed years of education,
income, full time employment, and migration. The analysis is based on a data set constructed by linking
22the World War I Draft Registration Cards, which contain the near universe of males born between January 1887 and August 1897, to the 1940 U.S. Census. The exogenous variation in stress is derived fromhurricanes that struck the U.S. South between June 1885 and October 1899.
The main results, which are based on a sample of white males born in the U.S. South, reveal negative e¯ects of in utero exposure on income, as well as smaller and less precisely estimated e¯ects on
educational attainment. The e¯ects on income are also present for individuals exposed during the first six
months of life, but otherwise not for exposure before the age of two. They are unlikely to be driven by
selective migration, mortality, measurement error in the dependent variable, or di¯erences in the likelihood of matching an individual across data sources. The concentration of the e¯ects during the prenatal
and neonatal periods, in combination with the nature of the shock, provides support for the interpretation that maternal stress during pregnancy represents the primary mechanism leading to long-term
consequences (Galea et al. 2005; Paxson et al. 2012; Currie and Rossin-Slater 2013).
Our preferred estimates suggest that the decrease in income cannot be fully explained by lower humancapital accumulation, but the absence of medical records precludes us from quantifying the contributionof poor health to the estimated long-run e¯ect. Nonetheless, Currie and Rossin-Slater (2013) present
limited evidence that in utero stress a¯ects a newborn’s overall health, a result that is corroborated by
Fuller (2014). Although we do not assess whether the e¯ects on income can be explained by poorer
overall health, and mental health especially, in adulthood, the intensive margin income estimates are
consistent with productivity representing an important factor that drives the results.
We also consider the short-run consequences of hurricane exposure for the poorer and more vulnerable black population. We provide evidence of increased mortality among black males exposed in utero
and during the neonatal period, which is consistent with previously documented findings that stress affects the gender ratio and results in a smaller number of surviving male fetuses. Concerns about labormarket segregation and data quality prevent us from analyzing long-run outcomes for blacks.
We view these results as particularly relevant given the macroeconomic evidence that exposure to
tropical cyclones can dampen a country’s growth and productivity over a twenty-year time horizon(Hsiang and Jina 2014). Our findings suggest that the consequences of hurricanes, and potentially othernatural disasters, can include costs such as reduced individual-level productivity, that do not manifest
themselves until long after the immediate aftermath of the disaster. Our results also contrast some ofthe estimates of the short- and medium-run e¯ects of hurricane exposure on the adult population, suchas Deryugina et al. (2014), Groen et al. (2016) and Ho et al. (2016), who document positive e¯ects of
exposure and associate the results with positive mortality selection, increased labor demand during thereconstruction, and the inflow of relief to the a¯ected locations. In the context of this study, where thesubject of the “experiment” is a relatively fragile population, storms were unanticipated, and relief e¯orts
were minimal, we find unambiguously negative long-term consequences. These costs to child development and productivity remain relevant to policymakers as the ongoing growth of coastal populations
has brought more people to locations at risk of extreme weather events, and add to the growing concern
within both the scientific community and governments about the impact of climate change (Dell et al.
2012; Burke et al. 2015; McNutt 2015; Burke et al. 2016).

23

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Ruggles, Steven, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek, Integrated
Public Use Microdata Series: Version 6.0, Minnesota: University of Minnesota, 2015.
Schwandt, Hannes, “The Lasting Legacy of Seasonal Influenza: In-utero Exposure and Human Capital
Development,” Working Paper, 2016.
Simeonova, Emilia, “Out of Sight, Out of Mind? Natural Disasters and Pregnancy Outcomes in theUSA,” CESifo Economic Studies, 2011, 57 (3), 403–431.
Sotomayor, Orlando, “Fetal and Infant Origins of Diabetes and Ill Health: Evidence from Puerto Rico’s
1928 and 1932 Hurricanes,” Economics and Human Biology, 2013, 11 (3), 281–293.
Torche, Florencia, “The E¯ect of Maternal Stress on Birth Outcomes: Exploiting a Natural Experiment,” Demography, 2011, 48 (4), 1473–1491.
Trivers, Robert and Dan Willard, “Natural Selection of Parental Ability to Vary the Sex Ratio ofO¯spring,” Science, 1973, 179 (4068), 90–92.
Tseng, Tzu-Yin Hazel, “The Hidden Costs of Natural Disasters: In Utero Environment and Mental
Health in Adulthood,” Working Paper, 2016.
U.S. Geological Survey, U.S. Geographic Names Information System (GNIS), Reston VA: U.S. Geological
Survey, 2014.
Walker, Francis A. and Charles W. Seaton, “Report on the Productions of Agricuture,” 1880.
Weatherford, Candis and William Gray, “Typhoon Structure as Revealed by Aircraft Reconnaissance.
Part II: Structural Variability,” Monthly Weather Review, 1988, 116, 1044–1056.
Webster, Peter, Gregory Holland, Judith Curry, and Hai-Ru Chang, “Changes in Tropical Cyclone
Number, Duration, and Intensity in a Warming Environment,” Science, 2005, 309 (5742), 1844–1846.
28

Tables
Table 1: Linkage Rates from the WWI Draft Registration Cards to U.S. Censuses
U.S. birth region(1)
U.S.-Born(2)
Non-South

(3)
South

30-100km(4)All

0-30km(5)
All(6)
In utero

Panel A: Linking WWI records to the 1900 U.S. Census
No match

0.532

0.494

0.657

0.654

0.701

0.708

Multiple matches

0.099

0.100

0.095

0.091

0.083

0.084

Unique match

0.369

0.406

0.248

0.255

0.216

0.207

Baseline sample

7656005

5845398

181060746420825557015774

Panel B: Linking WWI records to the 1940 U.S. Census
No match

0.391

0.370

0.455

0.453

0.475

0.476

Multiple matches

0.280

0.273

0.303

0.291

0.290

0.286

Unique match

0.329

0.357

0.242

0.255

0.234

0.238

Baseline sample

77554915867503

188798848089527114315740

Note: Panel A presents match rates from the WWI draft records to the 1900 U.S. Census. The linking variables
are first and last name, birth year and birth month, and state of birth. Panel B presents match rates from the
WWI draft records to the 1940 U.S. Census. The linking variables are first and last name, age in years at thetime of the census (birth year ± 3 years), and state of birth. The baseline population, shown in columns 3 to6, consists of all individuals in the data set of WWI Draft Registration Cards who were born between January
1887 and August 1897 in one of nine Southern U.S. states exposed to hurricanes during the sample period(Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Texas, and Virginia). Bothpanels exclude records with missing first or last name, and cases reporting only a first or last initial. Panel A
also excludes observations with missing birth month, since it is one of the matching variables, and thus thebaseline population is slightly lower. Each column shows match failure, multiple match, and unique match rates
for di¯erent samples, beginning with all U.S.-born males, excluding Alaska and Hawaii, in the WWI records(column 1). Next, match rates are presented separately for the non-South and South regions (columns 2 and 3),
where the South is restricted to the nine Southern states exposed to hurricanes. Columns 4 and 5 show match
rates for locations 30 to 100 kilometers away and 0 to 30 kilometers away, respectively, from hurricane paths that
hit the continental U.S. between 1885 and 1899. Finally, separate match rates are shown for individuals exposed
in utero to hurricanes (column 6), a subset of individuals in column 5.

29

Table 2: Baseline Characteristics from the 1880 U.S. Census
U.S. birth region(1)
U.S.-Born(2)
Non-South

(3)
South

Hurricane zone
(4)30-100km(5)0-30kmPanel A: County averages (Full population)
Black

0.146

0.047

0.360

0.438

0.444

Rural

0.928

0.910

0.968

0.956

0.960

On farm

0.565

0.558

0.579

0.550

0.507

Average farm value

18.9

25.2

6.4

7.0

6.1

Average farm size

229.8

150.8

387.5

281.9

326.1

Panel B: Individual characteristics (White males age 20 to 40)
Labor force participation rate

0.934

0.933

0.940

0.937

0.933

Employed in agriculture

0.450

0.412

0.658

0.576

0.591

Occupational income score

20.3

20.8

17.5

18.6

18.1

Occupational earnings score

33.8

35.6

23.4

27.5

25.9

Occupational education score

9.3

9.5

8.3

8.9

8.4

Average age

28.3

28.3

28.2

28.2

28.3

Married

0.535

0.524

0.595

0.564

0.580

Family size

2.6

2.6

3.0

3.0

3.0

0.416

0.402

0.492

0.459

0.478

56822144806489875725236353191201

Has children in household
N

Note: This table reports summary statistics from the 1880 U.S. Census of Population. Panel A presentscounty averages computed by collapsing individual-level data for U.S.-born black and white males, age 20
and up, at the county level, while Panel B shows individual means for U.S.-born white males, age 20 to 40.
Sources: Ruggles et al. (2015) for the 1880 census. We thank Hoyt Bleakley for kindly sharing data on farm
size and farm value obtained from the U.S. Censuses of Agriculture.

30

Table 3: Population Statistics from the 1940 U.S. Census
U.S. birth region

Match to WWI

Final sample (exposure)31(1)
U.S.-Born(2)
Non-South

(3)
South

(4)
Non-South

(5)
South

(6)
0-30km(7)30-100km(8)
In utero

Reported any wage income

0.642

0.657

0.564

0.651

0.570

0.734

0.734

0.733

50+ USD non-wage income

0.398

0.386

0.458

0.406

0.466

0.370

0.369

0.358

Wage and salary income

1467.11503.81255.81529.11324.91359.01362.21294.9Predicted business income

1006.91064.2756.6

1075.3

783.3

814.6

848.5

772.2

Occupational income score

26.2

26.6

24.4

26.6

24.9

26.2

26.1

25.9

Occupational earnings score

52.3

53.6

45.9

53.3

47.3

51.7

52.0

50.9

Occupational education score

14.1

14.4

12.7

14.4

13.2

13.9

13.7

13.3

Worked 50+ weeks

0.702

0.700

0.708

0.711

0.721

0.724

0.731

0.710

Employed in agriculture

0.189

0.167

0.295

0.183

0.285

0.240

0.232

0.239

Married

0.845

0.839

0.879

0.860

0.892

0.896

0.897

0.893

Highest grade completed

8.7

8.9

7.8

8.9

8.0

8.1

8.0

8.0

Primary school graduate

0.471

0.495

0.353

0.507

0.362

0.355

0.381

0.356

Middle school graduate

0.148

0.145

0.159

0.150

0.164

0.158

0.153

0.157

High school graduate

0.167

0.170

0.154

0.167

0.158

0.175

0.164

0.168

College completion

0.054

0.056

0.044

0.056

0.046

0.057

0.049

0.055

70033685825991

11773772115346

340661

19226

39625

1392

N

Note: This table reports summary statistics from the 1940 U.S. Census by region, for the population and for the linked WWI-1940 sample, and by
distance from hurricane paths for the final analysis sample. All columns restrict attention to U.S.-born white males with non-missing values of themain outcome variables: wages and imputed self-employment income, hours of work, and years of completed education. They also exclude individuals
who were incarcerated, serving in the military, working for religious orders, or enrolled in school in 1940. Columns 1 to 3 report means for individuals
aged 42 to 53 in 1940, while the remaining columns also include some individuals aged 39 to 41, or 54 to 56, due to inexact matches on age to WWI
records. Source: authors’ calculations based on the restricted-use version of 1940 U.S. Census complete count file. See Appendix A.3 for details on
how predicted business income is computed.

Table 4: E¯ects on Education and Labor Market Outcomes(1)
Years ofeducation(2)
Middle school
graduation(3)
Log income(1939 USD)

(4)
Worked50+ weeks(5)
Log income(1939 USD)

(6)
Worked50+ weeks

0.060⇤⇤⇤(0.019)0.013(0.014)

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.131(0.100)0.023(0.015)0.073⇤⇤⇤(0.021)0.016(0.014)

Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes

32

Post-storm conception

0.030(0.092)0.013(0.011)0.024(0.019)0.002(0.012)0.021(0.016)0.003(0.011)

Hurricane in utero

0.132(0.105)0.022(0.015)0.075⇤⇤⇤(0.022)0.018(0.014)0.062⇤⇤⇤(0.019)0.015(0.013)Hurricane age [0,6] months

0.001(0.135)0.011(0.021)

0.051⇤(0.027)0.012(0.015)0.049⇤⇤(0.023)0.013(0.015)

Hurricane age [7,12] months

0.183(0.134)0.016(0.018)0.010(0.028)0.008(0.015)0.008(0.026)0.004(0.015)

Hurricane age [1,2]0.069(0.106)0.010(0.014)0.015(0.021)0.008(0.013)0.022(0.020)0.009(0.012)

Education controls
Mean of Y
P-value (Equality of coe˘cients)
N

No
8.001
0.440

No
0.374
0.145

No
6.907
0.008

No
0.729
0.938

Yes
6.907
0.008

Yes
0.729
0.958

58851

Notes: Each column and panel displays coe˘cients from a separate regression of an educational or labor market outcome in 1940 on a measure ofhurricane exposure: an indicator variable equal to one if a hurricane track passed within 30 kilometers of the individual’s location of birth during the inutero period (Panel A), or separate indicators for exposure in utero, age 0 to 6 months, age 7 to 12 months, age 1 to 2 years, and conception up to 1 year
after the storm (Panel B). In columns 3 and 5, income is the sum of wage income reported in the 1940 census, and business or farm income imputed using
data from the 1950 census (See Appendix A.3 for details). Each specification controls for linkage quality measures, an indicator for individuals born inlocations within 30 kilometers of a hurricane during the sample period, state-specific linear time trends, as well as county of birth, conception year, andconception week fixed e¯ects. The samples restrict attention to individuals with non-missing occupational outcomes, incomes, and years of education in
1940. Standard errors are clustered by county of birth. Point estimates marked ***, **, and * are statistically significant at the 1, 5, and 10 percent levels,
respectively. Probability of exposure within the sample: .03 (conception 0 to 1 year after hurricane), .024 (in utero), .014 (age 0 to 6 months), .015 (age 7
to 12 months), and .029 (age 1 to 2 years).

Table 5: The Role of Storm Categories and Pregnancy Trimesters(1)
Years ofeducation(2)
Middle school
graduation(3)
Log income(1939 USD)

(4)
Worked50+ weeks(5)
Log income(1939 USD)

(6)
Worked50+ weeks

Panel A: E¯ects of in utero hurricane exposure by trimester
Hurricane 1st trimester

0.413⇤⇤(0.167)0.044⇤(0.023)0.104⇤⇤(0.041)

0.005(0.023)0.062⇤(0.037)0.012(0.023)Hurricane 2nd trimester

0.059(0.175)0.014(0.023)0.053(0.042)0.012(0.022)0.047(0.039)0.011(0.022)Hurricane 3rd trimester

0.058(0.170)0.010(0.022)0.068⇤(0.040)0.044⇤⇤(0.022)0.074⇤⇤(0.034)0.045⇤⇤(0.021)Panel B: E¯ects of in utero exposure to hurricanes and interaction with Category 2 or 3 hurricanes

33

Hurricane in utero

0.157(0.123)0.028⇤(0.016)0.080⇤⇤⇤(0.025)0.020(0.014)0.065⇤⇤⇤(0.022)0.017(0.013)

In utero ⇥ Category 2 or 3

0.141
(0.246)0.035(0.039)0.032(0.052)0.011(0.045)0.022(0.043)0.012(0.044)

Education controls
Mean of Y
P-value (Equality of trimesters)
N

No
8.001
0.101

No
0.374
0.468

No
6.907
0.693

No
0.729
0.310

Yes
6.907
0.884

Yes
0.729
0.205

58851

Notes: See Table 4 for details on sample and controls. Panel A shows separate coe˘cients for exposure during each trimester of pregnancy, but is otherwise
identical to Panel B of Table 4. In Panel B, the variable of interest is an interaction of an indicator for in utero exposure to a hurricane with indicators forwhether a Category 1 or a Category 2 to 3 hurricane were experienced in the location of birth while in utero. The regression also includes interactions of
the storm category indicators with each of the remaining indicators for hurricane exposure by age. Standard errors are clustered by county of birth. Point
estimates marked ***, **, and * are statistically significant at the 1, 5, and 10 percent levels, respectively. Probability of exposure within sample: .008 (1sttrimester), .008 (2nd trimester), and .008 (3rd trimester). Probability of storms by category: .019 (Category 1) and .004 (Category 2 or 3).

Table 6: E¯ects on Gender Ratio in 1900, By Race
Pooled(1)1886-1896

Whites(2)
1892-1896(3)
1886-1896

Blacks(4)
1892-1896(5)
1886-1896(6)
1892-1896

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.024⇤⇤(0.011)0.023⇤(0.012)0.008(0.020)0.010(0.026)0.044⇤⇤(0.017)0.051⇤⇤⇤(0.017)

Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes
0.011(0.009)0.019(0.012)0.024(0.017)0.047⇤(0.024)0.008(0.018)0.015(0.022)Hurricane in utero

0.027⇤⇤(0.012)0.032⇤⇤(0.014)0.008(0.021)

0.003(0.027)0.048⇤⇤(0.018)0.055⇤⇤⇤(0.021)

Hurricane age [0,6] months

0.026⇤(0.014)0.036⇤⇤(0.015)0.002(0.025)0.030(0.027)0.052⇤⇤⇤(0.020)0.051⇤⇤(0.022)Hurricane age [7,12] months

0.022(0.015)0.006(0.017)0.041⇤(0.025)0.012(0.028)0.012(0.020)0.015(0.023)Hurricane age [1,2] years

0.002(0.011)0.001(0.017)0.005(0.019)0.017(0.037)0.004(0.015)0.017(0.022)

Mean of Y
N

1.074
43392

1.063
17110

1.129
32128

1.130
14160

1.087
25088

1.077
11446

34

Post-storm conception

Note: This table presents results from models where the dependent variable is the (male/female) gender ratio in the 1900 U.S. Census. An observation
is a county of residence-by-month of conception cell. Dade and Monroe counties, at the southern tip of Florida, are dropped from the sample. Eachspecification also excludes counties with no births, and counties with no male or female births, in at least one month during the sample period. All
regressions weight observations by the number of births in the county of residence. All columns include as controls state-specific linear trends, 1900
county of residence fixed e¯ects, and conception year and month fixed e¯ects. Standard errors (in parentheses) are clustered at the residence (birth)county level. Point estimates marked ***, **, and * are statistically significant at the 1, 5, and 10 percent levels, respectively.

Table 7: E¯ects on Migration by 1900 and 1940
Migration by 1900
(1)
InterCounty(2)
InterState

Migration by 1940

(3)
OutsideSouth(4)InterCounty(5)InterState(6)
OutsideSouth

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.005(0.010)0.003(0.006)0.001(0.003)0.012(0.015)0.027⇤⇤(0.012)0.001(0.007)

Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes

35

Post-storm conception

0.008(0.009)0.004(0.005)0.004(0.003)0.002(0.012)0.011(0.011)0.007(0.008)Hurricane in utero

0.008(0.011)0.001(0.006)0.001(0.003)0.010(0.015)0.028⇤⇤(0.012)0.001(0.007)

Hurricane age [0,6] months

0.002(0.014)0.007(0.006)0.000(0.003)0.002(0.018)0.018(0.018)0.012(0.009)Hurricane age [7,12] months

0.004(0.012)0.004(0.008)0.002(0.004)0.019(0.017)0.012(0.014)0.001(0.009)Hurricane age [1,2]0.015(0.013)0.007(0.006)0.001(0.003)0.007(0.013)0.006(0.012)0.015⇤(0.008)Mean of Y
P-value (Equality of coe˘cients)
N

0.218
0.907

0.046
0.776
79549

0.013
0.563

0.386
0.726

0.260
0.056
58851

0.097
0.017

Note: This table presents results from models where the dependent variables are indicators for migrating to di¯erent destinations by 1900
(columns 1 to 3) or 1940 (columns 4 to 6). The dependent variables are indicators for: county of residence di¯erent from county of birth,
but living in the same state; state of residence di¯erent from state of birth; and residing outside the South. In addition to the nine hurricane
states, the “South” includes Arkansas, Delaware, Kentucky, Maryland, Oklahoma, Tennessee, and West Virginia. Estimation is based on
samples of individuals uniquely linked from the WWI draft records to the 1900 U.S. Census (columns 1 to 3) or the 1940 U.S. Census(columns 4 to 6). See Table 4 for details on the sample and controls. In columns 1 to 3, the sample also restricts attention to individuals withmothers aged 15 to 49 at the time of birth, and the regressions include as controls indicators for mother’s age at birth (age 20 to 24, 25 to34, 35 to 39, and 40 and up). Standard errors are clustered at the birth county level. Point estimates marked ***, **, and * are statisticallysignificant at the 1, 5, and 10 percent levels, respectively.

Figures
Figure 1: Birth Locations and Hurricane Exposure
Hurricane exposure

Sample Size

30km radii exposure zone

130-100km radii buffer zone

10

US counties in 1880

100
1,000

Note: This figure plots the latitude and longitude coordinates of the city or town of birth for locations within 100 kilometers of the path of ahurricane that hit the continental U.S. between 1885 and 1899. The coordinates are overlayed on a map of U.S. county boundaries for 1880.
Green circles indicate birth locations within 30 kilometers of a hurricane path during the sample period, while red circles denote locations 30
to 100 kilometers away. The diameter of the circles is proportional to the number of observations at each location. The sample consists of all
U.S.-born white males satisfying the sample restrictions described in Table 4. Sources: Minnesota Population Center (2011) for boundary file;authors’ calculations based on WWI Draft Registration Cards (n.d.) and U.S. Geological Survey (2014) for coordinates.

Figure 2: Estimated Completeness of WWI Draft Registration
Non−South

South

1.2

WWI coverage rate

1

.8

.6

.4

.2

0
1885

1890

1895

1900 1885

1890

1895

1900

Birth year

Note: This figure plots the completeness rate of the WWI draft against year of birth, by birth region: the “hurricane zone” Southern states(“South”, right panel) and the rest of the county (“Non-South”, left panel). The solid purple lines denote the average completeness rate by birthcohort and the dashed lines indicate the states with the highest and lowest coverage in each region. The non-South excludes Alaska, Hawaii,
and the District of Columbia. The rate of completeness is calculated as the ratio of the number of individuals in a birth cohort who registered
for the draft, to the number of individuals from the same birth cohort who were enumerated in the 1910 U.S. Census. Since the census only
reports age in years on the day of the census, the birth cohort from the WWI records for year t includes individuals born between 16 April inyear t 1 and 15 April (census day) in year t.

36

Appendix Tables
Table A1: Robustness – Selection of Birth Locations
Coordinates + centroids(1)
Log income(2)50+ weeks

County centroids only(3)
Log income(4)50+ weeks

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.058⇤⇤⇤(0.021)0.014(0.013)0.036⇤(0.020)0.015(0.013)

Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes
Post-storm conception

0.017(0.019)0.006(0.010)0.014(0.016)0.011(0.010)

Hurricane in utero

0.059⇤⇤⇤(0.021)0.017(0.013)0.044⇤⇤(0.021)0.019(0.013)Hurricane age [0,6] months

0.045⇤(0.026)0.012(0.014)0.066⇤⇤(0.026)0.010(0.015)

Hurricane age [7,12] months

0.018(0.025)0.013(0.014)0.012(0.026)0.015(0.014)

Hurricane age [1,2]0.005(0.020)0.008(0.011)

0.007(0.021)

0.003(0.012)

Mean of Y
P-value (Equality of coe˘cients)
N

6.891
0.027
65781

0.727
0.960
65781

6.890
0.237
67001

0.727
0.914
67001

Note: The table presents results based on di¯erent restrictions on the assignment of coordinates to the birth
location, which determine exposure to a hurricane. Columns 1 and 2 identify exposure to hurricanes based on thecoordinates of the reported birth location and add exposure based on birth county centroid when the exact location
is missing, while columns 3 and 4 define exposure based only on the coordinates of the birth county centroid. In
columns 1 and 3, the dependent variable is log income (1939 USD), and in columns 2 and 4 it is an indicator forworking at least 50 weeks in a year. Aside from including observations with birth location known only at thecounty level, the regressions impose the same restrictions as the main estimation sample. See Table 4 for details on
the sample and controls. Standard errors are clustered by county of birth. Point estimates marked ***, **, and *are statistically significant at the 1, 5, and 10 percent levels, respectively.

37

Table A2: Probability of Matching WWI Records to the 1900 and 1940 U.S. Censuses
Match to 1900
(1)
Unique match(2)
Multiple matches

Match to 1940
(3)
Final sample(4)Unique match(5)
Multiple matches(6)
Final sample

0.006(0.004)0.004(0.003)

Panel A: E¯ects of in-utero exposure to hurricanes
Hurricane in utero

0.004(0.005)0.002(0.002)0.005(0.004)0.006(0.004)

Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes
0.005(0.004)0.002(0.002)0.005(0.003)0.001(0.004)0.001(0.003)0.001(0.003)Hurricane in utero

0.006(0.005)0.002(0.002)0.007(0.004)0.006(0.005)0.007(0.005)0.004(0.003)Hurricane age [0,6] months

0.002(0.005)0.005⇤⇤(0.002)0.002(0.004)0.007(0.005)0.008(0.005)0.002(0.004)

Hurricane age [7,12] months

0.003(0.004)0.003(0.002)0.004(0.004)0.004(0.005)0.003(0.005)0.003(0.004)

Hurricane age [1,2]0.006(0.004)0.002(0.002)0.007⇤⇤(0.003)0.001(0.004)0.001(0.004)0.003(0.003)

Mean of Y
P-value (Equality of coe˘cients)
N

0.243
0.968

0.041
0.020

0.174
0.791
455888

0.257
0.332

0.184
0.531

0.129
0.550

38

Post-storm conception

Note: This table reports the results of models where the dependent variables are indicators that equal one if an individual is matched from the WWI Draft Registration
Cards database to a unique record (column 1) or more than one record (column 2) in the 1900 U.S. Census, or the same outcomes for matches to the 1940 U.S. Census(columns 4 and 5). In columns 3 and 6, the dependent variables are indicators for the subset of unique matches to the 1900 census that appear in the migration
regression sample in Table 7, and the subset of unique matches to the 1940 census that are part of the main estimation sample (Table 4), respectively. In addition to
the restrictions on the baseline sample described in Table 1, this table drops individuals for whom the coordinates of the birth location are unknown, and individuals
whose birth state is not uniquely identified. All specifications include an indicator for birth locations within 30 kilometers of a hurricane path during the sample
period, state-specific trends, and county of birth, conception year, and conception week fixed e¯ects as controls. Standard errors (in parentheses) are clustered at thebirth county level. Point estimates marked ***, **, and * are statistically significant at the 1, 5, and 10 percent levels, respectively.

Table A3: Interaction with Urbanization Rate and Farm Value in 1880
(1)Years ofeducation(2)
Middle school
graduation(3)
Log income(1939 USD)

(4)
Worked50+ weeks(5)
Log income(1939 USD)

(6)
Worked50+ weeks

Panel A: E¯ects of in utero exposure and interaction with county urbanization rate
Hurricane in utero

0.171(0.108)0.033⇤⇤(0.015)0.082⇤⇤⇤(0.025)0.030⇤⇤(0.015)0.066⇤⇤⇤(0.022)0.027⇤(0.014)

In utero ⇥ Urbanization rate

0.004
(0.004)0.001⇤(0.001)0.001(0.001)0.001⇤⇤⇤(0.000)0.001(0.001)0.001⇤⇤⇤(0.000)Mean of Y
N

8.001

0.374

6.907

0.729

6.907

0.729

58851
Panel B: E¯ects of in utero exposure and interaction with below median farm value indicator

39

Hurricane in utero

0.180(0.143)0.024(0.020)0.066⇤⇤(0.027)0.029(0.018)0.048⇤(0.025)0.026(0.017)

In utero ⇥ (Farm value < p(50))

0.107(0.211)

0.004(0.029)0.021(0.044)0.024(0.027)0.032(0.038)0.023(0.026)

Education controls
Mean of Y
N

No
8.001

No
0.374

No
6.907

No
0.729

Yes
6.907

Yes
0.729

58808

Note: This table reports results from estimating the specifications in panel B of Table 4, with each of the hurricane exposure indicators interacted with the urbanization
rate of the birth county in 1880 (Panel A), and an indicator for whether the average farm value of the birth county in 1880 was below the median among counties in theestimation sample (Panel B). The urbanization rate is defined as the percentage of county residents (0 to 100) living in an urban area with at least 2,500 residents. Farmvalue measures the per acre fair market value of farms, including land, fences, and buildings, and is obtained from the 1880 Agricultural Census. Since urbanization
rate and farm value are defined at the county level, the main e¯ects of each variable are subsumed in the county fixed e¯ects. See Table 4 for details on the sample andcontrols. Standard errors are clustered at the birth county level. Point estimates marked ***, **, and * are statistically significant at the 1, 5, and 10 percent levels,
respectively.

Table A4: Robustness – Logarithm of Wage and Salary Income
Restricted Sample

Relaxed Sample

(1)

(3)

(2)

Full Sample

(4)

(5)

(6)

0.055⇤⇤(0.025)0.072(0.087)0.068(0.084)

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.061⇤⇤(0.029)0.060⇤⇤(0.027)

0.056⇤⇤(0.028)Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes

40

Post-storm conception

0.019(0.027)0.022(0.023)0.028(0.026)0.027(0.022)0.019(0.075)0.034(0.076)

Hurricane in utero

0.067⇤⇤(0.030)0.065⇤⇤(0.027)0.057⇤(0.029)0.056⇤⇤(0.025)0.096(0.094)0.096(0.089)Hurricane age [0,6] months

0.054(0.035)0.060⇤(0.032)0.057(0.037)0.056(0.035)0.061(0.108)0.073(0.107)

Hurricane age [7,12] months

0.026(0.041)

0.012(0.038)0.009(0.038)0.006(0.037)0.134(0.105)0.126(0.104)

Hurricane age [1,2]0.033(0.031)

0.032(0.028)0.006(0.028)0.007(0.026)0.094(0.077)0.107(0.078)

Education controls
Mean of Y
P-value (Equality of coe˘cients)
N

No
6.908
0.268

Yes
6.908
0.145

No
6.878
0.167

Yes
6.878
0.098

No
Yes
4.779
4.779
0.878
0.937
68172

37110

47368

Note: This table presents results from models with the natural logarithm of wage and salary income as the dependent variable, with varying
restrictions on the sample. Individuals who report zero wages or salary are assigned $1 of income. In columns 1 and 2, the sample restricts
attention to individuals reporting no more than $50 in business and farm income, and only includes individuals not reporting any wage andsalary income if they are out of the labor force, and have no reported industry or occupation. Columns 3 and 4 report results from a relaxedsample that adds individuals with more than $50 in business and farm income, while columns 5 and 6 present results from a sample that also
adds all individuals not reporting any wage and salary income. All regressions include the same controls as the main specifications in column
3 of Table 4. Standard errors are clustered by county of birth. Point estimates marked ***, **, and * are statistically significant at the 1, 5, and10 percent levels, respectively.

Table A5: Measurement Error in Gestational Age
Dep var: Log income
(1)
37

(2)
38

(3)
40

Dep var: Worked 50+ weeks
(4)
41

(5)
37

(6)
38

(7)
40

(8)
41

0.016
(0.014)0.017(0.014)0.016(0.014)0.018(0.014)

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.073⇤⇤⇤(0.022)0.068⇤⇤⇤(0.021)0.070⇤⇤⇤(0.021)

0.067⇤⇤⇤(0.021)Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes

41

Post-storm conception

0.014(0.019)0.013(0.020)0.020(0.020)0.023(0.020)0.010(0.012)0.007(0.012)0.004(0.012)0.000(0.012)Hurricane in utero

0.076⇤⇤⇤(0.023)0.072⇤⇤⇤(0.022)0.073⇤⇤⇤(0.022)0.070⇤⇤⇤(0.021)0.019(0.014)0.020(0.014)0.018(0.014)0.020(0.013)Hurricane age [0,6] months

0.049⇤(0.027)0.051⇤(0.027)

0.053⇤(0.027)0.055⇤⇤(0.027)0.012(0.015)0.012(0.015)0.012(0.015)0.013(0.015)

Hurricane age [7,12] months

0.010(0.028)0.011(0.028)0.010(0.028)0.009(0.028)0.009(0.015)0.009(0.015)0.009(0.015)0.009(0.015)

Hurricane age [1,2]0.016(0.021)0.016(0.021)0.014(0.021)0.017(0.021)0.008(0.013)0.008(0.013)0.008(0.013)0.007(0.013)P-value (Equality of coe˘cients)
Prob. in utero exposure
N

0.015
0.022

0.022
0.023

0.011
0.024

0.010
0.025
58851

0.972
0.022

0.962
0.023

0.949
0.024

0.860
0.025

Note: The table shows the robustness of the main results to di¯erent assumptions about the gestational age. In the main specification, we assume that the gestational age is
39 weeks, while in this table, it ranges from 37 to 41 weeks. In columns 1 to 4, the dependent variable is the log of total income (1939 USD), and in columns 5 to 8 it isan indicator for having worked at least 50 weeks in 1939. See Table 4 for details on sample and controls. Standard errors are clustered by county of birth. The dependent
variable means are: 6.91 (log income) and .73 (worked 50+ weeks).

Table A6: Robustness – Definition of Treatment
No donut hole
(1)
20km

(2)
40km

Exclude [d,d+20] km donut hole
(3)
50km

(4)
20km

(5)
40km

(6)
50km

0.052⇤⇤(0.022)0.034(0.023)

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.064⇤⇤⇤(0.025)0.067⇤⇤⇤(0.019)0.034⇤⇤(0.016)0.081⇤⇤⇤(0.031)Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes
0.032(0.023)0.007(0.018)0.002(0.013)0.034(0.029)0.003(0.021)0.001(0.018)Hurricane in utero

0.059⇤⇤(0.027)0.073⇤⇤⇤(0.020)0.037⇤⇤(0.017)0.076⇤⇤(0.032)0.058⇤⇤⇤(0.022)0.037(0.023)Hurricane age [0,6] months

0.033(0.038)0.074⇤⇤⇤(0.023)0.039⇤(0.020)0.024(0.041)

0.058⇤⇤(0.027)0.026(0.028)Hurricane age [7,12] months

0.010(0.034)0.018(0.030)0.002(0.021)

0.006(0.041)

0.003(0.034)0.005(0.027)

Hurricane age [1,2] years

0.024(0.026)0.022(0.020)0.002(0.018)0.035(0.030)0.027(0.025)0.011(0.023)

Mean of Y
P-value (Equality of coe˘cients)
Prob. in utero exposure
N

6.907
0.030
0.015
58851

6.907
0.009
0.032
58851

6.907
0.193
0.049
58851

6.923
0.030
0.014
44164

6.885
0.240
0.038
36093

6.863
0.704
0.050
32397

42

Post-storm conception

Note: See Table 4 for details on sample and controls. Columns 1 and 4 restrict the exposure region to a 20 kilometer-radii bandwidth around the storm track,
while columns 2 and 5 extend it to 40 kilometers, and columns 3 and 6 extend it to 50 kilometers. Columns 4 to 6 also exclude a 20 kilometer-radii bandwidth("donut hole" region) immediately beyond the exposure zone. Standard errors are clustered by county of birth. Point estimates marked ***, **, and * arestatistically significant at the 1, 5, and 10 percent levels, respectively.

Table A7: Robustness – Aggregated Data
(1)
Years ofeducation(2)
Middle school
graduation(3)
Log income(1939 USD)

(4)
Worked50+ weeks(5)
Log income(1939 USD)

(6)
Worked50+ weeks

0.053⇤⇤(0.022)0.022(0.015)

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.265⇤⇤(0.110)0.036⇤⇤(0.015)0.081⇤⇤⇤(0.026)0.027⇤(0.015)

Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes

43

Post-storm conception

0.010(0.104)0.005(0.013)0.012(0.021)

0.015(0.012)0.011(0.017)0.015(0.012)Hurricane in utero

0.258⇤⇤(0.114)0.033⇤⇤(0.016)0.081⇤⇤⇤(0.027)0.028⇤(0.015)0.054⇤⇤(0.022)0.023(0.015)

Hurricane age [0,6] months

0.159(0.146)0.018(0.019)0.068⇤⇤(0.033)0.010(0.016)0.084⇤⇤⇤(0.028)0.013(0.015)

Hurricane age [7,12] months

0.175(0.134)0.006(0.018)0.003(0.031)

0.005(0.017)0.021(0.028)0.009(0.017)

Hurricane age [1,2]0.151(0.101)0.021(0.013)0.031(0.024)0.013(0.013)0.015(0.021)0.010(0.012)

Education controls
Mean of Y
P-value (Equality of coe˘cients)
N

No
8.045
0.027

No
0.385
0.057

No
6.868
0.006

No
0.724
0.157

Yes
6.868
0.005

Yes
0.724
0.217

26924

Note: This table presents results from models analogous to those presented in Table 4, with the exception that the data have been collapsed at the county ofbirth-by-conception month level. The dependent variables are county of birth-by-conception month means of the highest grade completed (column 1), theprobability of middle school graduation (column 2), the natural logarithm of total income in 1939 USD (columns 3 and 5), and the probability of working atleast 50 weeks in 1939 (columns 4 and 6). In panel A, the coe˘cient on in utero exposure takes the value of one if at least one individual was exposed in utero to ahurricane in a given county and conception month cell (the coe˘cients in panel B are similarly defined). Each column controls for the percentage of individuals
with exactly matching first and last names, the average absolute deviation in ages between the WWI records and the 1940 census, state-specific linear trends, aswell as county of birth, conception year, and conception month fixed e¯ects. Columns 5 and 6 also include a control for the birth county-by-conception monthaverage for years of completed education rounded to the nearest integer. Standard errors are clustered at the county level. Point estimates marked ***, **, and* are statistically significant at the 1, 5, and 10 percent levels, respectively.

Appendix Figures
Figure A1: Linkage Rates from the WWI Draft Registration Cards to U.S. Population Censuses(a) Birth year and month (matching to 1940 census)(b) Birth year and month (matching to 1900 census)
1
WWI to 1900 unique match rate

WWI to 1940 unique match rate

1

.8

.6

.4

.2

0

.6

.4

.2

0

1886m1

1888m1

1890m1

1892m1Birth month

Non−South

1894m1South

1896m11898m1

1886m1

1888m1

Hurricane Zone

1890m1

1892m1Birth month

Non−South(c) Commonness of name (matching to 1940 census)

1894m1South

1896m11898m1

Hurricane Zone(d) Commonness of name (matching to 1900 census)
1
WWI to 1900 unique match rate

1
WWI to 1940 unique match rate

.8

.8

.6

.4

.2

0

.8

.6

.4

.2

0
2

4

6

8
10
12
14
Bins of name frequency
First name

16

18

20

Surname

2

4

6

8
10
12
14
Bins of name frequency
First name16

18

SurnameNote: In panels (a) and (b), this figure plots the rate of unique matches from the WWI Draft Registration Cards to the 1940 and 1900 U.S.
Population Censuses, respectively, against the month and year of birth, by U.S. geographic regions: Non-South, South, and locations everwithin 100 kilometers of a hurricane during the sample period from 1885 to 1899 (“Hurricane Zone”). The latter is contained within the ninestates comprising the South: Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Texas and Virginia. Panels (c)and (d) plot the rate of unique matches to the 1940 and 1900 censuses, respectively, against ventiles of name frequency within the population of
WWI Draft records, by first name and surname.

4420

Figure A2: E¯ects of In Utero Exposure on Log of Total Income, Dropping States One-by-one

Coefficient on in utero exposure

0

−.05

−.1

−.15
FL

TX

NC

MS

LA

GA

AL

VA

SC

State dropped from sample
Note: Each line in the figure represents a 95-percent confidence interval, while the square marker denotes the point estimate, for the e¯ect ofin utero exposure to hurricanes on the log of total income, from estimating Equation 2 using a sample that deviates from the main specification
by dropping all observations from a particular state. The lines are ordered by the size of the in utero exposure coe˘cients. Each specification
includes the same controls as panel B of Table 4. Standard errors are clustered by county of birth.

45

Figure A3: E¯ects of In Utero Exposure on Log of Total Income, Dropping Storms One-by-one(a) Ignoring Exposure from Dropped Storm(b) Keeping Exposure from Dropped Storm0

Coefficient on in utero exposure

Coefficient on in utero exposure

0

−.05

−.1

−.15

−.05

−.1

−.15
Storms dropped from sample one by one

Storms dropped from sample one by one

Note: Each line in the figure represents a 95-percent confidence interval, while the square marker denotes the point estimate, for the e¯ect of inutero exposure to hurricanes on the log of total income, from estimating Equation 2 by dropping one storm at a time from the sample. The lines
are ordered by the size of the in utero exposure coe˘cients. We follow two procedures for dropping storms to deal with individuals exposed tomultiple storms: ignoring exposure to the dropped storms (Figure A3a) and accounting for exposure to dropped storms when individuals were
exposed to multiple storms (Figure A3b). In either case, individuals born in locations within 100 kilometers of multiple hurricanes remain in
the sample. Twelve of the estimates in Figure A3b are identical to the main estimates in column 3 and Panel B of Table 4, given that the set ofbirth locations within 100 kilometers of some hurricane paths are a subset of birth locations within 100 kilometers of other storms, and thus
no observations are dropped from the sample. Each specification includes the same controls as panel B of Table 4. Standard errors are clusteredby county of birth.

46

Figure A4: E¯ects of In Utero Hurricane Exposure on Log of Total Income, by Distance Bands around
Hurricane Path.1

Coefficient on in utero exposure

.05
0
−.05
−.1
−.15
−.2
−.25
0−10km10−20km20−30km30−40km40−50kmRadius band around hurricane path
Note: This figure plots the coe˘cients on in utero exposure, interacted with indicators for distance bands around the hurricane path (0 to 10,
10 to 20, 20 to 30, 30 to 40, and 40 to 50 kilometers), all estimated from a regression with the log of total income as the dependent variable.
Each line in the figure represents a 95-percent confidence interval, while the square marker denotes the point estimate. The specification extends
Equation 2 to include indicators for whether a birth location overlapped each of the five distance bands around a hurricane that occurred
during the sample period, as well as interactions of the distance band dummies with each of the exposure indicators: conception up to one year
post-storm, in utero, age 0 to 6 months, age 6 to 12 months, and age 1 to 2 years old. See Table 4 for details on the sample and controls.

47

Figure A5: Robustness to Tolerance for Mismatched Names
Log total income

Worked 50+ weeks

.15

.1

.05

0

−.05

−.1

−.15
0

.05

.1

.15

.2

0

.05

.1

.15

.2

Maximum Jaro−Winkler distance for matching names

Note: This figure plots coe˘cients on in utero exposure and 95 percent confidence intervals against the maximum Jaro-Winkler string distancetolerated for an observation to be considered matched, by the dependent variable in the regression: the natural log of total annual income(reported wage and salary income in addition to estimated business and farm income) in 1939, and an indicator for working at least 50 weeks in
1939. Aside from the changes to the Jaro-Winkler threshold, each regression is identical to the specification presented in panel B of Table 4.

Figure A6: Measurement Error Due to Matching – Similarity of Names(a) Log income
No similar records(b) Weeks worked
One similar record.15

No similar records

One similar record

Two similar records

Three similar records

.15

.1

.1

.05

.05

0

0

−.05

−.05

−.1

−.1

−.15

−.15

Two similar records

Three similar records

.15

.15

.1

.1

.05

.05

0

0

−.05

−.05

−.1

−.1

−.15

−.15
0

.05

.1

.15

.2

0

.05

.1

.15

.2

Maximum Jaro−Winkler distance for similar names

0

.05

.1

.15

.2

0

.05

.1

.15

Maximum Jaro−Winkler distance for similar names

Note: This figure plots coe˘cients on in utero exposure and 95 percent confidence intervals, from regressions with the natural log of total
income (panel a), or an indicator for working at least 50 weeks in 1939 (panel b), as the dependent variable, against a range of Jaro-Winkler
string distance thresholds for a record to be considered similar to the best match (⇢), by the maximum number of similar records in the 1940
census allowed for an observation to be included in the sample (ranging from 0 to 3 similar records). A similar record is defined as having a birthyear within one year of the matched record, and a Jaro-Winkler similarity score averaged over the first and last name satisfying (1 + ⇢) J s J
where J is the Jaro-Winkler score of the matched record, Js is the Jaro-Winkler score of a similar record, and ⇢✏[0, 0.1]. Aside from the changes
to the Jaro-Winkler threshold and the restrictions on the number of similar records, each regression is identical to the specification presented inpanel B of Table 4.

48.2

For Online Publication: Online Appendix
A

Data Appendix

A.1

Hurricane tracks

We reconstruct hurricane paths using the HURDAT2 database of best-track estimates for Atlantic
hurricanes produced by the National Hurricane Center (NHC) at the National Oceanic and Atmospheric Administration (NOAA).42 The HURDAT2 database contains all storms that occurred between
1851 and 2015 in the North Atlantic basin, and matched or exceeded the intensity of a tropical depression.
It contains a standardized set of latitude and longitude coordinates for the location of a storm every six
hours, which are estimated from storm observations in historical sources.43 We use GIS software to join
the coordinates for a storm into a line segment. When the duration of a storm extends across multiple
weeks, we split the storm track into multiple segments while extending each segment by one data point(equivalent to the movement of the storm over 6 hours) at each week-to-week transition to avoid missing
the area between two coordinates when computing exposure to the storm.
Saffir-Simpson Hurricane Wind Scale
The Sa˘r-Simpson Hurricane Wind Scale classifies hurricanes into five categories according to windspeed: 119-153 kilometers per hour (kph) (Category 1), 154-177 kph (Category 2), 178-208 kph (Category3), 209-251 kph (Category 4), and 252 kph or higher (Category 5). We exclude observations of tropicaldepressions (up to 64 kph) and tropical storms (65-118 kph) that appear in the database. Each category of
hurricane is associated with an expected severity of damage to property and infrastructure that is basedon a modern setting, but should still be relevant to the historical period of study. Category 1: homes
can have minor damage to the roof and vinyl sidings, while large branches of trees will snap. Category 2:
homes can sustain major roof and siding damage and shallow rooted trees may snap. Category 3: homes
may incur major damage or removal of roof decking, trees will snap, and water will be unavailable forextended period of time. Category 4: homes can sustain severe damage with loss of most of the roof
structure and some exterior walls. Most trees will snap, and most of the area will be uninhabitable for an
extended period of time. Category 5: a high percentage of homes will be destroyed and most of the area
will be uninhabitable for an extended period of time.44
42

The data was originally accessed on March 29, 2013 (http://www.aoml.noaa.gov/hrd/hurdat/easyread2012.html).
The database has since been updated to include storms up to 2015 and is currently located at
http://www.aoml.noaa.gov/hrd/hurdat/hurdat2-1851-2015-021716.txt. For more details on the re-analysis project that
updated the original HURDATA database in 2012, see http://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html.
43
For documentation on each storm in the database, see http://www.aoml.noaa.gov/hrd/hurdat/metadata_dec12.html
44
See http://www.nhc.noaa.gov/aboutsshws.php for more information.

49

A.2

World War I Draft Registration Cards

Historical BackgroundOn 18 May 1917, six weeks after the U.S. declared war on Germany, the U.S. government passed the
Selective Service Act, which required all males living in the U.S between the ages of 18 and 45, regardlessof citizenship status, to register for the draft. Anyone already in active military service by June 1917
was not required to register. Three separate registrations were held for the World War I draft. The firstregistration took place on 5 June 1917, and covered men born between 6 June 1886 and 5 June 1896 (those
aged 21 to 31 at the time). The Second Registration took place on 5 June 1918, and covered males born
between 6 June 1896 and 5 June 1897 (those who had turned 21 since the previous registration), as wellas those covered by the First Registration who had not already registered. The Third Registration washeld on 12 September 1918 and covered males born between 11 September 1897 and 12 September 1900
as well as those born between 11 September 1872 and 5 June 1886, and anyone else who was not in active
military service and had failed to register on the previous occasions (Ancestry.com 2005). The Selective
Service System (SSS) was established under the Provost Marshal General O˘ce (PMGO) to oversee theregistration and drafting of men into military service. A local board was responsible for about 30,000
residents in a city or county. On average, a district board had authority over 30 local boards, each ofwhich registered approximately 5,000 men (Nudd 2005).
The WWI Draft Registration Cards include an individual’s name, exact date of birth, and place ofbirth at either the county, city, or township level. The database contains nearly 24 million records,
which represents about 23 percent of the estimated U.S. population in 1918 and about 93 to 98 percent
of all men residing in the U.S. who were born between 1872 and 1900, and enumerated in the 1910
census.45 The Provost Marshal General, Enoch Crowder, estimated that between 2.4 and 3.6 million
men avoided military service by not registering for the draft (Chambers 1987), which would account for
10 to 15 percent of the eligible population and would be consistent with Figure 2 showing evidence ofunder-registration among cohorts born in the second half of 1880s.46
The decision to evade the draft registration was likely influenced by the risk of conscription. The
military established a five-tier classification system, with most drafted men coming from Class 1. Federal
and state o˘cials, ministers, and pilots were among those assigned to a fifth group (Class 5) that was not
subject to induction into military service. Registrants could be placed in Classes 2 through 4 if they were
granted a deferment, which depended upon income, occupational and social status, and the presence ofdependents (Nudd 2005). The military established recruitment quotas as a function of a state’s draft-age
population and the number of volunteer servicemen from the state. Given that fewer volunteers fromthe South had joined the military, Southern counties were subject to higher quotas for the draft, and thus
faced stronger incentives to avoid registration (Keith 2001).
45
There is some uncertainty in the share of the population due to records with missing gender or multiple transcribed values
for age in the 1910 census.
46
Under-registration for the WWI draft would only be a concern for the empirical analysis if it was related to hurricane
exposure, which is unlikely to have been the case.

50

Geocoding Birth Locations
We geocode the birth locations in the World War I Draft Registration Cards database by matchingbirth place strings to place names in the Geographic Names Information System (GNIS) database (U.S.
Geological Survey 2014). The GNIS file contains the universe of U.S. physical and cultural geographic
features, and assigns latitude and longitude coordinates to each location. We also match the birth placestrings in the WWI records against place names and counties listed in the 1900, 1910, and 1920 U.S.
Censuses. We begin by extracting and cleaning place name strings in each file, then separately match thelist of place names in each historical census and the GNIS file to the place names in the WWI records,
allowing for phonetic di¯erences in the place name strings.
In some cases, the GNIS file will contain multiple locations with the same name in di¯erent counties
of a particular state. To identify the correct match, we make use of the WWI records linked to the1900 census. For each unique birth place string in the WWI records, we identify the modal county ofresidence in 1900. Next, we use GIS software to overlay the coordinates of locations in the GNIS file on1900 county boundaries, and prioritize GNIS locations in 1900 counties that match the modal county ofresidence for a WWI birth place string.47 We can also identify the correct location when the WWI record
provides both the location (city or town) and county of birth. When the matches to the place names in
the historical censuses and the contemporary GNIS file are in conflict, we prioritize matches based on
the quality of the linkage.
Although the instructions for the WWI Draft Registration Cards asked respondents to record theircity and state of birth, some respondents only reported the county and state of birth. In either case,without knowing both the city and county, it is not always possible to uniquely identify a location. In
some cases, a city in one county will have the same name as a di¯erent county in the same state. Suchambiguity can introduce significant measurement error to the determination of hurricane exposure status,
and thus we exclude these conflicting cases from the main estimation sample, in addition to ambiguous
cases where a city has the same name as the county in which it is located.
To account for changes in county boundaries over time, we assign all latitude and longitude coordinates to 1880 counties, the most recent census year prior to the start of our sample period. In specifications where exposure to hurricanes is defined at the county level we also include WWI records that only
contain the county of birth, which we identify by linking the birth place strings to lists of county names
from the 1900 to 1920 censuses. In all situations, we do not assign a birth location to a WWI record thathas potential matches in multiple counties.

A.3

U.S. Census of Population

The 1940 Census of Population data are extracted from the restricted-use complete count file createdby IPUMS-USA in conjunction with Ancestry.com, and accessed at the National Bureau of Economic
Research. A drawback of the 1940 census is that information on income is limited to wage and salaryearnings. When it comes to business, farm, or self-employment income, enumerators only asked for47

We require at least 50 percent of WWI records with a given birth place string to reside in the modal county of residence in
1900, and exclude birth place strings that appear fewer than ten times in the WWI records.

51

a “yes” or “no” response to the question of whether the individual had more than $50 in non-wage
income. In our final sample, 38 percent of individuals report having at least $50 in non-wage, non-salary
income. We use the IPUMS 1% sample of the 1950 census (Ruggles et al., 2015), which reported businessincome in addition to wage earnings, to impute business, farm, and self-employment income following
the procedure used by Feigenbaum (2015b).
We restrict attention to white males aged 39 to 57 with non-missing business earnings, years of education, occupation codes, and industry codes in the 1950 census, and convert reported business earnings
into 1939 dollars using the CPI.48 We estimate business earnings as a function of age and years of education fixed e¯ects, state of residence, as well as industry code and occupation code for the 1950 census
sample, and use the estimated coe˘cients to predict business earnings for the 1940 sample. Among individuals in the 1940 census who reported having at least $50 in business earnings, we assign total income
consisting of reported wage and salary income plus imputed business earnings, or only the latter in cases
where zero wage and salary income was reported.49
IPUMS Occupational Status Variables
Here we reproduce explanations of the occupational status variables constructed and described by
IPUMS (Ruggles et al. 2015):
OCCSCORE Occupational income score: Assigns each occupation a value representing the median
total income (in hundreds of 1950 dollars) of all persons with that particular occupation in 1950.
ERSCOR50 Occupational earnings score, 1950 basis: Reports the percentage of persons in occupations
having lower standardized median earnings than the respondent’s occupation. The scores werebased on the earnings levels of the employed civilian labor force aged 16 and above, excluding
persons who did not work in the past one year.
EDSCOR50 Occupational education score, 1950 basis: Indicates the percentage of people in the respondent’s occupational category who had completed one or more years of college.
NPBOSS50 Nam-Powers-Boyd occupational status score, 1950 basis: A measure of occupational statusthat gives equal weights to the median earnings and educational attainment associated with eachcategory in the OCC1950 variable. It can be interpreted as the percentage of persons in the civilian
labor force employed in occupations with combined levels of education and earnings below thatoccupation. The scores can vary from 0 to 100.

48
Specifically, we use a chained CPI with 1982-84 base year. We perform the same analysis for black males when we reportresults for the black population.
49
Following Feigenbaum (2015b), we use a multi-level model which produces estimated fixed e¯ects that are closer to themean in cells with smaller sample sizes (Hill and Gelman, 2007).

52

Online Appendix Tables
Table O1: Hurricanes Making Landfall, 1885 to 1899
Year

Date

Category

Strength (kph)States Exposed

Panel A: Hurricanes making continental U.S. landfall
1885
1886
1886
1886
1886
1886
1886
1887
1887
1887
1888
1889
1891
1893
1893
1893
1893
1894
1894
1896
1896
1897
1898
1898
1899
1899

Aug 25
Jun 14
Jun 21
Jul 01Aug 20
Sep 23
Oct 13
Jul 27
Sep 21
Oct 19Aug 17
Sep 23Jul 06
Aug 28
Sep 07
Oct 13
Oct 02
Sep 26
Oct 09Jul 07
Sep 29Sep 13
Aug 03
Oct 02
Aug 01
Aug 18

2
2
1
1
2
1
1
1
1
1
2
1
1
2
1
1
2
1
2
2
3
1
1
2
2
2

170
160
150
130
160
140
140
120
140
120
160
120
130
165
130
150
175
140
160
160
185
140
130
165
160
165

FL, NC, SC
TX
FL, GAFL
TX
TX
LA, TX
AL, FL
TX
LA
FL, LA, MS
LA
TX
GA, NC, SC
LA
NC, SC, VA
AL, LA, MS
FL, GA, SC
FL, GA, VA
FL
FL, GA, NC, SC
FL, LA, TX
FL
FL, GAFL
NC

Panel B: Hurricanes within 30km of continental U.S. landfall
1886
1888
1888
1898

Jul 17
Jun 16
Oct 09Aug 30

1
1
2
1

140
130
175
140

Panel A presents a list of Category 1 to 5 hurricanes making continental U.S.
landfall between 1885 and 1899. The category and wind speed associated witheach storm represent the maximum values recorded after the storm madelandfall. Panel B lists hurricanes that pass within 30 km of continental U.S.
landfall. The category and wind speed associated with each storm represent
the maximum values recorded at any point during the storm’s path. In PanelA, the date refers to the first landfall date of the hurricane, while in Panel B,
it refers to the first day on which a storm reaches hurricane strength.

53

Table O2: Match Rates of Birth Locations in the WWI Draft Registration Cards
All WWI Records(1)
All

Matched to 1940

(2)
(3)
(4)
30-100km 0-30km In utero

(5)
All

Final sample

(6)
(7)
(8)
30-100km 0-30km In utero

(9)
All

(10)
(11)
30-100km 0-30km(12)
In utero

Birth county unknown

0.042

0.000

0.000

0.000

0.036

0.000

0.000

0.000 0.000

0.000

0.000

0.000

Birth county known

0.067

0.064

0.109

0.109

0.066

0.064

0.109

0.109 0.000

0.000

0.000

0.000

City-(di¯erent)-county conflict

0.136

0.176

0.086

0.072

0.130

0.176

0.086

0.072 0.066

0.092

0.008

0.007

City-(same)-county conflict

0.122

0.132

0.148

0.139

0.117

0.132

0.148

0.139 0.162

0.154

0.180

0.169

Birth location known

0.633

0.627

0.657

0.680

0.652

0.627

0.657

0.680 0.772

0.754

0.812

0.824

1887988

87462

41359

2874 328455

87462

41359

2874 76196

52523

23673

1690

N

54

Notes: The table presents match rates of birth locations for individuals registered in the WWI draft and born in one of the nine Southern states exposed to hurricanes (columns 1 to4). In subsequent columns, we restrict attention to white males matched to the 1940 U.S. Census (columns 5 to 8), and records satisfying the restrictions imposed on our final analysis
sample (columns 9 to 12). Within each group of columns, we present match rates for four samples: individuals born in one of nine hurricane states, in locations 30 to 100 kilometers
away from hurricane tracks during the sample period, in locations 0 to 30 kilometers away, and individuals exposed in utero to hurricanes. In each column we first show the share of
WWI observations with an unknown birth location, or with only birth county known. City-(different)-county conflict refers to individuals born in a city or town in a county with thesame name as another county in the same state, while City-(same)-county conflict referes to individuals born in a city or town with the same name as the county in which it is located.

Table O3: Robustness – Results for the Black Population(1)
Years ofeducation(2)
Middle school
graduation(3)
Log income(1939 USD)

(4)
Worked50+ weeks(5)
Log income(1939 USD)

(6)
Worked50+ weeks

0.001(0.030)0.046⇤⇤(0.019)Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.057(0.126)0.002(0.013)0.003(0.032)0.045⇤⇤(0.019)Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes

55

Post-storm conception

0.037(0.124)0.001(0.011)

0.023(0.024)0.023(0.018)0.019(0.023)0.022(0.017)

Hurricane in utero

0.077(0.129)0.002(0.013)0.007(0.033)0.040⇤⇤(0.020)0.001(0.032)0.041⇤⇤(0.020)

Hurricane age [0,6] months

0.136(0.160)0.021(0.014)0.010(0.041)0.018(0.027)0.018(0.039)0.021(0.027)

Hurricane age [7,12] months

0.001(0.172)0.014(0.017)0.020(0.037)0.041⇤(0.022)0.023(0.035)0.040⇤(0.022)Hurricane age [1,2]0.064(0.138)0.017(0.011)

0.004(0.028)0.001(0.016)0.009(0.027)0.003(0.016)

Education controls
Mean of Y
P-value (Equality of coe˘cients)
N

No
4.954
0.950

No
0.105
0.625

No
6.121
0.892

No
0.583
0.007

Yes
6.121
0.823

Yes
0.583
0.007

24107

Note: This table reports results from specifications that are identical to those presented in Table 4, with the exception that the models are estimatedon a sample of blacks instead of whites. See the notes to Table 4 for more details on the sample restrictions and controls. Standard errors are clusteredat the county level. Point estimates marked ***, **, and * are statistically significant at the 1, 5, and 10 percent levels, respectively.

Table O4: E¯ects of Hurricane Exposure on Farm Values
Pre- and post-exposure years(1)[1880,1890](2)[1890,1900]

Hurricane exposure

0.175(2.731)0.773(1.320)Mean of Y
N

11.17
184

8.57
210

Note: This table reports estimates for the e¯ects of hurricane
exposure on average county-level farm values. Farm values aredefined as per acre fair market values of farms, including land,fences, and buildings, and are obtained from the 1880, 1890 and1900 Agricultural Censuses. The pre and post periods are 1880
and 1890 in column 1, and 1890 and 1900 in column 2. The
window of exposure to hurricanes is 1 to 2 years prior to thepost period and the area of exposure is a 0 to 30 kilometer-radii
band around the storm path. Each sample contains the set ofcounties within 100 kilometers of a hurricane path during thetreatment period, while each specification includes an indicator
for the post period and county fixed e¯ects. We exclude counties
with missing farm values in any of the census years.

56

Table O5: Robustness – Restricting the Control Group to Pre-storm Births
Control: Pre-storm births
(1)

(2)

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.067⇤⇤⇤(0.024)0.054⇤⇤(0.022)

Panel B: In utero or age 0 to 2 exposure to hurricanes
Hurricane in utero

0.078⇤⇤⇤(0.026)

0.064⇤⇤⇤(0.024)

Hurricane age [0,6] months

0.043(0.030)0.047⇤(0.025)

Hurricane age [7,12] months

0.007(0.031)0.011(0.028)Hurricane age [1,2]0.021(0.024)0.024(0.021)

Education controls
Mean of Y
N

No
6.909

Yes
6.909
36004

Note: This table presents results based on a sample which restricts attention to individuals
born before a sample period hurricane and those exposed in utero. In comparison to the main
specification, the indicator for post-storm conception is excluded from the estimation model
given that it is always equal to zero in the restricted sample. All regressions include the same
controls as the main specifications in column 3 of Table 4. Standard errors are clustered bycounty of birth. Point estimates marked ***, **, and * are statistically significant at the 1, 5,
and 10 percent levels, respectively.

57

Table O6: Robustness – Occupational Scores as Dependent Variable(1)
Occupational
income score(2)
Occupational
education score(3)
Occupational
earnings score(4)
Nam-Powers-Boyd score(5)
Occupationallog income

1.299⇤(0.778)0.025⇤⇤(0.012)

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.463(0.320)0.898⇤(0.484)1.530⇤(0.830)Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes

58

Post-storm conception

0.392(0.296)0.278(0.379)1.014(0.763)0.841(0.658)0.003(0.012)Hurricane in utero

0.418(0.345)0.949⇤(0.500)1.443(0.893)

1.255(0.822)0.027⇤⇤(0.013)Hurricane age [0,6] months

0.629(0.432)

1.381⇤⇤(0.652)

1.483(1.106)1.265(1.095)0.027(0.019)Hurricane age [7,12] months

0.149(0.458)0.954(0.631)0.285(1.240)0.410(1.070)0.002(0.017)

Hurricane age [1,2] years

0.403(0.321)0.584(0.468)0.608(0.753)0.364(0.686)0.011(0.010)Mean of Y
P-value (Equality of coe˘cients)
N

26.1
0.070

13.8
0.016

51.9
0.061

49.8
0.139

7.6
0.289
56723

58593

Note: In columns 1 to 4, the dependent variables are measures of occupational status created by IPUMS (Ruggles et al. 2015). See Table 4 fordetails on the sample and controls, and the Online Appendix for a description of the dependent variables. In column 5, the dependent variable
is an occupational income score computed by the authors based on average 1950 log income by three-digit occupation code, for white males aged
42 to 53 in the 1 percent sample of the 1950 census (Ruggles et al. 2015). 1950 income includes wage and salary income, in addition to business,farm, and self-employment income. Standard errors are clustered at the birth county level. Point estimates marked ***, **, and * are statisticallysignificant at the 1, 5, and 10 percent levels, respectively.

Table O7: Robustness to Trimming Tails of Income Distribution
(1)
Bot
5%

(2)
Bot
10%

(3)
Top
5%

(4)
Top
10%

(5)
Bot/Top5%(6)
Bot/Top10%

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.048⇤⇤(0.022)0.054⇤⇤⇤(0.019)0.059⇤⇤⇤(0.022)0.064⇤⇤⇤(0.020)0.033(0.022)0.041⇤⇤(0.017)

Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes

59

Post-storm conception

0.023(0.016)0.021(0.015)0.019(0.019)0.022(0.019)0.019(0.016)0.020(0.014)

Hurricane in utero

0.049⇤⇤(0.023)0.054⇤⇤⇤(0.020)0.061⇤⇤⇤(0.022)0.065⇤⇤⇤(0.021)

0.033(0.023)0.040⇤⇤(0.017)

Hurricane age [0,6] months

0.047(0.029)0.041⇤(0.023)0.055⇤⇤(0.026)0.032(0.024)0.051⇤⇤(0.024)0.022(0.019)Hurricane age [7,12] months

0.005(0.023)0.004(0.022)0.006(0.028)0.008(0.027)0.000(0.022)0.000(0.018)Hurricane age [1,2]0.003(0.018)0.002(0.016)0.010(0.021)0.014(0.019)0.003(0.018)0.003(0.016)

Education controls
Mean of Y
P-value (Equality of coe˘cients)
N

No
7.010
0.028
55871

No
7.079
0.007
52947

No
6.829
0.040
55909

No
6.764
0.023
52967

No
6.933
0.046
52929

No
6.940
0.016
47063

Note: This table reports results from estimating the specification shown in column 3 of Table 4, with di¯erent sample restrictions. Each column excludes agroup of observation in the tails of the sample income distribution: the bottom 5 percent (column 1), the bottom 10 percent (column 2), the top 5 percent(column 3), the top 10 percent (column 4), the top and bottom 5 percent (column 5), and the top and bottom 10 percent (column 6). See Table 4 for details
on the sample and controls. Standard errors are clustered at the birth county level. Point estimates marked ***, **, and * are statistically significant at the
1, 5, and 10 percent levels, respectively.

Table O8: Robustness to Restrictions on Range of Conception Years
Range of conception years in sample(1)
1886-1888(2)
1889-1891(3)
1892-1896

Birth (b) or conception (c) years dropped(4)c1889-1891(5)b1891-1892(6)b1896-1897

0.072⇤⇤⇤(0.021)

0.053⇤⇤(0.026)

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.027(0.040)0.087(0.111)0.073⇤⇤⇤(0.027)0.071⇤⇤⇤(0.022)

Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes
0.043(0.054)0.056(0.044)0.037(0.027)0.017(0.022)0.025(0.020)0.022(0.021)

Hurricane in utero

0.125⇤⇤(0.060)0.064(0.119)0.067⇤⇤(0.030)0.077⇤⇤⇤(0.023)0.076⇤⇤⇤(0.022)0.055⇤⇤(0.026)

Hurricane age [0,6] months

0.191⇤⇤(0.081)0.024(0.048)0.027(0.035)0.055⇤⇤(0.027)0.052⇤⇤(0.027)0.047(0.032)Hurricane age [7,12] months

0.148⇤(0.080)0.065(0.113)0.015(0.034)0.015(0.031)

0.012(0.029)0.006(0.027)

Hurricane age [1,2]0.159⇤(0.083)0.028(0.033)0.011(0.035)0.038(0.030)0.047⇤(0.025)0.016(0.023)

Mean of Y
P-value (Equality of coe˘cients)
N

6.873
0.380
13029

6.912
0.351
15982

6.918
0.033
29840

6.905
0.019
42869

6.905
0.004
53546

6.909
0.088
51129

60

Post-storm conception

Note: This table reports results from estimating the specification shown in column 3 of Table 4, with di¯erent sample restrictions. The first threecolumns split the sample into three groups based on the range of conception years: 1886 to 1888, 1889 to 1891, and 1892 to 1896, respectively. Columns
4 to 6 drop selective cohorts from the sample: the 1889 to 1891 conception years, births between 15 April 1891 and 15 April 1892, and births after
16 April 1896, respectively. See Table 4 for details on the sample and controls. Standard errors are clustered at the birth county level. Point estimates
marked ***, **, and * are statistically significant at the 1, 5, and 10 percent levels, respectively.

Table O9: Robustness to Dropping Individuals Employed in Agriculture(1)
Years ofeducation(2)
Middle school
graduation(3)
Log income(1939 USD)

(4)
Worked50+ weeks(5)
Log income(1939 USD)

(6)
Worked50+ weeks

0.048⇤⇤(0.023)0.013(0.016)

Panel A: E¯ects of in utero exposure to hurricanes
Hurricane in utero

0.055(0.115)0.023(0.017)0.052⇤⇤(0.024)0.015(0.017)

Panel B: Post-storm conception, in utero, or age 0 to 2 exposure to hurricanes

61

Post-storm conception

0.014(0.109)0.011(0.013)0.011(0.022)0.006(0.014)0.013(0.019)0.005(0.013)Hurricane in utero

0.079(0.123)0.024(0.017)0.058⇤⇤(0.024)0.019(0.017)0.051⇤⇤(0.023)0.016(0.016)

Hurricane age [0,6] months

0.154(0.151)

0.022(0.022)0.067⇤⇤(0.031)0.027(0.017)0.054⇤⇤(0.026)0.023(0.016)

Hurricane age [7,12] months

0.289⇤(0.171)

0.023(0.021)

0.027(0.029)0.006(0.018)0.002(0.028)0.001(0.017)

Hurricane age [1,2]0.033(0.112)0.009(0.015)0.008(0.024)0.008(0.015)0.010(0.023)0.008(0.015)

Education controls
Mean of Y
P-value (Equality of coe˘cients)
N

No
8.508
0.344

No
0.422
0.119

No
7.070
0.134

No
0.712
0.789

Yes
7.070
0.165

Yes
0.712
0.790

45032

Note: Each specification drops individuals working in the agricultural sector, which we identify based on 1950 IPUMS occupational codes which areassigned to individuals in the 1940 census. We define individuals in the agricultural sector as having a 1950 occupational code of 100 (farmers, owners andtenants), a 1950 industry code of 105 (agriculture), or a Duncan Socioeconomic Index value of 14 (farmers, owners and tenants). See Table 4 for details on
the sample and controls. Standard errors are clustered at the birth county level. Point estimates marked ***, **, and * are statistically significant at the 1, 5,
and 10 percent levels, respectively.

Online Appendix Figures
Figure O1: Kernel Density Plot for Log of Total Income by Exposure Status.5

Density

.4

.3

.2

.1

0
4

5
6
7
8
Log income (wages/salaries + business/farm), 1939 USD
In utero exposed

9[0,100] km, not exposed

Note: This figure shows kernel density plots for the log of total income (1939 USD) produced using the Epanechnikov kernel function. The
solid red line depicts the kernel density plot for individuals in the main estimation sample who were exposed in utero to a hurricane. The
dashed blue line represents the kernel density plot for individuals born within 100 kilometers of a hurricane that hit the birth location during
the sample period who were not exposed to a storm. That is, the sub-sample excludes individuals born within 30 kilometers of a hurricane who
were exposed to the storm in utero or between birth and age two, or were conceived up to one year after the storm. Total income combines
reported wage and salary income, in addition to estimated business, farm, and self-employment income. Both measures are truncated at $5000(1939 USD). For clarity, the figure excludes a handful of observations with income below $50.

Figure O2: Observations Exposed In Utero

Sample size
1
10100
US counties in 1880

Note: This figure plots the latitude and longitude coordinates of the city or town of birth for individuals exposed in utero to hurricanes that hit
the continental U.S. between 1886 and 1897, where birth locations are defined as exposed if they are within 30 kilometers of the hurricane path.
The coordinates (shown in blue) are overlayed on a map of U.S. county boundaries for 1880 and the diameter of the circles is proportional to
the number of observations at each location. The sample consists of all U.S.-born white males satisfying the restrictions described in Table 4.
Sources: Minnesota Population Center (2011) for boundary file; authors’ calculations based on WWI Draft Registration Cards (n.d.) and U.S.
Geological Survey (2014) for coordinates.

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Figure O3: E¯ects of Age 0 to 6 Month Exposure on Income, Dropping Storms One-by-one(b) Keeping Exposure from Dropped Storm

Coefficient on age 0 to 6 months exposure

Coefficient on age 0 to 6 months exposure(a) Ignoring Exposure from Dropped Storm

0

−.05

−.1

−.15

0

−.05

−.1

−.15
Storms dropped from sample one by one

Storms dropped from sample one by one

Note: Each line in the figure represents a 95-percent confidence interval, while the square marker denotes the point estimate, for the e¯ect ofage 0 to 6 exposure to hurricanes on the log of total income, from estimating Equation 2 by dropping one storm at a time from the sample.
The lines are ordered by the size of the age 0 to 6 month exposure coe˘cients. We follow two procedures for dropping storms to deal withindividuals exposed to multiple storms: ignoring exposure to the dropped storms (Figure O3a) and accounting for exposure to dropped storms
when individuals were exposed to multiple storms (Figure O3b). In either case, individuals born in locations within 100 kilometers of multiple
hurricanes remain in the sample. Twelve of the estimates in Figure O3b are identical to the main estimates in column 3 and Panel B of Table 4,given that the set of birth locations within 100 kilometers of some hurricane paths are a subset of birth locations within 100 kilometers of otherstorms, and thus no observations are dropped from the sample. Each specification includes the same controls as panel B of Table 4. Standard
errors are clustered by county of birth.

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