Firm Adaptation in Production Networks:
Evidence from Extreme Weather Events in Pakistan
Clare Balboni

Johannes Boehm

Mazhar Waseem ∗July 18, 2024

Abstract
This paper considers how far private adaptation may reduce future vulnerability to climate risks.
Using data on monthly firm-to-firm transactions from Pakistan, we find that flood-affected firms
are more likely to relocate to safer ground, and shift purchases towards suppliers in less flood-prone
regions and reached via less flood-prone roads. The results indicate that firms are imperfectly informed about flood risk, and update their beliefs following floods. We quantify aggregate impacts
using a spatial model of endogenous production network formation. The findings suggest that climate change impacts will be mediated as firms learn from the experience of increasingly frequentdisasters.
Keywords: flooding, adaptation, firms, production networks, environment, transportation, remote sensing, climate change, Pakistan
JEL Codes: L25, O14, O18, O53, Q52, Q54, R11, R41∗

Balboni: Department of Economics, London School of Economics and Political Science, London WC2A
2AE, UK, c.a.balboni@lse.ac.uk.
Boehm: Sciences Po Department of Economics, Paris 75007, France, johannes.boehm@sciencespo.fr. Waseem: Manchester University Department of Economics, Manchester M13 9PL,
United Kingdom, mazhar.waseem@manchester.ac.uk. We thank Tatyana Deryugina, Rafael Dix-Carneiro, Kelsey Jack,
Gabriel Kreindler, Glenn Magerman, Julien Martin, Yuhei Miyauchi, David Nagy, Ezra Oberfield, Mar Reguant, Julien
Sauvagnat, Eric Verhoogen, Juanma Castro Vincenzi and Daniel Xu for helpful comments and Aaron Berman, Cathy
Cai, Coly Elhai, Niklas Filkorn, Helen Gu, Thuy (Peter) Le, Dexin Li, Sam Pomerantz and Ye Sun for excellent research
assistance. We thank TGIR Huma-Num (CNRS UAR 3598) for computational infrastructure support. We are gratefulto PEDL (ERG 5567 and 7253), STEG (LRG 620), and IGC (PAK-22228) for financial support. The views expressed
are not necessarily those of CEPR or FCDO.

1Introduction

Climate change presents a global threat to human populations and economic growth. Despite growing
policy and research focus on mitigating climate risks, it is now clear that mitigation efforts will beinsufficient to prevent many of their damaging effects. Foremost among these is the increased likelihoodand severity of extreme weather events (IPCC, 2021). Estimating the costs of climate change, anddesigning appropriate policies to moderate damages, requires an understanding of how those affectedby climate disasters respond to these changing circumstances (Carleton et al., 2022; Bilal and RossiHansberg, 2023). In particular in developing economies, where capacity for centralized policymaking
is often weak (Greenstone and Jack, 2015), the burden of adaptation often lies disproportionately withprivate actors. This paper considers how firms—which represent the central locus for the locationof economic activity and are of first-order importance for the welfare of populations—anticipate andadapt to climate-related shocks.
Estimating firm adaptation to climate change, and its role in shaping aggregate growth trajectories,
is challenging because both risk exposure and adaptation margins may involve complex network effects.
Firms are exposed to spatially concentrated disaster risk not only directly because of their production
taking place in risky locations, but also indirectly via exposure of their suppliers or buyers (Barrot
and Sauvagnat, 2016; Boehm et al., 2019; Carvalho et al., 2021), or transportation infrastructure thatlinks firms to their trading partners (Korovkin et al., 2024). Adaptive behavior—actions taken byfirms to reduce their risk exposure—can therefore take place along several margins: firms may exitor contract activities in risky locations, relocate towards less disaster-prone regions, adjust their mixof trading partners, or shift routes towards those less exposed to disaster risk. Capturing exposureand adaptation margins therefore requires detailed knowledge of production linkages, as well as aconvincing methodology to distinguish changes in expectations over supply partners’ outcomes fromchanges in costs or other determinants of supply chain formation.
Our empirical analysis provides evidence that firms affected by natural disasters undertake adaptive
production and sourcing decisions along all of the margins of location, supplier and supply route choicein the aftermath of major floods. We leverage detailed data on transactions between firms, as wellas measures of both firm- and supply route-level exposure to natural disasters, to attribute theseadjustments to forward-looking decisions over future risk exposure rather than the direct disruptive
impacts of flooding. This finding has crucial implications for our understanding of the role of climate
risk in firm decision-making, complementing a recent literature examining individual decision-making
and learning in relation to climate risks (Deryugina, 2013; Kala, 2017; Patel, 2023). While a worsening
trajectory of natural disaster risk will have damaging impacts for firms, our results suggest that thesewill be mediated by firms responding adaptively as more information becomes available. More broadly,
the complex adaptive behavior we identify is informative about the forward-looking behavior of firms
in the presence of risk, which plays a central role in a large range of economic and policy questions.
The context of our study is Pakistan, one of the countries most exposed to extreme weather
worldwide (Eckstein et al., 2021). We study firm and production network adaptation in Pakistanfrom 2011 to 2018 at a highly granular spatial and temporal scale using a series of novel datasets. We
leverage georeferenced monthly microdata on the near-universe of formal firm-to-firm sales transactions
to capture the key adaptation margins available to firms at a high frequency and level of precision.
We complement the transaction records with data on over six billion observations from GPS trackers
installed on more than 15,000 commercial trucks over the same period to measure the extent to which1supply routes are affected by natural disasters. Flood disruptions to firm activities and the road
network are measured by intersecting these with satellite-derived data on major flood events. To
capture how far responses to these events may reduce vulnerability to future floods, we supplement
this data with high-resolution measures of flood risk derived from a global flood hazard model.
We first document severe but short-lived disruption of firm activities and traffic induced by floodingof firm premises and roads. Sales and purchases of the mean flooded firm decline by 1.32% and 0.41%
respectively in the month of recorded flooding, though both recover within six months. Pronounced
increases in the probability of exit are observed following severe flood events. Flooding of roads also
leads to large but brief disruptions to traffic flows: mean truck speeds decline by 0.8km/hr and truckday counts by 16-20% immediately following floods, with reversion of both outcomes to pre-exposurelevels within a month. The core of our analysis then turns to the question of whether these significant
but temporary flooding disruptions induce firms to undertake longer term adaptive changes in orderto reduce their vulnerability to future flooding.
We provide the first micro-level evidence of firm-level adaptive relocation by studying whether
flooded firms relocate towards areas less prone to flooding. The results suggest that the average
flooded firm sees a 1.79% increase in the odds of relocating more than 10km away over the ten-year
study period relative to those that are not flooded. Importantly, this relocation is adaptive in the sensethat flooding induces firms to relocate systematically towards less flood-prone locations: the average
flooded firm that relocates more than 10km sees a 3.79cm reduction in the expected flood depth it
would experience during a 1-in-100 year flood. District-level gravity specifications also suggest that
relocating firms respond to recent flooding in deciding on a destination location, substantively avoiding
locations that have recently been flooded within origin-destination district pair moves. These firmlevel findings complement a recent climate migration literature which considers the response to extreme
weather events of populations (Boustan et al., 2012; Mueller et al., 2014), night lights (Kocornik-Minaet al., 2020; Elliott et al., 2015), and employment (Indaco et al., 2021).
Given that firms may be exposed to climate risk not only directly but also via vertical linkages,
we use transaction-level data to examine adaptation through supplier choice. Diversification may
ameliorate expected flood losses by reducing dependence on individual suppliers, and spreading risk
across suppliers with uncorrelated shocks (Cole et al., 2013; Meltzer et al., 2021; Boehm and Sonntag,
2022; Castro-Vincenzi et al., 2024). Consistent with this, we find that firms increase the number ofsuppliers from which they source following flooding of their suppliers, but that this response endures
for less than a year. Combining transaction-level data with data on flood risk reveals that these firms
more persistently shift the composition of their supplier base towards less flood-prone suppliers. This
adaptive behavior is also evident among a firm’s non-flooded suppliers – suggesting a role for forwardlooking adaptation rather than simply the mechanical effect of no longer being able to source fromflood-affected sellers – and persists for at least four years after flood exposure.1 These results suggestthat accounting for network-based adaptation margins is important, and demonstrate the sophisticatednature of firms’ adaptive responses beyond the direct flood exposure of production sites.
The vulnerability of the firm network is predicated not only on the flood risk of firms, but also on
the riskiness of the trading links connecting them. We use data on flood-induced road disruptions to
examine adaptation via firms’ choice of supply routes. Route-level specifications leverage the bilateralnature of the transaction-level data to fully isolate adaptive behavior by using buyer-seller, buyer1

This is consistent with extensive margin evidence from financial data that temperature and flood shocks at supplierlocations that exceed expectations may induce customers to terminate relationships and choose replacement supplierswith lower expected climate risk (Pankratz and Schiller, 2022).

2

time and seller-time fixed effects. These control for any direct effects of floods on firms and ruleout potentially confounding shocks that may affect flooded firms even after their sales and purchases
have recovered, such as local labor market disruptions or correlated cost shocks. The results suggestthat firms respond to short-lived flood-induced disruptions to road transportation by reducing theirdependence on supply partners reached via flood-prone routes. Despite pre-flood traffic flows being
restored within a month, firms do not switch back to sourcing from these suppliers once access isrestored. This provides our most cleanly identified evidence of firms undertaking long-term adaptationin response to transient shocks, and highlights the importance of accounting for route- as well as firmlevel adaptation margins.
Taken together, these results provide evidence that firms anticipate future flood risk and undertake
adaptive actions following exposure to major flood events. Quantifying the economy-wide implicationsof this adaptation for the vulnerability of the production network is challenging given that aggregate
effects will reflect firm connections via multi-step linkages and general equilibrium forces. We therefore
develop a quantitative spatial model of endogenous production network formation that captures firmto-firm linkages and general equilibrium effects in order to estimate aggregate impacts.
The model features firms that are subject to both idiosyncratic and aggregate flood shocks that
reduce firm productivity. Firms are imperfectly informed about the (joint) distribution of these risks,
but update their beliefs in response to flood shocks. The framework builds on recent advances inmodeling production network formation under uncertainty (Kopytov et al., 2022) to incorporate thisimperfect information. Before flood shocks are realized, firms search for suppliers in different locations, taking into account their beliefs over potential partners’ flood risk. These search decisions affectthe distribution of supplier draws that the firm receives. Once shocks have been realized, firms makeproduction and sourcing decisions conditional on these draws to minimize costs. We incorporate insights from the spatial trade literature, leveraging extreme value distributions to yield tractable gravity
equations describing sourcing shares (following Eaton and Kortum, 2002; Oberfield, 2018; and Boehm
and Oberfield, 2020, 2022). These gravity equations allow us to parameterize flood-induced productivity shocks and identify adaptive changes in firms’ supplier search decisions from observed changes
in sourcing shares, without imposing parametric assumptions about the belief-updating process. The
structure of the model therefore allows us to estimate the aggregate impacts of adaptive changes infirms’ supplier choice and to simulate policy counterfactuals.
We parameterize flood-induced productivity shocks at the level of locations comprising proximate
firms with similar supplier flood exposure, which update their beliefs in a similar way following flood
events. Accounting for direct productivity losses as well as general equilibrium impacts of supplier disruption, the estimated economy-wide increase in the household cost index for the floods in our sample
ranges from 0.05% to 0.3%. We use the model to estimate the impacts of adaptation undertaken in theaftermath of these floods via counterfactual simulations that consider how damages from subsequent
floods change if we remove adaptation following previous floods. This exercise reveals that adaptation
following the 2012 floods helped reduce damages from subsequent floods affecting similar locationsin 2013 and 2015, which would have been 5% and 1% higher respectively under sourcing shares thatprevailed before the 2012 floods. However, adaptation in the aftermath of the 2012 floods is estimatedto have worsened the impacts of the 2014 floods, which affected spatially disjoint regions in lower floodrisk areas. This highlights that post-flood adaptation need not always ameliorate damages from future
flood events, especially when idiosyncratic flooding affects areas that are not particularly flood-prone,
which may become increasingly pertinent as flooding incidence responds to climate change.

3

The paper’s findings suggest that natural disaster risk plays an important role in firm decisionmaking and that the realization of climate shocks influences firm expectations in a meaningful way.
This manifests via adaptation along location, supplier and route choice margins, with complex systemwide effects as a result of manifold inter-linkages in production networks. A significantly worsening
trajectory of flood events is predicted in Pakistan (World Bank and ADB, 2021) and globally (Kirezci
et al., 2020) over the coming decades as climate change unfolds. As such, these responses will haveprofound implications for how firms will adapt to an increasingly risky environment, a key factor
informing assessments of the costs of climate change. Our results indicate that firms are imperfectly
informed about climate-related disaster risk, highlighting the potential for policies addressing such
frictions to ameliorate climate damages.
The remainder of the paper proceeds as follows. Section 2 describes the datasets used in theanalysis. Section 3 provides evidence for the disruptive impacts of flood events on firm production
and road transportation in Pakistan. Section 4 examines firm and supply chain adaptation in theaftermath of flood events. Section 5 uses a quantitative model of production network formation andadaptation under supply chain uncertainty in our empirical setting to understand the importance ofadaptive decisions for aggregate outcomes. Section 6 concludes.

2

Data

The empirical setting for our analysis is Pakistan, one of the world’s most vulnerable countries to theeffects of extreme weather (Eckstein et al., 2021), where rapid industrialization is proceeding alongsideincreasing vulnerability to the effects of climate change. Floods are preeminent among these: thecountry frequently ranks in the top deciles for per capita flood losses globally (Guha-Sapir et al.,2022), with major floods involving severe disruption to firms and infrastructure. The 2022 floods
alone are estimated to have resulted in damages of $40 billion (PMO, 2022) (roughly 11.5% of 2021GDP), 30% of which are accounted for by damages to infrastructure and non-residential structures(World Bank, 2022). Transportation infrastructure is especially affected by flooding: the 2022 floods
damaged over 8000 miles of roads and 392 bridges (Congressional Research Service, 2022), while the2010 floods are reported to have damaged 10% of the country’s road network (World Bank, 2010).
In this context, the analysis draws on four novel georeferenced micro-datasets from 2011-2018 inorder to characterize flood-induced disruption to firms and production network linkages, and identify
adaptive adjustments at a fine temporal and spatial resolution. Firm-to-firm transaction data allows us
to identify production network linkages, disruption to firms and relationships, and examine adaptation
via firm location and supplier choice. We use GPS tracker signals from commercial trucks to identify
disruption to transportation routes and examine adaptation via trading route choice. We identify theflood exposure of firms and roads in the data by intersecting these geocoded datasets with satellite
images of flood extents. Finally, detailed data on flood risk from an advanced flood hazard model helps
us to characterize adjustments as adaptive to the extent they reduce the flood risk of firms’ premises
and supply network dependencies.
2.1

Firm transactions data

Data on firm outcomes comes from the near-universe of formal firm-to-firm monthly sales transactions
for all VAT-registered firms over July 2011-June 2018 from Pakistan’s Federal Board of Revenue (FBR).
At the firm level, these data contain information on reporting firms’ name, industry and address at the4beginning and end of the study period. The data also contain monthly information on all transactions
where at least one party is registered for VAT, as well as total sales, purchases, exports and imports asreported monthly by each firm. We construct three firm-level monthly sales and purchases variables:
one given by the firm’s reported sales (purchases); a second given by the sum of transaction-level sales(purchases) reported by the firm; and a third which aggregates the union of transaction-level purchases(sales) reported by the firm and its trading partners.
The data contains reports of all firms in Pakistan registered to pay VAT, which is required for allimporters, wholesalers and distributors, as well as manufacturers and retailers with revenue exceeding
10 million rupees in the previous tax reporting period and an annual utility bill above 800,000 rupees.2
This yields a raw dataset containing information on 419,517 firms which either self-report or arereported upon in the reports of VAT-registered firms.
We take a number of steps to exclude incomplete or potentially misreported transaction data. We
exclude firms that have been identified as—or transact exclusively with—‘invoice mills’, firms that
exploit breaks in the supply chain to purchase and sell VAT invoices without conducting any realbusiness (Waseem, 2019; Keen and Smith, 2006). This removes 4% of firms in the sample. Also
excluded are 29% of firms for which there is insufficient address information to geocode the firm’s
location and therefore for which we cannot identify flood exposure. A large fraction of the remaining
firms report very infrequently or not at all (the latter appearing in the dataset only by virtue of havingtransactions reported upon by their VAT-registered transaction partners). Given that measurement
for such firms is likely to be poor and that singleton observations will not be informative for studying
the effects of flooding, we also exclude firms that report at most twice in any transaction measure.3
The full set of sample restrictions reduces the firm count considerably to 73,336, but excludes firmsthat account for only 2.9% of aggregate sales and 3.4% of aggregate purchases.
The resulting data represents a large fraction of the economic aggregates reported in national
accounts. In the restricted sample, aggregate manufacturing value added accounts for 89% of reported
manufacturing GDP in the last year of the sample.4 To capture entry and exit of firms while allowing
for the potentially confounding effect of irregular reporting, we define a firm as entering on the dateof their first report (self-reported or reported by a transaction partner) if this is more than a year
since the beginning of our panel, and as exiting on the date of their last report if this is more than ayear from the end of our panel. All observations for a given firm before their date of entry or after
their date of exit are set to missing. Summary statistics describing the firms and transactions in the
restricted sample are included in Tables A.1 and A.2, respectively.
Address information for firms in the sample was used to geocode firm locations using the Google
Maps API.5 The location of firms in the sample is shown in Panel (a) of Figure 1 and displays a strong
concentration of firms in Pakistan’s major industrial provinces of Punjab and Sindh. For 60% of firms,
sufficient address information is available to geocode addresses in 2011 and 2019 separately.
2

These thresholds were raised from 5 million rupees in July 2016 and from 700,000 rupees in July 2015, respectively.
For the same reason, all transaction-level specifications restrict attention to firm-pairs for which we observe at least
one transaction across the study period.
4
Total value added in our restricted sample accounts for approximately 20% of total GDP. This is likely because thereported aggregates include sectors which are not subject to VAT—agriculture, certain services, and the informal sector.
5
For those firms for which no address information was available from the FBR’s firm transactions data, where possible
we used address information scraped from the FBR’s Active Taxpayer Lookup Portal. Where multiple addresses areavailable for a firm, we use the primary ‘business’ address. We drop a small number of firms reporting two businessaddresses which are more than 5km apart.
3

5

Figure 1. Spatial distribution of firms, roads, and flood risk(a) Firms(b) Roads(c) Flood risk

Notes: Flood risk in panel (c) is defined as the maximum across fluvial and pluvial flood risk, both measured as
the expected flood depth in meters for a 1 in 100 year return period.

2.2

GPS tracker data from commercial trucks

In order to study the disruptive effects of flooding on firm-to-firm trade via transportation network disruptions, we obtain high-frequency data from GPS trackers installed in more than 15,000 commercial
trucks in Pakistan from a large original equipment manufacturer. The data provider sells tracking devices and associated tracking and fleet management solutions to truck manufacturers, logistics
providers, industrial and insurance companies. The data comprises more than six billion observations
showing the precise location, timestamp, and speed of trucks traveling on Pakistan’s road network from2012 to 2018. As such, the data yield accurate information on truck supply routes, traffic conditions,and disruptions.
Figure 2. N-55 Indus Highway flooding disruption(a) Jul 27-30, 2015(b) Jul 31-Aug 1, 2015

Notes: The maps display the location and speed of trucks from GPS tracker data in the area surrounding the N55
highway near Vehova in Punjab Province before (panel (a)) and after (panel (b)) reported flooding.

Figure 2 displays the capacity of this data to capture flood-induced disruption to roads at anextremely fine spatial and temporal resolution. The Figure shows the area surrounding the N55
highway near Vehova in Punjab Province, which at 09:15 on 31 July 2015 was reported by Pakistan’s
6National Disaster Management Authority (2015) to have been hit by “floodwater coming from Koh-eSuleman Range” which “swept away a 300-foot portion of the highway” (Dawn.com, 2015). The left
hand panel shows normal traffic running along the north-south highway directly to the left of the Indus
River in the four days leading up to the flood from 27-30 July. The right hand panel shows the abrupt
cessation of traffic along the route in the direct aftermath of the flooding from 10:00 on 31 July to
10:00 on 1 August. In Section 3.2, we use weekly road edge level regressions to document a systematic
pattern of such flood-induced disruptions to road traffic in our sample.
We study flood-induced disruption to firm-to-firm supply routes by constructing firm-pair-routelevel measures of travel speeds and disruption over time. To do so, we obtain Open Street Map dataon Pakistan’s road network comprising motorways, trunk roads, primary, secondary and tertiary roads
and their links, shown in Figure 1b. These are split at road endpoints and intersections to yield anedge-level dataset onto which we project the GPS tracker observations according to the closest edge
within 10 meters of the observation coordinates.6 Consecutive observations are filtered out where
the between-observation elapsed time is more than 30 minutes (periods during which the truck islikely parked) or the Euclidean distance is more than 20km (from which sensible route information
cannot be inferred). Using the remaining data observations, we find the shortest distance between eachconsecutive pair of observations along the edge network and—based on the observations’ timestamps
at both points—infer the average speed at which the truck traveled on all edges between them. We
aggregate speeds first to the day-truck-edge level, and then by taking the mean to the week-edge level,also taking note of the number of truck-day observations within each week-edge (“day-truck count”).
Figures A.1 and A.2 suggest that the resulting edge-time level dataset captures travel speeds well.
Figure A.1 compares calculated speeds in the full sample to speeds reported by the trackers themselvesat the time of each observation, for each road type in 2012. This comparison demonstrates that
calculating travel speeds using the method described above overcomes selection bias in the reported
speeds arising from the fact that GPS trackers are disproportionately likely to report when vehicles
are starting, stopping, braking, or turning, which accounts for the mass of observations at very low
reported travel speeds. In contrast, calculated speeds follow a smooth distribution with a sensible
distribution by road type. Figure A.2 compares calculated speeds for an area of Lahore in 2015 to
those reported for 2010 in Japan International Cooperation Agency (2012), and finds a high degree ofoverlap in both the magnitude and spatial distribution of reported speeds.
The edge-week level data are used to construct the least-time route and travel time between eachbuyer-seller pair on average across non-flooded weeks, and during each week when flood events arerecorded. Buyer and seller firm locations are projected onto the road network and the least-time route
between them calculated using average edge-level speeds over the relevant period, weighting by edge
length.
2.3

Flooding data

Data on flood events in Pakistan from 2011-2018 are obtained from the United Nations Satellite Centre(UNOSAT) flood portal.7 This service provides satellite imagery of major flood events generated in
response to requests from organizations such as UN entities, member states, government offices andNGOs, most often to aid disaster response efforts.8 These images allow us to map the exact location
6

All observations with coordinates more than 10 meters from any road edge in our data are discarded.
http://floods.unosat.org/geoportal/catalog/main/home.page.
8
We cross-reference the floods identified from this source with major flood events identified in other key natural
disaster datasets (the EM-DAT dataset of the Centre for Research on the Epidemiology of Disasters, the Dartmouth
77

of floods as detected from satellites, from which we extract a reference water layer.9 Flood-affected
firms and roads are identified by intersecting the resulting flood areas with georeferenced firm androad locations.
For firm-level specifications, we aggregate satellite images to the monthly level, which yields a totalof 7 monthly flood events over 2011-2018. The aggregate extent of flooding during years in which weobserve flood events during our sample is shown in Figure A.3. We capture flooding of firm locations
using the maximum share of a 2km buffer surrounding the firm’s geocoded location that is flooded
during a given flood event. As described in Table A.1, using this definition 28% of firms in the sample
are ever flooded during the sample period and 4% are flooded more than once.
Given that roads are often disrupted by floods for shorter durations, and the extremely fine temporal resolution of the GPS tracker data, we consider flood-induced road disruption at the weekly
level. Satellite images grouped at the weekly level yield a total of 11 flooded weeks during the sample
period for which we observe GPS network data (2012-2018). Road network edges are intersected withthe union of flood polygons observed in each week. At the buyer-seller level, 46% of ordinary-time
shortest routes experience flooding at least once during the sample period.
2.4

Flood risk data

Given our focus on adaptation to flood risk, we supplement our data on flood exposure with data on
flood risk. These data come from Fathom-Global, which uses a global flood hazard model combinedwith detailed terrain and hydrography data. The resulting datasets comprise rasters at a resolution of
90 meters representing fluvial and pluvial flood risk (measured as the expected flood depth in meters)with return periods of 1 in 10 years, 1 in 50 years and 1 in 100 years.10 For each return period, we takethe maximum of the projected fluvial and pluvial flood risk. Panel (c) of Figure 1 maps the Fathom
flood risk across Pakistan for a return period of 1 in 100 years; equivalent maps for return periodsof 1 in 10 and 1 in 50 years are shown in Figure A.4. These maps demonstrate a significant degreeof overlap between flood-prone locations and areas with a high density of firms, which are shown inPanel (a) of Figure 1.
The flood risk of a firm location is calculated as the weighted average Fathom flood risk depth index
in the 2km buffer surrounding a firm’s geocoded location, cropped to erase the baseline water layer.
The distribution of firms’ flood risk for each return period is shown in Figure A.5. As expected, longer
return periods are associated with more density in the right tail, and in each case the distribution forfirms that are ever flooded during the sample is rightward shifted. Similar patterns are evident for the
distribution of flood risk of routes connecting firm pairs, calculated as the average Fathom flood risk
depth index of all edges along the route, weighted by edge length, as shown in Figure A.6.
Flood Observatory and Sentinel Asia) to confirm that major flood events described in these sources are captured inour data. Relative to these sources, the UNOSAT data provides the advantage of exact flood locations and extents asobserved from satellites during our study period.
9
The reference water layer comprises rivers, lakes and other existing bodies of water obtained fromhttps://download.geofabrik.de/asia/pakistan.html.
10Fathom-Global 2.0 is based on LISFLOOD-FP, a two-dimensional hydrodynamic model designed to simulate floodplain inundation over complex topography (Bates, 2010). The key datasets used are the MERIT-Hydro global hydrography dataset and the MERIT-DEM global terrain dataset, which have been corrected for urban developments (Yamazaki
et al., 2017; Yamazaki et al., 2019), as well as a database of flood defense infrastructure.

8

3

Floods and supply chain disruption

In this section, we present motivating evidence for the disruptive impacts of flooding on firm andnetwork activity. Understanding how flooding of firms and roads disrupts firm operations is an important outcome in its own right that has received limited empirical attention in developing country
contexts (Hu et al., 2019; Rentschler et al., 2021; Zhou and Botzen, 2021). This is especially pertinent
in Pakistan given the country’s extreme vulnerability to acute flooding (Eckstein et al., 2021) andunder-developed disaster insurance market.11 Analyzing the dynamic effects of floods on firm operations will also be informative for our subsequent examination of firm adaptation. If firms undertake
long-term actions in response to floods, this may reflect adaptive behavior or mirror persistent direct
impacts of flooding on firm operations. We use evidence on the duration of direct impacts of floods
from these specifications to help disentangle these two effects in Section 4.
3.1

Direct impacts of firm flooding

We first consider the direct impact of flooding of a firm’s premises on its operations, as measured bysales and purchases, using the following specification:
yit =

12
X

βτ FloodExtenti,t−τ + αim(t) + αiy(t) + αt + εit(1)τ =−12τ ̸=−2where yit denotes log declared aggregate monthly sales or purchases for firm i in month-year t; andFloodExtentit is the maximum share of firm i’s 2km buffer that is flooded during month-year t. αim(t) ,αiy(t) , and αt are firm-month, firm-year and month-year fixed effects respectively, which control for
firm-specific seasonality, firm-specific yearly shocks, and aggregate time trends.12 Standard errors are
clustered at the firm level. As we observe multiple instances of flooding for a small share of firms, we
restrict attention to each firm’s first observed flooding event-month during the study period in thisand all subsequent specifications unless otherwise noted. We choose the period two months before thefirm’s first recorded flood as the omitted reference period, and shade the period from τ = −1 to τ = 0
as the period during which the firm is likely to have first experienced flooding. This reflects the factthat there is a lag between the onset of flooding and the date at which UNOSAT satellites capture
flood extents.13
The results of estimating this specification, shown in Figure 3, display intuitive reductions in boththe sales and purchases of flood-hit firms in the direct aftermath of flooding events. The immediate
impacts are statistically significant and economically large: during the month of impact, sales decline
by 1.32% and purchases by 0.41% for the mean treated firm, which sees 1.31% of its 2km buffer
flooded.14 These large impacts are, however, relatively short-lived, with recovery of both sales andpurchases to levels close to their pre-flooding levels within six months.15 Reassuringly, trends are flat
11

An estimated 3% of damages caused by flooding and earthquakes is covered by insurance and risk retention funds
in any given year (ADB, 2021).
12
Results are robust to replacing month-year fixed effects with district-month-year fixed effects or Fathom flood risk
decile-month-year fixed effects, as demonstrated in Figures A.7 and A.8, respectively.
13
In Appendix C.1, we consider the robustness of results to using alternative estimators to address potential challenges
associated with two-way fixed effects regressions including treatment lags and leads with variation in treatment timing.
14
Results where FloodExtentit is replaced by an indicator variable capturing whether a firm sees 0-5%, 5-10% or morethan 10% of its buffer flooded are shown in Figures A.9 and A.10. These results suggest that estimated effects are driven
by firms that see a large share of their buffer flooded.
15
The fact that sales and purchases appear not to fully recover to pre-purchase levels is consistent with firms incurring9

in the full pre-treatment window for sales outcomes and in the period up to two months before recorded
flooding for purchases outcomes. The slight decline in purchases in the month before flooding, while
insignificant, may reflect anticipatory contractions of purchases once imminent floods are forecast, as
well as lags between the onset and satellite capture of flooding.
Figure 3. Impact of flooding on firm sales and purchases(a) Log sales(b) Log purchases

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales andpurchases as specified in equation (1). The unit of observation is a firm-month-year. The 95%
confidence intervals rely on standard errors clustered at the firm-level.

Given these sizeable impacts of flooding on firm operations, Figure A.11 considers whether floods
are sufficiently disruptive as to result in the exit of the worst-affected firms. The results in the pooled
sample show a modest positive impact on firm exit in the direct aftermath of flooding, while strong
effects are observed for some individual flood events.16
3.2

Direct impacts of road flooding

Flooding may disrupt firm and supply chain network activity not only via direct damage to firmbuildings, equipment and stocks, but also as a result of disruptions to the road network. Such effects
may be substantial: for instance, the World Bank (2010) estimates that the devastating floods of 2010
damaged 10% of Pakistan’s road network. Our GPS data provides a unique opportunity to study
the firm- and network-level effects of such disruptions given the fine-grained lens they provide intoflood-induced road closures. Given that roads closed due to flooding are often reopened rapidly, we
examine flood-induced road disruptions at the weekly level using the following specification:
yiw =

20
X

βτ · 1(i flooded at w − τ and w − τ ∈ y(w)) · FloodExtenti,y(w) + αi + αdw + εiw(2)τ =−10τ ̸=−2where yiw is an outcome for road edge i during week w; y(w) is the year of week w; FloodExtenti,y(w)is the share of the total road length of i that is flooded in the first week of flooding during y(w); αi
are road edge fixed effects; and αdw are district-week fixed effects.
To capture alternative measures of road disruption, we consider several outcome variables. Panel(a) of Figure 4 shows the impact on the mean speed of trucks traveling on the edge; panel (b) on
the log day-truck count; and panel (c) on an indicator variable denoting whether the road edge iscosts to change their supplier base adaptively following floods, see Section 4.
16
For instance, following the severe floods in 2014, the mean firm first treated in 2014 sees a 0.40 percentage point
increase in the probability of exit, as shown in the second panel of Figure A.11.

10

‘disrupted’, defined as having a day-truck count on the edge that is lower than the fifth percentile forthe relevant edge across all weeks.17
Figure 4. Impact of flooding on road traffic
4
3

0.2

0.2

0
−1

0

estimates

1

−0.2

−0.4

time relative to event
Note: N_obs = 13395395(a) Mean truck speed

0

−0.1

≤10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
≥2
0

≤10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
≥2
0

−2

0.1

time relative to event
Note: N_obs = 11704088(b) Day-truck count

≤10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
≥2
0

estimates

estimates

2

time relative to event
Note: N_obs = 11714830(c) Disruption (< 5th percentile)

Notes: The panels plot OLS estimates of the effect of road flooding on different traffic outcomes following equation (2).
The unit of observation is a road edge-week. Panel (a) excludes observations with day-truck counts < 10. Panels (b) and(c) drop edges for which the first percentile of day-truck counts is zero. The 95% confidence intervals rely on standard
errors clustered at the district-time level.

The results of these specifications paint a consistent picture of sizeable but brief disruptions totraffic induced by flooding of roads. The point estimates suggest that mean truck speeds at the roadedge-week level decline by 0.8km/hr in the week in which flooding is recorded, though estimates are
not statistically significant at conventional levels, and return to pre-flooding levels by the following
week. Day-truck counts, shown in panel (b), show a more pronounced decline in the range of 16-20%,
with reversion to pre-flooding levels within a month of flooding. A disruption indicator based on a
threshold of the fifth percentile of edge-level day-truck counts shows an increase of 11.7pp in the week
in which flooding is recorded, with reversion within a fortnight.
The results in this section suggest that floods have sizeable disruptive impacts on firm operations
and road traffic. While effects on firm exit are likely mostly permanent, the event study plots ofimpacts on intensive margin sales and purchases and road traffic are transient, persisting for a matterof only months or weeks respectively. This dynamic pattern is informative for our understanding ofpotential adaptive responses. If we see long-term firm responses to flooding of firms or roads, theresults in this section suggest that these are not driven by long-term disruption to intensive marginfirm operations or roads. In this context, we consider in the next section whether firm responses toflood events are consistent with adaptation.

4

Evidence for firm and supply chain adaptation

In this section, we turn to the key question of whether firms undertake actions following flood events
that are adaptive in the sense of reducing their vulnerability to future flooding. We consider several
potential margins along which firms may reduce their future flood risk in the aftermath of flood exposure. First, flooded firms may relocate towards areas that are less exposed to flood risk. Second, firms
17

The day-truck count is the number of different trucks travelling on a given edge during a given week, counting eachtruck more than once if they travel on the edge on different days of the week. For the specifications examining mean
speed in panel (a), we consider the set of edge-week observations for which we have at least 10 day-truck observations
with valid speed since mean speed is poorly measured when trucks pass very infrequently. The regressions (b) and (c)exclude edges where the first percentile of the day-truck counts is zero; these are edges that are infrequently traversedby trucks in our dataset.

11may adjust their choice of supply partners to lower indirect flood exposure, either via diversification bytransacting with a larger number of supply partners, or by shifting towards less flood-prone suppliers.
Finally, firms may respond to flooding of key supply routes by reducing their dependence on supply
partners reached via flood-prone routes.
The adaptation margins we consider may capture both firms’ forward-looking decision-making
to reduce their future vulnerability to flooding, and more mechanical reductions in flood risk if, forinstance, even random relocation of flooded firms (which on average have higher flood risk) should
lower the flood risk of the average flooded firm. Both forward-looking and mechanical responses reduce
firms’ flood risk and are therefore of interest in understanding firm adaptation to flood risk. However,separating mechanical from forward-looking adaptation is important in considering policies that may
help to facilitate adaptation. In Sections 4.3 and 4.4, we provide evidence that isolates forward-looking
adaptive behavior in supplier and route choice adaptation, and hence suggests that adaptive responses
at least in part reflect forward-looking actions to reduce future flood risk.
4.1

Location choice

We first consider the impact of flooding on firm relocation decisions for the 60% of firms for which
we have a geocoded firm location in both 2011 and 2019. In these specifications, we consider whether
flooding induces firms to relocate, and how far flooding prompts firms to move towards less flood-prone
locations.
Firm locations are geocoded from address strings associated with each firm in 2011 and 2019.
Small differences in the address strings (for instance the same street address being entered with andwithout a building number) may result in different geocodes being assigned in the two years evenwhen a firm has not moved. Summary statistics in Appendix Table A.3 reveal that there is a non-zerodifference between the 2011 and 2019 location of 68% of firms, an implausibly high relocation rateover an eight-year horizon which is likely predominantly accountable to these small discrepancies inaddress information leading to local discrepancies in geocode locations. Defining firm relocation basedon a threshold of 10km gives a more plausible relocation rate of 13%, so we use this as the thresholdfor defining a firm ‘move’.18
The following logit specification is used to examine the impact of flooding on the probability offirm relocation during the study period:P r (Movei ) = F (βFloodExtenti + αzd )(3)where M ovei is an indicator denoting whether firm i moved during the sample period; and F loodExtenti
is the maximum share of firm i’s 2km buffer that is flooded during the firm’s first experienced flood
during the study period. We consider specifications including district-level fixed effects αd and fixed
effects αzd at the level of district × decile of Fathom flood risk (with a 1 in 100 year return period)in order to restrict attention to within-district variation among firms with similar levels of underlying
flood risk. Standard errors are clustered at the district level.
The results, shown in columns (1) and (2) of Table 1, suggest that firm flooding increases theprobability that firms relocate during the sample period. In the specification with district fixed effects(district × Fathom flood risk decile fixed effects), flooding results in a 1.79% (2.10%) increase in
the odds of relocating more than 10km for the mean flooded firm, relative to 1 in 15 mean odds of18

Results are consistent using other thresholds, as shown in Tables A.4 to A.6.

12

relocating more than 10km among non-flooded firms.
Table 1. Impact of flooding on firm relocation and location flood risk

Move Dummy∆ Flood Risk

(1)

(2)

(3)

(4)

Max Share of 2km Buffer Flooded

1.583∗∗(0.754)

1.849∗∗(0.805)-1.952∗(1.007)-0.450(0.526)

District FE
District × Fathom 1 in 100 FE
R2
N

Yes
0.046
43,848

Yes
Yes
0.067
43,395

0.127
5,737

Yes
0.449
5,596

Notes: Columns (1) and (2) display logit estimates of the effect of flooding on the probability of relocating by >10km following equation (3). Columns (3) and (4) report OLS
estimates of the effect of flooding on the change in flood risk of firms moving by >10km
as specified in equation (4). Observations are firms geocoded in 2011 and 2019. Standard
errors (in parentheses) are clustered at the district level. R2 refers to McFadden’s Pseudo
R2 for columns (1) and (2). ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Firm relocation may help firms to adapt to flood risk if they relocate towards areas with lower
underlying flood risk. We investigate this using the following specification:△FloodRiski = βFloodExtenti + αzd + εi(4)where △FloodRiski measures the change in Fathom flood risk between firm i’s 2019 and 2011 addresses
in units of expected flood depth under a 1 in 100 year flood; with F loodExtenti and αzd as above.
Standard errors are clustered at the district level.
The results, shown in columns (3) and (4) of Table 1, suggest that flooding indeed induces firms
to relocate to less flood-prone locations. Using within-district variation, the mean treated firm thatmoved more than 10km, which has 1.94% of its buffer flooded, sees a 3.79 cm reduction in expected 1 in
100 year flood depth. Column (4) of Table 1 also displays a negative effects when restricting to within
district-risk decile variation, with an intuitive reduction in magnitude and statistical significance.
In a final specification relating to relocation, we consider evidence that relocating firms take into
account recent flood history in deciding on a destination location. Intuitively, this specification tests
whether flooded firms that relocate during the sample period are more likely to move to destination
areas that are flooded if, at the time when the relocating firm’s area was flooded, the destination area
had not yet been flooded.19 This is illustrated in Figure 5: restricting attention to firms that relocatefrom the same origin district to the same destination district, do we see that firms flooded in 2014
(who were in a position to have witnessed 2013 flooding) are less likely than those flooded in 2012
(who had not witnessed 2013 flooding) to relocate to areas of the destination district flooded in 2013?
19

Recall that firm addresses are only observed at the beginning and end of the study period, so we do not observe whena relocating firm moves. The strategy of this regression assumes that firms are more likely to move after having been
flooded. If that was not the case, the coefficient in equation (5) is unlikely to be non-zero. For locations in destination
districts that are not flooded during the sample period, td is set to a time period beyond the end of the sample. Firms
whose origin address is not flooded during the study period are dropped from the estimation.

13

Figure 5. Illustration of differential relocation based on destination flood history
OriginDestination

floodedin 2014notfloodedfloodedin 2012floodedin 2013

We examine this using the following gravity Poisson specification:
Xoto dtd = αod + αoto + αtd + β 1 (to − td > 12) + εoto dtd(5)where Xoto dtd denotes the number of firms that relocate from areas of an origin district o that are
flooded at time to to areas of a destination district d that are flooded at time td ; αod are origin district× destination district fixed effects; αoto are fixed effects for the area of origin district o flooded attime to ; αtd are fixed effects capturing destination areas flooded at time td ; and 1 (to − td > 12) is anindicator that takes the value one if the flooding of area oto post-dates that of area dtd by more than
12 months. Standard errors are clustered at the level of origin-destination district pairs.
The results of this analysis in Table 2 suggest that, within origin-destination district pairs, firms
relocating from origin district regions that are flooded more than 12 months after destination district
regions are indeed half as likely to relocate to the latter regions as earlier-flooded firms. Firms therefore
appear to take past flooding of destination locations into account when deciding where to move,systematically avoiding those destination regions that they have seen flooded.
Table 2. Impact of destination flood history on relocation flows

Number of Firms Moved
Destination flooded 12mo prior-0.735∗∗∗(0.254)

Origin × Destination FE
Origin × Flood Event (month) FE
Flood Event of Destination FE
Move Distance Restriction
N

Yes
Yes
Yes
>10km
1,412

Notes: The table displays the Poisson pseudo-maximum-likelihood estimateof the effect of flood history on relocation flows following equation (5). The
unit of observation is the area of an origin-district first flooded in a given
year-month paired with the area in a destination district which was neverflooded or first flooded in a given year-month. We only consider firms movingby >10km and location-pairs with positive flows. The standard error (inparentheses) is clustered at the origin-district-by-destination-district level.
∗p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

14

4.2

Diversification of suppliers

In addition to location choice, another margin along which firms might adapt to flood risk is in theirchoice of supply partners. If flooding increases the risk that a firm’s suppliers will be unable to meettheir commitments, firms may hedge this risk by diversifying their supplier base or shifting towards lessflood-prone suppliers.20 We first consider whether firms adapt to flood risk by increasing the numberof suppliers from which they source. This may help to reduce dependence on individual suppliers andspread risk across suppliers with uncorrelated shocks, in line with a literature examining diversificationin other contexts (Cole et al., 2013; Meltzer et al., 2021; Boehm and Sonntag, 2022; Castro-Vincenzi,
2022; Castro-Vincenzi et al., 2024).
We test whether firms diversify their supplier base in response to flooding of their own premises
using the following specification:
yit =

12
X

βτ FloodExtenti,t−τ + αim(t) + αiy(t) + αt + εit(6)τ =−12τ ̸=−2where yit denotes firm i’s log number of suppliers in month-year t; FloodExtentit is the maximum shareof firm i’s 2km buffer that is flooded during month-year t; and αim(t) , αiy(t) , and αt are firm-month,firm-year and month-year fixed effects respectively. Standard errors are clustered at the firm level.21
It may be more intuitive to expect firms to diversify suppliers in response to flooding of the suppliers
themselves rather than their own premises. We test this using the following specification, where thecoefficients of interest are the β1,τ terms, including controls for the firm’s own flood status:
ybt =

12
X

β1,τ SellerFloodb,t−τ +τ =−12τ ̸=−212X

β2,τ OwnFloodb,t−τ + αbm(t) + αby(t) + αt + εbt(7)τ =−12τ ̸=−2where ybt denotes the log number of suppliers of buyer firm b during month-year t. SellerFloodbt are thetreatment terms, based on the firm’s first observed supplier flooding event. Given that many firm-pairs
transact only infrequently (see Section 2.1), a buyer may be affected by flooding of those suppliers
from which it sources but with which it happens not to transact in the month under consideration.
In constructing the treatment variable, we therefore define a buyer firm’s suppliers as those firms
from which the buyer firm has made any purchases in the prior three months.22 SellerFloodb,t−τ isthe maximum share of the 2km buffer flooded across all suppliers that account for more than 10% of
firm b’s purchases within the three-month window. OwnFloodbt are controls for the firm’s own flood
status during the first observed supplier flooding event, based on the maximum share of firm b’s 2km
buffer that is flooded during month-year t. αbm(t) , αby(t) , αt are as previously, and standard errors
are clustered at the firm level.
The results of both specifications, shown in Figure 6, do not find evidence for diversification in
20

All specifications investigating supplier choice restrict attention to firms that did not relocate more than 10km overthe sample period, in order to remove the potential effects of relocating firms switching suppliers to those based in their
new location. All results are robust to including relocating firms, as shown in Appendix C.8.1. Supplier diversification
results further restrict the sample to cases where buyer and seller reports coincide exactly.
21
The findings are robust to instead considering
alternative measure of diversification given
P as an outcome variable2 an−1by the inverse Herfindahl index, defined by ( Nwhere N is the number of total suppliers,
i=1 (Share of Purchasesi ) )as shown in Figure A.12.
22
All results that use this assumption are robust to alternatively defining a buyer firm’s suppliers based on a six or
twelve month window, see Appendix C.7.

15

response to flooding of a firm’s own premises (panel (a)), but suggest that firms do diversify suppliers
in response to supplier flooding (panel (b)). In the case of the latter, for the mean treated firm, whose
maximally flooded supplier sees 1.37% of its buffer flooded, the number of suppliers increases by 1.74%
by three months after supplier flooding was recorded, with no evidence of pre-trends. This responseis relatively short-lived, with reversion to pre-exposure levels within a year.
Figure 6. Supplier Diversification: Impact of flooding on log number of suppliers(a) Own flooding(b) Supplier flooding

Notes: Panels (a) and (b) plot OLS estimates of the effect of own flooding and supplier flooding
on the log number of suppliers following equations (6) and (7), respectively. Observations are
firm-month-years whose 2011 and 2019 addresses are known and ≤ 10km apart. We restrict
attention to transactions for which buyer and seller reports coincide precisely. The 95% confidence intervals rely on standard errors clustered at the firm-level.

4.3

Choice of suppliers

Beyond changes in the number of a firm’s suppliers, another potential margin of adaptation is to change
the characteristics of their suppliers by shifting towards a portfolio of suppliers less prone to flooding.
We consider two dimensions of such a decision. In this section, we examine how far flood-affected
firms shift the composition of their suppliers towards suppliers located in less flood-prone regions. In
the next section, we consider whether floods affecting transportation infrastructure also induce firms
to reduce dependence on suppliers reached via flood-prone routes.
We study changes in the risk profile of a buyer firm’s suppliers if the buyer itself, or any of their
suppliers (again based on the preceding 3-month window), is flooded. This reflects the fact that firms
deciding on the risk profile of their supplier base may take into account both their experience of supply
chain disruptions caused by flooded suppliers, and their own direct experience of floods. The empirical
specification is as follows:∆ybt∗ = β1 OwnF loodbt∗ + β2 SellerF loodbt∗ + αd(b)t∗ + ϵbt∗

(8)where t∗ denotes the month-year of a flood event; OwnF loodbt∗ is the maximum share of buyer b’s
2km buffer that is flooded at t∗ ; SellerF loodbt∗ is the maximum share of the 2km buffer flooded at t∗
across all sellers which account for at least 10% of b’s purchases over the previous three months; andαd(b)t∗ are buyer district × time fixed effects. We additionally report results using buyer district ×time × buyer 1 in 100 year flood risk decile or buyer district × time × buyer industry fixed effects.
The set of observations consists of all firm-by-flood-year-month pairs (b, t∗ ) for which the 2011 and2019 addresses are known and less than 10km apart. The dependent variable, ∆ybt∗ , captures thechange in the sales-weighted average flood risk of all of b’s suppliers in the three months before versus

16

after flood exposure, given by:P∆ybt∗ =s∈Sb (t∗ ,t∗ +3],P

Ps∈Sb (t∗ ,t∗ +3]t∈(t∗ ,t∗ +3] Salesbst Risks

Pt∈(t∗ ,t∗ +3] Salesbst

P−s∈Sb (t∗ −3,t∗ ]PPs∈Sb (t∗ −3,t∗ ]t∈(t∗ −3,t∗ ] Salesbst Risks

Pt∈(t∗ −3,t∗ ] Salesbst(9)

where Risks indicates seller s’s expected flood depth under a 1 in 100 year flood; Salesbst represents
the total sales from seller s to buyer b in year-month t; and Sb (t1 , t2 ] is the set of b’s suppliers over(t1 , t2 ].
The results are shown in Table 3. Buyers do not appear to adjust the flood-risk composition of their
suppliers in response to flooding of their own premises, but do respond to supply chain disruptions
caused by flooding of their suppliers by shifting towards less flood-prone suppliers. The magnitudes
are sizable and quite consistent across specifications including different fixed effects; for instance, inthe central specification including district × time fixed effects, the mean treated observation (whichsees a maximum flood extent among its sellers’ buffers of 1.76%) experiences a 1.11cm reduction in
the sales-weighted average supplier flood risk for a 1 in 100 year flood event.
Table 3. Impact of supplier flooding on supplier flood risk

∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

-0.0648(0.0929)-0.631∗∗∗(0.165)-0.0913(0.0897)-0.650∗∗∗(0.169)-0.0908(0.118)-0.753∗∗∗(0.190)Yes
Yes
0.0115
144,566

0.0330143,861

Yes
0.0610139,302

Notes: The table reports OLS estimates of the effects of own and supplier flooding
on the change in suppliers’ sales-weighted average flood risk following equation(8). Observations are firm-by-flood-year-month pairs for which the 2011 and 2019addresses are known and ≤ 10km apart. Standard errors (in parentheses) areclustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

The shift towards less flood-prone suppliers may be driven by forward-looking adaptation, but isalso consistent with the mechanical effect of being forced to source less from flood-hit suppliers while
their operations are disrupted. To disentangle these effects, we consider whether flooding induces firms
to shift towards less flood-prone suppliers among the subset of their suppliers that are not flooded, andtherefore for which such mechanical effects are shut down. This provides the first opportunity to isolate
adaptation that derives from firms’ forward-looking decision-making to reduce future vulnerability toflooding, rather than also being consistent with more mechanical drivers of adaptation.
Table 4 estimates equation (8) where the dependent variable is calculated as the change in salesweighted average flood risk of buyer b’s suppliers that are not hit by a flood shock. While the coefficienton the supplier treatment is smaller than in the previous specification (meaning that some of the
reduction in supplier risk is mechanically coming from reducing purchases from the flooded supplier),
17

Table 4. Impact of supplier flooding on flood risk of non-flooded suppliers∆ Risk of Non-Flooded Suppliers(1)-0.115(0.0935)-0.272∗∗∗(0.0993)

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(2)
-0.108(0.0895)-0.281∗∗∗(0.0941)(3)-0.147(0.121)-0.268∗∗(0.108)Yes
Yes
0.0090144,423

0.0324
143,718

Yes
0.0586
139,164

Notes: The table reports OLS estimates following equation (8) of the effects of
own and supplier flooding on the change in sales-weighted average flood risk amongsuppliers that are not flooded (defined as less than 5% overlap of the 2km buffer withthe flood polygon). Appendix Figure A.9 shows that such firms see no reduction insales compared to the control group. Appendix Figure A.7 shows robustness checks
for varying thresholds in this definition. Observations are firm-by-flood-year-monthpairs whose 2011 and 2019 addresses are known and ≤ 10km apart. Standard errors(in parentheses) are clustered at the time × district level.
∗p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

it remains large and statistically significant. This suggests that, when a buyer experiences floodingof any of their suppliers, this induces them to shift towards safer suppliers even among the subset
of suppliers not disrupted by flooding. In terms of magnitudes, the mean treated firm sees a 0.48cm
reduction in the sales-weighted average flood risk among its non-flooded suppliers.
We next consider the persistence of the shift towards less flood-prone suppliers among firms whose
suppliers experience flooding, using the following specification:∆ybt =

48
X
τ =−12s.t. τ3 ∈N,τ ̸=−3

β1,τ SellerFloodb,t−τ +48
Xβ2,τ OwnFloodb,t−τ + αd(b)t + εbt(10)τ =−12s.t. τ3 ∈N,τ ̸=−3

where ∆ybt represents the change in sales-weighted average flood risk among suppliers of firm b (defined
as in equation (9)) from the previous three-month-window to that ending in the time t of the observation (b, t) (i.e., from (t − 6, t − 3] to (t − 3, t]); SellerFloodb,t−τ indicates the maximum share of the 2km
buffer flooded across b’s suppliers during b’s first observed supplier flooding event; OwnFloodb,t−τ isthe share of b’s buffer flooded during that event; and αd(b)t are buyer-district-by-time fixed effects.23
Given this specification, a short-lived shift towards less flood-prone suppliers would yield an initial
negative coefficient of interest β1,τ , followed by positive coefficients in later time periods as the buyer
reverts back towards more flood-prone suppliers. Conversely, a persistent shift would be consistent
with an initial negative coefficient, without evidence of positive coefficients thereafter.
23

We include ever treated observations only at the lags of interest τ(t) = −12, −9, ..., 45, 48 to prevent treated observations from confounding α\d(b)t .

18

Figure 7. Dynamic impact of supplier flooding on supplier flood risk

Notes: The panel plots OLS estimates of the effect of supplier flooding on thechange in suppliers’ sales-weighted average flood risk following equation (10).
Observations are all firm-year-months for which the 2011 and 2019 addresses
are known and ≤ 10km apart. The 95% confidence intervals rely on standard
errors clustered at the time × district level.

Figure 7 plots the coefficients of interest β1,τ for rolling three-month on three-month windows out tofour years after the date of first observed supplier flooding. The results are consistent with buyer firms
persistently shifting towards less flood-prone suppliers over this horizon. The first period coefficientis strongly negative, as firms with flooded suppliers shift their supplier mix towards less flood-prone
suppliers in the subsequent three months, consistent with the results of Table 3. Importantly, there is
no reversion towards higher flood risk suppliers up to four years later.24 A persistent response is also
evident when restricting attention to non-flooded suppliers, as shown in Figure A.13.
4.4

Evidence from route-level flooding

Floods may affect firm activities via transportation disruption as well as flooding of firm premises, as
shown in Section 3.2. We next consider whether firms adapt to flooding of transportation routes byreducing their dependence on supply partners reached via flood-prone routes.
The route-level specifications leverage the bilateral nature of transaction-level data to fully isolate
adaptive behavior from potentially confounding shocks that may affect flooded firms. In particular,
using variation from pairwise route-level flooding allows us to include buyer-time and seller-time fixed
effects, thereby absorbing any shocks to buyers and sellers, including those that may persist even after
flooded firms’ sales and purchases have recovered.25 We estimate event study regressions of the form:
ybst =

36
X

βτ ShareRouteFloodedbs,t−τ + ηage(b,s),t + αbs + αbt + αst + εbst(11)

τ =−12τ ̸=−2where ybst is an outcome at the buyer-seller-time level (sales in the (b, s) relationship at time t, or an
24

Figure C.39 presents results excluding firms which experience flooding of a supplier (accounting for at least 10%of purchases in the three months before the flood) in more than one flood event. The robustness of the results to thisrestriction indicates that the persistence of the effect is not driven by repeated exposures.
25
For instance, it is possible that, even once flooded firms’ sales and purchases have recovered, local labor market
or correlated cost shocks (e.g. through credit markets, see Choudhary and Jain, 2022) might continue to affect theiroperations and contribute to persistent changes in outcomes at the firm level.

19

indicator variable denoting whether sales are positive); and ShareRouteFloodedbst is the share of theordinary-time (i.e. during non-flooded weeks) shortest-time route between b and s flooded at time t.
We consider all events where the shortest-time route between b and s is flooded for the first time after
entry of b and s. ShareRouteFloodedbst is calculated at the weekly level and the maximum for weeks
during a given month is used to generate monthly-level variables. A set of indicator variables for theage of the buyer-seller relationship, ηage(b,s),t , is included given evidence for strong life-cycle effects inbuyer-seller relationships (see Figure A.14). αbs , αbt , and αst are, respectively, buyer-seller, buyertime, and seller-time fixed effects. As such, we identify outcomes from variation within buyer-seller
relationships, controlling for time-varying buyer and seller characteristics. In the extensive margin
specifications, the set of observations consists of all triples (b, s, t) where b and s transact at least
twice, and b and s have both entered by time t. In the intensive margin specifications, the set ofobservations (b, s, t) is all triples where s has positive sales to b at t.
In the baseline specification, we restrict attention to manufacturing firms only. This reflects thefact that transactions between services (which account for the majority of excluded firms under thisrestriction) and agricultural firms are unlikely to represent shipments of physical goods between firmpremises and as such disruptions to the route between firms may not be pertinent. The robustness of
the results when this restriction is not imposed – as well as to alternative sample restrictions outlinedin the discussion of robustness tests in Section 4.6 – is shown in Appendix Table C.27.
Figure 8 shows the baseline extensive margin results. After a flood hits the route between abuyer-seller pair, the likelihood of the relationship remaining active declines compared to non-flooded
relationships. Trends are flat in the year-long window prior to flood exposure, increasing confidence
that this effect is driven by the impact of flooding. To illustrate the magnitude of the estimates,a point estimate of -0.3 six months after treatment implies that the transaction probability in that
period declines by 0.12 percentage points for the median flooded route (which sees 0.4% of its length
flooded).26 The figure shows that the decline is persistent for at least three years, far beyond theduration of road disruptions, which typically last less than one month (see Section 3.2). Conditionalon a transaction occurring, we do not find any adjustment in the transaction magnitude following aflood (Figure A.15). This suggests that substitution away from supply partners reached by flooded
routes is driven by transactions ceasing rather than intensive margin reductions in transaction volumes.
26
For comparison, the unconditional probability of a relationship being active in our panel—meaning not before thefirst transaction of each firm pair—is 18.45%.

20

Figure 8. Impact of road flooding on extensive margin sales in buyer-seller relationship
0.4
0.2

P(Sales > 0)

0
−0.2
−0.4
−0.6
−0.8
−1
−1.2
<-12 -10

-5

0

5
10
15
20
Months after first flood of shortest path25

30

35 >36

Notes: The graph shows the response of the probability of sales
being positive in the (b, s) relationship around the first time
the shortest path between b and s gets flooded (after entry ofb and s) following equation (11). The sample consists of all
buyer-seller-weeks such that b and s are manufacturing firms, do
not relocate and transact at least twice. Regression conditions
on both b and s having entered, and includes b × s, s × t, andb × t fixed effects and months-since-first-sale dummies. The
95% confidence intervals rely on standard errors clustered at therelationship level.

These results provide strong evidence of adaptation using clean exogenous assignment of the treatment: controlling for any direct effects of floods on buyers or sellers themselves, as well as buyer-seller
fixed effects, short-lived flood disruption of transportation routes between buyer-seller pairs results inpersistent cessation of transactions between them.
4.5

Mechanisms underlying adaptive responses

The results in this section suggest that firms adapt to flood risk in the aftermath of flood events via
relocation towards less flood-prone locations, diversification and shifts in the supplier mix away fromthose in more flood-prone locations and reached via flooded routes. Distinguishing between alternative
mechanisms that may underlie these adaptive responses will be informative for policy that aims toinfluence firm behavior in relation to climate risks: for instance, whether and for how long information
treatments might be effective in inducing adaptation will depend on whether firms adapt as a resultof rational learning or behavioral biases based on recent experience.
Rational learning would suggest that firms affected by floods change their beliefs over the underlying
distribution of flood risk and adaptive actions reflect a rational response to this. Such channels havebeen studied in a recent literature examining individual decision-making in relation to climate risks(Lybbert et al., 2007; Moore, 2017; Kala, 2017; Patel, 2023), though evidence from firm behavior
remains scarce (Kremer et al., 2019). A second mechanism posits that floods instead change theimportance of flood risk in firm decision-making, for instance by increasing the salience of climate risk.
Such ‘availability bias’ might induce flood-hit firms to infer erroneously that they are subject to higher
flood risk relative to a firm with identical statistical information, simply by virtue of recent experience(Tversky and Kahneman, 1973; Kahneman, 2011; Bordalo et al., 2021), behaviors that have been
documented in individuals’ decisions to purchase weather insurance and responses to surveys about21

climate change (Gallagher, 2014; Turner et al., 2014; Karlan et al., 2014; Deryugina, 2013).27
While mechanisms predicated on experience-based updating should imply persistent responses,
availability heuristics would predict larger impacts from more recent floods, with ‘forgetting’ as flood
events recede into the more distant past. We observe firm and network behavior over eight years, a
relatively long timespan relative to the frequency of flooding. The evidence in Section 4.1 suggestingthat firms undertake adaptive relocation is consistent with a permanent response following a temporaryshock. Flood-induced adaptive shifts in supplier mix persist for at least four years (Figure 7), and shifts
away from suppliers reached via flooded routes for at least three years (Figure 4), without evidenceof attenuation in either case. While it is possible that behavior may revert over longer timescales, thefact that these adaptive responses persist for as long as is measurable in our data is inconsistent withsalience effects being first-order in the medium-run.
4.6

Robustness

Appendix C considers the robustness of the reduced form evidence on adaptive behaviors to using
alternative estimators that aim to overcome potential challenges with the use of two-way fixed effects
regressions including treatment leads and lags (Appendix C.1); excluding industries for which supply
disruptions of the nature considered in the analysis may not be pertinent (Appendix C.2-C.4); considering only transaction observations where buyer and seller reports coincide exactly (Appendix C.5);
and considering floods with return periods of 1 in 10 or 1 in 50 rather than 1 in 100 years (Appendix
C.6). The central results are all qualitatively robust to these alternative specifications.

5

Quantifying the aggregate impacts of adaptation

The evidence in the previous section suggests that flooding induces firms to undertake adaptive adjustments that reduce their exposure to flood risk. In this section, we quantify the importance of theseadaptive responses for the aggregate vulnerability of the production network to future flooding.
While the reduced form results in Section 4 are informative in identifying evidence for adaptation,a simple aggregation of these estimates is unlikely to yield an accurate estimate of their economy-wide
impacts. This results from the fact that aggregate effects will reflect firm connections via multi-step
linkages and general equilibrium forces, as well as direct impacts on affected firms.28 We therefore estimate the aggregate impacts of adaptation by constructing a quantitative spatial model of production
network formation and adaptation which models general equilibrium and indirect effects explicitly.
Section 5.1 outlines the theoretical model. Section 5.2 describes how we bring the model to thedata in our empirical setting, and Section 5.3 estimates the model to quantify the implications of
post-flood adaptation observed in our sample for the damages imposed by subsequent floods.
27
A third possible alternative is that floods lower the fixed cost of making changes that the firm may already havewished to make. Such a mechanism is potentially consistent with the adaptive relocation results in Section 4.1: firmsthat wished to relocate to safer areas but previously found the fixed cost of doing so too high may use the ‘opportunity’afforded by the need to rebuild to do so in a less flood-prone location. It is possible, though arguably less intuitive, toapply a similar logic to the shift towards safer suppliers described in Table 3 if there are large human capital or systems
costs to switching suppliers. Such a mechanism cannot, however, account for flood-induced supplier diversification(Section 4.2) and the finding in Table 4 that supplier flooding induces buyers to shift their non-flooded supplier mix
towards less flood-prone firms.
28
For example, a firm may substitute towards suppliers which are less likely to be directly flooded, but which may
nevertheless be subject to flood shocks because they purchase from flood-prone regions.

22

5.1

Theoretical model

The model builds on recent models of production network formation under uncertainty (Kopytov
et al., 2022) but allows for imperfectly informed firms that learn about underlying flood risk fromflood shock realizations. Firms are subject to idiosyncratic and aggregate flood risk, about which they
update their beliefs in response to floods. Before flood shocks are realized, firms search for suppliers
taking into account their beliefs over the flood risk of potential partner firms. Once floods haveoccurred, firms then choose suppliers to minimize costs conditional on the outcomes of their search.
We incorporate extreme value distribution assumptions in this framework (following Oberfield, 2018
and Boehm and Oberfield, 2020, 2022) in order to yield tractable empirical estimates of sourcing shares
which can be inverted to yield adaptive changes in supplier search decisions.29 Despite the model’s richmicrofoundations, this allows us to estimate the aggregate benefits of adaptation without imposing
assumptions about the nature of the belief updating process.
5.1.1

Model setup

The economy consists of N locations indexed by n. Location n contains an exogenous number offirms Jn . Each firm sells a good which is considered differentiated by the representative household,
but which is perfectly substitutable with goods produced by other firms when used as an intermediate
input in production.
5.1.2

Households

Households have constant relative risk aversion preferences over consumption of a bundle of goods
comprising individual varieties produced by firms in different locations. Households’ expenditure
shares on goods from different locations are assumed to be constant and given by βn :1 1−ρu(q) =q ,1−ρ

ρ > 0,

q=


N 
Y
qn βn
n=1

βn

Z
,

qn =

qn (j)

ε−1
ε

 ε

ε−1

dj

Jn

Utility maximization yields demand qn (j) for each variety and the ideal price index pn in each location:−εqn (j) = βn pε−1n (pn (j)) ,

Z
pn =

1−ε

pn (j)

1/(1−ε)
dj

Jn

,

p=

N
Y

pβnn

n=1

Isoelastic demand means that firms choose a constant markup over marginal cost, pn (j) = ε/(ε −1)cn (j), where cn (j) denotes the marginal cost of production of firm j in location n. The Lagrange
multiplier on the household’s budget constraint is λ = u′ (q)/p = q −ρ /p.
5.1.3

Production

Production takes place in two stages. In the first, firms search optimally for suppliers in different
locations given their beliefs over flood risk. This yields combinations of suppliers and idiosyncratic
productivity realizations (‘techniques’) that firms can use to produce. In the second stage, shocks are
realized and firms choose the technique with which they will produce to maximize profits. We startby describing the second stage, where technique draws and shock realizations are taken as given.
29

See also Eaton et al. (2022) for a related model with matching intensities.

23Second stage: Sourcing and production
Search results in the arrival of techniques ϕ, consisting of a supplier s and a match-specific factoraugmenting productivity z. Each technique describes a production function:yj (ϕ) = an(j) bn(j) ξj lj1−α (z(ϕ)xj )α(12)where lj and xj are the quantity of equipped labor and intermediate inputs respectively; z is thematch-specific productivity draw; an(j) is a deterministic time-invariant productivity level associatedwith the location n of firm j; bn(j) is a location-specific productivity shock common to all firms in n,interpreted as coming from floods; and ξj is a firm-specific idiosyncratic flood shock.
Suppliers set prices at their marginal cost cs when they sell to downstream firms, i.e. buyers havefull bargaining power. Trade is subject to location-specific iceberg costs such that, for each unit to be
used as an input in production, τn(j)n(s) ≥ 1 units must be shipped. Denoting the cost of one unit ofequipped labor by w, the marginal cost of production using technique ϕ is:
cj (ϕ) =1
cs (ϕ) α
w1−α τn(j)n(s)an(j) bn(j) ξj
z (ϕ)

(13)

In this setup, sourcing decisions depend on suppliers’ production costs cs , which in turn depend on
their own sourcing decisions, and so on. In order to characterize the aggregate equilibrium, we impose
two key functional form assumptions. Following Kortum (1997) and Oberfield (2018), we assume
that the distribution of match-specific productivity draws z is such that the number of techniquedraws where the supplier is in location n′ and that yield a match-specific productivity z greater thana threshold z̄ is Poisson distribution with mean mnn′ z̄ −ζ , where mnn′ describe search effort in thefirst stage. The parameter ζ governs the tail of the distribution of match-specific productivity draws:
higher ζ implies on average more similar draws, such that a buyer will be more willing to substituteto a different supplier when a supplier experiences an idiosyncratic cost shock. Second, we place aζ/αfunctional form assumption on the distribution of the idiosyncratic flood shock by assuming that ξjfollows a positive one-sided stable distribution characterized by its Laplace transform:


ζ/α
β
−uξj
E e
= eu

These assumptions allow us to characterize the distribution of firm production costs in each location:
Lemma 1. Conditional on the realization of the aggregate flood shocks b, the cost distribution of firms
in location n is Weibull: 
P (cj > c|b) = exp −  an(j) bn(j)

ζβ/α

w

"
X

1−α −ζβ/α

#β 
−ζ −ζ
mnn′ τnn′ c̄n′

 cζβ/α n′where:c̄−ζn =an(j) bn(j)ζw

1−α −ζ



!α
X
n′

−ζ −ζ
mnn′ τnn′ c̄n′



α
Γ 1−
β


(14)

Sourcing shares follow the gravity form, with search efforts mnn′ as bilateral trade flows shifters:24

Corollary 1. The expenditure share of location n on inputs from n′ is:−ζ −ζ
mnn′ τnn
Xnn′′ c̄n′
=P
−ζ
Xn
c̄−ζ
ñ mnñ τ(15)nñ ñ

The first stage of the firms’ production problem endogenizes search efforts mnn′ as the optimal
ex-ante investments in the face of uncertainty over flood outcomes, given beliefs over flood risk.
First stage: Search
Before location-specific flood shocks bn have been realized, firms in location n have beliefs over the
distribution of these shocks described by the information set In . Firms are owned by the representative
household and maximize profits π discounted by the households’ stochastic discount factor λ, subject
to a resource constraint. In the first stage, a firm j in location n chooses search efforts mnn′ to solve:
max E (λπj (mn1 , . . . , mnN )|In )
mnn′

(16)

s.t. g(mn1 , . . . , mnN ) = m̄mnn′ ≥ 0 for all n′
We assume that g is such that the solution matrix m(I) = (mnn′ (In ))nn′ to this problem is unique.
5.1.4

Equilibrium

An equilibrium of the economy is a matrix of search efforts m(I) and cost indices c̄n such that (i) m(I)solves the firms’ optimal search problem (16); (ii) conditional on the realization of shocks, firms choose
techniques to minimize costs and markups to maximize profits; (iii) conditional on the realization ofshocks, the representative household maximizes utility; and (iv) goods and labor markets clear.
Lemma 2. Let α > 0. Then for each realization of the aggregate shocks bn an equilibrium exists andis unique.
Changes in the household price index p in response to flood shocks or changes in search efforts m
can be characterized as a function of shocks, elasticities, and pre-shock equilibrium outcomes following
Dekle et al. (2007). Denoting ratios of a variable in one equilibrium to another x̂ = x′ /x yields:ˆ X, b̂, m̂
p̂ X, b̂, m̂ = β · c̄

(17)



ˆ X, b̂, m̂ satisfies:
where X ≡ (Xni /Xn )n,i and c̄c̄ˆn X, b̂, m̂= b̂−ζ
n"
X Xnn′
n′

5.1.5

Xn

m̂nn′ c̄ˆn′#−ζ −α/ζX, b̂, m̂

(18)

Dynamics

The static economy described above is played out in each time period, which are linked by the dynamics
of firms beliefs I over the distribution of floods. Changes in beliefs alter ex-ante search decisions for
suppliers, m(I), which in turn shift sourcing shares according to the gravity equation (15).

25

5.2

Quantitative implementation

This section describes how we take the model in Section 5.1 to the data in our empirical setting. Our
aim is to use the model-implied gravity equation (15) to identify adaptation as enduring changes insourcing shares, consistent with the evidence in Section 4 that firms persistently shift sourcing shares
towards less flood-prone suppliers following flood events. We then estimate the consequences of thisadaptation for the network’s aggregate vulnerability to future flooding in Section 5.3.
To estimate the model, we need to specify empirical counterparts for locations and time periodsin the model. Locations comprise groups of firms and are defined for each flood event by two firm
characteristics, chosen to reflect the model’s key assumption that all firms in a location n have thesame beliefs. First, location definitions reflect whether or not any of a firm’s suppliers experience
flooding of more than 10% of their buffer in the flood event under consideration.30 This reflects theevidence in Table 4 that firms whose suppliers are flooded update their beliefs differentially.31 Second,the definition of locations accounts for the district in which the firm is located, to permit heterogeneityin adaptation according to the proximity of flooding. Sales transactions between firms in each location
are aggregated in order to interpret them through the lens of the model.
Estimation of the model is based on three time periods around each flood event. The flooded period
is defined as the six month period starting when flooding is first recorded (consistent with the dynamics
of direct flood impacts in Section 3), during which the flood reduces TFP in affected locations. The
six months prior to this are defined as the pre-flood period, when no flood is present and bn = 1 forall locations n. The six months subsequent to the flooded period are defined as the post-flood period,
when the temporary disruptive effects of the flood have subsided and all observed changes in sourcing
shares are interpreted as being driven by changes in beliefs.32 Technological productivity levels an ,elasticity parameters and trade costs τnn′ are assumed constant over the three periods of each flood
event.33 Flood shocks b and ξ are realized independently across flood events.
Based on this specification of locations and time periods, the next section describes how we parameterize flood-induced productivity shocks. Section 5.2.2 then uses the model to identify adaptivechanges in firms’ search decisions over potential suppliers in response to each flood in our sample.
5.2.1

Estimating flood-induced productivity shocks

To quantify the consequences of adaptive changes in search decisions, we require a parameterizationof location-level productivity shocks induced by flooding. We assume that productivity shocks bn ineach location n are related in a log-linear way to ShareFloodedn , the share of firms in n experiencing
flooding of more than 10% of their 2-kilometer buffer:
log bn = η log(1 + ShareFloodedn )
30

(19)

A threshold of 10% is chosen to define flood status given non-linearities in the impact of flooding according to theshare of the firm’s buffer flooded, see Figures A.9 and A.10.
31
Here a firm’s suppliers are defined as those that account for more than 10% of the firm’s expenditures in the threemonths prior to flooding. We restrict attention to firms that report at least ten times in the 13 months of the panel thatprecede the first flood event in 2012 to ensure that changes in sourcing shares over the six-month periods considered in
the estimation are likely to capture true changes in sourcing behavior rather than noise resulting from sporadic reporting.
32
This definition of time periods ensures that, for each flood event, the pre- and post-flood periods span the same six
months of the calendar year, ameliorating concerns that differences in sourcing behavior may reflect seasonal effects.
33
Trade costs could be made stochastic akin to ξ, but as long as they are i.i.d across buyer-supplier pairs, it would
not change the identification and counterfactual.

26

We estimate η by substituting this expression into the gravity equation (15) for sourcing shares expressed in changes from the pre-flood period to the flooded period, during which time search decisions
mnn′ are assumed unchanged:


\
ζ
Xnn′ tζ= exp −ζ log ĉn′ + log c̄ˆn + log b̂n
Xnt
α
α

(20)

where variables with hats again denote changes from the pre-flood period to the flood period (x̂ =xduring /xpre for any variable x) and the changes in cost indices c̄ˆ are implicitly defined as a functionof b̂ and pre-period sourcing shares by:"#−α/ζ
X Xnn′ −ζc̄ˆn = b̂−1
c̄ˆ ′
n
Xn n
′

(21)

n

Estimation of equation (20) using observed sourcing shares, together with values for the parameters
α and ζ, can be used to obtain an estimate of η. We set the input share α equal to the averageannual share of reported purchases to sales, which is 0.77.34 The trade elasticity ζ is calibrated to be
4 following Simonovska and Waugh (2014).
This estimation yields an estimate of η = −0.42, which implies that a location where all firms saw
flooding of more than 10% of their 2-kilometer buffer would experience a 30% reduction in TFP.35 Thisestimate of η, together with the observed share of firms in each location n experiencing flooding of
more than 10% of their 2-kilometer buffer, yields the productivity cost of flooding bn in each locationfrom equation (19). These values are mapped for each flood event in Figure 9. While the majority oflocations do not experience direct reductions in TFP in a given flood event, the maximum decrease
across locations ranges from 20% to 29%.
34

In this calculation we ignore firms that report fewer than three times in a year, and firms that have purchase-to-sales
ratios exceeding 3. This value is larger than most materials shares reported in the literature because purchases fromfirms can also include capital.
35
We ensure that all observations of shares are finite and reduce the influence of outliers by estimating Equation (20)on the set of cell pair × events (n, i, t∗ ) where, prior to the flood, the purchases of n from i account for at least half apercent of n’s purchases, and winsorizing the change in purchase shares at the 99th percentile.

27

Figure 9. Estimated productivity shocks from floods in sample

Notes: The figure maps the productivity shocks log b̂ estimated from equation (19) as
described in this section. We obtain log b̂ at the district level by taking the average acrossboth locations in the district weighted by pre-flood period sales to households.

The impact of flooding on firm costs is likely to extend beyond direct location-level TFP reductions
via the general equilibrium effects of flood exposure of firms’ suppliers (as well as that of their suppliers’
suppliers, and so on). The total increase in firm costs in each location accounting for such effects is
calculated from Equation (21) and plotted for the flood events in our sample in Figure 10. While cost
increases are more widespread than the direct productivity impacts in Figure 9, the latter can be seen
to dominate the general equilibrium impact of suppliers’ exposures.

28

Figure 10. Estimated increases in firm costs across locations from floods in sample

Notes: The figure displays the increases in firm costs log ĉ estimated from equation (21) as
described in this section. We obtain log ĉ at the district level by taking the average acrossboth locations within the district weighted by pre-flood period sales to households.

The economy-wide impact of each flood event can be estimated as the increase in the households’
cost index p. This is calculated by aggregating location-level cost increases using Equation (17), where
the final demand shares βn are calibrated to the share of each location’s sales to out-of-network buyers.
The results yield estimated increases in the households’ cost index as a result of the 2012, 2013, 2014
and 2015 floods of 0.05%, 0.30%, 0.06% and 0.16% respectively.36
5.2.2

Recovering adaptation

Adaptation is captured in the model by changes in sourcing behavior resulting from changes in firm
beliefs once the direct disruptive impacts of flooding have passed. We identify changes in searchdecisions m̂nn′ of firms in location n over potential suppliers in location n′ using the gravity equation
36

To benchmark the magnitude of these estimates, estimated total annual direct economic losses from all categories
of natural disasters in Pakistan between 2000 and 2013 averaged 1.16% of national GDP, including substantial losses
from the severe 2010 floods and 2005 earthquake (ADB, 2021).

29

(15) expressed in changes between the pre- and post-flood periods:
\
Xnn′ tζˆˆ= exp log m̂nn′ − ζ log c̄n′ + log c̄n
Xnt
α

(22)

where variables with hats denote changes from the pre-flood period to the post-flood period (x̂ =xpost /xpre for any variable x). Consistent with the empirical result in Section 4.2 that flood-induced
diversification of suppliers is not persistent, we impose the restriction that, for all n, the sum ofeach firm’s log search efforts across upstream locations remains constant between pre- and post-floodP
37 The change in the cost indices as a result of adaptation, the c̄ˆ inperiods, i.e.
n′ log m̂nn′ = 0.
ˆ = c̄
ˆ (X, 1, m̂), following Equation (18).
equation (22), is given by c̄This system of equations can be solved directly for changes in search decisions m̂nn′ from the period
before to after each flood event, which characterize adaptation. These adaptive sourcing decisions
reflect the aggregate effect of firms’ changing exposure due to upstream firms exiting or moving awayfrom affected areas, and buyers choosing to purchase from less risk-prone areas, thereby capturing
all relevant adaptation margins highlighted in Section 4.38 Furthermore, this approach to identifying
adaptation is based on the central intuition that changes in beliefs over flood risk can drive changes
in sourcing behavior, but does not require us to take a stance on how floods reveal information
about flood risk or how firms update their beliefs. The first stage problem in Equation (16) could be
replaced by many alternative formulations to accommodate, for instance, forward-looking expectations,adjustment costs, or behavioral biases, without affecting the estimation of m̂nn′ and consequently thequantification of the impacts of adaptation. The approach also requires only minimal data—only the
pre- and post-flood sourcing shares are necessary—and elasticities α and ζ which are well-studied in
the literature.
5.3

Estimating the implications of adaptation for the damages from subsequent floods

The central aim of the model is to estimate the aggregate implications of adaptation undertaken byfirms in the aftermath of floods for the vulnerability of the production network to future flooding.
In this section, we use the estimates of flood-induced productivity shocks and post-flood changes insearch decisions from Section 5.2 to quantify the impacts of observed post-flood adaptation for the
response of the economy to subsequent floods.39
We consider the example of adaptive changes in sourcing shares undertaken following the observed
flood in 2012, and estimate the impacts of this adaptation for the damages imposed by subsequent
floods in the sample. We use the model to quantify this by estimating the change in the household
price index resulting from a subsequent flood (for example, the flood in 2013) in two scenarios: onein which the sourcing shares are those that prevailed in the period before the 2012 flood, and one inwhich the sourcing shares correspond to those in the period after the 2012 flood.
We recover adaptive changes in sourcing behavior following the 2012 flood as described in Section
5.2.2. Estimation of Equation (22) reveals that the 2013 flood would have resulted in a 5% higher
37

P′
This assumption corresponds to a constraint of g(mn1 , . . . , mnN ) =n′ log mnn = log m̄ in the firm’s search
problem described in Equation (16). A microfoundation for this constraint could be that the manager needs to spend
log m units of time to search for a mass m of potential suppliers in a location, and the total time available to the manager
in which to search for suppliers is constant.
38
The model holds fixed household sourcing shares across locations β given our focus on firms’ adaptive decisions.
39
These effects are difficult to identify using reduced-form regressions given that firms undertake adaptive actions
following exposure so that subsequent treatment is potentially a function of prior exposure.

30

increase in the household price index in the absence of adaptation following the 2012 flood, as captured
by changes in the pre- to post-2012 flood sourcing shares.40 Similarly, damages from the 2015 flood
would have been 1% higher. While this suggests that adaptation in the aftermath of the 2012 flood
helped in ameliorating damages from the subsequent floods in 2013 and 2015, this need not be thecase for all future flood shocks. In contrast, adaptation following the 2012 floods relocated sourcing
activity towards areas that would subsequently be affected by the 2014 floods, and as a result damages
from the 2014 floods would have been 4% lower without adaptation following the 2012 flood.
To understand these patterns, consider the spatial distribution of flood impacts shown in Figures
9 and 10. The 2012 and 2013 floods can be seen to affect very similar areas. Consistent with this,sourcing share changes undertaken following the 2012 floods shift activity away from regions that will
face flood exposure in 2013, and as such attenuate damages from a 2013 flood scenario. Conversely, the2014 floods afflict quite different regions, such that adaptive changes in sourcing shares following the2012 floods are associated with higher damages in this case. The 2015 flood is intermediate between
these two cases, affecting both areas that were and were not flooded in 2012, so that adaptation
following the 2012 floods had more muted protective impacts with respect to the damages imposed bya 2015 flood scenario.
The economy-wide aggregate figures mask substantial heterogeneity in the effects of adaptation
across regions. While overall damages in a 2013 flood scenario would have been 5% higher in theabsence of adaptation following the 2012 floods, flood damages would have been at least twice as large
in four regions. The benefits of adaptation are also widely felt in this case, with 94% of locations
seeing higher estimated damages in the absence of adaptation. Conversely, aggregate damages in a2015 flood scenario would have been 1% higher without adaptation following the 2012 floods, but witha smaller interquartile range of location-specific changes from -1% to 7%, and only 66% of locations
seeing benefits from adaptation.
The heterogeneous impacts of adaptation following the 2012 flood across subsequent floods in thesample raises a question as to the conditions under which post-flood adaptation may be expected tohelp or hurt in the face of future disasters. Some guidance on this question may be derived fromconsidering how far the locations flooded in each of the floods in our sample coincide with regions ofhigh flood risk in the Fathom data. Intuitively, for the majority of floods, the flood risk distribution
among flooded firms is rightward shifted relative to that of their non-flooded counterparts, as shownin panels (a), (b) and (d) of Figure 11. Flood events are, however, stochastic, and may also hit lower
flood risk areas. Indeed, panel (c) of Figure 11 suggests that firms affected by the 2014 flood areon average less flood-prone than those that are not. This is exactly the flood for which adaptation
following the 2012 flood worsened outcomes – and is the outlier in other pairwise comparisons of the
impact of post-flood adaptation on the damages from subsequent flood events (see Appendix Table
A.8). This suggests that post-flood adaptation may be more likely to reduce damages from future
floods on average where floods affect flood-prone regions, but that the converse may be more likely for
idiosyncratic events that affect areas that are not especially flood prone.
40

Aggregation weights locations by their share of out-of-network sales in the period preceding the adaptation year.

31Figure 11. Density of sales across 1 in 100 year flood risk by flood exposure(a) 2012(b) 2013(c) 2014(d) 2015

Notes: The figure plots the density of sales by flood exposure in different flood events. Dashed lines indicate
sales-weighted average flood risk. Sales refer to total declared sales between July 2011 and August 2012.
Flood risk is defined as the expected flood depth in meters for a 1 in 100 year return period. Flood exposure
indicates whether the firm had a positive share of its buffer flooded during the flood event.

This examination of the implications of adaptation following a given flood for the impact of subsequent flood events highlights that adaptation can result in quantitatively important reductions in thedamages from future flooding that afflicts similar locations. Importantly, however, such adaptation
does not always help to reduce the damages from future floods, and indeed may worsen the impactsof some future flood shocks, especially those affecting spatially disjoint regions. The pattern of gains
and losses from post-flood adaptation for future vulnerability will depend intuitively on the spatial
correlation between areas that firms shift their sourcing towards or away from following one flood
event, and areas adversely affected by a subsequent event.

6

Conclusion

The results of this paper suggest a consequential role for natural disaster events — a key manifestationof climate change — in influencing its impacts by inducing firm-level adaptation. We find that, while
even major floods result in only temporary disruption to production and transportation links, these
prompt persistent shifts in firm location, supplier and route choice that reduce firms’ vulnerability to
the recurrence of such events in the future. These responses are enduring, consistent with flood events
causing firms to update their beliefs about underlying flood risk.
The interdependent nature of firm supply chains and the central role of vertical linkages in adaptation suggest that firm adjustments may have important general equilibrium implications for other32

firms and the resilience of the aggregate firm network. We estimate a spatial equilibrium model offirm production and sourcing decisions to capture such spillovers and estimate general equilibrium
effects. This exercise reveals that firms’ adaptive behavior following floods observed in our sample has
quantitatively meaningful implications for the damages imposed by future flood events.
The fact that firms learn from flood experience, and respond by undertaking adaptive actions to
reduce their vulnerability, raises the optimistic prospect that private adaptation may go some way tomitigating the projected impacts of a rapidly changing climate. But recent experience and significant
remaining uncertainty about future climate impacts are cause to sound a note of caution. In the last
15 years, Pakistan has experienced two ‘1 in 100 year’ floods, and average annual flood losses continue
to reach catastrophic levels. Importantly, the structural estimation reveals that post-flood adaptation
does not always reduce aggregate damages from future floods, and may exacerbate costs imposed byfloods affecting low flood risk areas. Such considerations will be especially important as climate change
alters the distribution and severity of flooding across regions in uncertain ways.
The paper’s findings raise important policy questions about whether complementary approaches
might effectively induce adaptation – for instance, could providing accurate information to firms on
flood risk be sufficient to induce meaningful adaptation, or do firms only respond to costly flood
experience? The finding that firms anticipate and adapt to flood risk also opens up an exciting research
agenda on firm expectations about long-range climate change trajectories. Dynamic effects may beespecially interesting if, for instance, belief updating and adaptive behaviors attenuate over time
as major flood events become more common, consistent with evidence for shorter-lived employment
impacts of natural disasters in contexts where they are experienced more often (Belasen and Polachek,2008). Given sharply deteriorating projections of natural disaster incidence as climate change proceeds,
understanding such dynamics will be crucial in anticipating future adaptation and damages.

33

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37

A

Supplementary Tables and Figures
Table A.1. Firm-level summary statistics

Buyers = Sellers
Share of firms with 2011 and 2019 geocodes
Share of firms ever flooded
Share of firms flooded > once
Share of firms ever had important recent supplier flooded
Share of firms had important recent supplier flooded > once
Median firm age at end of sample period (years)
Average probability of firm exit in given month

Log total declared sales
Share of months with positive declared sales
Log self-reported sales
Share of months with positive self-reported sales
Log all aggregated sales
Share of months with positive aggregated sales
Log total declared purchases
Share of months with positive declared purchases
Log self-reported purchases
Share of months with positive self-reported purchases
Log all aggregated purchases
Share of months with positive aggregated purchases

73,336
60%
28%
4%
19%
2%
32
0.25%
Monthly
Mean

SD

14.74
49%
14.84
50%
14.78
51%
14.45
45%
14.46
47%
14.27
52%

2.07
34pp
2.08
33pp
2.14
33pp
2.15
34pp
2.22
34pp
2.38
34pp

Annual
Mean

SD

16.73

2.15

17.01

2.10

16.86

2.20

16.35

2.20

16.42

2.25

16.28

2.39

The table reports descriptive statistics on the firm level. We impose standard restrictions on firms and transaction
partners. For the self-reported and all aggregated transaction variables, we do not restrict transaction partners to
the standard sample. This is because these variables are only used to compute reporting frequencies for sample
restrictions. All firms are considered both buyers and sellers because the sample is restricted to firms that reportat least three nonzero values for each transaction measure. Sales and purchases may be measured in one of three
ways: (1) declared as aggregate by a firm, (2) aggregated based on self-reported transaction values, or (3) aggregatedbased on self-reported and reverse-reported transaction values. All sales and purchases are denominated in Pakistani
Rupee (PKR) before logging. The flooding of an important recent supplier follows the definition in sections (4.2)
and (4.3). It is a buyer-year-month observation in which a seller accounting for ≥ 10% of buyer purchases over thepreceding three months was flooded. Standard deviations for shares of year-months in which a variable is positive arecomputed as the standard deviation across firms of the corresponding within-firm share. Other standard deviations
are computed across all observations. Years refer to fiscal years, which last July through June.

38

Table A.2. Transaction-level summary statistics

Transaction panel observations (transaction > 0)
Buyer-seller pairs ever reported
Share active pairs among possible combinations

Log transaction value
Transactions per pair in years with ≥ 1
Transactions per pair per year over sample period if ≥ 1
Months between transactions of pair if ≥ 2
Distinct suppliers per buyer over sample period if ≥ 1
Distinct quarterly suppliers per buyer if ≥ 1
Share of quarterly buyer purchases from average supplier
Share of sellers supplying ≥ 10% of buyer’s quarterly purchases
Distinct buyers per seller over sample period if ≥ 1
Distinct quarterly buyers per seller if ≥ 1
Share of quarterly seller sales to average buyer
Share of buyers purchasing ≥ 10% of seller’s quarterly sales

15,473,279
1,657,933
0.031%
Mean

SD

12.43
4.30
1.33
2.13
25.35
7.77
52%
72%
29.23
10.57
46%
65%

2.27
3.81
2.27
4.10
81.41
24.15
37pp
34pp
111.81
54.97
38pp
36pp

Transactions refer to buyer-seller-year-month observations with a positive transaction value. All sales
and purchases are denominated in Pakistani Rupee (PKR) before logging. Both the number of distinct
active buyer-seller-pairs and the number of possible buyer-seller-pairs are defined as permutations. Thatis, we count firm A selling to firm B and firm B selling to firm A as two distinct buyer-seller pairs. In
defining quarterly partner variables, a firm’s buyers (suppliers) refer to companies purchasing (selling) a
positive amount to the firm in a given quarter. Years refer to fiscal years, which last July through June.

39

Figure A.1. Histograms of calculated speed and reported speed using 2012 data
1e6

Computed speed v.s reported speed (pooling, with 2012 data)

Computed speed v.s reported speed (motorway, with 2012 data)c_speedr_speedc_speedr_speed3000004250000
3
count

count

2000002150000100000

1

0

3.0

50000

0

1e6

20

40

60
km/h

80

100

0

120

Computed speed v.s reported speed (trunk, with 2012 data)
1.0

c_speedr_speed

2.5

0

20

40

60
km/h

80

100

120

1e6 Computed speed v.s reported speed (primary, with 2012 data)c_speedr_speed

0.8

2.0
count

count

0.6
1.5

0.4

1.0

0.2

0.5
0.0

0

20

40

60
km/h

80

100

0.0

120

0

Computed speed v.s reported speed (secondary, with 2012 data)

80

100

120

c_speedr_speed300000300000250000250000

count

count

60
km/h

350000350000200000200000150000150000100000100000

50000

50000
0

40

Computed speed v.s reported speed (tertiary, with 2012 data)c_speedr_speed400000

20

0

20

40

60
km/h

80

100

0

120

0

20

40

60
km/h

80

100

120

Notes: The histograms plot the count of computed speeds in the full sample (blue)
and of speeds reported by the trackers (orange) for different road types in 2012.

Figure A.2. Comparison of calculated and reported truck speeds in Lahore
The Project for Lahore Urban Transport Master Plan in the Islamic Republic of Pakistan

FINAL REPORT: VOLUME I of II

CHAPTER 7 – MASTER PLAN 2030
Figure 7.2.2 Current (2010) Road Network Performance(a) Calculated speeds, 2015(b) Reported speeds, 2010

Notes: Panel (a) maps computed 2015 speeds and panel (b) maps 2010 speeds reported in Japan
International Cooperation Agency (2012) for the same area in Lahore.

Source: JICA Study Team

40
7-23

Figure A.3. Flood extent maps during sample period(a) 2011: Aug.-Sep.

(c) 2013(b) 2012(d) 2014(e) 2015

Notes: The figure maps the aggregate extent of flooding in the sample period in different years in which we observe
flood events. Panel a) omits the January 2011 flood since it lies outside the sample period.

41

Figure A.4. Fathom flood risk maps of Pakistan for return periods of 10 and 50 years(a) 1 in 10 years return period(b) 1 in 50 years return period

Notes: The maps display flood risk across Pakistan for a 1 in 10 and a 1 in 50 year return period.
Flood risk is defined as the maximum across pluvial and fluvial flood risk, measured as expectedflood depth in meters.

Figure A.5. Distribution of firms by Fathom flood risk(a) 1 in 10 years return period(b) 1 in 50 years return period(c) 1 in 100 years return period

Notes: The histograms display the density of firms’ flood risk by whether a firm’s buffer was flooded at least once overthe sample period. Flood risk is measured as expected flood depth in meters. The histograms are truncated at 1.5m.

Figure A.6. Distribution of firm-pair routes by Fathom flood risk(a) 1 in 10 years return period(b) 1 in 50 years return period(c) 1 in 100 years return period

Notes: The histograms display the density of firm-pair-route flood risk by whether the route was flooded at least once
over the sample period. Flood risk is measured as the length weighted average expected flood depth in meters of edges
along a give route. The histograms are truncated at 1.5m.

42Figure A.7. Impact of flooding on firm sales and purchases using district × time FEs(a) Log sales(b) Log purchases

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
following equation (1). Here, we use district × time FEs instead of time FEs. The unit ofobservation is a firm-month-year. The 95% confidence intervals rely on standard errors clusteredat the firm-level.

Figure A.8. Impact of flooding on firm sales and purchases using flood-risk-decile × time FEs(a) Log sales(b) Log purchases

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
following equation (1). Here, we use flood-risk-decile × time FEs instead of time FEs. The floodrisk decile is defined as the decile of a firm’s expected flood depth in meters for a 1 in 100 year
return period. The unit of observation is a firm-month-year. The 95% confidence intervals relyon standard errors clustered at the firm-level.

Figure A.9. Impact of binned own flooding on firm sales(a) 0-5%(b) 5-10%(c) >10%Notes: The panels plot the estimated effect of flooding on log declared sales from a single OLS regression specifiedI
analogously to equation (1). Here, we include dummy treatment variables Di,t−τ:= 1{FloodExtenti,t−τ ∈ I}
for each flood extent bin I ∈ {(0%, 5%], (5%, 10%], (10%, 100%]} instead of the buffer share flooded. The unit ofobservation is a firm-month-year. The 95% confidence intervals rely on standard errors clustered at the firm-level.

43

Figure A.10. Impact of binned own flooding on firm purchases(a) 0-5%(b) 5-10%(c) >10%Notes: The panels plot the estimated effect of flooding on log declared purchases from a single OLS regression
I
specified analogously to equation (1). Here, we include treatment dummies Di,t−τ:= 1{FloodExtenti,t−τ ∈ I}
for each flood extent bin I ∈ {(0%, 5%], (5%, 10%], (10%, 100%]} instead of the buffer share flooded. The unit ofobservation is a firm-month-year. The 95% confidence intervals rely on standard errors clustered at the firm-level.

Figure A.11. Impact of flooding on firm exit(a) All floods(b) September 2014 flood

The figure displays the estimated impact of firm flooding on firm exit, specified as follows:
yit =

12
X

βτ FloodExtenti,t−τ + αdt + εit(23)τ =−12τ ̸=−2where the unit of observation, (i, t), is a firm-month-year; yit is an indicator variable equal to one if
firm i exits in month-year t; FloodExtenti,t−τ is the share of firm i’s buffer flooded in its first flood
month; and αdt are district-month-year fixed effects. As in section (2.1), we define a firm’s exit dateas the year-month of its last report if this is more than a year from the end of the panel. Panel (b)only includes firms which are either never flooded or first flooded in September 2014. We display 95%
confidence intervals.

44

Table A.3. Share and number of firms by distance moved
Share of Firms Moved
0.68
0.47
0.39
0.24
0.13
0.09
0.07
43877

Moved >0km
Moved >1km
Moved >2km
Moved >5km
Moved >10km
Moved >15km
Moved >20km
Observations# of Firms Moved
29,69920,47417,004
10,638
5,755
3,795
2,928

Notes: The sample is restricted to firms with 2011 and 2019 geocodes.

Table A.4. Impact of flooding on firm relocation (0, 5, 15km move thresholds)

Move Dummy
(1)

(2)

(3)

(4)

(5)

(6)

Max Share of 2km Buffer Flooded-0.222(0.739)1.685∗∗(0.702)0.783(0.830)-0.432(0.754)1.534∗(0.906)1.426∗(0.862)District FE
District × Fathom 1 in 100 FE
Move Dummy Threshold
McFadden’s Pseudo R2
N

Yes

Yes

Yes

0km
0.005
43,8315km
0.021
43,84115km
0.061
43,845

Yes
0km
0.017
43,515

Yes
5km
0.041
43,487

Yes
15km
0.088
43,152

Notes: The columns display logit estimates of the effect of flooding on the probability of relocating by >0km,>5km, or >15km following equation (3). Observations are firms geocoded in 2011 and 2019. Standard errors(in parentheses) are clustered at the district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table A.5. Impact of flooding on flood risk of firm’s location (no, 0, 5, 15km move restrictions)∆ Flood Risk

Max Share of 2km Buffer Flooded
District FE
District × Fathom 1 in 100 FE
Move Distance Restriction
R2
N

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

-1.370∗-1.906∗-2.331∗-1.969∗∗-0.543∗-0.752∗(0.779)(1.110)(1.351)(0.822)(0.285)(0.424)-0.598(0.581)-0.458(0.392)

Yes

Yes

Yes

Yes

None
0.029
43,866>0km
0.039
29,684>5km
0.086
10,623

>15km
0.161
3,780

Yes
None
0.190
43,754

Yes
>0km
0.268
29,569

Yes
>5km
0.424
10,481

Yes
>15km
0.479
3,652

Notes: The table reports OLS estimates of the effect of flooding on firms’ change in flood risk as specified in equation (4). Observations
are firms geocoded in 2011 and 2019 or those which additionally moved by >0km, >5km, or >15km. Standard errors (in parentheses)
are clustered at the district level. R2 refers to McFadden’s Pseudo R2 . ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

45

Table A.6. Impact of destination flood history on relocation flows (0, 5, 15km move restrictions)

Number of Firms Moved
(1)

(2)

(3)

Dest. flooded 12mo prior-1.766∗∗∗(0.281)-0.803∗∗∗(0.225)-0.827∗∗∗(0.293)

Origin × Destination FE
Origin × Flood Event (month) FE
Flood Event of Destination FE
Move Distance Restriction
N

Yes
Yes
Yes
>0km
1,626

Yes
Yes
Yes
>5km
1,469

Yes
Yes
Yes
>15km
1,304

Notes: The table displays Poisson pseudo-maximum-likelihood estimates of the effectof flood history on relocation flows following equation (5). The unit of observationis the area of an origin-district first flooded in a given year-month paired with thearea in a destination district which was never flooded or first flooded in a given yearmonth. We only consider firms moving by >0km, >5km, or >15km and location-pairs
with positive flows. The standard errors (in parentheses) are clustered at the origindistrict-by-destination-district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Figure A.12. Supplier Diversification: Impact of flooding on suppliers’ log inverse HHI(b) Supplier flooding(a) Own flooding

Notes: Panels (a) and (b) plot OLS estimates of the effect of own flooding and supplier flooding
on the log inverse Herfindahl index of a buyer’s suppliers in a given month following equations(6) and (7), respectively. Observations are firm-month-years whose 2011 and 2019 addresses are
known and ≤ 10km apart. We restrict attention to transactions for which buyer and seller reports
coincide precisely. The 95% confidence intervals rely on standard errors clustered at the firm-level.

46

Table A.7. Impact of flooding on supplier flood risk

∆ Flood Risk of Suppliers Flooded by≤ 5%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
-0.115(0.0935)-0.272∗∗∗(0.0993)(2)-0.108(0.0895)-0.281∗∗∗(0.0941)≤ 10%(3)-0.147(0.121)-0.268∗∗(0.108)Yes(4)-0.0860(0.0956)-0.483∗∗(0.200)(5)-0.0835(0.0890)-0.504∗∗(0.205)

Yes
Yes

0.0090144,423(6)-0.120(0.125)-0.508∗∗(0.225)0.0324
143,718

Yes
Yes
0.0586
139,1640.0098144,494

0.0323
143,789

Yes
0.0586139,235

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on thechange in sales-weighted average flood risk among suppliers flooded by ≤ 5% or ≤ 10% during the flood risk windows.
Observations are firm-by-flood-year-month pairs whose 2011 and 2019 addresses are known and ≤ 10km apart. Standard
errors (in parentheses) are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Figure A.13. Dynamic impact of supplier flooding on flood risk of ≤ 5% flooded suppliers

Figure A.13 plots OLS estimates of a sequence of separate regressions examining the impact of
supplier flooding on the sales-weighted average flood risk among suppliers flooded by ≤ 5%. The
specification for the estimate at lag l ∈ {−12, −9, −6, ..., 48} is defined analogously to equation (8),but with the dependent variable lagged by l months:∆yb(t∗ +l) = β1 OwnF loodbt∗ + β2 SellerF loodbt∗ + αd(b)t∗ + ϵbt∗

(24)

where t∗ denotes the month-year of a flood event; OwnF loodbt∗ is the maximum share of buyer b’s 2km
buffer that is flooded at t∗ ; SellerF loodbt∗ is the maximal maximum share of the 2km buffer flooded
at t∗ across all sellers which account for ≥ 10% of b’s purchases over the previous three months; andαd(b)t∗ are buyer district × time fixed effects. ∆yb(t∗ +l) , denotes the change in the sales-weighted
average flood risk of b’s suppliers from (t∗ + l − 6, t∗ + l − 3] to (t∗ + l − 3, t∗ + l] which were notflooded by >5% before or at t∗ + l. Otherwise, flood risk is defined analogously to equation (9). The
set of observations consists of all firm-by-flood-year-month pairs (b, t∗ ) for which the 2011 and 2019addresses are known and ≤ 10km apart. To ensure later lags capture long term effects, we exclude47

all buyers which are flooded themselves or experience supplier flooding (defined like the treatment) in
the period before or at t∗ + l but excluding the flood event around t∗ . The 95% confidence intervals
rely on standard errors clustered at the time × district level.
Figure A.14. Probability of having positive sales in the relationship, by month after first sale1

P (Salesbst > 0)

0.8

0.6

0.4

0.2
0

5

10

15

20

25

30

35

40

45

50

Months after first sale in relationshipThe graph shows the unconditional probability of thebuyer-seller relationship having positive sales (vertical axis),n months after the first sale in the relationship (horizontalaxis).

Figure A.15. No impact of road flooding on intensive margin sales in buyer-seller relationship86
4
2

Log Sales

0
−2
−4
−6
−8
−10
−12
<-12 -10

-5

0

5
10
15
20
Months after first flood of shortest path25

30

35 >36

The graph shows the response of log sales in the (b, s)
relationship around the first time the shortest path betweenb and s gets flooded (after entry of b and s) following
equation (11). Observations are buyer-seller-weeks in the
manufacturing sector. Regression conditions on b and shaving positive sales, and includes b × s, s × t, and b × t
fixed effects and months-since-first-sale dummies. The 95%
confidence intervals are clustered at the relationship level.

48

Table A.8. Percentage difference in household price index in flood year in absence of adaptation
following adaptation year flood

Adaptation year
2012
2013
2014

Flood year
2013 2014 2015
5%

-4%
-14%

1%
-1%
3%

The table displays counterfactual changes in thehousehold price index estimated as described in
section 5.2

49

B

ProofsLemma 3 (Shanbhag and Sreehari, 1977). If Z is a standard exponential random variable and X isa positive α-stable random variable defined byα
E e−uX = e−uZ αis also a standard exponential random variable.
X

and independent from Z, then
Proof.

P

Z
X

α



 Z
h
i
1/α α
1/α
1/α
1/α
> u = P Z > u X = e−u x dF (x) = E e−u X = e−(u ) = e−u

Lemma 4. Let X be Fréchet distributed withθP (X > x) = e−T x



α
β
and Y independent from X such that E e−uY = e−u . Then X/Y 1/θ is Fréchet distributed with

P

X
Y 1/θ

α


i
h
θβ
> x = exp −T β x α

Proof. We have that T (X)θ is standard exponential: x 1/θ 
P T (X) > x = P X >
= e−x
T


θ

From the Shabhag-Sreehari lemma above we know that

T (X)
Y

P

θ



!β

> x = e−x .

Rearrange to get



X
Y 1/θ



X
Y 1/θ

P
P

α θβ
α

α



> T −β x


−β



!
= e−x

α

> T x
= e−x



h
i
X α
β θβ
α
>
u
=
exp
−T
u
P
Y 1/θ
θβ

Lemma 5. Let X be Frechet with−ζP (X < x) = e−ax .

50Then log X has the characteristic functionhit log X

χ (log X) (t) = E e

i



it
=a Γ 1−
ζ
it
ζ


.

Proof.
h

Z ∞

i

−ζ

χ (log X) (t) = E e=
eit log x aζx−ζ−1 e−ax dx0Z ∞−ζaζx−ζ−1+it e−ax dx=
0Z ∞Z ∞   itu − ζ −u=xit e−u du =
e du
a
0
0
Z ∞
it
− it
ζ
(u) ζ e−u du
= a
0


it
it
= aζ Γ 1−ζ
it log X−ζf (x) = aζx−ζ−1 e−ax
where we’ve used the substitutionu = u −1/ζ=a
1 1 u −1/ζ−1
−
=
aζ a
du
=
dx
1
− xζ+1 du =
ζa

ax−ζ
x
dx
du
−aζx−ζ−1dx

Lemma 6 (Lemma 1 in the main text). Conditional on the realization of the aggregate flood shocks
b, the cost distribution of firms in n is Weibull: 
P (cj > c|b) = exp −  an(j) bn(j)

ζβ/α

w1−α

"
−ζβ/α X

#β 
−ζ −ζ
mnn′ τnn′ c̄sn′n′where:ζc̄−ζn = (an bn )w

1−α −ζ



!α
X
n′

−ζ −ζ
mnn′ τnn′ c̄n′



α
Γ 1−
β

Proof. Let Fi (c) be the CDF of firm’s costs in a location i. We have1cs (ϕ) α1−αcj (ϕ) =w
τn(j)n(s)an(j) bn(j)t ξjt
z (ϕ)

51



 cζβ/α cs (ϕ) α11−αwτn(j)n(s)>cP (cj (ϕ) > c|b, ξ) = P
an(j) bn(j)t ξjt
z (ϕ)




1/α 1/αcs (ϕ)−1
1−α −1/α= P> τn(j)n(s) w
an(j) bn(j)t ξjt
c
z (ϕ)


The distribution of effective cost from techniques with a supplier in n′ follows
P



Z Z n

o
cs
−ζ−1
>c
= exp −mnn′
1< c dFn′ (cs )ζz
dz
z
z


Z Z n
o
cs
−ζ−1 ζ
< 1 dFn′ (cs )ζuc du= exp −mnn′
1
u


Z Z n
o
cs
1
= exp −mnn′ cζ< 1 dFn′ (cs )ζu−ζ−1 duuhiζ= exp −mnn′ c̄−ζ
s c

c

s

where we have used the substitutionsu = czdu/dz = cz −ζ−1 dz = u−ζ−1 cζ duand where we have used the notation
Z Z n
o
cs
−ζ
c̄s
=
1
< 1 dFn′ (cs )ζu−ζ−1 du
u
Z Z
= −
1 {t > 1} (cs )−ζ dFn′ (cs )ζtζ−1 dtZ=(cs )−ζ dFn′ (cs )Let cmin (j) be the lowest cost that j can achieve, and cmin,n′ the lowest cost it can achieve by sourcing
from n′ , thenP cmin,n′ > c|b, ξ




= P

= exp

cs (ϕ)z (ϕ)

min,n′−mnn′ c̄−ζ
n′



−1
τn(j)n′w

1−α −1/α!

1/α 1/αan(j) bn(j) ξjc

1−α −1/α1/α ζ ζ/αan(j) bn(j) ξjc−1> τn(j)n′w



is Weibull distributed. Hence
P (cmin > c|b, ξ) =Y

P cmin,n′ > c|b, ξ



n′

"
= exp −w


1−α −1/α[an bn ξj ]1/α

ζ

!
X

−ζ −ζ
mnn′ τnn′ c̄n′#cζ/αn′where we write shorthand n for n(j). Conditional on b an ξ, the minimum cost is Weibull distributed.

52

Apply now Lemma 4,

P

X
Y 1/θ



> x1/αihθβ= exp −T β x αwith
X = (cj |b) ξjt

ζ
−1/α
T =
w1−α
[an bn ξj ]1/α

!
X

−ζ −ζ
mnn′ τnn′ c̄n′n′θ = ζ/αY= ξ ζ/αto get "P ((cj ) > x|b) = exp −w


1−α −1/α[an bn ]1/α

ζ



!#β
X

−ζ −ζ
mnn′ τnn′ c̄n′xζβ/α n′

= exp −w


1−α −ζβ/α

 X
−ζ −ζ
ζβ/α
[an bn ]mnn′ τnn′ c̄n′!βxζβ/α n′which is the first part of the statement of the Lemma. For the second part, use the definition of c̄ andLemma 5:
h i
h i
−ζ
−ζ
= E X ζ = [an bn ]ζ w1−αc̄−ζn =E c

!α
X
n′

−ζ −ζ
mnn′ τnn′ c̄n′



α
Γ 1−
β

Lemma 7 (Lemma 2 in the main text). Let α > 0. Then for each realization of the aggregate shocks
bn an equilibrium exists and is unique.
Proof. The proof of Lemma 2 follows directly from Theorem 1 in Alvarez and Lucas (2007) with
β := 1 − α and θ := α/ζ.

53

C

Robustness Specifications

C.1

Results using Sun and Abraham (2021) estimator and event-by event regressions

A recent literature has highlighted potential challenges with the use of two-way fixed effects regressions
including treatment leads and lags, since variation in treatment timing may give rise to contaminationof coefficients on lead or lag terms by effects from other periods (Callaway and Sant’Anna, 2021; Sun
and Abraham, 2021). While the major floods in our sample are generally close to a year apart sothat such effects may not be first order, we re-run all key results using the estimator proposed in Sun
and Abraham (2021). This estimator aims to overcome the challenges that may be associated with
two-way fixed effects event study regressions by using never-treated (or, if these are not available,
last-treated) firms to form the control group. It relies on a binary treatment variable. Since Figure
A.9 suggests that the impact of flooding is concentrated among firms which have more than 10% of
their buffer flooded, we consider a firm treated only if the treatment variable is greater than 10%, anddrop all firms with a treatment variable ϵ (0%, 10%]. We use never treated firms as the control cohort.
As an additional test, we present results separately by flood event, restricting the sample to firms
which are either never treated or treated only in a given flood event. The flood events in our sample
period are Aug-Sep 2011, Sep 2012, Aug 2013, Sep 2014, and Jul-Aug 2015. For flood events lasting
two months, we define event time relative to the first month of the event. This specification shuts
down variation in treatment timing while using the standard continuous treatment variable. Since
the panel begins in July 2011 and ends in June 2018, coefficients for earlier and later periods areomitted. One coefficient in each fiscal year is omitted in the sales, purchases, and log number ofsupplier regressions because the full set of treatment variables, the firm-fiscal-year fixed effects andthe firm-month-of-the-year fixed effects are perfectly collinear when restricting to a single flood event.
We include the number of treated firms, Ntreated , with each specification below.
Robustness analyses in this section are included only for those specifications estimated with twoway fixed effects models.
C.1.1

Impact of flooding on firm sales and purchases
Figure C.1. Impact of flooding on firm sales and purchases (Sun & Abraham)(a) Log sales(b) Log purchases

Ntreated = 681

Ntreated = 681

Notes: The panels plot the effect of flooding on log declared sales and purchases. We use themethod in Sun and Abraham (2021) to estimate equation (1) but with a binary treatment
variable based on a 10% cutoff. Observations are firm-month-years which are never flooded or
flooded by > 10% in their first flood month. The 95% confidence intervals rely on standard
errors clustered at the firm-level.

54

Figure C.2. Impact of flooding on firm sales and purchases (Aug-Sep 2011)(a) Log sales(b) Log purchases

Ntreated = 990

Ntreated = 990

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
as specified in equation (1). Observation are firm-month-years which are never flooded or firstflooded in the Aug-Sep 2011 event. The 95% confidence intervals rely on standard errors clusteredat the firm-level.

Figure C.3. Impact of flooding on firm sales and purchases (Sep 2012)(a) Log sales(b) Log purchases

Ntreated = 37

Ntreated = 37

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
as specified in equation (1). Observation are firm-month-years which are never flooded or firstflooded in the Sep 2012 event. The 95% confidence intervals rely on standard errors clusteredat the firm-level.

55

Figure C.4. Impact of flooding on firm sales and purchases (Aug 2013)(a) Log sales(b) Log purchases

Ntreated = 3602

Ntreated = 3602

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
as specified in equation (1). Observation are firm-month-years which are never flooded or firstflooded in the Aug 2013 event. The 95% confidence intervals rely on standard errors clusteredat the firm-level.

Figure C.5. Impact of flooding on firm sales and purchases (Sep 2014)(a) Log sales(b) Log purchases

Ntreated = 7870

Ntreated = 7870

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
as specified in equation (1). Observation are firm-month-years which are never flooded or firstflooded in the Sep 2014 event. The 95% confidence intervals rely on standard errors clusteredat the firm-level.

56

Figure C.6. Impact of flooding on firm sales and purchases (Jul-Aug 2015)(a) Log sales(b) Log purchases

Ntreated = 9507

Ntreated = 9507

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
as specified in equation (1). Observation are firm-month-years which are never flooded or firstflooded in the Jul-Aug 2015 event. The 95% confidence intervals rely on standard errors clusteredat the firm-level.

C.1.2

Supplier Diversification

We omit the impact of own flooding in the September 2012 flood in this section since only five firms
with a non-missing dependent variable experienced flooding of more than 10% of their buffer during
this episode.
Figure C.7. Impact of flooding on log number of suppliers (Sun and Abraham estimator)(a) Own flooding(b) Supplier flooding

Ntreated = 216

Ntreated = 125

Notes: The panels plot the effect of own flooding or supplier flooding on log number of suppliers.
We use the method in Sun and Abraham (2021) to estimate equations (6) and (7), but with abinary treatment variable based on a 10% threshold. Observations are firm-month-years whose
2011 and 2019 addresses are known, ≤ 10km apart and which are never treated or treated by >10% in their first treatment month. We restrict attention to transactions for which buyer and seller
reports coincide precisely. The 95% confidence intervals rely on standard errors clustered at thefirm-level.

57

Figure C.8. Impact of flooding on log number of suppliers (Aug-Sep 2011)(a) Own flooding(b) Supplier flooding

Ntreated = 442

Ntreated = 281

Notes: The panels plot OLS estimates of the effect of own flooding or supplier flooding on log
number of suppliers following equations (6) and (7). Observations are firm-month-years whose
2011 and 2019 addresses are known, ≤ 10km apart and which are never treated or first treated in
the Aug-Sep 2011 event. We restrict attention to transactions for which buyer and seller reports
coincide precisely. The 95% confidence intervals rely on standard errors clustered at the firm-level.

Figure C.9. Impact of supplier flooding on log number of suppliers (Sep 2012)

Ntreated = 38
Notes: The panel plots OLS estimates of the effect of supplier flooding on log number of suppliers
following equation (7). Observations are firm-month-years whose 2011 and 2019 addresses are
known, ≤ 10km apart and which are never treated or first treated in the Sep 2012 event. We
restrict attention to transactions for which buyer and seller reports coincide precisely. The 95%
confidence intervals rely on standard errors clustered at the firm-level.

58

Figure C.10. Impact of flooding on log number of suppliers (Aug 2013)(a) Own flooding(b) Supplier flooding

Ntreated = 1651

Ntreated = 1108

Notes: The panels plot OLS estimates of the effect of own flooding or supplier flooding on log
number of suppliers following equations (6) and (7). Observations are firm-month-years whose
2011 and 2019 addresses are known, ≤ 10km apart and which are never treated or first treatedin the Aug 2013 event. We restrict attention to transactions for which buyer and seller reports
coincide precisely. The 95% confidence intervals rely on standard errors clustered at the firm-level.

Figure C.11. Impact of flooding on log number of suppliers (Sep 2014)(a) Own flooding(b) Supplier flooding

Ntreated = 3994

Ntreated = 1854

Notes: The panels plot OLS estimates of the effect of own flooding or supplier flooding on log
number of suppliers following equations (6) and (7). Observations are firm-month-years whose
2011 and 2019 addresses are known, ≤ 10km apart and which are never treated or first treatedin the Sep 2014 event. We restrict attention to transactions for which buyer and seller reports
coincide precisely. The 95% confidence intervals rely on standard errors clustered at the firm-level.

59

Figure C.12. Impact of flooding on log number of suppliers (Jul-Aug 2015)(a) Own flooding(b) Supplier flooding

Ntreated = 4730

Ntreated = 1772

Notes: The panels plot OLS estimates of the effect of own flooding or supplier flooding on log
number of suppliers following equations (6) and (7). Observations are firm-month-years whose
2011 and 2019 addresses are known, ≤ 10km apart and which are never treated or first treated in
the Jul-Aug 2015 event. We restrict attention to transactions for which buyer and seller reports
coincide precisely. The 95% confidence intervals rely on standard errors clustered at the firm-level.

C.1.3

Supplier choice

Figure C.13. Dynamic impact of supplier flooding on flood risk of all suppliers (Sun and Abraham)

Ntreated = 198
Notes: The panel plots estimates of the effect of supplier flooding on the change in sales-weighted average floodrisk among all suppliers. We use the method by Sun and Abraham (2021) to estimate equation (10) with abinary treatment variable based on a 10% threshold. Observations are all firm-year-months for which the 2011
and 2019 addresses are known, ≤ 10km apart and which are either never treated or treated by >10% in their firsttreatment month. The 95% confidence intervals rely on standard errors clustered at the time × district level.

60Figure C.14. Dynamic impact of supplier flooding on flood risk of all suppliers (Aug-Sep 2011)

Ntreated = 481
Notes: The panel plots estimates of the effect of supplier flooding on the change in sales-weighted average floodrisk among all suppliers following equation (10). Observations are all firm-year-months for which the 2011 and2019 addresses are known, ≤ 10km apart and which are never treated or first treated in the Aug-Sep 2011 event.
The 95% confidence intervals rely on standard errors clustered at the time × district level.

Figure C.15. Dynamic impact of supplier flooding on flood risk of all suppliers (Sep 2012)

Ntreated = 52
Notes: The panel plots estimates of the effect of supplier flooding on the change in sales-weighted average floodrisk among all suppliers following equation (10). Observations are all firm-year-months for which the 2011 and2019 addresses are known, ≤ 10km apart and which are never treated or first treated in the Sep 2012 event. The
95% confidence intervals rely on standard errors clustered at the time × district level.

Figure C.16. Dynamic impact of supplier flooding on flood risk of all suppliers (Aug 2013)

Ntreated = 1360
Notes: The panel plots estimates of the effect of supplier flooding on the change in sales-weighted average floodrisk among all suppliers following equation (10). Observations are all firm-year-months for which the 2011 and2019 addresses are known, ≤ 10km apart and which are never treated or first treated in the Aug 2013 event. The
95% confidence intervals rely on standard errors clustered at the time × district level.

61

Figure C.17. Dynamic impact of supplier flooding on flood risk of all suppliers (Sep 2014)

Ntreated = 2595
Notes: The panel plots estimates of the effect of supplier flooding on the change in sales-weighted average floodrisk among all suppliers following equation (10). Observations are all firm-year-months for which the 2011 and2019 addresses are known, ≤ 10km apart and which are never treated or first treated in the Sep 2014 event. The
95% confidence intervals rely on standard errors clustered at the time × district level.

Figure C.18. Dynamic impact of supplier flooding on flood risk of all suppliers (Jul-Aug 2015)

Ntreated = 2829
Notes: The panel plots estimates of the effect of supplier flooding on the change in sales-weighted average floodrisk among all suppliers following equation (10). Observations are all firm-year-months for which the 2011 and2019 addresses are known, ≤ 10km apart and which are never treated or first treated in the Jul-Aug 2015 event.
The 95% confidence intervals rely on standard errors clustered at the time × district level.

62

C.2

Results excluding electricity and gas producers

We examine how far the results are affected by industries for which supply disruptions of the nature
considered in the analysis may not be pertinent. The first of these robustness checks excludes fromthe sample the 1% of firms, accounting for 14% of aggregate sales, with two-digit industry identifiers
corresponding to electricity, gas and extraction of crude petroleum.41 This accounts for the fact that,while firms purchase these inputs regularly, these are monopolies that firms are unable to substituteaway from. The results in this case are very similar to the baseline results.
C.2.1

Impact of flooding on firm sales and purchases
Figure C.19. Impact of flooding on firm sales and purchases (excl. electricity and gas)(a) Log Sales(b) Log Purchases

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
as specified in equation (1). Observations are firm-month-years excluding electricity and gas
producers. The 95% confidence intervals rely on standard errors clustered at the firm-level.
41

The two-digit industry code corresponding to electricity, gas and extraction of crude petroleum also includes steam
and air conditioning suppliers.

63

C.2.2

Firm Location

Table C.1. Impact of flooding on firm relocation and location flood risk (excl. electricity and gas)

Max Share of 2km Buffer Flooded
District FE
District × Fathom 1 in 100 FE
R2
N

Move Dummy∆ Flood Risk

(1)

(2)

(3)

(4)

1.569∗∗

1.912∗∗-1.954∗(0.758)(0.800)(1.009)-0.451(0.537)

Yes

Yes

0.045
43,525Yes
0.068
43,074

0.127
5,663

Yes
0.449
5,525

Notes: Columns (1) and (2) display logit estimates of the effect of flooding on the probability of relocating by >10km following equation (3). Columns (3) and (4) report OLS
estimates of the effect of flooding on moving firms’ change in flood risk as specified inequation (4). Observations are firms fully geocoded in 2011 and 2019 excluding electricity
and gas producers. The flood risk regressions only include firms which moved by >10km.
Standard errors (in parentheses) are clustered at the district level. R2 refers to McFadden’s Pseudo R2 for columns (1) and (2). ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table C.2. Impact of destination flood history on relocation flows (excl. electricity and gas)

Number of Firms Moved
Dest. flooded 12mo prior-0.718∗∗∗(0.250)

Origin × Destination FE
Origin × Flood Event (month) FE
Flood Event of Destination FE
Move Distance Restriction
N

Yes
Yes
Yes
>10km
1,392

Notes: The table displays the Poisson pseudo-maximum-likelihood estimateof the effect of flood history on relocation flows following equation (5). The
unit of observation is the area of an origin-district first flooded in a given yearmonth paired with the area in a destination district which was never flooded
or first flooded in a given year-month. We only consider firms moving by>10km which are not electricity or gas producers and location-pairs withpositive flows. The standard error (in parentheses) is clustered at the origindistrict-by-destination-district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

64

C.2.3

Supplier Diversification
Figure C.20. Impact of flooding on log number of suppliers (excl. electricity and gas)(a) Own flooding(b) Supplier flooding

Notes: The panels plot OLS estimates of the effect of own flooding or supplier flooding on log numberof suppliers following equations (6) and (7), respectively. Observations are firm-month-years whose 2011
and 2019 addresses are known, ≤ 10km apart and not electricity or gas producers. We restrict attention
to transactions for which buyer and seller reports coincide precisely and which do not involve electricity
or gas producers. The 95% confidence intervals rely on standard errors clustered at the firm-level.

C.2.4

Supplier Choice
Table C.3. Impact of supplier flooding on supplier flood risk (excl. electricity and gas)

∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

0.0277(0.0401)-0.630∗∗∗(0.156)-0.0160(0.0373)-0.662∗∗∗(0.163)0.0637(0.0450)-0.768∗∗∗(0.183)

Yes
Yes
0.0088138,8850.0253
138,174

Yes
0.0542
133,913

Notes: The table reports OLS estimates of the effects of supplier and own flooding
on the change in sales-weighted average flood risk among all suppliers following
equation (8). Observations are firm-by-flood-year-month pairs for which the 2011
and 2019 addresses are known and ≤ 10km apart. We exclude electricity and gas
producers from buyers and sellers. Standard errors (in parentheses) are clusteredat the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

65

Table C.4. Impact of supplier flooding on supplier flood risk (excl. electricity and gas)

Dependent Variable: ∆ Flood Risk of Suppliers Flooded by≤ 1%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
0.0134(0.0209)-0.0633∗(0.0383)(2)0.0163(0.0209)-0.0782∗(0.0411)≤ 5%(3)0.0288(0.0264)-0.0462(0.0403)Yes(4)
0.0112(0.0236)-0.202∗∗∗(0.0638)(5)0.00536(0.0257)-0.222∗∗∗(0.0663)

Yes

0.0221
137,571(7)0.0307(0.0298)-0.421∗∗(0.187)(8)0.0116(0.0258)-0.449∗∗(0.197)

Yes
Yes
0.0508133,334(9)0.0530(0.0377)-0.455∗∗(0.214)

Yes

Yes
0.0056138,283≤ 10%(6)0.0278(0.0295)-0.209∗∗∗(0.0678)0.0058
138,715

0.0219138,001

Yes
Yes
0.0505
133,757

0.0066
138,792

0.0226
138,079

Yes
0.0509133,834

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on the change in sales-weighted average flood risk
among suppliers flooded by ≤ 1%, ≤ 5%, or ≤ 10% during the flood risk windows. Observations are firm-by-flood-year-month pairs for which the 2011 and2019 addresses are known and ≤ 10km apart. We exclude electricity and gas producers from buyers and sellers. Standard errors (in parentheses) are clusteredat the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Figure C.21. Dynamic impact of supplier flooding on supplier flood risk (excl. electricity and gas)(b) Suppliers flooded by ≤ 5%(a) All Suppliers

Notes: Panels (a) and (b) plot OLS estimates of the effect of supplier flooding on the change in sales-weighted average flood risk among all suppliers and suppliers flooded by ≤ 5% following equations (10) and (24), respectively.
Observations are firm-year-month pairs for which the 2011 and 2019 addresses are known and ≤ 10km apart. We
exclude electricity and gas producers from buyers and sellers. Panel (b) excludes buyers which experience own
or supplier flooding before the lag. The 95% confidence intervals rely on standard errors clustered at the time ×
district level.

C.3

Results excluding capital purchases

We next consider the robustness of results to excluding transactions involving capital goods, giventhat lumpy capital purchases are likely to be infrequent and may be less prone to flood-induced supply
disruptions. To do so, we remove all transactions in which either the buyer or the seller has a primary
product code which maps to a capital good, identified using Part I of the Fifth Schedule of the Customs
Act.42
42https://www.fbr.gov.pk/categ/customs-tariff/51149/70853/131188

66

C.3.1

Impact of flooding on firm sales and purchases
Figure C.22. Impact of flooding on firm sales and purchases (excl. capital purchases)(a) Log Sales(b) Log Purchases

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
as specified in equation (1). Observations are firm-month-years excluding firms transacting
primarily in capital goods. The 95% confidence intervals rely on standard errors clustered at thefirm-level.

C.3.2

Firm location

Table C.5. Impact of flooding on firm relocation and location flood risk (excl. capital purchases)

Move Dummy∆ Flood Risk

(1)

(2)

(3)

(4)

Max Share of 2km Buffer Flooded

1.583∗∗(0.754)

1.849∗∗(0.805)-1.952∗(1.007)-0.450(0.526)

District FE
District × Fathom 1 in 100 FE
R2
N

Yes
0.046
43,848

Yes
Yes
0.067
43,395

0.127
5,737

Yes
0.449
5,596

Notes: Columns (1) and (2) display logit estimates of the effect of flooding on theprobability of relocating by >10km following equation (3). Columns (3) and (4) report
OLS estimates of the effect of flooding on moving firms’ change in flood risk as specified
in equation (4). Observations are firms geocoded in 2011 and 2019 which do not primarilytransact in capital goods. The flood risk regressions only include firms which moved by>10km. Standard errors (in parentheses) are clustered at the district level. R2 refers to
McFadden’s Pseudo R2 for columns (1) and (2). ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

67

Table C.6. Impact of destination flood history on relocation flows (excl. capital purchases)

Number of Firms Moved
Dest. flooded 12mo prior-0.735∗∗∗(0.254)

Origin × Destination FE
Origin × Flood Event (month) FE
Flood Event of Destination FE
Move Distance Restriction
N

Yes
Yes
Yes
>10km
1,412

Notes: The table displays the Poisson pseudo-maximum-likelihood estimateof the effect of flood history on relocation flows following equation (5). The
unit of observation is the area of an origin-district first flooded in a given
year-month paired with the area in a destination district which was neverflooded or first flooded in a given year-month. We only consider firms movingby >10km which do not primarily transact in capital goods and locationpairs with positive relocation flows. The standard error (in parentheses) isclustered at the origin-district-by-destination-district level.
∗

C.3.3

p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Supplier Diversification
Figure C.23. Impact of flooding on log number of suppliers (excl. capital purchases)(a) Own flooding(b) Supplier flooding

Notes: The panels plot OLS estimates of the effect of own flooding or supplier flooding on log numberof suppliers following equations (6) and (7), respectively. Observations are firm-month-years whose
2011 and 2019 addresses are known, ≤ 10km apart and do not primarily transact in capital goods. We
restrict attention to transactions for which buyer and seller reports coincide precisely and which do not
involve firms primarily transacting in capital goods. The 95% confidence intervals rely on standard
errors clustered at the firm-level.

68C.3.4

Supplier Choice
Table C.7. Impact of supplier flooding on supplier flood risk (excl. capital purchases)∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

-0.0734(0.106)-0.514∗∗∗(0.144)-0.0462(0.0919)-0.527∗∗∗(0.150)-0.119(0.133)-0.604∗∗∗(0.166)

Yes
Yes
0.0111125,091

0.0355
124,384

Yes
0.0671
120,023

Notes: The table reports OLS estimates of the effects of supplier and own flooding on the change in sales-weighted average flood risk among all suppliers following
equation (8). Observations are firm-by-flood-year-month pairs for which the 2011
and 2019 addresses are known and ≤ 10km apart. We exclude firms primarilytransacting in capital goods from buyers and sellers. Standard errors (in parentheses) are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table C.8. Impact of supplier flooding on supplier flood risk (excl. capital purchases)

Dependent Variable: ∆ Flood Risk of Suppliers Flooded by≤ 1%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
-0.126(0.108)-0.138∗(0.0702)(2)-0.0982(0.0911)-0.0951(0.0782)≤ 5%(3)-0.173(0.138)-0.103(0.0932)Yes(4)-0.118(0.110)-0.198∗∗(0.0770)(6)-0.165(0.139)-0.177∗(0.0979)

Yes
Yes

0.0102
124,576(5)-0.0777(0.0951)-0.169∗∗(0.0836)≤ 10%

0.0385
123,859(7)-0.0971(0.111)-0.345∗∗(0.161)0.0098
124,953(9)-0.146(0.141)-0.352∗(0.190)

Yes
Yes

Yes
0.0669119,528(8)-0.0496(0.0921)-0.332∗(0.171)0.0364
124,246

Yes
Yes
0.0663
119,8930.0100125,0220.0355
124,315

Yes
0.0652
119,960

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on the change in sales-weighted average flood risk
among suppliers flooded by ≤ 1%, ≤ 5%, or ≤ 10% during the flood risk windows. Observations are firm-by-flood-year-month pairs whose 2011 and 2019addresses are known and ≤ 10km apart. We exclude firms primarily transacting in capital goods from buyers and sellers. Standard errors (in parentheses)
are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

69

Figure C.24. Dynamic impact of supplier flooding on supplier flood risk (excl. capital purchases)(b) Suppliers flooded by ≤ 5%(a) All Suppliers

Notes: Panels (a) and (b) plot OLS estimates of the effect of supplier flooding on the change in sales-weighted
average flood risk among all suppliers and suppliers flooded by ≤ 5% following equations (10) and (24), respectively. Observations are firm-year-month pairs for which the 2011 and 2019 addresses are known and ≤ 10km
apart. We exclude firms primarily transacting in capital goods from buyers and sellers. Panel (b) excludes buyers
which experience own or supplier flooding before the lag. The 95% confidence intervals rely on standard errors
clustered at the time × district level.

C.4

Results using manufacturing firms

We restrict attention to the 37% of firms, accounting for 53% of sales, with industry codes corresponding to manufacturing sectors. The majority of firms excluded under this restriction are services
firms, with a smaller number of firms in the agricultural sector. Services and agricultural firms may
be expected to face distinct flood-related disruptions relative to the production network effects thatare the focus of the current analysis.
C.4.1

Impact of flooding on firm sales and purchases
Figure C.25. Impact of flooding on firm sales and purchases (manufacturing sample)(a) Log Sales(b) Log Purchases

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
as specified in equation (1). Observations are firm-month-years in the manufacturing sector. The
95% confidence intervals rely on standard errors clustered at the firm-level.

70C.4.2

Firm location
Table C.9. Impact of flooding on firm relocation and location flood risk

Move Dummy∆ Flood Risk

(1)

(2)

(3)

(4)

Max Share of 2km Buffer Flooded

1.026(0.958)1.046(0.931)-1.292(0.987)-0.0300(0.552)District FE
District × Fathom 1 in 100 FE
R2
N

Yes

Yes

0.052
17,422

Yes
0.072
17,070

0.146
2,752

Yes
0.459
2,660

Notes: Columns (1) and (2) display logit estimates of the effect of flooding on theprobability of relocating by >10km following equation (3). Columns (3) and (4) report
OLS estimates of the effect of flooding on moving firms’ change in flood risk as specified
in equation (4). Observations are firms geocoded in 2011 and 2019. The flood risk
regressions only include manufacturing firms which moved by >10km. Standard errors(in parentheses) are clustered at the district level. R2 refers to McFadden’s Pseudo R2
for columns (1) and (2). ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table C.10. Impact of destination flood history on relocation flows

Number of Firms Moved
Dest. flooded 12mo prior-0.846∗∗∗(0.284)

Origin × Destination FE
Origin × Flood Event (month) FE
Flood Event of Destination FE
Move Distance Restriction
N

Yes
Yes
Yes
>10km
784

Notes: The table displays the Poisson pseudo-maximum-likelihood estimateof the effect of flood history on relocation flows following equation (5). The
unit of observation is the area of an origin-district first flooded in a given yearmonth paired with the area in a destination district which was never flooded
or first flooded in a given year-month. We only consider manufacturingfirms moving by >10km and location-pairs with positive relocation flows.
The standard error (in parentheses) is clustered at the origin-district-bydestination-district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

71

C.4.3

Supplier Diversification
Figure C.26. Impact of flooding on log number of suppliers (manufacturing sample)(a) Own flooding(b) Supplier flooding

Notes: The panels plot OLS estimates of the effect of own flooding or supplier flooding on the log
number of suppliers following equations (6) and (7), respectively. Observations are firm-monthyears in the manufacturing sector whose 2011 and 2019 addresses are known and ≤ 10km apart.
We restrict attention to transactions for which buyer and seller reports coincide precisely and which
only involve manufacturers. The 95% confidence intervals rely on standard errors clustered at thefirm-level.

C.4.4

Supplier Choice
Table C.11. Impact of supplier flooding on supplier flood risk (manufacturing)∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

0.174∗∗∗(0.0385)-0.670∗∗∗(0.122)0.177∗∗∗(0.0416)-0.667∗∗∗(0.126)0.203∗∗∗(0.0648)-0.674∗∗∗(0.126)

Yes
Yes
0.019552,7690.0472
52,238Yes
0.067051,413

Notes: The table reports OLS estimates of the effects of supplier and own flooding on the change in sales-weighted average flood risk among suppliers following
equation (8). Observations are firm-by-flood-year-month pairs for which the 2011
and 2019 addresses are known and ≤ 10km apart. We restrict buyers and sellers
to manufacturing firms. Standard errors (in parentheses) are clustered at the time× district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

72

Table C.12. Impact of supplier flooding on supplier flood risk (manufacturing)

Dependent Variable: ∆ Flood Risk of Suppliers Flooded by≤ 1%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
0.0522(0.0509)-0.0678(0.0832)(2)
0.0533(0.0557)-0.0549(0.0808)≤ 5%(3)
0.0832(0.0558)-0.0588(0.0757)

Yes(4)
0.124∗∗(0.0529)-0.221∗∗(0.108)(6)0.150∗∗(0.0718)-0.214∗∗(0.108)(7)
0.143∗∗∗(0.0469)-0.365∗∗(0.150)Yes
Yes

0.016752,049(5)0.126∗∗(0.0565)-0.216∗∗(0.107)≤ 10%

0.043851,518(8)0.155∗∗∗(0.0482)-0.360∗∗(0.150)

Yes
Yes

Yes
0.063350,7030.015552,655(9)0.176∗∗(0.0685)-0.362∗∗(0.152)0.042052,123

Yes
Yes
0.0622
51,2990.015652,7120.042652,183Yes
0.062851,356

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on the change in sales-weighted average floodrisk among suppliers flooded by ≤ 1%, ≤ 5%, or ≤ 10% during the flood risk windows. Observations are firm-by-flood-year-month pairs whose 2011 and2019 addresses are known and ≤ 10km apart. We restrict buyers and sellers to manufacturing firms. Standard errors (in parentheses) are clustered at thetime × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Figure C.27. Dynamic impact of supplier flooding on supplier flood risk (manufacturing)(b) Suppliers flooded by ≤ 5%(a) All Suppliers

Notes: Panels (a) and (b) plot OLS estimates of the effect of supplier flooding on the change in sales-weighted
average flood risk among all suppliers and suppliers flooded by ≤ 5% following equations (10) and (24),respectively. Observations are firm-year-month pairs for which the 2011 and 2019 addresses are known and ≤
10km apart. We restrict buyers and sellers to manufacturing firms. Panel (b) excludes buyers which experienceown or supplier flooding before the lag. The 95% confidence intervals rely on standard errors clustered at thetime × district level.

C.5

Results using precisely coinciding buyer and seller reports only

While the firm transactions data described in Section 2.1 offers a unique lens into supply chain relationships in Pakistan, these data may be subject to misreporting by firms in order to reduce their tax
liability (Waseem, 2019). We exclude ‘invoice mills’ from our estimation sample in order to overcome
an especially pernicious documented source of such behavior. In order to rule out other potential
sources of misreporting, we consider the robustness of our results to considering only those 42% ofmonthly transaction observations (representing 22% of total sales) where buyer and seller reports
coincide exactly.
To the extent that buyer and seller reports of the same monthly-level transactions reflect strategic
misreporting rather than random error, we expect the two parties to have conflicting incentives tomisreport: while sellers will wish to understate their sales to reduce their VAT liability, the converse istrue for buyers who will wish to overstate their purchases. Using the fact that we observe independent73

reports of pair-level monthly transactions from the buyer and seller, we can investigate the potential
importance of such biases. In this robustness specification, we take an extremely stringent approach
to ruling this out by restricting attention to cases where buyer and seller reports match exactly andas such where misreporting is highly unlikely.
C.5.1

Supplier choice
Table C.13. Impact of supplier flooding on supplier flood risk (coinciding reports)∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

0.0451(0.0925)-0.984∗∗∗(0.216)-0.0347(0.123)-0.965∗∗∗(0.222)0.0624(0.0942)-0.956∗∗∗(0.229)Yes
Yes
0.011186,7860.0301
86,184Yes
0.0601
83,675

Notes: The table reports OLS estimates of the effects of supplier and own flooding on the change in sales-weighted average flood risk among all suppliers following
equation (8). Observations are firm-by-flood-year-month pairs for which the 2011
and 2019 addresses are known and ≤ 10km apart. We restrict attention to transactions for which buyer and seller reports coincide precisely. Standard errors (inparentheses) are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗p < 0.01

Table C.14. Impact of supplier flooding on supplier flood risk (coinciding reports)

Dependent Variable: ∆ Flood Risk of Suppliers Flooded by≤ 1%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
0.0777(0.0599)-0.100(0.119)(2)
0.0661(0.0712)-0.0763(0.121)≤ 5%(3)0.0943(0.0643)-0.0769(0.126)Yes(4)0.0411(0.0724)-0.451∗∗∗(0.118)≤ 10%(6)0.0497(0.0809)-0.410∗∗∗(0.121)

Yes
Yes

0.007385,941(5)-0.00569(0.101)-0.428∗∗∗(0.119)0.025285,330(7)0.0600(0.0821)-0.765∗∗∗(0.276)0.0081
86,654(9)0.0671(0.0932)-0.728∗∗(0.289)

Yes
Yes

Yes
0.0591
82,838(8)0.00608(0.106)-0.745∗∗∗(0.281)0.027486,054

Yes
Yes
0.057883,5500.009386,7280.0283
86,128Yes
0.057883,621

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on the change in sales-weighted average floodrisk among suppliers flooded by ≤ 1%, ≤ 5%, or ≤ 10% during the flood risk windows. Observations are firm-by-flood-year-month pairs whose 2011 and2019 addresses are known and ≤ 10km apart. We restrict attention to transactions for which buyer and seller reports coincide precisely. Standard errors (inparentheses) are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

74

Figure C.28. Dynamic impact of supplier flooding on supplier flood risk (coinciding reports)(b) Suppliers flooded by ≤ 5%(a) All Suppliers

Notes: Panels (a) and (b) plot OLS estimates of the effect of supplier flooding on the change in sales-weighted average flood risk among all suppliers and suppliers flooded by ≤ 5% following equations (10) and (24), respectively.
Observations are firm-year-month pairs for which the 2011 and 2019 addresses are known and ≤ 10km apart.
Panel (b) excludes buyers which experience own or supplier flooding before the lag. We restrict attention totransactions for which buyer and seller reports coincide precisely. The 95% confidence intervals rely on standard
errors clustered at the time × district level.

75

C.6

Floods with return periods of 1 in 10 years and 1 in 50 years

Our central results relating to flood risk consider the combined fluvial and pluvial flood risk, measured
as the expected flood depth in each location associated with a 1 in 100 year flood. This represents
the most expansive definition of flood risk captured by the Fathom flood risk data, as can be seen bycomparing Panel (c) of Figure 1 with both panels in Figure A.4. In this section we demonstrate therobustness of results to measuring flood risk in relation to 1 in 10 year floods or 1 in 50 year floods.
C.6.1

Firm location

Table C.15. Impact of flooding on firm relocation and location flood risk (1 in 10 year return period)

Max Share of 2km Buffer Flooded

Move Dummy∆ Flood Risk

(1)

(2)

(3)

(4)

1.583∗∗(0.754)1.509∗∗(0.745)-1.245(0.871)-0.0891(0.530)District FE
District × Fathom 1 in 10 FE
R2
N

Yes
0.046
43,848

Yes
Yes
0.066
43,665

0.072
5,737

Yes
0.380
5,689

Notes: Columns (1) and (2) display logit estimates of the effect of flooding on theprobability of relocating by >10km following equation (3). Columns (3) and (4) report
OLS estimates of the effect of flooding on moving firms’ change in flood risk as specified
in equation (4). Here, flood risk is measured for a 1 in 10 instead of a 1 in 100 year
return period (for both the dependent variables in columns (3) and (4) and the FEs incolumns (2) and (4)). Observations are firms geocoded in 2011 and 2019. The flood risk
regressions only include firms which moved by >10km. Standard errors (in parentheses)
are clustered at the district level. R2 refers to McFadden’s Pseudo R2 for columns (1)and (2). ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

76

Table C.16. Impact of flooding on firm relocation and location flood risk (1 in 50 year return period)

Max Share of 2km Buffer Flooded

Move Dummy∆ Flood Risk

(1)

(2)

(3)

(4)

1.583∗∗(0.754)1.950∗∗(0.947)-1.791∗(0.997)-0.317(0.529)District FE
District × Fathom 1 in 50 FE
R2
N

Yes

Yes

0.046
43,848Yes
0.068
43,522

Yes
0.447
5,588

0.111
5,737

Notes: Columns (1) and (2) display logit estimates of the effect of flooding on theprobability of relocating by >10km following equation (3). Columns (3) and (4) report
OLS estimates of the effect of flooding on moving firms’ change in flood risk as specified
in equation (4). Here, flood risk is measured for a 1 in 50 instead of a 1 in 100 year
return period (for both the dependent variables in columns (3) and (4) and the FEs incolumns (2) and (4)). Observations are firms geocoded in 2011 and 2019. The flood risk
regressions only include firms which moved by >10km. Standard errors (in parentheses)
are clustered at the district level. R2 refers to McFadden’s Pseudo R2 for columns (1)and (2). ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

C.6.2

Supplier choice

Table C.17. Impact of supplier flooding on supplier flood risk (1 in 10 year return period)∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

-0.0513(0.0596)-0.409∗∗∗(0.155)-0.0562(0.0608)-0.431∗∗∗(0.160)-0.0537(0.0763)-0.492∗∗∗(0.185)

Yes
Yes
0.0073
144,566

0.0192
144,230

Yes
0.0551
139,302

Notes: The table reports OLS estimates of the effects of supplier and own flooding
on the change in sales-weighted average flood risk among all suppliers following
equation (8). Here, flood risk is measured for a 1 in 10 instead of a 1 in 100 year
return period. Observations are firm-by-flood-year-month pairs for which the 2011
and 2019 addresses are known and ≤ 10km apart. Standard errors (in parentheses)
are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

77

Table C.18. Impact of supplier flooding on supplier flood risk (1 in 10 year return period)

Dependent Variable: ∆ Flood Risk of Suppliers Flooded by≤ 1%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
-0.0799(0.0558)-0.0853(0.0703)(2)-0.0879(0.0575)-0.0898(0.0672)≤ 5%(3)-0.0979(0.0729)-0.0835(0.0770)Yes(4)-0.0854(0.0566)-0.233∗∗∗(0.0777)≤ 10%(6)-0.0958(0.0736)-0.249∗∗∗(0.0845)

Yes
Yes

0.0058144,007(5)-0.0810(0.0584)-0.242∗∗∗(0.0753)0.0191
143,668(7)-0.0624(0.0598)-0.414∗∗(0.175)(8)-0.0530(0.0592)-0.431∗∗(0.179)

Yes
Yes

Yes
0.0551
138,752

0.0060144,423(9)-0.0709(0.0779)-0.456∗∗(0.198)0.0187144,086

Yes
Yes
0.0544
139,1640.0069144,494

0.0189144,157

Yes
0.0543
139,235

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on the change in sales-weighted average flood risk
among suppliers flooded by ≤ 1%, ≤ 5%, or ≤ 10% during the flood risk windows. Here, flood risk is measured for a 1 in 10 instead of a 1 in 100 year return
period. Observations are firm-by-flood-year-month pairs whose 2011 and 2019 addresses are known and ≤ 10km apart. Standard errors (in parentheses) areclustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Figure C.29. Dynamic impact of supplier flooding on supplier flood risk (1 in 10 year return period)(b) Suppliers flooded by ≤ 5%(a) All Suppliers

Notes: Panels (a) and (b) plot OLS estimates of the effect of supplier flooding on the change in sales-weighted
average flood risk among all suppliers and suppliers flooded by ≤ 5% following equations (10) and (24), respectively. Here, flood risk is measured for a 1 in 10 instead of a 1 in 100 year return period. Observations are
firm-year-month pairs for which the 2011 and 2019 addresses are known and ≤ 10km apart. Panel (b) excludes
buyers which experience own or supplier flooding before the lag. The 95% confidence intervals rely on standard
errors clustered at the time × district level.

78

Table C.19. Impact of supplier flooding on supplier flood risk (1 in 50 year return period)∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

-0.0636(0.0862)-0.558∗∗∗(0.167)-0.0827(0.0786)-0.593∗∗∗(0.174)-0.0846(0.109)-0.667∗∗∗(0.194)

Yes
Yes
0.0100144,566

0.0300143,843

Yes
0.0582
139,302

Notes: The table reports OLS estimates of the effects of supplier and own flooding
on the change in sales-weighted average flood risk among all suppliers following
equation (8). Here, flood risk is measured for a 1 in 50 instead of a 1 in 100 year
return period. Observations are firm-by-flood-year-month pairs for which the 2011
and 2019 addresses are known and ≤ 10km apart. Standard errors (in parentheses)
are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table C.20. Impact of supplier flooding on supplier flood risk (1 in 50 year return period)

Dependent Variable: ∆ Flood Risk of Suppliers Flooded by≤ 1%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
-0.109(0.0874)-0.120(0.0846)(2)-0.0999(0.0795)-0.128(0.0797)≤ 5%(3)-0.146(0.114)-0.101(0.0942)Yes(4)-0.109(0.0877)-0.259∗∗∗(0.0934)≤ 10%(6)-0.141(0.113)-0.256∗∗(0.102)

Yes
Yes

0.0078144,007(5)-0.0991(0.0791)-0.270∗∗∗(0.0896)0.0303
143,282(7)-0.0823(0.0899)-0.468∗∗(0.200)0.0078144,423(9)-0.115(0.117)-0.494∗∗(0.227)

Yes
Yes

Yes
0.0560138,752(8)-0.0719(0.0799)-0.492∗∗(0.207)0.0289143,703

Yes
Yes
0.0561
139,1640.0088144,494

0.0294
143,774

Yes
0.0562
139,235

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on the change in sales-weighted average flood risk
among suppliers flooded by ≤ 1%, ≤ 5%, or ≤ 10% during the flood risk windows. Here, flood risk is measured for a 1 in 50 instead of a 1 in 100 year return
period. Observations are firm-by-flood-year-month pairs whose 2011 and 2019 addresses are known and ≤ 10km apart. Standard errors (in parentheses) areclustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

79Figure C.30. Dynamic impact of supplier flooding on supplier flood risk (1 in 50 year return period)(b) Suppliers flooded by ≤ 5%(a) All Suppliers

Notes: Panels (a) and (b) plot OLS estimates of the effect of supplier flooding on the change in sales-weighted average
flood risk among all suppliers and suppliers flooded by ≤ 5% following equations (10) and (24), respectively. Here, floodrisk is measured for a 1 in 50 instead of a 1 in 100 year return period. Observations are firm-year-month pairs for which
the 2011 and 2019 addresses are known and ≤ 10km apart. Panel (b) excludes buyers which experience own or supplier
flooding before the lag. The 95% confidence intervals rely on standard errors clustered at the time × district level.

80

C.7

6-month or 12-month partner window for indirect treatment specifications

The central results define a buyer firm’s suppliers as those firms from which the buyer firm has madepurchases in the prior three months. This section considers the robustness of results to instead using
a 6-month or 12-month window to define a buyer firm’s suppliers.
C.7.1

Supplier diversification
Figure C.31. Impact of supplier flooding on log number of suppliers (6 month window)

Notes: The panel plots OLS estimates of the effect of supplier flooding on the log number of suppliers
following equation (7). Here, we define the treatment variable based on a six instead of a three month
supplier window. Observations are firm-month-years whose 2011 and 2019 addresses are known and ≤
10km apart. We restrict attention to transactions for which buyer and seller reports coincide precisely.
The 95% confidence intervals rely on standard errors clustered at the firm-level.

Figure C.32. Impact of supplier flooding on log number of suppliers (12 month window)

Notes: The panel plots OLS estimates of the effect of supplier flooding on the log number of suppliers
following equation (7). Here, we define the treatment variable based on a twelve instead of a threemonth supplier window. Observations are firm-month-years whose 2011 and 2019 addresses are knownand ≤ 10km apart. We restrict attention to transactions for which buyer and seller reports coincide
precisely. The 95% confidence intervals rely on standard errors clustered at the firm-level.

81C.7.2

Supplier choice
Table C.21. Impact of supplier flooding on supplier flood risk (6 month window)∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

-0.00582(0.0799)-0.633∗∗∗(0.135)-0.103(0.0845)-0.659∗∗∗(0.138)-0.0141(0.0965)-0.729∗∗∗(0.151)

Yes
Yes
0.0107
155,182

0.0312
154,474

Yes
0.0553
149,598

Notes: The table reports OLS estimates of the effects of supplier and own flooding on the change in sales-weighted average flood risk among all suppliers following
equation (8). Here, we use a 6 instead of a 3 month window to define suppliers.
Observations are firm-by-flood-year-month pairs for which the 2011 and 2019 addresses are known and ≤ 10km apart. Standard errors (in parentheses) are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table C.22. Impact of supplier flooding on supplier flood risk (6 month window)

Dependent Variable: ∆ Flood Risk of Suppliers Flooded by≤ 1%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
-0.0621(0.0830)-0.129(0.0855)(2)-0.0934(0.103)-0.123(0.0836)≤ 5%(3)-0.0718(0.104)-0.125(0.0878)(4)-0.0656(0.0844)-0.298∗∗∗(0.0936)

Yes

≤ 10%
(6)
-0.0684(0.106)-0.311∗∗∗(0.0971)

Yes
Yes

0.0083
154,643(5)-0.0992(0.101)-0.304∗∗∗(0.0909)

0.0319
153,929(7)-0.0319(0.0884)-0.482∗∗∗(0.163)0.0084
155,051(9)-0.0398(0.111)-0.518∗∗∗(0.180)

Yes
Yes

Yes
0.0542
149,070(8)-0.0892(0.101)-0.496∗∗∗(0.167)0.0307
154,344

Yes
Yes
0.0535
149,473

0.0090155,1160.0305
154,409

Yes
0.0534
149,538

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on the change in sales-weighted average flood risk
among suppliers flooded by ≤ 1%, ≤ 5%, or ≤ 10% during the flood risk windows. Here, we use a 6 instead of a 3 month window to define suppliers. Observations
are firm-by-flood-year-month pairs whose 2011 and 2019 addresses are known and ≤ 10km apart. Standard errors (in parentheses) are clustered at the time ×
district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

82

Figure C.33. Dynamic impact of supplier flooding on supplier flood risk (6 month window)(b) Suppliers flooded by ≤ 5%(a) All Suppliers

Notes: Panels (a) and (b) plot OLS estimates of the effect of supplier flooding on the change in sales-weighted
average flood risk among all suppliers and suppliers flooded by ≤ 5% following equations (10) and (24), respectively. Here, we use a 6 instead of a 3 month window to define suppliers. Observations are firm-year-month pairs
for which the 2011 and 2019 addresses are known and ≤ 10km apart. Panel (b) excludes buyers which experienceown or supplier flooding before the lag. The 95% confidence intervals rely on standard errors clustered at thetime × district level.

Table C.23. Impact of supplier flooding on supplier flood risk (12 month window)∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

-0.00923(0.0676)-0.622∗∗∗(0.120)-0.123∗(0.0651)-0.650∗∗∗(0.123)0.0112(0.0759)-0.733∗∗∗(0.136)

Yes
Yes
0.0107
164,6810.0294
163,976

Yes
0.0548
158,820

Notes: The table reports OLS estimates of the effects of supplier and own flooding
on the change in sales-weighted average flood risk among all suppliers following
equation (8). Here, we use a 12 instead of a 3 month window to define suppliers. Observations are firm-by-flood-year-month pairs for which the 2011 and 2019addresses are known and ≤ 10km apart. Standard errors (in parentheses) are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

83

Table C.24. Impact of supplier flooding on supplier flood risk (12 month window)

Dependent Variable: ∆ Flood Risk of Suppliers Flooded by≤ 1%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
-0.169∗(0.0913)-0.128∗(0.0770)(2)-0.249∗(0.136)-0.130(0.0807)≤ 5%(3)-0.177∗(0.106)-0.131(0.0808)(4)-0.193∗∗(0.0840)-0.434∗∗∗(0.0846)

Yes

≤ 10%
(6)
-0.182∗(0.0996)-0.456∗∗∗(0.0872)

Yes
Yes

0.0075
131,808(5)-0.315∗∗∗(0.110)-0.449∗∗∗(0.0858)

0.0297131,088(7)-0.143(0.103)-0.659∗∗∗(0.188)0.0084132,180(9)-0.129(0.119)-0.703∗∗∗(0.206)

Yes
Yes

Yes
0.0560126,563(8)-0.328∗∗∗(0.107)-0.683∗∗∗(0.197)0.0293
131,469

Yes
Yes
0.0550126,9230.0096132,2360.0301
131,525

Yes
0.0564
126,978

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on the change in sales-weighted average flood risk
among suppliers flooded by ≤ 1%, ≤ 5%, or ≤ 10% during the flood risk windows. Here, we use a 12 instead of a 3 month window to define suppliers.
Observations are firm-by-flood-year-month pairs whose 2011 and 2019 addresses are known and ≤ 10km apart. Standard errors (in parentheses) are clusteredat the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Figure C.34. Dynamic impact of supplier flooding on supplier flood risk (12 month window)(b) Suppliers flooded by ≤ 5%(a) All Suppliers

Notes: Panels (a) and (b) plot OLS estimates of the effect of supplier flooding on the change in sales-weighted
average flood risk among all suppliers and suppliers flooded by ≤ 5% following equations (10) and (24), respectively. Here, we use a 12 instead of a 3 month window to define suppliers. Observations are firm-year-month pairs
for which the 2011 and 2019 addresses are known and ≤ 10km apart. Panel (b) excludes buyers which experienceown or supplier flooding before the lag. The 95% confidence intervals rely on standard errors clustered at thetime × district level.

C.8

Results including moving firms

The central results investigating supplier choice restrict attention to firms that did not relocate morethan 10km over the sample period. This section considers the robustness of results to including
relocating firms.

84

C.8.1

Supplier Diversification

Figure C.35. Supplier Diversification: Impact of flooding on log number of suppliers (incl. movers)(a) Own flooding(b) Supplier flooding

Notes: The panels plot OLS estimates of the effect of own flooding or supplier flooding on the log
number of suppliers following equations (6) and (7), respectively. The unit of observation is a firmmonth-year. Here, we do not drop relocating firms. We restrict attention to transactions for whichbuyer and seller reports coincide precisely. The 95% confidence intervals rely on standard errors
clustered at the firm-level.

C.8.2

Supplier Choice
Table C.25. Impact of supplier flooding on supplier flood risk (incl. movers)∆ Supplier Flood Risk

Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)

(2)

(3)

-0.0502(0.0438)-0.495∗∗∗(0.144)-0.0775∗(0.0418)-0.506∗∗∗(0.146)-0.0477(0.0468)-0.623∗∗∗(0.157)

Yes
Yes
0.0095217,2890.0275
216,535

Yes
0.0545208,992

Notes: The table reports OLS estimates of the effects of supplier and own flooding on the change in sales-weighted average flood risk among all suppliers following
equation (8). Observations are all firm-by-flood-year-month pairs. Here, this includes relocating firms. Standard errors (in parentheses) are clustered at the time× district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

85

Table C.26. Impact of supplier flooding on supplier flood risk (incl. movers)

Dependent Variable: ∆ Flood Risk of Suppliers Flooded by≤ 1%
Own Max Flood Extent
Suppliers’ Max Flood Extent
Time × District FE
Time × District × Risk Dec. FE
Time × District × Industry FE
R2
N

(1)
-0.0504(0.0461)-0.112∗(0.0656)(2)-0.0641(0.0420)-0.107∗(0.0619)≤ 5%(3)-0.0558(0.0496)-0.120∗(0.0693)Yes(4)-0.0592(0.0463)-0.213∗∗∗(0.0773)≤ 10%(6)-0.0632(0.0498)-0.239∗∗∗(0.0780)

Yes
Yes

0.0082
216,282(5)-0.0803∗(0.0440)-0.212∗∗∗(0.0743)0.0293
215,536(7)-0.0558(0.0454)-0.406∗∗(0.168)(8)-0.0813∗(0.0429)-0.410∗∗(0.171)

Yes
Yes

Yes
0.0557208,0100.0079217,018(9)-0.0568(0.0481)-0.454∗∗(0.182)0.0277
216,271

Yes
Yes
0.0537208,726

0.0085
217,1390.0272
216,391

Yes
0.0527
208,845

Notes: The table reports OLS estimates following equation (8) of the effects of supplier and own flooding on the change in sales-weighted average flood risk
among suppliers flooded by ≤ 1%, ≤ 5%, or ≤ 10% during the flood risk windows. Observations are all firm-by-flood-year-month pairs. Here, this includes
relocating firms. Standard errors (in parentheses) are clustered at the time × district level. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Figure C.36. Dynamic impact of supplier flooding on supplier flood risk (incl. movers)(b) Suppliers flooded by ≤ 5%(a) All Suppliers

Notes: Panels (a) and (b) plot OLS estimates of the effect of supplier flooding on the change in sales-weighted
average flood risk among all suppliers and suppliers flooded by ≤ 5% following equations (10) and (24), respectively. Observations are firm-by-flood-year-month pairs. Here, this includes relocating firms. Panel (b) excludes
buyers which experience own or supplier flooding before the lag. The 95% confidence intervals rely on standard
errors clustered at the time × district level.

C.9

Results excluding repeated exposures

In the standard event studies, we consider a firm treated at the intensity of its first treatment in all
periods following this first treatment. Given that treatment in a subsequent flood event could affectestimates for later treatment lags, in this section we present event study results restricting attention
to firms which are either never treated or treated only in one flood event. For the two-month flood
events (Aug-Sep 2011 and Jul-Aug 2015), we define event time relative to the first month of the event.

86C.9.1

Impact of flooding on firm sales and purchases
Figure C.37. Impact of flooding on firm sales and purchases (excl. repeated exposures)(a) Log sales(b) Log purchases

Notes: The panels plot OLS estimates of the effect of flooding on log declared sales and purchases
as specified in equation (1). Observations are firm-month-years which are flooded in no or one
flood event. The 95% confidence intervals rely on standard errors clustered at the firm-level.

C.9.2

Supplier diversification

Figure C.38. Supplier Diversification: Impact of flooding on log number of suppliers (excl. repeatedexposures)(a) Own flooding(b) Supplier flooding

Notes: The panels plot OLS estimates of the effect of own flooding or supplier flooding on the log
number of suppliers following equations (6) and (7), respectively. Observations are firm-month-years
whose 2011 and 2019 addresses are known, ≤ 10km apart, and which are flooded in no or one flood
event. We restrict attention to transactions for which buyer and seller reports coincide precisely. The
95% confidence intervals rely on standard errors clustered at the firm-level.

87C.9.3

Supplier Choice

Figure C.39. Dynamic impact of supplier flooding on flood risk of all suppliers (excl. repeatedexposures)

Notes: The panel plots estimates of the effect of supplier flooding on the change in sales-weighted average floodrisk among all suppliers following equation (10). Observations are firm-year-months for which the 2011 and2019 addresses are known, ≤ 10km apart and which are treated in no or one flood event. The 95% confidence
intervals rely on standard errors clustered at the time × district level.

C.10

Robustness of route-level flooding impacts
Table C.27. Impact of route-level flooding on probability of relationship being active

Dependent variable: 1(Salesbst > 0)

(Shortest Path Floodedbst∗ ) × Postt
Years since first sale FE
Buyer × Time FE
Seller × Time FE
Buyer × Seller FE
Sample
N
R2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

-0.009***(0.001)-0.010***(0.001)-0.002***(0.001)-0.005***(0.001)-0.006***(0.001)-0.010***(0.002)-0.007***(0.002)

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Baseline

NonManCap

CV onlyMov2015 only

1 Exposure

11,193,406
0.546

33,856,461
0.558

9,471,209
0.585

10,288,183
0.505

26,884,973
0.536

11,193,406
0.546

8,021,948
0.562

The table reports the response of the probability of sales being positive in the (b, s) relationship around the first time the shortest
path between b and s gets flooded following equation (11) but with a static treatment. Observations are buyer-seller-weeks for whichb and s are both active. Robust standard errors in parentheses, clustered at the relationship level. Sample abbreviations are as
follows. Baseline: both buyer and seller are manufacturing firms; at least two months of transactions, excluding relationships whereat least one firm moves between 2011 and 2019. NonMan: like baseline, but includes non-manufacturing firms. Cap: like baseline, but
excluding firms that are capital goods suppliers. CV: like baseline, but including only transactions that are reported by both buyer
and seller and that are in agreement. Mov: like baseline, but includes also firms that move. 2015 only: consider only floods from2015. 1 Exposure: only relationships where the shortest path gets flooded at most once after the first transaction in the relationship.

88