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Eyal Frank is an environmental economist.
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A natural experiment in economics refers to an empirical study in w...
RESEARCH ARTICLE SUMMARY
◥
ECOLOGICAL ECONOMICS
The economic impacts of ecosystem disruptions:
Costs from substituting biological pest control
Eyal G. Frank
INTRODUCTION:
Scientists have long theorized
that declines in biodiversity and continued deg-
radation of ecosystem functioning would lead
to meaningful negative impacts on human well-
being. Quantifying those impacts is challenging
because of the limited measurements available
on wildlife and plant populations as well as
the ethical and feasibility constraints involved
with randomly manipulating ecosystems at
scales that would allow for the testing of key
theoretical predictions. This work makes a con-
tribution to our understanding of the relation-
ship between ecosystem functioning and human
well-being by using a natural experiment—an
occurrence resulting from unexpected changes
in environmental conditions that approximates
a randomized control trial. Specifically, I use
the sudden emergence of a deadly wildlife dis-
ease in insect-eating bats—known as white-
nose syndrome—to quantify the benefits from
their provision of biological pest control. I val-
idate previous theoretical predictions that
farmers respond by substituting bats with in-
secticides; however, because those are toxic
compounds, by design, this substitution leads
to higher human infant mortality rates in the
areas affected by the bat die-offs.
RATIONALE: Ecologists have established, through
experimental and observational studies, that
insect-eating bats can limit crop pest popula-
tions. A long-standing prediction has been
that if bat populations were to decline, so
would their provision of biological pest con-
trol, and farmers would have to compensate
with insecticides. Epidemiologists and public
health experts have been concerned about
the health impacts of pesticides even before
Rachel Carson’sseminalworkinSilent Spring.
The wildlife disease that is killing bats, with
mortality rates averaging at above 70%, began
spreading in the United States in 2006 as a
result of an invasive fungus species. The grad-
ual expansion of the disease provides a setting
that approximates random manipulation of
bat population levels, which allowed me to
estimate how farm operations and human
health change differentially before and after a
location experiences a negati ve shock to bio-
logical pest control.
RESULTS: I used annual data at the county
level on insecticide use and estimated that
after the onset of bat die-offs, farmers in the
county increase their insecticide use by 31.1%,
on average. This demonstrates the substitu-
tion between a declining natural input and a
human-made input—providing the first empir-
ical validation of a fundamental theoretical
prediction in environmental economics. I pro-
ceeded to document that infant mortality rates
due to internal causes of death (i.e., not due to
accidents or homicides) increased by 7.9%, on
average, in the affected counties. This result
highlights that real-world use levels of insecti-
cides have a detrimental impact on health, even
when used within regulatory limits, which high-
lights the difficulties of assessing the public
health impacts of pesticides when regulating
them individually.
The staggered expansion of the wildlife dis-
ease supports the causal interpretation of the
results. Any additional alternative explanation
would need to change along the expansion
path of the wildlife disease around the same
timing of its expansion. In additional analyses,
I demonstrate that changes in crop composi-
tion, in other types of mortality, or in eco-
nomic conditions fail to explain the observed
results, even when controlling for fine-scaled
andflexibletimetrends.
CONCLUSION: These findings help validate pre-
vious theoretical predictions regarding well-
functioning ecosystems, where interactions
between natural enemies—insect-eating bats
and cro p pes ts—allow farmers to use lower
amounts of toxic substitutes. Not only are
these results informative about natural enemy
interactions generally, and biological pest con-
trol more specifically, they also highlight the
direct agricultural and health benefits that
bats provide. White-nose syndrome is but one
of many threats that bats face, including those
that are shared with multiple other species,
such as habitat loss and climate change.
Improving our understanding of how changes
in biodiversity affect human well-being will be
important when designing and implementing
conservation policies. These findings inform
ongoing efforts, such as pursuing the ambi-
tious goal to place 30% of land and marine
areas under protection by 2030, and highlight
the importance of continued monitoring of bio-
diversity levels, as in the assessments released
by the Intergovernmental Science-Policy Plat-
form on Biodiversity and Ecosystem Services.
▪
RESEARCH
The list of author affiliations is available in the full article online.
Email: eyalfrank@uchicago.edu
Cite this article as E. G. Frank, Science 385, eadg0344
(2024). DOI: 10.1126/science.adg0344
READ THE FULL ARTICLE AT
https://doi.org/10.1126/science.adg0344
Ecosystem Human health
Agricultural system
Expansion of fungus
causing white-nose
syndrome
Insect-eating
bats
Biological pest
control
Infant health
Decreases Increases Directly observed in the study Indirectly observed in the study
Insecticide
use
Schematic framework linking the ecosystem and human health as being intermediated by the agricultural system. The figure depicts the main elements in
the study and the theoretical predictions made regarding how (i) bat die-offs due to an invasive fungus species lead to lower provision of biological pest control,
(ii) in turn causing farmers to compensate with higher insecticide use, and (iii) resulting in negative impacts on human infant health. The two solid lines highlight the
observed relationships examined in this study.
Frank, Science 385, 1062 (2024) 6 September 2024 1of1
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RESEARCH ARTICLE
◥
ECOLOGICAL ECONOMICS
The economic impacts of ecosystem disruptions:
Costs from substituting biological pest control
Eyal G. Frank
1,2,3
Biodiversity loss is accelerating, yet we know little about how these ecosystem disruptions affect human
well-being. Ecologists have documented both the importance of bats as natural predators of insects
as well as their population declines after the emergence of a wildlife disease, resulting in a potential decline
in biological pest control. In this work, I study how species interactions can extend beyond an ecosystem
and affect agriculture and human health. I find that farmers compensated for bat decline by increasing
their insecticide use by 31.1%. The compensatory increase in insecticide use by farmers adversely affected
health—human infant mortality increased by 7.9% in the counties that experienced bat die-offs. These
findings provide empirical validation to previous theoretical predictions about how ecosystem disruptions can
have meaningful social costs.
R
ecent research has documented increases
in biodiversity losses (1, 2), which are
suggestedtohavedireconsequenceson
par with climate change (3–5). Econo-
mists and ecologists have repeatedly
theorized that the reductions in the abun-
dance and diversity of animals and p lants
(6–11) will result in large social costs (11–15),
but well-established empirical estimates for
these costs are sorely lacking. Despite grow-
ing interest in assessing the economic impacts
of ecosystem degradation (16–18), efforts to
quantify these costs are complicated by the dif-
ficulties in establishing changes in biodiversity
as the cause of economic losses (19, 20). Eco-
nomic theory on sustainability emphasizes the
importance of being able to compensate for
the decline in natural capital with human-
made substitutes (21–24). However, it remains
empirically unknown whether such substitu-
tions occur and whether they are equivalent
to natural inputs in their intended and un-
intended consequences.
In the absence of rigorous quantitative evi-
dence on the social costs of biodiversity losses,
we risk making ill-informed policy decisions
regarding the tension betwe en preser ving
nature or allowing additional economic de-
velopment at its expense. The vast scale of
ecosystems makes it challenging to isolate
the effect of a change in ecosystem functioning
on human well-being. An ideal experiment
would involve randomly manipulating wildlife
populations; however, this would rarely be
feasible or ethical in practice. As a r esult,
valuations of ecosystems have mostly relied on
nonexperimental settings (16, 25). The lack of
experimental or quasi-experimental variation
limits our ability to interpret valuation studies,
even in terms of orders of magnitude.
In this work, I test whether substitutions
between natural and human-made capital oc-
cur in the context of biodiversity losses. To do
so, I quantify the extent to which insecticides
can provide an economically viable substitute
for natural enemies as biological control agents
for insect pests. Bats are widely known to con-
sume large numbers of pest insects, such that
in the absence of pest control by bats, farmers
mightfacecroplossesunlesstheycompensate
with insecticides (26, 27). However, farmers
might not experience a decline in biological
pest control if other, unaffected species, such
as birds, can fill the role, and farmers might
not need to increase their insecticide use if
they respond by switching to less-vulnerable
crops or increasing plot diversity (27, 28). Al-
though the substitution from one form of pest
control to another
—from bats to insecticides—
might allow farmers to compensate for the
loss of biological pest control, it might also
cause several knock-on effects, such as detri-
mental health consequences, because agri-
chemicals, of which insecticides are a subset,
have been linked to negative health effects
(29–32).
My empirical approach relies on a natural
experiment to recover credible estimates for
the role that insect-eating bats perform in
agroecosystems and the benefits that they
provide. In my analysis, I use the 2006 emer-
genceofwhite-nosesyndrome(WNS)—afatal
disease caused by an invasive fungus species,
which has resulted in extremely high mortality
rates in bat populations in North America—as
a natural experiment. Specifically, I compare
how insecticide use, infant health, and farm
operations evolve after counties experience
bat die-offs relative to counties that have yet
to experience them.
Importance of bats in the provision of
biological pest control
Existing research in ecology documents that
bats provide biological pest control through
their high population size and predation rates
on a variety of insects, many of which are crop
pests. Insectivorous bats consume 40% and
above of their body weight in insects each
night (27). The contribution of insect-eating
bats to biological pest control has been well
documented across several settings, where
studies have demonstrated that bats limit the
growth of insect populations in forests (33, 34)
and agricultural plots (35), in particular limit-
ing insects that damage produce (36). Results
from field experiments that prevented bats
from accessing treatment plots showed an in-
crease in the densities of arthropods by 59 to
84% in pantropical forests, a 66% increase in
fungal growth, and a 56 to 68% increase in leaf
damage from moth pests in corn fields in the
US “Corn Belt” (33–35). Studies that mimic bat
presence through acoustic signals have docu-
mented lower infestation of insects on maize
plots and that insects sought fewer mating
opportunitie s, had lowered pheromone excre-
tion, and produced fewer larvae—validating the
capacity of bats to limit insect populations
beyond direct predatory pressure ( 27 ).
Damages from crop pests can substantially
reduce agricultural productivity. In the US,
about 13% of crops are estimated to be de-
stroyed by insects each year (37), which rep-
resents a loss of $27.6 billion a year. Previous
work has extrapolated estimates for single
crop types, small geographic areas, and a small
number of bat species to suggest that the value
of avoided costs due to the pest control pro-
vided by bats in the US is between $3.7 billion
and $53 billion a year (26 ). Although it pro-
vides a rough order of magnitude, the extrap-
olation assumes that out of t he variety of
management practices (27), farmers will com-
pensate for the decline in bats by using more
insecticides; however, the study does not em-
pirically validate this crucial assumption.
Emergence of WNS and its validity as a
natural experiment
WNSisaninfectiouswildlifediseasethatde-
velops in certain bat species after exposure to
an invasive cold-loving fungus species (Pseudo-
gymnoascus destructans). The disease receives
its name because the fungus grows around the
nose of the bat and creates a cluster of white
flakes (38). The main threat that WNS presents
to bats is premature awakening from their
hibernation. Facing scarce food supplies and a
need for an increased calorie intake because
of the low temperatures, infected bats gener-
ally do not survive the winter (39). By 2010,
mortality rates of infected populations were
between 30 and 99%, with a mean of 73%,
characterized by rapid disease dynamics that
RESEARCH
1
Harris School of Public Policy, University of Chicago, Chicago,
IL, USA.
2
Center for Economic Policy Research, Paris, France.
3
National Bureau of Economic Research, Cambridge, MA, USA.
Email: eyalfrank@uchicago.edu
Frank, Science 385, eadg0344 (2024) 6 September 2024 1of6
Downloaded from https://www.science.org on September 07, 2024
can lead to local extinctions within 5 to 6 years
(38).TheearliestevidenceofWNSintheUS,
from Albany, New York, dates to February of
2006 (39). As of 2024, 12 of the roughly 50 in-
sectivorous bat species in the US are nega-
tively affected by WNS [see (40) for the most
up-to-date details about affected species and
the spatial extent of WNS]. Sequencing the
fungus’s DNA, researchers have determined
that it originated in Europe and was likely
introduced accidentally by trade or travel (e.g.,
brought over on the shoes or backpacks of
hikers) (38, 39, 41).
After the initial detection of WNS, every year,
new counties become classified as WNS coun-
ties (Fig. 1). The pattern of county contagion
appears to follow the migration path of bats as
well as hiking trails along the Appalachian
Mountains. The expansion of WNS remains
a complex function of environmental condi-
tions, host genetics, and behavioral responses
of both bats and people (42), which makes it
difficult for ecologists to predict its expansion.
Because the fungus can survive even without
an available bat host, an exposed county re-
mains in exposed status even when bat popu-
lations have been extirpated. However , because
the fungus has an upper temperature limit of
20°C (43) and is extremely sensitive to ultra-
violet light (44), it is unlikely to survive outside
the caves that bats use during the day and
throughout the winter.
The sudden and unexpected emergence of
WNS provides a natural experiment that ap-
proximates an ideal experiment where coun-
ties get fully randomly assigned to high or low
bat population levels (45). In this setting, the
natural experiment plausibly provides “go od
as random” assignment with respect to county
and farm characteristi cs, but the spatial con-
figuration of counties plays a role in determ-
ining WNS status. This approach builds on
previous use of natural experiments in envi-
ronmental settings (46)aswellastheirsuit-
ability in studying keyston e species (6, 47). As
the fungus expands its range each year, more
counties become affected by WNS (fig. S1 sum-
marizes the number of WNS-susceptible bat
species across counties, and fig. S4 summarizes
the growth in the number of WNS counties
each year). This allowed me to estimate the
effects of declining bat populations on the
outcomes of insecticide use, infant mortality,
and farm operations (hereafter, outcomes of
interest) as a function of time since exposure
to WNS (see Materials and methods sum-
mary section for more details).
The effects of declining biological pest control
after bat population losses
Evidence for higher insecticide use after bat
population losses
Insecticide use increased in the years after
WNS detection relative to that in non-WNS
counties (Fig. 2A, positive event-time coeffi-
cients;totherightofthedashedline).Insec-
ticide use in WNS-confirmed and non-WNS
counties did not trend differently in the years
before the emergence of WNS (Fig. 2A, nega-
tive event-time coefficients; to the left of the
dashed line). The unexpected invasion of the
fungus and difficulties in predicting where
WNS will develop—approximating random
perturbations to bat population levels—support
a causal interpretation of these findings. Fo-
cusing on the post-WNS period and pooling
event-time coefficients in 2-year intervals al-
lowed me to increase the precision of the esti-
mates (table S3), which allowed me to reject
the null hypothesis of no change at the 95%
confidence level for each of the estimated
coefficients.
After WNS detection, insecticide use was
about 1 kg/km
2
higher in the WNS-confirmed
counties compared with the non-WNS coun-
ties, relative to the year before WNS detection.
After more than 5 years from initial exposure,
insecticide use was, on average, more than
2kg/km
2
higher. These results reflect sub-
stantial increases, above 25%, relative to a
mean of ~8 kg/km
2
. Increasing compensatory
behavior by farmers is consiste nt with both
increasingbatdie-offsaswellasfarmerslearn-
ing about the new pest pressure over time and
adjusting to it.
The results hold when I control for weather
controls or change the sample weights (table
S4),whenIallowtimetrendstochangeflex-
ibly at the state level instead of the census
region level (fig. S7), when I include substate
linear trends (figs. S2, S3, and S8), or when I
limit the sample to include non-WNS counties
that are closer to the WNS counties and, as a
result, are a potentially better comparison
group. I verify that fungicide and herbicide use
do not see a similar increase in WNS counties
(fig. S10) because they do not directly substitute
for biological pest control as insecticides do.
Evidence for higher infant mortality after bat
population losses
Insecticide use is one source of agrichemical
pollution that has been linked with negative
health outcomes (29–32). Wind and water ero-
sion can carry pesticides off of the target field,
generati ng off-farm exposure (48, 49). Insecti-
cides are transported away from the farms,
contributing to ambient levels of agrichem-
ical pollution. Overall detections of insecticides
in water samples across the US, including
those not adjacent to farms, are higher during
the agricultural production season of April to
September (fig. S11), reflecting potential ex-
posure to insecticides away from the targeted
fields.
To test for the potential health consequences
of increased insecticide use, I used county-level
data on annual infant mortality—commonly
WNS Epicenter
Detection
Year:
2006
2007
2008
2009
2010
2011
2012
2013
2014
Post-2015
Control Counties
Main Sample
Excluded
Counties:
WNS Post-2015
WNS Suspected
Spatial Spillovers
Fig. 1. Expansion of WNS across counties in the United States. This map (using the Lambert azimuthal
equal-area projection) shows the official detection year, as classified by the Fish and Wildlife Service
from 2006 to 2018, of WNS in each US county. The main sample (counties in states with a solid black
line) in the analysis compares the counties that are classified as WNS-confirmed counties (highlighted in
yellow to green) with counties in states that had at least one WNS-confirmed county by 2014 (shown in dark
gray). The main analysis excludes counties in states with the first WNS detection after 2014, where only
the fungus is detected and counties are WNS suspected, or the non-WNS counties that are adjacent to the
WNS-confirmed counties where spatial spillovers might affect the counties. In total, the sample contains
1185 counties, in 27 states, where 245 counties ever become exposed to WNS.
RESEARCH | RESEARCH ARTICLE
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used to study negative health impacts of en-
vironmental pollution (29–32)—due to internal
causes (not accidents or homicides). I found
that the internal infant mortality rate (IIMR)
increased in the years after WNS detection
(Fig. 2B). As with the results on insecticide
use, there does not appear to be a s ystem-
atic difference between WNS-confirmed and
non-WNS counties in the years before WNS
detection (the coefficients to the left of the
dashed line, pre-WNS years, are all close to
zero, contain zero in their 95% confidence in-
tervals, and do not appear to systematically
increase as the counties approach their switch
to confirmed WNS status). In addition, mothers
giving birth before and after WNS emergence
are similar in terms of a set of observable
characteristics (fig. S12).
In additional analyses, I confirmed that the
results hold when I control for weather , con-
trol for population shares in different age
groups, and use different sample weights
(table S5). For completeness, I also report
nearly identical results for the infant mor-
tality rate (IMR) that pools internal and ex-
ternal causes of death (table S6) and report
that there is no difference in the IMR due to
external causes of death (table S7). External
causes of death (accidents and homicides)
provide a placebo because we would have no
reason to expect that bat die-offs and increased
insecticide use would affect those outcomes.
The fact that IMR due to external causes of
death does not change in WNS counties also
helps to alleviate any concerns about changes
in vital statistics measurement that are some-
how systematically correlated with the spread
of WNS. Finally, I report no meaningful changes
to other birth outcomes, such as birth weight,
gestation, and other metrics of newborn health
(table S8). This is in accordance with other
studies that have detected higher IMR due to
environmental pollution but saw no change in
other birth outcomes (50, 51). On average, in-
secticide use increased by 2.7 kg/km
2
(Fig. 3A),
and the IIMR increased by 0.54 deaths per
1000 live births (Fig. 3B) after WNS emer-
gence, reflecting increases of 31.1 and 7.9%
relative to the mean levels in the population-
weighted sample. This suggests that when
insecticide use increased by 1%, the IIMR in-
creased by 0.25%.
These findings agree with previous estimates
on environmental pollution and infant health.
Using the same data for infant health, earlier
work has estimated that a 1% decline in am-
bient air pollution levels led to a 0.3% decline
in the IIMR (50). Work focusing on agricul-
tural water pollution in India found that a 1%
increase in agrichemicals in the water led to a
0.46% increase in infant mortality (28). Recent
studies examining the impacts of higher pes-
ticide use have estimated increases in infant
mortality, in deaths per 1000 live births, of 0.31
in the US and 0.93 in Brazil (31, 32).
Evidence for lower farm profits after bat
population losses
Changes in biological pest control could lead
farmers to change the amount of land under
cultivation. If the increase in insecticide use
fails to fully substitute for the quality of pest
control provided by bats, then crop quality
might degrade, leading to lower crop revenue.
To test for these impacts on farm operations, I
estimated the effects of WNS emergence on
chemical expenditures, land in agricultural pro-
duction, and crop sales that aggregate agricul-
tural crop production in dollar terms.
I did not find evidence that the percent of
land used for cropland cultivation or the per-
cent of harvested acres changed by a mean-
ingful amount (Fig. 3C). However, although
farmers might be planting and harvesting at
levels similar to those before WNS emergence,
the productivity and value of those crops might
decline because of higher insect pest pressure.
Damages from insect pests could lower the
quality of the agricultural output even when
overall production does not decrease substan-
tially, resulting in lower prices paid to the
farmer and lower revenue. Documenting the
effect of environmental conditions on crop
quality is challenging because we often lack
A
B
Fig. 2. Increase in insecticide use and infant mortality after WNS detection. (A and B) Each panel
shows the regression results of comparing insecticide use (A) or IIMR (B) between counties that experience
bat die-offs, before and after the bat mortality shocks, with counties that do not experience them. Each dot is
a coefficient from the regression, and each line shows the 95% confidence interval. Insecticide use is
weighted by the number of 2002 cropland acres, and the IIMR is weighted by the number of live births.
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detailed data on crop quality and prices paid
to farmers. However, previous work has estab-
lished that a decline in apple quality due to
extreme weather conditions has led to a larger
negative impact on farm revenue compared
with a reduction in yield (52).
I estimate that in the years after WNS detec-
tion, crop revenue—measured in thousands of
dollars per square kilometer—dropped by 7.96
(Fig. 3C), reflecting a decline of 28.9% relative
to the mean. The findings on lower crop sales
agree with recent research that has documented
a 2.5% decline in agricultural land rental rates
in counties where bat populations were nega-
tively affected by WNS (53). Chemical expendi-
ture, which accounts for all agrichemical inputs,
such as pesticides and fertilizer, dropped by
23.4%, but this is relative to a lower baseline
amount relative to crop sales such that the
change in levels is much smaller (a drop of less
than 1000 dollars per square kilometer).
Although I would have expected to see an
increase in chemical expenditure because of
the increase in insecticide use, the net drop in
chemical expenditure is consistent with the
notion that farmers maximize profits not yields.
In other words, farmers might increase their
insecticide use until the point where the mar-
ginal benefit is at least as high as the marginal
cost. Farmers could also substitute by reduc-
ing one chemical class and increasing another .
Because marginal benefits are often assumed
to be diminishing but marginal costs are in-
creasing, and we observe declining revenues,
the new optimal input use might be lower
and, as a result, lower overall chemical ex-
penditure. In additional analyses, I found in-
conclusive evidence for input substitution. I
estimated a sharp decline in herbicide use in
WNS-confirmed counites—consistent with
farmers substituting one input for another to
maintain costs constant—when weighting the
sample by baseline acres (fig. S10B) but not
when using population weights (Fig. 3A).
Through a variety of additional tests, I ex-
plored different dimensions of heterogeneity,
such as species richness—where counties with
more susceptible species might experience a
larger reduction in biological pest control—
and estimated WNS severity (tables S9 to S11)
and examined the magnitude of the spatial
spillovers (table S12) as well as the degree to
which spatial correlation affects the precision
of the estimates (table S13). I report results for
changes in a variety of crop yields (fig. S13);
yet, data limitations limit the interpreta tion of
those results. I confirm that it is spatial prox-
imity to a WNS-confirmed county and not the
lagged values of the outcomes that have pre-
dictive power for the expansion of WNS (table
S14), address potential confounders (figs. S14
and S15 and tables S15 to S23), and verify that
the results are not sensitive to the composition
ofthesample(figs.S9andS16)andthatthe
results are not driven by spurious correlation
(fig.S17)aswellasusingalternative estimators
(fig. S18 and table S24). Finally, I report analysis
for insecticide use that also incorporates toxic-
ity indicators (figs. S19 to S21 and table S25).
Discussion
Disruption to biological pest control has a mean-
ingful impact on human well-being. I dem-
onstrate how declines in insect-eating bat
population levels induce farmers to substitute
with insecticides, consequently resulting in
a negative health shock to infant mortality.
These findings provide empirical validation
to theoretical predictions in ecological, agro-
nomic, and epidemiological studies. An impor-
tant contribution I make relative to prior work
is using an emergent wildlife disease, WNS,
as an approximation to an experiment that
would have randomly manipulated biological
pest control by bats.
I evaluate the magnitude of the losses at-
tributable to the decline in bat populations
and estimate total agricultural losses, crop re-
venue, and chemical expenditure on the mag-
nitude of $26.9 billion (2017 dollars) for the
A
B
CD
Fig. 3. Average changes in pesticide use, infant mortality, and farm operations between WNS and non-WNS counties before and after WNS introduction.
This figure summarizes the differences between WNS counties and non-WNS counties after the exposure to WNS. Each panel reports the coefficient and 95%
confidence interval from a separate regression. (A) Results for the different pesticide classes. (B) Results for IMRs by all causes, internal causes, or external causes.
(C) Results for agricultural land areas. (D) Results for farm operations (total crop revenue, total chemical expenditure, and the difference between the two). All
observations are population weighted.
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WNS-confirmed counties across the 2006-to-
2017 period. To anchor this magnitude, con-
sider that crop revenue across the entire US in
2017 alone was around $190 billion (2017 dol-
lars). For the health damages, I use the US
Environmental Protection Agency (EPA)’srec-
ommended central mortality risk reduction
value (also referred to as the value of statistical
life) of $9.24 million (2017 dollars) and estimate
that the additional 1334 infant deaths reflect
damages valued at $12.4 billion. Combined,
these amount to damages of $39.4 billion, or
$1932.20 per capita, in the WNS-confirmed
counties from 2006 to 2017 (see the supple-
mentary materials for more detailed calcula-
tions). The findings in this Research Article
highlight two policy implications: monitoring
and regulating agrichemical pollution levels
and the conservation funding and protections
for bat populations.
Increasing the capacity to monitor chemical
pollutants could allow for more precise re-
search on the health effects of pesticide expo-
sure. Currently, legislation around pesticides
regulates compounds individually. This means
that various permutations are not tested, and
as a result, we have only observational data to
alert us to the effects of agrichemical mixtures.
Our ability to learn about these mixture effects
could greatly improve if pesticide use data be-
came more readily available at more fine-scaled
spatiotemporal resolutions.
Bat populations face a combination of threats
owingtolandusechangesthatdegradetheir
habitats, the expansion of onshore wind en-
ergy (54), and heightened sensitivity to the
impacts of climate change (55). Because bats
have a low fecundity, any los ses that we ex-
perience today might require several years, if
not decades, of effor ts to rebuild bat popula-
tions. If the costs of conserving bat popula-
tionsarelowanddamagesfromourcurrently
best-available substitutes for their biological
pest control—pesticides—are high, then con-
serving bats can be beneficial for society.
Additional funding for wildlife population
monitoring and evaluating mitigation strat-
egies for the adverse effects detailed above
could greatly contribute to stabilizing and re-
covering bat population levels.
Beyond the specific empirical setting in this
study, we stand to gain a few broader insights.
These findings will likely generalize to other
locations that will become negatively affected
by WNS—because it is still spreading—as long
as those are similar in terms of their ecosystem
and agricultural characteristics. Even outside
of North America, the results of this analysis
inform us about the value of biological pest
control, namely from bats. More broadly, the
evidence in this work demonstrates that na-
tural enemy interactions in ecosystems can
generate meaningful impacts on human well-
being. However, broad applicability has clear
limits. It is important to note that the esti-
mates regarding how farmers respond to bat
die-offs reflect short-term economic effects
and responses, to which farmers might adapt
differently over longer time horizons, espe-
cially if newer technologies become available.
It would be almost impossible, and ill-advised,
to use these results to evaluate how the vast
patterns of changes in biodiversity—e.g., the
declines in insect populations or the reintro-
duction of apex predators—create costs and
benefits through socioecological channels.
Contributing to a growing body of work on
quantifying the value of nature, this study dem-
onstrates the usefulness of quasi-experimental
methods to overcome the challenges in study-
ing the importance of species and ecosystem
functions. Disruptions to ecosystems and wild-
life populations present us with an opportu-
nity to learn more about what is lost in their
absence. Given the limitations of field experi-
ments, natural experiments allow us to gain
an approximation of those ideal experimen-
tal conditions. The implementation of quasi-
experimental methods in this work builds
and extends on previous work by combining
knowledge and tools from ecology and eco-
nomics to provide credible estimates for the
social costs of biodiversity losses and the path-
ways through which they operate .
Across many species and ecosystems, making
informed decisions about conservation poli-
cies and priorities requires a rigorous evidence
base. Even if manipulating wildlife popula-
tions across large areas is not feasible, we
can still use other sources of disruptions to
ecosystem functioning to learn about their
importance to humanity.
Materials and methods summary
I used ordinary least squares regression to link
the outcomes of interest at the county-year
level (see table S2 for summary statistics and
fig. S5 for a summary of the trends in each
time series). In the main sample, I include
counties that either get classified as WNS
counties or counties that do not get classified
as WNS counties and are at least one neigh-
boring degree of separation from WNS coun-
ties. In other words, I exclude non-WNS counties
adjacent to WNS counties. In addition, I re-
strict the sample to counties that reside in
states that had counties classified as WNS
counties by 2014. Finally, I exclude counties
classified as suspected of being affected by
WNS because their status is ambiguous.
To estimate how the outcomes develop in
the years before and after the arrival of the
fungus and the emergence of WNS in that
specific county, I include binary variables that
represent the leads and lags from the timing of
WNS onset (see fig. S6 for results that examine
delays in WNS detection). This research desi g n
is often referred to as stagger ed difference-i n-
differences. When counties switch from a non-
WNS to a WNS county, they enter the treat-
ment group, and they are compared with
counties that never enter the treatment group
or those that enter later in the sample. The
regression captures how the outcomes evolve
over time in two sets of counties (WNS and
non-WNS) before and after the change in WNS
status. Although counties have different spa-
tial configurations in terms of how many neigh-
boring counties are in WNS or non-WNS status,
the regression focuses on the temporal switch-
ing from non-WNS to WNS status and does not
adjust for the spatial structure of WNS in
neighboring counties. Specifically, I report esti-
mation results from a regression specification
of the following form
y
ct
¼ Sb
t
m
ct
þ l
c
þ d
rt
þ e
it
The dependent variable is at the county-year
level, except for outcomes obtained from the
agricultural census, which is available at the
county level in 5-year intervals. The set of bi-
nary indicators m
ct
areequaltoonewhenthe
county is t years away from becoming classified
as a WNS county; they are zero otherwise. This
means that they are always zero for non-WNS
counties. The regression performs a double-
demeaning at the county- and census region–
by-year levels. This is achieved by including a
set of binary variables for each county- and cen-
sus region–year pair , l
c
and d
rt
, respectively.
The standard errors are clustered at the coun-
ty level.
In the regression results, each coefficient,
b
t
, represents the difference in means between
WNS and non-WNS counties. These differ-
ences are interpreted relative to a reference
level of the difference in means in the year
immediately before WNS emergence. The in-
terpretation here is that small and imprecise
coefficients are capturing the pattern of WNS
and non-WNS counties evolving along parallel
trends (in other words, not having differential
trends before the onset of WNS), but if the
WNS counties begin to diverge from the non-
WNS counties, those coefficients become sig-
nificantly different from zero.
The key assumptions for causal interpreta-
tion of the coefficients obtained from this re-
search design are that (i) counties would have
had their outcomes evolve along parallel trends
in the absence of WNS exposure, (ii) the on-
going expansion of WNS is quasi-random con-
ditional on lagged outcomes, and (iii) non-WNS
counties are not experiencing spillovers from
the WNS counties. The first assumption is sup-
ported by, but cannot be directly tested, the
coefficients on the leads being close to zero
and not exhibiting a pretrend. The second as-
sumption is supported by results that fail to
find that lagged outcomes predict WNS sta-
tus (table S14). The third assumption is more
plausible after excluding the non-WNS counties
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th at are adjacent to WNS counties (see table
S12 for evidence of spatial spillover to adjacent
counties). For more details, see the methods
sectioninthesupplementarymaterials.
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AC KN OW LE D GM E NT S
I am grateful to D. Almond, G. Heal, S. Naeem, and W. Schlenker for
their advice and support. I thank J. Anttila-Hughes, B. Balmford,
A. Baum, Z. Burivalova, F. Burlig, T. Cameron, T. Carleton, J. Colmer,
A. D’Agostino, G. Dwyer, A. Ebenstein, G. Englander, R. Fishman,
M. Greenstone, M. Hardy, S. Hsiang, K. Ito, A. Jina, R. Kellogg,
S. Kolstoe, K. Meng, A. Missirian, N. Ngo, K. Oremus, Y. Reingewertz,
J. Rising, and Y. Shem-Tov for their helpful comments. I thank
S. Banjara, S. Gerstner, and P. Rodrigue for excellent research
assistance. I also thank T. Cheng, J. Reichard, and W. Stone for their
help in obtaining the data. All errors are mine. Funding: This project
was supported by a research fellowship from the Columbia Global
Policy Initiative, with the support of the Endeavor Foundation.
Author contributions: E.G.F. led all stages of the research design,
analysis, and writing. Competing interests: The author declares
that they have no competing interests. Data and materials
availability: All data (except for the health outcomes) and code are
available on Zenodo (56), and the data to replicate the figures are
available in the supplementary materials. Researchers can obtain
access to the health data used in the analysis by applying through the
US National Center for Health Statistics [see details at https://www.
cdc.gov/nchs/nvss/nvss-restricted-data.htm (57)]. License
information: Copyright © 2024 the authors, some rights reserved;
exclusive licensee American Association for the Advancement of
Science. No claim to original US government works. https://www .
science.org/about/science-licenses-journal-article-reuse
SUPPLEMENTARY MATERIALS
science.org/doi/10.1126/science.adg0344
Materials and Methods
Supplementary Text
Figs. S1 to S21
Tables S1 to S25
References (58–107)
Data S1 to S9
Submitted 28 November 2022; resubmitted 7 February 2024
Accepted 1 August 2024
10.1126/science.adg0344
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Here are some great articles summarizing the research findings of this article:
- https://www.nytimes.com/2024/09/05/climate/bats-pesticides-infant-mortality.html
- https://www.vox.com/down-to-earth/370002/bats-link-babies-death-study-white-nose-syndrome
- https://news.uchicago.edu/story/collapse-bat-populations-increased-infant-mortality-rate-study-finds
Eyal Frank is an environmental economist.
Here are some of his other papers: https://scholar.google.com/citations?user=eQyqPJIAAAAJ
>> "Biodiversity loss is accelerating, yet we know little about how these ecosystem disruptions affect human well-being. Ecologists have documented both the importance of bats as natural predators of insects as well as their population declines after the emergence of a wildlife disease, resulting in a potential decline in biological pest control. In this work, I study how species interactions can extend beyond an ecosystem and affect agriculture and human health. I find that farmers compensated for bat decline by increasing their insecticide use by 31.1%. The compensatory increase in insecticide use by farmers adversely affected health—human infant mortality increased by 7.9% in the counties that experienced bat die-offs. These findings provide empirical validation to previous theoretical predictions about how ecosystem disruptions can have meaningful social costs."
A natural experiment in economics refers to an empirical study in which the researcher takes advantage of naturally occurring events or circumstances that mimic the conditions of a controlled experiment. In such cases, the "treatment" is not intentionally applied by researchers but occurs due to external factors, such as policy changes, natural disasters, or other external shocks, which affect some individuals, groups, or regions but not others. This creates an opportunity to observe the impact of a particular variable or intervention as if it were randomly assigned, allowing for causal inference.
For example, if a government implements a new policy in one region but not in another, economists might compare outcomes in the two regions to estimate the policy's effects, treating the policy implementation as a natural experiment.
The key advantage of natural experiments is that they provide a way to establish causality without the ethical or logistical challenges of a controlled experiment.