WPS7714
Policy Research Working Paper 7714
Insuring Growth
The Impact of Disaster Funds on Economic
Reconstruction in Mexico
Alain de Janvry
Alejandro del Valle
Elisabeth Sadoulet
Finance and Markets Global Practice Group
June 2016
Policy Research Working Paper 7714
Abstract
Climate change has considerably increased the likelihood of the impact of disaster funds on local economic activ
of experiencing extreme weather events. Governments in ity. The main finding is that access to disaster funding
developing countries have a limited capacity to smooth boosts local economic activity between 2 and 4 percent
the losses created by extreme weather, and could poten in the year following the disaster. Another finding is that
tially benefit from the introduction of disaster funds, that is, the positive impact of disaster funds on local economic
exante budgeting allocations for postdisaster reconstruc recovery can persist for as long as a year and a half after
tion. So far the implementation of disaster funds has been the disaster. Consistent with these findings, we additionally
limited, in part because it is still unclear whether disaster show that access to disaster funding leads to a large and
funds provide a costeffective way of coping with these sustained 76 percent increase in the growth of local con
losses. By taking advantage of the sharp rules that govern struction employment. This labor market impact slightly
the municipallevel eligibility for reconstruction funds in precedes the overall increase in local economic activity.
Mexico, this paper provides some of the first estimates
This paper is a product of the Disaster Risk Financing and Insurance Program (DRFIP), a partnership of the World Bank’s
Finance and Markets Global Practice Group and the Global Facility for Disaster Reduction and Recovery, with funding
from the UK Department for International Development. It is part of a larger effort by the World Bank to provide open
access to its research and make a contribution to development policy discussions around the world. Policy Research Working
Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at adelvalle@gsu.edu,
alain@berkeley.edu, or esadoulet@berkeley.edu.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Insuring Growth: The Impact of Disaster Funds on
Economic Reconstruction in Mexico
Alain de Janvry∗ Alejandro del Valle† Elisabeth Sadoulet∗‡
JEL Classiﬁcation: Q54, H12, H84, I32, O10.
Keywords: Disaster Risk Financing and Insurance; Financial Institutions; Climate Change
and Disaster Risk; Shocks and Vulnerability to Poverty; Public Finance.
∗
University of California at Berkeley
†
Georgia State University
‡
We are grateful to Juan Jose Miranda, Oscar Ishizawa, and Liam Wren Lewis for feedback on earlier drafts of
this paper, as well as seminar participants at Ferdi, COP21, the Paris School of Economics, GSU, and the World
Bank for useful comments. We have also beneﬁted from comments and suggestions made by Michael Anderson,
Daniel Clarke, Karen Macours, Stephen Shore, and Eric Strobl. We are also indebted to Salvador Perez and
Juan Miguel Adaya for facilitating the data that made this project possible. We thank Danamona Andrianari
manana, Debasish Kaushik, and Kunal Puranik for excellent research assistance. Please send correspondence to:
adelvalle@gsu.edu, alain@berkeley.edu or esadoulet@berkeley.edu
1 Introduction
With continuing greenhouse gas emissions in excess of the absorptive capacity of land and
oceans, inexorably contributing to climate change, damages from extreme weather events are
likely to increase in the coming decades (Emanuel, 2013; Mendelsohn et al., 2012). Governments
in developing countries have a limited ability to smooth the shocks created by extreme weather
events. In particular, budget reallocations following a disaster tend to be ineﬃcient, and often
result in costly liquidity crunches (Mahul, 2011). For these reasons, exante risk ﬁnancing
instruments like national disaster funds are a primary focus of Sovereign Disaster Risk Financing
eﬀorts. Given the large resources required to set up these funds, a key question is whether
disaster funds provide a cost eﬀective tool to cope with the losses created by natural disasters.
Taking advantage of a unique natural experiment and dataset, this paper provides some of the
ﬁrst evidence on the causal impact of disaster funds on economic recovery.
We circumvent endogeneity in the provision of rapid reconstruction funds by using a fuzzy
regression discontinuity design that exploits the rules that govern municipal level eligibility to
reconstruction funds from the Mexican disaster fund Fonden.1 Since only municipalities that
have oﬃcially experienced a disaster, as determined by event thresholds, are eligible for disaster
funds, we recover causal estimates of the impact of disaster funds on local economic activity in
three steps. In the ﬁrst step, we compare the post disaster economic outcomes of municipalities
that are just below and above the thresholds that deﬁne the occurrence of the disaster. In
the second step, we account for data restrictions by estimating how moving from just below to
just above the threshold increases the likelihood of being eligible for Fonden. In the third step,
we recover the causal impact of Fonden on local economic activity by rescaling the coeﬃcient
derived in the ﬁrst step by the coeﬃcient derived in the second step.
We measure changes in economic activity at the municipal level using high resolution satellite
imagery that allows us to measure the intensity of light as observed from outerspace. Because
our night light measure is a good proxy of subnational level economic activity we are able to
track the diﬀerential economic performance created by the rapid provision of reconstruction
funds.
Our main ﬁnding is that municipalities with access to Fonden grew between 2 to 4% more than
those without Fonden in the year following the disaster. Conservative beneﬁt cost ratios, for the
same time period, are in the 1.52 to 2.89 range. The paper additionally documents the impact
of Fonden over the post disaster period. We ﬁnd that the economic expansion triggered by the
program peaks 15 months after the disaster, and that from this point onward municipalities
without access to Fonden begin to catchup. Consistent with the previous ﬁndings, the paper
additionally documents a large and sustained increase in the growth of construction employment
1
A municipality is a second level administrative unit in Mexico. There are currently 2461 municipalities.
2
that slightly precedes the overall increase in local economic activity. At its peak this labor
market eﬀect is in the order of 76%.
The rest of the paper is organized as follows. Section 2 gives a brief background of Mexico’s
disaster fund Fonden. Section 3 explains the identiﬁcation strategy. Section 4 discusses the
datasets used. Section 5 presents the results. Section 6. shows that nights lights are a good
proxy of subnational economic activity in Mexico. Section 7 concludes.
2 Background
Fonden is a fund set up by the Mexican government to manage the risk created by natural
disasters. Fonden was ﬁrst introduced in 1996, but it was not used operationally until 1999.
The program is ﬁnanced by a protected budget appropriation and through the placement of
catastrophe bonds. Mexico is the ﬁrst developing country to use this type of bonds to transfer
part of its catastrophe risk to capital markets (Cardenas et al., 2007). While FONDEN was
designed to ﬁnance some exante risk management interventions through the complementary
Fopreden fund, its primary purpose is funding emergency relief operations and reconstruction
eﬀorts (Mahul, 2011). Currently, the bulk of Fonden funds is spent on the reconstruction of
lowincome housing and public infrastructure (World Bank, 2012).
Crucially for our paper, Fonden disbursement of funds has been determined by a clear set of
operational rules since its onset. These rules balance accountability and transparency concerns
with a timely disbursement of funds. The operational rules have been revised on ﬁve occasions,
but since 2004, they include the event thresholds used in this paper. The process for accessing
and executing reconstruction funds can be divided into four steps: (i) declaration of a natural
disaster; (ii) damage assessment and request for reconstruction funding; (iii) disbursement of
resources and reconstruction; (iv) public reporting of postdisaster activities.
Step (i) uses the event thresholds deﬁned in the operational rules to determine whether a
declaration of disaster can be issued. Since this veriﬁcation process is central to our identiﬁcation
strategy, we discuss this step in detail in the following section.
Step (ii) begins within 24 hours of the declaration of disaster when a damage assessment com
mittee is formed. This committee, comprised of both federal and state representatives, visits
the aﬀected area and determines the extent of damages. The ﬁndings and supporting documen
tation, including itemized reconstruction costs, are veriﬁed by an interministerial commission
before being sent for ﬁnal approval to Fonden. In the majority of cases, this process is com
pleted within three months. One important feature of this setup is that Fonden funding will be
proportional to the damages experienced in a municipality.
3
Step (iii) diﬀers depending on the ownership of the damaged asset. Federal assets are fully
covered by Fonden and reconstruction is executed by a federal entity. State or municipal
government assets only have partial Fonden coverage (50% in most cases), and reconstruction is
executed by both federal entities and private service providers. The partial Fonden coverage has
been successful at enticing local governments to participate in the Fonden process. Nonetheless,
the greater degree of coordination with local governments implies that federal owned assets are
likely to be reconstructed in shorter time frames. Average projected reconstruction time is seven
months.
Step (iv) involves the publishing of detailed records on postdisaster allocations. This real time
publication occurs via the website of the Mexican Secretariat of the Interior. Readers interested
in further details are referred to Chapter 3 in World Bank (2012).
Following this disbursement process, in a year of frequent and severe natural disasters like 2010,
the program responded to 54 disasters, with some of those disasters aﬀecting areas that spanned
over 94 municipalities. During that year alone Fonden provided $6.4 billion (PPP, constant 2005
international dollars) in rapid reconstruction funds. In an average year Fonden responds to 33
disasters, and on average 14 municipalities are aﬀected per disaster. Average municipal Fonden
expenditures amount to roughly $4.2 million and median expenditures to $1 million. Overall
median yearly program expenditures amount to $972 million, or $8.3 per capita.
Mechanisms to mitigate the impact of disasters
As previously mentioned, the bulk of Fonden expenditures is related to reconstruction eﬀorts.
These eﬀorts include the reconstruction of federal and state roads, the provision of funds to
reconstruct lowincome housing, and the rebuilding of hydraulic, health, and educational in
frastructure. These expenditures can provide a double gain to economic development. First,
by coping with the losses created by natural disasters. Second, by enabling local governments
and households to reallocate resources from safer but ineﬃcient lowrisk lowreturn productive
activities to more risky higheryielding activities.
Our current analysis estimates the impact of Fonden on economic activity at diﬀerent points
in the postdisaster period. The focus of our analysis takes place in the 20042013 period when
road expenditures accounted for the bulk of overall expenditures.2 Accordingly, we expect
Fonden to aﬀect economic activity primarily by enabling municipalities to quickly rebuild their
road network following a disaster.
2
The shares of expenditure by category vary year by year depending on the type of damages that have
occurred. During the period of analysis, road expenditures accounted for 57% of overall expenditures, and for
more than 65% of expenditures in ﬁve of the years analyzed.
4
3 Empirical Strategy
This paper assesses the impact of Fonden by comparing economic outcomes across essentially
identical municipalities who happen to diﬀer in their eligibility to Fonden funding. This oc
curs because the event thresholds used to determine whether a natural disaster has occurred
eﬀectively discretize a continuous measure of the intensity of a disaster into an eligible and
a noneligible group. It is thus possible to observe a set of municipalities that experienced a
natural disaster of a similar magnitude, but where only a subset of those municipalities are
“oﬃcially” considered to have experienced a natural disaster.
The details of this process are as follows. The process begins with a request from the state
governor. This request contains a list of municipalities that are believed to have experienced
damages as a result of a natural disaster. The request is veriﬁed by an independent technical
agency that compares measurements of the intensity of the disaster to the thresholds set out
in Fonden operational guidelines. For example, in the case of heavy rainfall, the national
water commission CONAGUA compares the rainfall at the weather station representative of
the requested municipality to the Fonden heavy rain threshold, that is, rainfall greater or equal
to the percentile 90 of historic rain recorded at that weather station. CONAGUA will then list
the municipalities that pass the threshold, and a declaration of disaster will be issued for this
set of municipalities. As previously mentioned, the declaration of disaster is important because
it determines eligibility to Fonden funding.
Fonden covers a broad range of events including: earthquakes, forest ﬁres, heavy rainfall, tropical
storms, hurricanes, landslides, hail storms, areal ﬂooding, and riverine ﬂooding. In this paper
we will focus on heavy rainfall events because of data availability. Heavy rainfall events account
for as much as 68% of all events, and for as much as 51% of overall program expenditures. In
spite of the sharp rule that determines when a heavy rainfall event has taken place, we use
a fuzzy regression discontinuity design because we are unable to perfectly distinguish between
heavy rainfall events and other types of events, and because we are unable to fully match
municipalities to the set of weather stations used for veriﬁcation.
In the fuzzy regression discontinuity design we estimate two causal eﬀects: the eﬀect of crossing
the percentile 90 threshold on the probability of receiving Fonden, and the eﬀect of crossing
the percentile 90 threshold on local economic activity. Speciﬁcally, we run the following regres
sions:
fmt = γ0 + γ1 zmt + γ2 g (rmt − cmt ) + γ3 g (rmt − cmt ) ∗ zmt + θt + vmt (1)
ymt = β0 + β1 zmt + β2 g (rmt − cmt ) + β3 g (rmt − cmt ) ∗ zmt + θt + εmt (2)
5
ˆ1
β
π
ˆ1 = (3)
γ
ˆ 1
Equation 1, the ﬁrst stage, is estimated by regressing a dummy that takes the value of one when
municipality m in year t receives Fonden, fmt , on an indicator variable that takes the value of
one when an observation falls above the threshold, zmt . That is, zmt = 1(rmt ≥ cmt ) where
rmt is the amount of rainfall on the day requested and cmt is the percentile 90 threshold. The
function g (rmt − cmt ) represents the relationship between the outcome and the forcing variable,
that is, millimeters of rainfall to the percentile 90 threshold. We will consider various ways of
modeling this relationship. First we will model g (·) as a ﬂexible polynomial function on either
side of the threshold and use the full sample to estimate it. Second, we will assume that g (·)
is linear and use a sample that falls within an optimal bandwidth to estimate it. To determine
the bandwidth we will use the methods proposed by Imbens and Kalyanaraman (2012) and
Calonico et al. (2014b).
Equation 2, the reduced form, is derived by regressing our measure of local economic activity,
ymt , on zmt . The estimation procedure is analogous to that of equation 1. Next, we derive
the impact of Fonden on local economic activity, π1 , by rescaling the reduced form coeﬃcient
of zmt by its ﬁrst stage coeﬃcient.3 Following Henderson et al. (2012) we include year ﬁxed
eﬀects in all of our speciﬁcations in order to ensure the comparability of satellite imagery over
time.
4 Data
For our analysis, we use data from several sources. We proxy changes in municipal level economic
activity by using imagery from the United States Air Force Defense Meteorological Satellite
Program (DMSP). Speciﬁcally, we use imagery gathered by three satellites: F15, F16 and F18.
These satellites are in a 101 min nearpolar orbit. This orbit implies that the satellites are
capable of observing every location on earth at roughly the same local time each day. In the
case of Mexico the satellite overpass occurs between 7 and 8 pm local time. These satellites use
Operational Linescan System (OLS) sensors to measure the intensity of earth based lights. The
National Oceanic and Atmospheric Administration (NOAA) has developed a methodology to
generate image composites that ﬁlter transient light observed in the raw images. Natural sources
of transient lights include, for example, the bright half of the lunar cycle, auroral activity, and
forest ﬁres, see Elvidge et al. (1997) for details on the ﬁltering process. The resulting stable
cloudfree night light composites measure, by and large, manmade lights. These measures of
night lights have been shown to be good proxies for economic activity at the country level
3
In order to derive standard errors for π
ˆ1 we will instead estimate the coeﬃcient using 2SLS.
6
(Henderson et al., 2011, 2012) and at lower levels of aggregation (Harari and La Ferrara, 2013;
Michalopoulos et al., 2014; Bundervoet et al., 2015; Alesina et al., 2016). Speciﬁcally, related
to our paper the work of Bertinelli and Strobl (2013) and Elliott et al. (2015) have shown,
respectively for the Caribbean and for China, that not only are night lights a reliable proxy of
the economic damages brought about by natural disasters, but that it is night lights measured
at lower levels of aggregation which provide the most reliable proxy. In section 6 we present
our own tests of the relationship between night lights and economic activity at the subnational
level in Mexico.
NOAA publicly provides composites in yearly frequency covering the 1992 to 2012 period. These
satelliteyear datasets have a spatial resolution of approximately 30 arc seconds, that is, each
pixel roughly represents a one kilometer square cells. Each pixel has an associated digital
number (DN) which represents the intensity of lights normalized across satellites in a scale
ranging from 0 (no light) to 63 (maximum light). Given the operational lifespan of the satellites
we will use data from the F16 satellite for the 2004 to 2009 period, and from the F18 satellite
for the 2010 to 2012 period. Following Henderson et al. (2011) our subnational measure of night
lights will be constructed by summing the DN across all pixels that are within the subnational
boundary, dividing the sum by the area of the observed pixels, and then taking the log of the
previous number. We will calculate this measure of brightness at both the state and municipal
level, and refer to it from this point on as log night lights. The key outcome variable will be the
change in log night lights between the year the disaster takes place and the following year.
In addition to the publicly available imagery, NOAA has produced, especially for this paper,
monthly frequency composites for the 2004 to 2013 period. These satellitemonth datasets
are derived using the same process and raw images as the annual composites. In addition to
calculating log night lights at the municipal and state level as we did with the annual composites.
We will construct two additional sets of night light measures from monthly composites. The
ﬁrst will take advantage of the overlapping coverage of the F15 and F16 satellite during the
2004 the 2007 period, and use as source data pixel level averages from the two satellites. The
second will calculate our log night lights measure from composites where we have excluded top
coded pixels, that is, pixels with a DN of 63.
Unfortunately, late sunset during summer months leads to missing log night lights data for some
parts of Mexico, every year, between June and August. In addition in 2009 due to degradation of
the sensor on board the F16 satellite we have up to 7 months of missing night light information.
In order to overcome the constrains created by missing data, we will take averages over monthly
log night lights. In our preferred speciﬁcation the key outcome variable is the log diﬀerence
between the average for the 12 months before the disaster and the average at various points
in the post disaster period. One important implication of using these averages in conjunction
with stable night light imagery is that our results will not be aﬀected by transient phenomena
7
such as cloud cover of ephemeral light sources.
In addition to night lights, we provide direct evidence of the impact of Fonden on local economic
activity by investigating its impact on employment. The employment dataset is constructed
from the labor force survey (LFS) ENOE. Speciﬁcally, we produce a quarteryear panel of
employment at the municipal level for the 2005 to 2013 period. The LFS is produced by the
Mexican statistical oﬃce INEGI. The survey samples 120,000 dwellings per quarter in both
urban and rural municipalities. It has a focus and structure similar to that of the Current
Population Survey, and at any given point in time, it provides information on roughly half
of Mexico’s municipalities. As in the case of night lights, the outcome of interest is the log
diﬀerence in employment between the quarter the disaster takes place and various quarters in
the postdisaster period.
The national water commission (CONAGUA) provided us with three datasets: (i) Data on
historical rainfall at the dayweather station level, this dataset spans the 1920 to 2015 period,
and contains the universe of weather stations. (ii) The weather stationmonth level triggers for
Fonden eligibility. (iii) The mapping between municipalities and representative weather stations.
These datasets where merged with numeric weather station identiﬁers when available, and
with string weather station identiﬁers when not. In spite of using natural language processing
algorithms, we are still unable to fully match municipalities to all the possible weather stations
used for veriﬁcation. The resulting dataset is a municipalmonth level panel that allows us to
observe the rainfall mm to the Fonden heavy rainfall threshold. For events spanning more than
one day the maximum was chosen.
Data on municipal level requests and approvals for disaster declarations were constructed from
the archives of Mexico’s oﬃcial diary. Data on municipal level Fonden expenditures, and
days between disaster and authorization of funds are provided by the Ministry of Finance
(MoF).
Given the introduction of the percentile 90 heavy rainfall rule in 2004 and the availability
of log night lights, we are primarily interested in two samples. When working with annual
composites, the set of relevant events will be those that occur between 2004 and 2011.4 In this
period we observe 3,083 municipal heavy rainfall requests. Of these, we have complete weather
and Fonden threshold information for 1,745. As shown in table 1, using 2000 census data, we
ﬁnd no evidence of systematic diﬀerences between municipalities with missing and complete
information. When we turn to monthly composites we are able to use the complete sample of
heavy rainfall request which covers the period between 2004 and 2013. In this case we observe
5,652 municipal heavy rainfall requests and we have complete information for 2,825. Tables 2
and 3 present summary statistics of the key variables for each sample.
4
Note that while annual night light data are available until 2012, we are interested in the change in night
lights between the year the disaster takes place and the following year.
8
5 Results
5.1 The impact of Fonden on local economic activity
Figure 1a plots the mean probability of receiving Fonden relative to rainfall millimeters (mm)
to the threshold. The optimal bin width is calculated following Calonico et al. (2014a).5 The
ﬁgure reveals a potential jump in the probability of receiving Fonden. Moving from just below
to just above the threshold increases the likelihood of receiving Fonden from about 60% to 80%.
The previous ﬁgure additionally plots a 4th order polynomial ﬁt estimated separately on each
side of the threshold. The polynomial ﬁt reveals that the underlying relationship between the
probability of receiving Fonden and rainfall mm to the threshold could be potentially captured
by a second or third order polynomial function of g (·).
In order to investigate the sensitivity of these results to the choice of binwidth, in ﬁgures 1c to 1f,
we respectively double and triple the number of bins. These ﬁgures produce consistent results,
thereby bolstering the idea that the probability of receiving Fonden increases by approximately
20 percentage points at the threshold.
Next in ﬁgure 1b we plot the mean change in log lights between the year a disaster occurs and
the following year relative to rainfall mm to the threshold. The ﬁgure reveals a clear jump
in brightness at the threshold. Moving from just below to just above the threshold increases
brightness by roughly 0.07 log points. As in the previous case, ﬁgures 1d and 1f, illustrate that
this result is robust to the choice of bin width. The ﬁgures further reveal that in the absence of
Fonden, night lights become dimmer as the relative intensity of the disaster increases, that is, as
the we move towards the threshold from the left. This ﬁnding is important because it suggests
that night lights are capable of measuring the economic slowdown brought about by natural
disasters. Moreover, consistent with the idea that Fonden reconstruction funding is proportional
to the damages, the ﬁgures also illustrate that the relationship between night lights and the
relative intensity of disaster disappears after the threshold has been crossed.
In order to illustrate that the underlying relationship between night lights and intensity of
disaster is not being driven by areas with sparse data, in ﬁgure 2 we restrict the sample to
observations that are within 150 mm of the threshold, and plot once again the mean change
in log lights in relation to the forcing variable. Consistent with the previous results, we ﬁnd,
that the negative relationship between night lights and intensity of disaster only occurs in the
absence of Fonden.
5
The number of bins is chosen using is a datadriven method whose objective is to create a plot that allows
us to detect discontinuities in the underlying regression function without imposing smoothness in the estimator.
Speciﬁcally, the algorithm chooses the optimal number of bins such that the Integrated Mean Square Error is
minimized, see Calonico et al. (2014a) for details.
9
Table 4 presents the regression analog of ﬁgures 1a and 1b. Panel A presents OLS estimates of
equation 1. Panel B presents OLS estimates of equation 2, and 2SLS estimates of the impact
of Fonden on local economic activity π1 . Columns 1 to 4 present various speciﬁcations of the
functions g (·). Speciﬁcally, in columns 1 and 2 we estimate using the full sample and assume
that function g (·) is a second or a third order polynomial. In columns 3 and 4, we assume that
g (·) is linear and restrict the sample to within 50.5 mm and 57.3 mm as determined by two
optimal bandwidth calculations. Standard errors are clustered at the municipal level.
Consistent with ﬁgure 1a, the estimates of panel A reveal that crossing the threshold increases
the probability of receiving Fonden between 13% to 19% and that this jump is statistically
signiﬁcant in all cases. Similarly, the reduced form estimates in panel B reveal that munici
palities above the threshold grew by roughly 0.04 log points more than municipalities below
the threshold. Once we rescale these reduced form coeﬃcients, we ﬁnd that Fonden led to an
increase in the range of 0.196 to 0.37 log points. While these coeﬃcients are only statistically
signiﬁcant at the margin, the estimated gains in night lights imply considerable increases in
local economic activity. Speciﬁcally, in the ﬁrst row of panel C, we calculate the impact of
Fonden on local economic activity by multiplying the IV estimates with the elasticity of log
night lights to GDP from the annual ﬂuctuations speciﬁcation, table 6 panel A column 2. Our
estimates indicate that municipalities with access to Fonden grew between 2 to 4% more than
those without Fonden in the year following the disaster.6
In order to get a better sense of the magnitude of the changes in economic activity we addi
tionally derive Fonden’s heavy rainfall beneﬁt cost ratios. This calculation is preformed in four
steps. First, we restrict the sample to 790 municipalities that received Fonden funds between
2004 and 2011 and for whom we have complete information on the resources allocated by the
program. Second, we proxy municipal GDP before the introduction of Fonden, by multiplying
UNDP estimates of municipal GDP per capita in 2000 by municipal population. Third, we
calculate the dollar value of the economic activity generated by FONDEN by multiplying mu
nicipal GDP by the product of the elasticity of light to GDP and our IV estimate of the impact
of Fonden, that is, we multiply the level of GDP by the additional growth created by Fonden.
Fourth, we sum across municipalities to derive the total beneﬁt and cost of the program.7
To account for the uncertainty in estimating the elasticity of light to GDP, and of estimating the
impact of Fonden on night lights, the third step of the calculation is preformed using coeﬃcients
drawn from two normal distributions. In each case the mean of the distribution is set to the
point estimate, and the standard deviation is set to the standard error. Steps three to four are
then repeated 5,000 times using random draws of coeﬃcients described on step three. We then
6
Note that the counterfactual is not no reconstruction response at all, but rather nonFonden discretionary
reconstruction response.
7
Since UNDP GDP estimates are in 2005 PPP USD we convert Fonden reconstruction expenditures to the
same units.
10
take the simulated total beneﬁts generated by Fonden and compute the mean and the standard
deviation.
The second row of panel C reports the average total beneﬁt from the simulations divided by
the total cost. Rows three and four report the total beneﬁt one standard deviation below and
above the mean divided by the total cost. All in all, while our ratios vary too broadly to clearly
pin down the eﬀect of Fonden, they are of a reasonable magnitude. The average beneﬁt cost
ratio, across speciﬁcations, lies in the 1.52 (with a probability of 0.66 of being greater than one)
to 2.89 (with a probability of 0.8 of being greater than one) range, and is therefore consistent
with previous estimates of ﬁscal multipliers. These costbeneﬁt ratios might, however, be too
conservative as they account for the overall expenditures of Fonden while assuming that the
beneﬁt of the program is only accrued in the year following the disaster.
5.2 The dynamic impact of Fonden on local economic activity
As previously mentioned, we turn to month frequency night lights data to document the dynamic
impact of Fonden on local economic activity. Figures 3a to 3d plot the mean change in log night
lights relative to rainfall millimeters (mm) to the threshold, at four key points in the post disaster
period. Speciﬁcally, the four outcome variables are the night lights log diﬀerence between the
average 12 months before the disaster and the average at 3, 5, 15, and 20 months after the
disaster. The ﬁgures suggest that the impact of Fonden can be broadly characterized in three
phases. In the very short run, while funds and reconstruction eﬀorts are being put into place,
we ﬁnd no diﬀerence between those municipalities just above and below the threshold. Between
5 and 15 months after the disaster, we see a clear jump in brightness at the threshold. At its
peak, 15 months, moving from just below to just above the threshold increases brightness by
roughly 0.1 log points. From 15 to 19 months, municipalities at the left of the threshold begin
to catch up to those at the right of the threshold. At 20 months there seems to be no diﬀerence
between municipalities with and without access to Fonden.
In order to provide a detailed account of the dynamic impact of Fonden, ﬁgures 4a to 4c plot
the coeﬃcients and 90% conﬁdence intervals derived from estimating equations 1 to 3. Each
plotted coeﬃcient corresponds to a separate OLS regression where the dependent variable is the
night lights log diﬀerence between the average 12 months before the disaster and the average at
various points in the post disaster period. We begin with a postdisaster average at 3 months,
and then we incrementally increase this average by a month for up to 30 months.
In order to ensure that our estimates of the impact of Fonden at diﬀerent points in time are
comparable, we use the following procedure. First, following Calonico et al. (2014b), we calculate
the optimal bandwidth for each of our dependent variables, that is, the change in local economic
activity at diﬀerent points in the postdisaster period. Second, using the bandwidths derived in
11
the ﬁrst step, we calculate an average optimal bandwidth. Third, we estimate equations 2 and
3 assuming a linear g (·) and restricting the sample to the average optimal bandwidth.
Figure 4a plots the coeﬃcients derived from estimating the ﬁrst stage. Consistent with the re
sults derived in the previous section we ﬁnd that crossing the threshold increases the probability
of receiving Fonden by roughly 16%. The ﬁrst stage coeﬃcient is the same at every point in
time because the sample is constructed to remain constant.
Figure 4b plots the reduced form coeﬃcients. Consistent with the ﬁndings and description of
ﬁgure 3, the coeﬃcients illustrate an incremental increase in brightness for municipalities above
the threshold that peaks at approximately 15 months. Next in ﬁgure 4c we plot the instrumental
variables coeﬃcients. These coeﬃcients are our sharpest estimates of the impact of Fonden on
night lights. The coeﬃcients illustrate a progressive build up of the impact of Fonden on night
lights which peaks 15 months after the disaster has taken place, 0.6 log points (t=1.9). The
coeﬃcients also reveal that Fonden has a statistically signiﬁcant impact on log night lights for
up to 17 months.
Given that the timing of the observed gains depends to a large extent on the ability of Fonden
to quickly mobilize funds for reconstruction, ﬁgure 4d plots the fraction of municipalities with
authorized funds at diﬀerent points in the post disaster period. Figure 4d suggest that in roughly
80% of the cases Fonden funding was authorized and ready to be used for reconstruction within
3 months of a disaster. Taking together the time required for disbursement of funds with the
average projected reconstruction time of 7 months suggest that, consistent with our ﬁndings,
the earliest impact of Fonden on local economic activity should occur between 3 to 10 months
after a disaster.
It is possible that reconstruction could be delayed or take longer to complete, thus extending
the period in which Fonden has a direct impact on economic activity. Interviews with Fonden
managers indicate that the bulk of delays correspond to projects where the assets damaged
are owned by state governments. This follows, as mentioned in section 2, from the additional
coordination eﬀorts requiered for state owned assets. Accordingly, our estimates of the impact
of Fonden in the ﬁrst few months following a disaster, are likely to correspond to the recon
struction of federal assets. Reconstruction of federal assets accounts for 43% of overall Fonden
expenditures.
Next in ﬁgure 5 we investigate the sensitivity of these results to our choice of speciﬁcation.
Figure 5a reports for convenience the IV estimates of the previous ﬁgure. Figure 5b reports
IV estimates from a speciﬁcation that uses the full sample and assumes a cubic g (·) function.
Figures 5c and 5d report IV estimates from speciﬁcations that assume a linear g (·) function
but that respectively restrict the sample to the minimum or the maximum optimal bandwidth.
While speciﬁcations 5b to 5d produce similar results to those of ﬁgure 5a, they suggest diﬀerent
12
time periods in which we are able to detect a statistically signiﬁcant eﬀect of Fonden on night
lights. For example, ﬁgure 5b indicates that Fonden has a statistically signiﬁcant eﬀect between
7 and 18 months, while ﬁgure 5d indicates that the impact of Fonden could be detected for up
to two years after the disaster.
In ﬁgures 6a and 6b we verify that our composite processing choices have had no bearing on the
results. Speciﬁcally, in ﬁgure 6a instead of using pixel level averages that use information from
overlapping satellites when available, we limit ourselves to information derived solely from the
F16 and F18 satellites. In ﬁgure 6b we additionally address the concern that our estimates might
be biased by the presence of top coded pixels, and exclude these very bright, but upper truncated
pixels from the analysis. While, as expected, these estimates are less precisely estimated than
those of ﬁgure 5c, they are of similar magnitudes and they further conﬁrm the time period over
which the impact of Fonden can be conﬁdently established.
All in all, these ﬁgures consistently indicate that Fonden has a statistically signiﬁcant impact
on night lights between 7 and 17 months after a disaster has taken place. The coeﬃcients
also suggest that the implied economic impact of Fonden is of considerable magnitude. In
ﬁgure 5a the 12 month point estimate is 0.57 log points (t=1.93). This coeﬃcient implies that
municipalities with access to Fonden grew 6.5% more than those without Fonden in the year
following the disaster.8
This estimate of the impact of Fonden is not directly comparable with the ones derived from
the annual light composites, because it is derived from a diﬀerent sample.9 Nonetheless, taken
at face value it does suggest that the estimates derived from annual composites are likely to be
lower bounds of the impact of Fonden.
The key diﬀerence between the estimates, is that the estimates derived from annual composites
are naturally constrained by the calendar year. Accordingly, these estimates will necessarily
average over events that can last as many as 12 or 23 months after the disaster has taken place.
Since the eﬀect of Fonden on night lights begin to decrease after 15 months it is reasonable for the
annual composites to provide smaller estimates of the impact of Fonden on night lights.
In order to further investigate the dynamics of the impact of Fonden in ﬁgures 6c we estimate
the impact of Fonden when the dependent variable is the night lights log diﬀerence between the
average 12 months before the disaster and a 9 month moving average in the post disaster period.
Figure 6d reports results from a similar exercise where we use a 12 month moving average. The
key ﬁnding, is that consistent with our previous results the impact of Fonden on night lights is
largest between 7 and 15 months in ﬁgure 6c and between 4 and 15 months in ﬁgure 6d. In all
8
We calculate the impact of Fonden on local economic activity by multiplying the IV estimate by the elasticity
of light to GDP from the annual ﬂuctuation speciﬁcation, table 6 panel B column 2.
9
Monthly composites cover a longer time period, thereby allowing us to use a larger sample of events, see
section 4 for details.
13
cases, our results indicate that the impact of Fonden is not permanent, and that its dynamics
can be characterized by a three phase process: setup for reconstruction, reconstruction, and
catch up by municipalities not covered by Fonden.
5.3 The dynamic impact of Fonden on construction employment
Next we turn to quarterly employment data. These data allow us to provide direct evidence
of the impact of Fonden on local economic activity, as well as to document the time path of
reconstruction work. While we ﬁnd no evidence of an overall eﬀect on employment, see ﬁgure
7, we do ﬁnd that Fonden has a considerable impact on construction employment. Speciﬁcally,
ﬁgures 8a to 8f plot the mean growth in construction employment, at diﬀerent points in the
post disaster period, relative to rainfall millimeters (mm) to the threshold. Speciﬁcally, the six
outcome variables are the log diﬀerence in construction employment between the quarter the
disaster takes place and the next six quarters, in one quarter steps.
In spite of the coverage of the LFS only allowing us to use half of the previous sample, we
ﬁnd that the time path of growth in construction employment is consistent with our previous
ﬁndings. In particular, in the very short run, we ﬁnd no evidence of diﬀerential growth in
employment at threshold. However, at 6 months or more, we begin to observe a diﬀerential
eﬀect at the threshold. This eﬀect appears to peak, in a very clear jump of roughly 25% 12
months after a disaster has taken place.
As in the previous case, we provide a more detailed account of the dynamic impact of Fonden
on employment growth by sequentially estimating equations 1 to 3 for each of the six quarters
following a disaster (3 to 18 months). Each plotted coeﬃcient corresponds to a separate OLS
regression. In all regressions, it is assumed that function g (·) is linear, and the sample is
restricted to the set of observations that fall within the average optimal bandwidth.
Figure 9a plots the ﬁrst stage coeﬃcients. These coeﬃcients provide strong evidence of a
ﬁrst stage among the municipalities that make up this smaller sample. Speciﬁcally, we ﬁnd
that crossing the threshold leads to an increase in the probability of receiving Fonden of 35%.
Next ﬁgure 9b plots the reduced form coeﬃcients, and ﬁgure 9c plots the 2SLS coeﬃcients.
As expected given the smaller sample size these coeﬃcient are noisly estimated. Nonetheless,
the estimated eﬀect sizes are of considerable economic magnitude. Moreover, the path of the
coeﬃcients strongly indicate that 6 months after a disaster, Fonden leads to a large and sustained
increase in the growth of employment in the construction sector. At its peak the point estimates
indicate, that Fonden increased construction employment by as much as 76%. This eﬀect size
is reasonable because municipalities are likely to operate as local labor markets,10 because
10
Less than 3% of LFS respondents report moving to a diﬀerent municipality in order to ﬁnd or keep their
current job.
14
the labor force required for reconstruction tends to be considerable, and because Fonden uses
reconstruction schemes that are speciﬁcally designed to employ the local community.11
5.4 Falsiﬁcation Exercises
The identifying assumption that underpins our regression discontinuity design is that potential
outcomes are smooth functions of the forcing variable as the threshold is crossed. One potential
threat to this assumption is that individuals might be able to game the Fonden assignment rule.
It is unlikely that third parties could have tampered with weather stations run by CONAGUA
not only because these weather stations serve a variety of purposes both civilian and military, but
also because few individuals outside of CONAGUA could have known the thresholds or be able to
identify the subset of weather stations used to determine Fonden eligibility. Nonetheless, in order
to investigate whether manipulation is possible, we examine the density of the forcing variable.
If Fonden is being manipulated then we would expect to observe bunching of observations to
the right of the threshold. Formally, we use the sorting test proposed by McCrary (2008). In
ﬁgure 10a we plot the density of the forcing variable as it crosses the threshold. The solid line
is the density of the forcing variable as estimated by local linear regression, the dashed lines
represent conﬁdence intervals. The overlapping conﬁdence intervals suggest that there is no
discontinuous change in the density across the threshold. The pvalue of the McCrary sorting
test associated with this graph is 0.42. We thus fail to reject the null that the forcing variable
is smooth across the threshold.
We can provide further support for the validity of the identifying assumption by establishing
that log night lights do not change discontinuously at the threshold in the years preceding a
natural disaster. Figure 10b plots the change in log night lights between two years before a
disaster has taken place and the following year. The ﬁgure reveals no apparent discontinuity at
the thresholds. Moreover, when we estimate the impact of Fonden it yields a small coeﬃcient
that is statistically indistinguishable from zero, 0.016 (t=0.45).
6 Night lights as proxies of subnational economic activity
While our primary interest lies in determining whether log night lights can predict changes in
municipal level GDP, in the absence of this type of data we begin our analysis by investigating
the relationship between log night lights and proxies of economic activity at the municipal
level. Speciﬁcally, we calculate by municipality, from the 2005 population conteo and the 2010
population census, the number of dwellings with the following characteristics: dwelling has non
11
In particular, Fonden housing reconstruction eﬀorts occur through the temporary work program. This
program is designed to hire homeowners to rebuild their own houses.
15
dirt ﬂoors, has television, has fridge, has washing machine. We additionally compile data from
various administrative records that allow us to observe at the municipal level: the number of
users of electricity, the number of car registrations, and the number of building licenses for both
industrial and residential use.
Given the frequency with which these data are available, panel A of table 5 uses a long diﬀerence
speciﬁcation in which we test whether the ﬁve year log change in the number of dwellings of
the various characteristics previously described is related to ﬁve year change in log night lights.
In all cases columns 1 to 4, we ﬁnd that the log night coeﬃcient is positive and that it is
sharply estimated. The R2 is in the 6 to 8% range. In panel B of the same table, we turn to
administrative records available in yearly frequency. In this case we use a log log speciﬁcation
that includes both municipal and year ﬁxed eﬀects. As in the previous case we ﬁnd that our
measure of log nights is positive albeit less precisely estimated in columns 3 and 4 where the
number of observations is limited.
Next, we investigate the relationship between log night lights and state level GDP.12 Given that
our primary purpose is to estimate an elasticity of light to GDP that would allow us to back
out the impact of Fonden on local economic activity, we restrict the sample to the 26 states
that have requested Fonden funding in the 20042011 period. We follow the approach taken
by Henderson et al. (2011) and focus primarily on determining whether night lights are able
to predict year to year growth, annual ﬂuctuations, recession and expansions, and long term
growth. Panel A of table 6 uses log night lights derived from annual composites. Panel B
of the same table uses log night lights derived from pixel averaged monthly composites. The
benchmark speciﬁcation regresses log GDP on log night lights, state ﬁxed eﬀects and year ﬁxed
eﬀects. Standard errors are clustered at the state level in all cases. Column 1 presents the result
of the benchmark speciﬁcation. We sharply estimate an elasticity of roughly 0.24.
Next, in column 2 we test whether night lights are capable of predicting annual ﬂuctuations by
extending the previous speciﬁcation to include state trends. Since we are primarily interested in
short term deviations from the state growth path, this is the key speciﬁcation for our analysis.
As in the previous case we sharply estimate an elasticity in the order of 0.11 (t=2.6). This
result is important because it suggests that night lights do a reasonably good job of predicting
annual ﬂuctuations in GDP.
In column 3 we test for ratchet eﬀects, that is whether, relative to the state mean over time,
increases and decreases in night lights are symmetrically related to increases and decreases in
GDP. This calculation is preformed in two steps: (i) We demean the data by regressing GDP and
lights on year and state ﬁxed eﬀects. (ii) We regress the GDP residuals on absolute value positive
lights residuals, and absolute value negative lights residuals. We ﬁnd that the coeﬃcients are
12
The source of state level GDP data is INEGI. GDP is measured in constant 2008 pesos.
16
very similar in magnitude and that they have the opposite signs, we thus conclude that night
lights are capable of picking up both economic expansions and economic downturns.
In terms of the R2 , note that the R2 reported in columns 1 and 2 is a within state R2 , it still
accounts for the role of year dummies. The R2 reported in column 3, in the range of 7 to
10%, reﬂects solely the contribution of night lights to explaining withinstate and withinyear
variation in GDP.
Last in column 4, we look at the ability of night lights to predict longterm growth. This is done
using a long diﬀerence speciﬁcation where we regress the change in log GDP between 2004 and
2012 on the change in log night lights between 2004 and 2012. We ﬁnd a positive and sharply
estimate elasticity, and an R2 in the 40 to 44% range. All in all, while our sample size is small
compared to those of other papers in the literature, our results validate the idea of using night
lights as a proxy for economic activity at the subnational level in Mexico. Interestingly, our
results for subnational data in Mexico are of very similar magnitude to those derived for the
World by Henderson et al. (2011) and for Africa by Bundervoet et al. (2015).
7 Conclusion
This paper exploits the sharp rules that govern eligibility to Fonden funding for postdisaster
reconstruction to derive some of the ﬁrst estimates of the causal impact of disaster funds on
local economic activity. Our results indicate that, in the year following the disaster, munici
palities with access to Fonden grew between 2 to 4% more than those without Fonden. The
reconstruction eﬀort led to a 76% increase in construction employment and this eﬀect preceded
in time the recovery in local economic activity. Conservative beneﬁtcost ratios, in the 1.52 to
2.89 range (respectively with probabilities of 0.66 and 0.8 of being larger than one), suggest
that Fonden has provided costeﬀective protection from the public service disruptions caused
by natural disasters. We additionally document the impact of Fonden over time. We ﬁnd that
the economic expansion generated by Fonden peaks roughly between 7 and 17 months after
a disaster has taken place, and that from that point onward municipalities without access to
Fonden begin to catch up. On the whole, given the scale of gains to local economic activity
brought about by availability and rapid disbursement of disaster funds, these results suggest
that policy makers in other countries could be encouraged to consider using predisaster funding
schemes such as Fonden to enhance their own response capabilities.
17
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8 Figures
19
Figure 1: First stage and reduced form at various bin widths
.2 .15 .1 .05 0 .05 .1 .15 .2 .25 .3 .35 .4 .45 .5
Change in log lights one year after disaster
1
.2 .3 .4 .5 .6 .7 .8 .9
Probability of Receivig FONDEN
.10
00
50
00
50
00
0
0
50
0
0
0
0
0
5
10
15
20
25
30
00
50
00
50
00
0
0
50
0
0
0
0
0
3
2
2
1
1
5
10
15
20
25
30
3
2
2
1
1
Rainfall mm to threshold
Rainfall mm to threshold IMSEoptimal bins Left: 44.15 Right: 61.92
(a) Prob. of receiving Fonden, First Stage (b) log night lights, Reduced Form
.2 .15 .1 .05 0 .05 .1 .15 .2 .25 .3 .35 .4 .45 .5
Change in log lights one year after disaster
1
.2 .3 .4 .5 .6 .7 .8 .9
Probability of Receivig FONDEN
.1 0
00
50
00
50
00
0
0
50
0
0
0
0
0
5
10
15
20
25
30
00
50
00
50
00
0
0
50
0
0
0
0
0
3
2
2
1
1
5
10
15
20
25
30
3
2
2
1
1
Rainfall mm to threshold
Rainfall mm to threshold IMSEoptimal bins Left: 22.08 Right: 30.96
(c) Prob. of receiving Fonden, First Stage (bins x 2) (d) log night lights, Reduced Form (bins x 2)
.2 .15 .1 .05 0 .05 .1 .15 .2 .25 .3 .35 .4 .45 .5
Change in log lights one year after disaster
1
.2 .3 .4 .5 .6 .7 .8 .9
Probability of Receivig FONDEN
.1 0
00
50
00
50
00
0
0
50
0
0
0
0
0
5
10
15
20
25
30
00
50
00
50
00
0
0
50
0
0
0
0
0
3
2
2
1
1
5
10
15
20
25
30
3
2
2
1
1
Rainfall mm to threshold
Rainfall mm to threshold IMSEoptimal bins Left: 14.72 Right: 20.64
(e) Prob. of receiving Fonden,First Stage (bins x 3) (f) log night lights, Reduced Form (bins x 3)
Note: The sample is composed by the municipalities that have requested Fonden funding. The ﬁgures plot the local
average at the midpoint of each bin, and a 4th order polynomial ﬁt estimated separately on each side of the threshold.
The size of the markers is proportional to the number of observations in each bin. The forcing variable is rainfall mm
to the percentile 90 threshold. The dependent variable for ﬁgures on the left is a dummy take the value of one when a
municipality receives Fonden. The dependent variable for ﬁgures on the right is the log change in night lights between
the year the disaster takes place and the following year. The optimal bin width is calculated following Calonico et al.
(2014a). In order to investigate the sensitivity over the choice of binwidth, in ﬁgures 1c to 1f, we double and triple the
number of bins.
Figure 2: Change in log night lights by rainfall mm to the threshold
Change in log lights one year after disaster
0 05 .1 15 .2 25 .3
.2 .15 .1 .05 . . .
50
35
20
05
0
5
0
5
0
5
0
15
30
45
60
75
90
5
0
5
0
9
7
6
4
3
1
10
12
13
15
1
1
1
1
Rainfall mm to threshold
IMSEoptimal bins Left: 14.84 Right: 12.4
Note: This ﬁgure plots the local average at the midpoint of each bin, and a 4th order polynomial ﬁt estimated separately
on each side of the threshold. The size of the markers is proportional to the number of observations in each bin. The
forcing variable is rainfall mm to the percentile 90 threshold. The sample has been restricted to observations where the
forcing variable is between 150 mm to 150 mm, approximately a 2% trim on each side. The dependent variable is the
log change in night lights between the year the disaster takes place and the following year. The optimal bin width is
calculated following Calonico et al. (2014a).
21
Figure 3: Dynamics of Fonden impact: reduced form at various post disaster periods.
Change avg 12 months before and average 3 after
Change avg 12 months before and average 5 after
.4
.4
.3
.3
.2
.2
.1
.1
0
0
.1
.1
.2
.2
.3
.3
.4
.4
100 50 0 50 100 100 50 0 50 100
Rainfall mm to threshold Rainfall mm to threshold
(a) log night lights, 0 to 3 months after (b) log night lights, 0 to 5 months after
Change avg 12 months before and average 15 after
Change avg 12 months before and average 20 after
.4
.4
.3
.3
.2
.2
.1
.1
0
0
.1
.1
.2
.2
.3
.3
.4
.4
100 50 0 50 100 100 50 0 50 100
Rainfall mm to threshold Rainfall mm to threshold
(c) log night lights, 0 to 15 months after (d) log night lights, 0 to 20 months after
Note: The ﬁgures plot the local average at the midpoint of each bin, and a 4th order polynomial ﬁt estimated separately
on each side of the threshold. The size of the markers is proportional to the number of observations in each bin. The
forcing variable is rainfall mm to the percentile 90 threshold. The dependent variable is the log change in night lights
between the average in the 12 months before the disaster and the cumulative average at diﬀerent points in the post
disaster period. The optimal bin width is calculated, for each ﬁgure, following Calonico et al. (2014a).
22
Figure 4: Dynamic impact of Fonden on night lights
.25
.15
Probability of Receiving Fonden
.1
.2
Night Lights (Log points)
.05
.15
0
.1
.05
.05
0 3 6 9 12 15 18 21 24 27 30 0 3 6 9 12 15 18 21 24 27 30
Months After the Disaster Months After the Disaster
90% Conf Int Impact of Fonden 90% Conf Int Impact of Fonden
(a) Prob of receiving Fonden, First Stage (b) log night lights, Reduced Form
.4
1
Night Lights (Log points)
.3
Fraction of Municipalities
.5
.2
0
.1
.5
0 3 6 9 12 15 18 21 24 27 30
Months After the Disaster
0
90% Conf Int Impact of Fonden 0 30 60 90 120 150 180 210 240 270 300 330 360
CCT optimal bandwidth 46 mm Days between disaster and authorization of funds
(c) log night lights, IV (d) Timing of Fund Authorization
In ﬁgures (a) to (c), the dependent variable is the log change in night lights between the average in the 12 months
before the disaster and the cumulative average at diﬀerent points in the post disaster period. Each plotted coeﬃcient
corresponds to a separate OLS regression where it is assumed that function g (·) is linear, the sample is restricted to the
set of observations that fall within the average optimal bandwidth. Speciﬁcally, the following procedure was used: (i)
The optimal bandwidth for each coeﬃcient was derived by following Calonico et al. (2014b). (ii) In order to guarantee
that all coeﬃcients are estimated on the same sample, the average optimal bandwidth is calculated. (iii) All regressions
are estimated within this average optimal bandwidth. The 90% conﬁdence intervals are calculated using standard errors
clustered at the municipal level. The night light data is derived from monthly composites where pixels have been averaged
for years in which there is coverage of more than one satellite. Figure (d) plots the fraction of municipalities with Fonden
funding in relation to the number of days between disaster and the authorization of funds.
23
Figure 5: Robustness checks I
.8
1
.6
Night Lights (Log points)
Night Lights (Log points)
.5
.2 .4
0
0 .2
.5
0 3 6 9 12 15 18 21 24 27 30
Months After the Disaster 0 3 6 9 12 15 18 21 24 27 30
Months After the Disaster
90% Conf Int Impact of Fonden
90% Conf Int Impact of Fonden
CCT optimal bandwidth 46 mm
(a) Average optimal BW CCT (b) Full sample cubic g (·)
1.5
2 1.5
Night Lights (Log points)
Night Lights (Log points)
1
.5 1
.5
0
0
.5
0 3 6 9 12 15 18 21 24 27 30 0 3 6 9 12 15 18 21 24 27 30
Months After the Disaster Months After the Disaster
90% Conf Int Impact of Fonden 90% Conf Int Impact of Fonden
CCT optimal bandwidth 40 mm CCT optimal bandwidth 50 mm
(c) Maximum optimal BW CCT (d) Minimum optimal BW CCT
In all ﬁgures the dependent variable is the log change in night lights between the average in the 12 months before the
disaster and the cumulative average at diﬀerent points in the post disaster period. In ﬁgures (a) (c) and (d), each
plotted coeﬃcient corresponds to a separate OLS regression where it is assumed that function g (·) is linear. In each case
the sample is restricted to the set of observations that fall within the average, the maximum or the minimum optimal
bandwidth. Speciﬁcally, the following procedure was used: (i) The optimal bandwidth for each coeﬃcient is derived by
following Calonico et al. (2014b). (ii) In order to guarantee that all coeﬃcients are estimated on the same sample, the
average, maximum, and minimum optimal bandwidth is calculated. (iii) Each set of regressions are estimated within
each of these bandwidths. In ﬁgure (b) each plotted coeﬃcient corresponds to a separate OLS regression where the full
sample is used and where it is assumed that function g (·) is cubic. The 90% conﬁdence intervals are calculated using
standard errors clustered at the municipal level. The night light data is derived from monthly composites where pixels
have been averaged for years in which there is coverage of more than one satellite.
24
Figure 6: Robustness checks II
1
1
Night Lights (Log points)
Night Lights (Log points)
.5
.5
0
0 .5
.5
0 3 6 9 12 15 18 21 24 27 30 0 3 6 9 12 15 18 21 24 27 30
Months After the Disaster Months After the Disaster
90% Conf Int Impact of Fonden 90% Conf Int Impact of Fonden
(a) Satellites F16 and F18 Only (b) Excluding top coded pixels
1.5
2
Night Lights (Log points)
Night Lights (Log points)
1
1
.5
0
0 .5
1
9
10
11
12
13
14
15
16
17
8
19
20
21
2
3
4
12
13
14
15
16
17
18
19
20
1
22
23
24
1
1
2
2
2
2
3
2
4
5
6
7
8
9



1
2
3
4
5
6
7
8
9



10
11
11
11
12
13
14
10
11
11
11
Months After the Disaster Months After the Disaster
90% Conf Int Impact of Fonden 90% Conf Int Impact of Fonden
(c) Post disaster 9 month moving average (d) Post disaster 12 month moving average
In ﬁgures (a) and (b) the dependent variable is the log change in night lights between the average in the 12 months
before the disaster and the cumulative average at diﬀerent points in the post disaster period. In ﬁgure (c) the dependent
variable is the log change in night lights between the average in the 12 months before the disaster and a 9 month moving
average in the post disaster period. In ﬁgure (d) the dependent variable is the log change in night lights between the
average in the 12 months before the disaster and a 12 month moving average in the post disaster period. In all ﬁgures
each plotted coeﬃcient corresponds to a separate OLS regression where it is assumed that function g (·) is linear. In
each case the sample is restricted to the set of observations that fall within the average optimal bandwidth. The speciﬁc
procedure is described in the footnote of ﬁgure 4 The 90% conﬁdence intervals are calculated using standard errors
clustered at the municipal level. In ﬁgure (a) the night light data is not averaged at the pixel level, instead of using data
from both the F15 and F16 satellites for the 20042007 period, we use only data from the F16 satellite. In ﬁgure (b) the
night light data is derived from composites where top coded observations have been dropped. In ﬁgure (c) and (d) the
night light data is derived from monthly composites where pixels have been averaged for years in which there is coverage
of more than one satellite.
25
Figure 7: Employment growth by rainfall mm to the threshold
.5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
.5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
Employment growth 1 quarters after disaster
Employment growth 2 quarters after disaster
.6
.6
100 50 0 50 100 100 50 0 50 100
Rainfall mm to threshold Rainfall mm to threshold
(a) 3 months After Disaster (b) 6 months After Disaster
.5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
.5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
Employment growth 3 quarters after disaster
Employment growth 4 quarters after disaster
.6
.6
100 50 0 50 100 100 50 0 50 100
Rainfall mm to threshold Rainfall mm to threshold
(c) 9 months After Disaster (d) 12 months After Disaster
.5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
.5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
Employment growth 5 quarters after disaster
Employment growth 6 quarters after disaster
.6
.6
100 50 0 50 100 100 50 0 50 100
Rainfall mm to threshold Rainfall mm to threshold
(e) 15 months After Disaster (f) 18 months After Disaster
Note: The ﬁgures plot the local average at the midpoint of each bin, and a 4th order polynomial ﬁt estimated separately
on each side of the threshold. The size of the markers is proportional to the number of observations in each bin. The
forcing variable is rainfall mm to the percentile 90 threshold. The dependent variable is the log change in employment
between the quarter the disaster takes place and various quarters in the post disaster period. The optimal bin width is
calculated, for each ﬁgure, following Calonico et al. (2014a).
26
Figure 8: Construction employment growth by rainfall mm to the threshold
Sector 4 Employment growth 1 quarters after disaster
Sector 4 Employment growth 2 quarters after disaster
.6 .5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
.6 .5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
100 50 0 50 100 100 50 0 50 100
Rainfall mm to threshold Rainfall mm to threshold
(a) 3 months After Disaster (b) 6 months After Disaster
Sector 4 Employment growth 3 quarters after disaster
Sector 4 Employment growth 4 quarters after disaster
.6 .5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
.6 .5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
100 50 0 50 100 100 50 0 50 100
Rainfall mm to threshold Rainfall mm to threshold
(c) 9 months After Disaster (d) 12 months After Disaster
Sector 4 Employment growth 5 quarters after disaster
Sector 4 Employment growth 6 quarters after disaster
.6 .5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
.6 .5 .4 .3 .2 .1 0 .1 .2 .3 .4 .5 .6
100 50 0 50 100 100 50 0 50 100
Rainfall mm to threshold Rainfall mm to threshold
(e) 15 months After Disaster (f) 18 months After Disaster
Note: The ﬁgures plot the local average at the midpoint of each bin, and a 4th order polynomial ﬁt estimated separately
on each side of the threshold. The size of the markers is proportional to the number of observations in each bin. The
forcing variable is rainfall mm to the percentile 90 threshold. The optimal bin width is calculated, for each ﬁgure,
following Calonico et al. (2014a).
27
Figure 9: Impact of Fonden on Employment growth in the Construction Sector
.6
.6
Employment Sector 4 (Log points)
Probability of Receiving Fonden
.4
.5
.2
.4
0
.3
.2
.2
3 6 9 12 15 18 3 6 9 12 15 18
Months After the Disaster Months After the Disaster
90% Conf Int Impact of Fonden 90% Conf Int Impact of Fonden
(a) Prob of receiving Fonden, First Stage (b) log construction employment, Reduced Form
1.5
Employment Sector 4 (Log points)
0 .5
.5 1
3 6 9 12 15 18
Months After the Disaster
90% Conf Int Impact of Fonden
(c) log construction employment, IV
Note: Each plotted coeﬃcient corresponds to a separate OLS regression where it is assumed that function g (·) is linear,
the sample is restricted to the set of observations that fall within the average optimal bandwidth. Speciﬁcally, the
following procedure was used: (i) The optimal bandwidth for each coeﬃcient was derived by following Calonico et al.
(2014b). (ii) In order to guarantee that all coeﬃcients are estimated on the same sample, the average optimal bandwidth
is calculated. (iii) All regressions are estimated within this average optimal bandwidth. The 90% conﬁdence intervals
are calculated using standard errors clustered at the municipal level.
28
Figure 10: Falsiﬁcation exercises
0.010
0.008
0.006
0.004
0.002
−100 −50 0 50 100
(a) Density of forcing variable across the threshold
0 05 .1 15 .2 25 .3
Change in log lights one year before disaster
.2 .15 .1 .05 . . .
50
35
20
05
0
5
0
5
0
5
0
15
30
45
60
75
90
5
0
5
0
9
7
6
4
3
1
10
12
13
15
1
1
1
1
Rainfall mm to threshold
IMSEoptimal bins Left: 7.42 Right: 10.63
(b) Placebo: Change in night lights one year before disaster
Note: Figure (a) plots the density of the forcing variable in relation to rainfall mm to the threshold. The solid line is the
density of the forcing variable as estimated by local linear regression, the dashed lines are the corresponding conﬁdence
intervals. The pvalue of McCrary sorting test associated with this graph is 0.42. Figure (b) plots the local average at
the midpoint of each bin, and a 4th order polynomial ﬁt estimated separately on each side of the threshold. The size
of the markers is proportional to the number of observations in each bin. The forcing variable is rainfall mm to the
percentile 90 threshold. The dependent variable is the change in log night lights between two years before a disaster has
taken place and the following year. The optimal bin width is calculated following Calonico et al. (2014a).
29
9 Tables
Table 1: Balance between municipalities with complete and missing information
Mean mun. with Mean mun. with Diﬀerence of means
missing information complete information (se)
No of Dwellings 9168.7 9036.6 132.0
(1480.9)
with non dirt ﬂoor 7901.9 7306.8 595.0
(1389.4)
with electricity 8591.0 8308.3 282.6
(1427.6)
with tap water 7803.4 7079.2 724.3
(1359.5)
with connection to sewage 7280.9 6628.0 653.0
(1363.6)
with toillets 8302.1 7917.8 384.3
(1406.6)
with tv 7842.3 7315.9 526.4
(1383.4)
with fridge 6674.5 5715.3 959.3
(1233.9)
with washing machine 5234.0 4088.3 1145.7
(1011.4)
with pc 948.4 647.1 301.3
(217.1)
Note: Standard errors in parentheses. Asterisks indicate statistical signiﬁcance at the 1% ***, 5% **, and 10% *
levels.Characteristics of dwellings are derived from 2000 census data. The sample is composed of municipalities
that have requested Fonden between 2004 and 2011. The missing information group corresponds to municipalities
missing either night lights, percentile 90 thresholds, or rainfall data in the same sample period.
30
Table 2: Summary statistics annual frequency night light data
Variable Obs Mean Std. Dev. P5 P95
Change in log light 1745 .04 .28 .43 .48
FONDEN=1 1745 .58 .49 0 1
Above threshold 1745 .36 .48 0 1
mm to threshold 1745 13.09 74.94 116.1 120.8
Inches to threshold 1745 .52 2.95 4.57 4.76
Rainfall mm 1745 93.21 79.5 4.5 265
Rainfall Inches 1745 3.67 3.13 .18 10.43
Note: Change in log lights refers to the change in log night light between the
year the disaster takes place and the following year. Average rainfall refers
to the average of maximum daily rainfall recorded for each municipality and
disaster declaration pair.
Table 3: Summary statistics monthly frequency night light data
Variable Obs Mean Std. Dev. P5 P95
Change in log light 2833 .05 .39 .35 .24
FONDEN=1 2833 .58 .49 0 1
Above threshold 2833 .36 .48 0 1
mm to threshold 2833 11.2 76.41 111.7 133.55
Inches to threshold 2833 .44 3.01 4.4 5.26
Rainfall mm 2833 90.2 80.81 2 259.5
Rainfall Inches 2833 3.55 3.18 .08 10.22
Note: Change in log lights refers to the change in log night light between the year
the disaster takes place and the following year. Average rainfall refers to the
average of maximum daily rainfall recorded for each municipality and disaster
declaration pair.
31
Table 4: The impact of Fonden
(1) (2) (3) (4)
Panel A: First Stage
Variables Fonden=1 Fonden=1 Fonden=1 Fonden=1
Above threshold (γ1 ) 0.194*** 0.140*** 0.129** 0.157***
(0.042) (0.0510) (0.0530) (0.0506)
Panel B: Reduced Form & 2SLS
Variables log light log light log light log light
Above threshold (β1 ) 0.038** 0.0382 0.0481** 0.0439**
(0.018) (0.0235) (0.0235) (0.0222)
F onden (π1 ) 0.196* 0.272 0.372 0.280*
(0.104) (0.199) (0.238) (0.167)
Observations 1,745 1,745 922 1,016
Speciﬁcation quadratic cubic linear linear
Sample Full Full Optimal bw Optimal bw
IK: 50.5 mm CCT: 57.3 mm
Panel C: Impact of Fonden on economic activity
Impact on local GDP % 2.21 3.07 4.20 3.16
Simulated beneﬁt cost ratios
Average 1.52 2.11 2.89 2.17
1 std dev. below Avg. 0.47 0.26 0.61 0.54
1 std dev. above Avg. 2.57 3.96 5.16 3.79
P(b/c>1) 0.66 0.71 0.80 0.76
Note: Standard errors clustered at the municipal level in parentheses. Asterisks indicate statistical
signiﬁcance at the 1% ***, 5% **, and 10% * levels. All regressions include year ﬁxed eﬀects. Panel
A presents OLS estimates of equation 1. Panel B presents OLS estimates of equation 2, and 2SLS
estimates of the coeﬃcient π1 . The label speciﬁcation referrers to the polynomial order of the function
g (rmt − c). In column 3, the optimal bandwidth was derived following Imbens and Kalyanaraman
(2012). In column 4 the optimal bandwidth was derived following Calonico et al. (2014b). The impact
on local GDP is derived by multiplying the 2SLS estimate by the elasticity of light to GDP. The
details of this calculation and of the simulated ﬁscal multiplier can be found in sections 6 and 5.1.
32
Table 5: Night lights and municipal proxies of economic activity
(1) (2) (3) (4)
Panel A: Change in dwelling characteristics between census 2005 and 2010
Variables ln dwellings ln dwellings ln dwellings ln dwellings
non dirt ﬂoors with tv with fridge with wash machine
ln (lights/area) 0.262*** 0.177*** 0.200*** 0.411***
(0.034) (0.026) (0.025) (0.048)
Observations 2,204 2,204 2,203 2,191
R2 0.056 0.079 0.057 0.083
Panel B: Other economic proxies derived from administrative records
Variables ln users ln car ln industrial ln residential
of electricity registrations building licenses building licenses
ln(lights/area) 0.035*** 0.063*** 2.548* 0.491
(0.011) (0.017) (1.438) (0.378)
Observations 15,498 17,024 667 1,542
Municipalities 2,118 2,118 246 459
(Within municipality) R2 0.120 0.570 0.081 0.055
Note: Standard errors clustered at the municipal level in parentheses. Asterisks indicate statistical signiﬁcance at the 1%
***, 5% **, and 10% * levels. Panel A uses a ﬁve year diﬀerence speciﬁcation and has no other controls. Panel B includes
both municipal and year ﬁxed eﬀects.
33
Table 6: Elasticity of night lights to State GDP
Base Annual Asymetric Long
Speciﬁcation Fluctuations Fluctuations Diﬀerence
(1) (2) (3) (4)
Variables ln(GDP) ln(GDP) Res ln(GDP) ln(GDP)
Panel A: log night lights derived from annual composites
ln(lights/area) 0.246** 0.113** 0.978***
(0.093) (0.043) (0.205)
+ Res ln(lights/area) 0.254*
(0.132)
 Res ln(lights/area) 0.239*
(0.131)
(Within state) R2 0.847 0.947 0.069 0.412
Panel B: log night lights derived from pixel averaged month composites
ln(lights/area) 0.236*** 0.115*** 0.936***
(0.060) (0.035) (0.247)
+ Res ln(lights/area) 0.263*
(0.141)
 Res ln(lights/area) 0.210**
(0.088)
(Within state) R2 0.852 0.948 0.096 0.444
Observations 234 234 234 26
State FE In demean .
Year FE In demean .
State Trend . . .
Note: Standard errors clustered at the state level in parentheses. Asterisks indicate statistical sig
niﬁcance at the 1% ***, 5% **, and 10% * levels. Mexico has 32 states. The sample has been
restricted to the 26 states that have applied for Fonden funding between 2004 and 2011.
34