Enterprise Recovery following Natural Disasters
Suresh de Mel, David McKenzie and Christopher Woodruff*
Abstract:
We use a combination of detailed panel data and a randomized experiment to assess the
role of capital in the recovery process of microenterprises in Sri Lanka following the
December 2004 tsunami. Disaster relief in low-income countries typically comes in the
form of relief aid rather than insurance payments. In our data aid is found to be
uncorrelated with the extent of business losses and to cover only a small fraction of
losses. Our results show that lack of access to capital inhibits the recovery process, with
firms receiving randomly given grants recovering profit levels two or more years before
other damaged firms. Access to capital appears to be particularly important for the retail
sector, whereas reductions in demand, disruptions in supply chains, and other disasterinduced changes appear to limit the extent to which capital alone can spur recovery in the
manufacturing and services sectors. Overall our data show that business recovery is much
slower than commonly assumed, providing a role for targeted aid to hasten
microenterprise recovery dramatically following such disasters.
JEL Classification Codes: O12, Q54, C93.
Keywords: Microeconomic Impact of Aid, Enterprise Recovery Following Natural
Disasters.
*
University of Peradeniya, World Bank, and University of Warwick, respectively. The authors thank
Susantha Kumara and Jayantha Wickramasiri and Michael Callen for outstanding research assistance and
the editor, two anonymous referees, Kathleen Beegle, Saroj Jha and Apurva Sanghi for comments. AC
Nielsen Lanka administered the surveys on which the data are based. Financial support from NSF grant #
SES-0523167, and the World Bank GFDRR and Norway Governance Trust Fund is gratefully
acknowledged.
“What seems more typical are the comments appearing six months to a year after a
disaster, expressing surprise at the speed with which the community has recovered, and
the prosperity that now reigns”
(Dacy and Kunreuther, 1969)
A series of catastrophic events in recent years has drawn increased attention of
both the public and researchers to the plight of those impacted by natural disasters. The
number of natural disasters reported by the press and included in the most comprehensive
disaster database is increasing. The Intergovernmental Panel on Climate Change (2007)
believes that the frequency of natural disasters is “likely” to increase as a consequence of
global warming. Some argue that the current increase results from more complete
reporting of disasters1, and others say that the future trend driven by climate change is
uncertain. But there is little disagreement that the impact of natural disasters is felt most
severely by households already living on the margins in low-income countries. The death
toll from disasters, for example, is typically much higher in low-income countries (Kahn
2005).
There is a large literature on how households in developing countries cope with
and respond to disasters and aggregate shocks. (See Skoufias (2003) for an overview.)
The poor suffer disproportionately because missing credit and formal insurance markets
limit their ability to smooth aggregate shocks. The informal risk-coping strategies that are
used to smooth idiosyncratic shocks break down when all members of a risk-sharing
group are affected (Morduch, 1999, Lustig 2000). As a result, transient shocks can have
permanent effects, either through a lowered ability of households to provide nutrition or
schooling for their children (Maccini and Yang, 2009; Ferreira and Schady, 2009), or
through the inability to repurchase productive assets, such as livestock, which are sold to
smooth consumption (Carter et al, 2007).
1
The trend of an increasing number of disasters shown in the EM-DAT data is likely due in part or in
whole to a more complete reporting of disasters. (See, for example, the discussion in Strömberg 2007.)
2
These same market failures that can limit the ability of households to recover
quickly from disasters are also likely to inhibit the recovery of microenterprises and small
businesses. Yet the existing literature has not looked at the process of enterprise recovery
in developing countries, and even in developed countries there are only a handful of
qualitative case studies. Until recent disasters such as Hurricane Katrina and the Asian
tsunami, the conventional wisdom, reflected in the quote from Dacy and Kunreuther’s
book, was that the economy recovers surprising quickly.2 Based on qualitative interviews
with small business owners shortly after Hurricane Katrina, Runyan (2006) concludes
that insurance is key: firms with insurance quickly replace destroyed assets, but those
without insurance do not, often because business records lost in the flooding and
destruction were required to access federal aid. While few micro and small business
owners in developing countries have insurance which covers natural disasters, large
government and non-profit aid flows are also common in developing countries after
natural disasters. The literature has examined the determinants of the magnitude of
international financial flows such as aid response to disasters (e.g. Eisensee and
Strömberg, 2007; Yang, 2008), but we know much less about whether the aid reaches
enterprise owners.
This paper provides the first microeconomic study of the recovery of the private
sector in a developing country following a major natural disaster. We use firm-level panel
data gathered from micro enterprises in southern Sri Lanka following the December 2004
Asian tsunami. In addition to surveys, the post-tsunami project involved a field
experiment providing grants to randomly selected enterprises. The grants allow us to
2
Most of the research by economists examining the aftermath of disasters focuses on short-term recovery
process, rather than the longer-term rebuilding process (Okuyama 2003). Analysis of longer term recovery
generally uses aggregate data such as building permits (Dacy and Kunreuther 1969). Two exceptions using
micro-level data are Smith and McCarty (1996), who analyze the demographic changes in southern Dade
Country, Florida following Hurricane Andrew in 1992, and Dolfman et al (2007) who estimate losses in
employment and wages in New Orleans following Katrina.
3
assess the importance of capital in the recovery process and measure the return to capital
immediately following a disaster. This is the focus of the paper.
Recovery in low-income countries differs from recovery in high-income countries
most notably in the flow of cash to households and enterprises. A large share of
households and businesses in high-income countries are covered by insurance against
disasters. While about 50 percent of losses resulting from Hurricane Andrew in Florida
and the Northridge earthquake in California were covered by insurance, for example, less
than 15 percent of the losses resulting from the tsunami were covered (Ferguson 2006).
Moreover, insurance coverage in low-income countries is typically limited to the largest
enterprises. Few households or small businesses have insurance to cover losses from
natural disasters. Relief aid flows serve as a substitute for insurance flows, both in paying
for the recovery and in stimulating economic activity. But our data suggest that only a
small part of disaster relief aid flows as cash directly to households and small
businesses.3 The majority of aid is channeled into infrastructure projects. Moreover, aid
agencies are seldom in a position to verify actual losses of households or small-scale
enterprises. So while insurance payments are closely related to the insured entity’s losses,
aid payments to individuals, households, or firms may not reflect the actual losses
suffered. How long does the process of rebuilding take, and what is the role of capital in
the recovery process? Our data are uniquely suited to shed light on these questions.
In spite of the fact that our data come from enterprises which are likely to be
credit constrained even in normal circumstances and uninsured against calamities, it is
unclear whether or not we should expect capital to be a binding constraint in the recovery
process. On one hand, there is strong evidence that even in normal circumstances, credit
and insurance market failures loom large for business owners in developing countries
3
See Oxfam (2005) for an argument that cash aid flowing directly to households should be more common
following disasters. Harvey (2005) reviews the state of knowledge on cash aid following disasters. These
discussions focus largely on household recovery rather than microenterprise recovery. There is also a
debate among microfinance practitioners about the role of loan forgiveness in disaster recovery. See
Mathison (2003) for a discussion of these issues.
4
(e.g. Banerjee and Duflo, 2005). Aggregate shocks such as natural disasters can limit the
effectiveness of informal financing and risk-coping mechanisms which households use to
overcome these constraints, potentially making access to capital even more of a
constraint on firm growth.
However, other impacts of disasters may instead be the main binding constraints,
and increasing access to capital without addressing these other constraints could therefore
have limited effect. First, firms face shifts in demand. The aggregate shift is generally
inward, but for some sectors—for example, construction materials—demand may shift
outward. Second, supply chains and trading relationships are destroyed, either
temporarily or permanently. The time required to rebuild supply chains and re-form
relationships depends on the specific sector. This effect is analogous to the
“disorganization” effects discussed in the literature on transition economies (Blanchard
and Kremer 1997; Roland and Verdier, 1999). We are able to cleanly identify the effects
on recovery of access to incremental capital. But we will explore the heterogeneity of this
impact in the retail and non-retail sectors for evidence of the relevance of other factors in
the recovery process.
We begin in Section 2 by discussing the extent of damage and recovery funds
provided by insurance and grants. In Section 3, we turn to the process of recovery,
identifying a proper comparison sample from which to calibrate recovery. Section 4 is the
heart of the paper. There, we use the random injections of capital to assess the importance
of access to liquid capital—over and above that available from other sources—in the
recovery process. We examine both the returns to the incremental capital and the effect
on recovery to the pre-tsunami trajectory. We show that the effect differs in the retail and
non-retail sectors, and that the results are robust to various comparison samples and ways
of defining the affected firms. Finally, we conclude in Section 5 section with a discussion
of policies that might quicken the recovery of microenterprises following a major
disaster.
5
Section 2: The Tsunami, Firm Losses and Sources of Recovery Funds
The December 2004 Indian Ocean tsunami produced catastrophic damage along
Sri Lanka’s eastern and southern coastlines. Official estimates put total deaths in Sri
Lanka at more than 35,000. More than half a million people were displaced when their
houses were damaged or destroyed. Estimated damage to infrastructure and other assets
exceeded $1.3 billion, around 7 percent of the country’s GDP.4 The tsunami’s impact was
concentrated in a narrow strip along the coast, and in the fishing and tourism sectors.
Aggregate Sri Lankan GDP fell only by between 0.5 and 1.0 percent (Jayasuriya et al,
2005). But two-thirds of the island’s fishing fleet was destroyed (Asian Development
Bank et al, 2005), and—in spite of the fact that the arrival of aid workers dampened the
blow—hotel bookings fell by 60 percent on Sri Lanka’s southern coast in 2005.
The international response to the disaster was rapid and strong. Governments,
international NGOs and the international financial institutions committed over $2 billion
in relief and recovery funds, of which $1.1 billion were dispersed and $0.6 billion
expended within 18 months of the disaster (Government of Sri Lanka, 2006). Of the total
aid pledged, $184 million was categorized as “relief” aid. Of the remainder, the largest
amounts were earmarked for recovery of the housing ($370 million), transportation ($245
million committed), and water ($190 million) infrastructure and for livelihood restoration
($219 million).
2.1 The survey data and intervention
We examine the impact of access to capital on recovery from this large disaster
using panel data from surveys we conducted with owners of microenterprises along the
southern coast of Sri Lanka after the tsunami. The sample includes 608 individuals with
4
This is the figure reported in the EM-DAT database. An assessment by the Asian Development Bank,
World Bank and Japanese Bank for International Cooperation estimated losses of roughly $1.0 billion and
replacement costs of $1.5 billion. The Sri Lankan government estimated recovery costs to be $1.8 billion.
6
information on assets, 205 of whom suffered damage of business assets during the
tsunami. Among the owners suffering no direct damage, 208 were located near enough to
the tsunami areas that they faced severe disruptions in their markets, and 195 were
located some distance away from impact areas and were unaffected by the tsunami. We
refer to the firms suffering damage as “directly affected,” those located in the same
neighborhoods as “indirectly affected,” and those located further from the coast as
“unaffected.” We conducted 11 surveys in all, with quarterly surveys between April
2005 and April 2007, and semi-annual surveys conducted in October 2007 and April
2008. We believe these data are unique in allowing us to examine the recovery process of
microenterprises in a low-income country.
The April 2005 microenterprise baseline survey asked owners which assets were
damaged or destroyed by the tsunami, and all waves of the survey ask about the repair or
replacement of those assets. The July 2005 survey has questions on grants and loans
obtained to replace assets. Enterprises in the panel were also subject to random capital
injections in May and November 2005, as described in more detail in Section 3 below.
Note that all of the data on asset damage and aid received are self reported. The small
scale enterprises and households which comprise the majority of the samples seldom
keep written records of assets or business transactions.
Following the baseline survey, we randomly selected 117 of the enterprises and
gave them either 10,000 LKR (about US$100) or 20,000 LKR (about US$200) either in
cash or in-kind. Two-thirds of the grants (78) were for 10,000 LKR, and one-third (39)
for 20,000 LKR. Half of the grants were provided in cash and half in-kind. The cash
grants were paid by check directly to the survey respondent. For the in-kind grants, the
owners selected the items to be purchased. They were accompanied to local stores or
markets by research assistants working for the project. Cash treatments were given
without restrictions; recipients were told they could purchase anything they wanted with
7
the cash. The treatment was framed as compensation for participating in the panel survey,
and enterprise owners were told that they would be eligible to win the grant only once.
The aim of the experiment was to help us understand the role of access to capital
in the process of enterprise recovery through the generation of an exogenous shock to
capital stock. We describe in detail the results of the experiment among undamaged firms
in de Mel, McKenzie and Woodruff (2008). Randomization was done by computer and
stratified according to district (Galle, Matara or Kalutara) and extent of tsunami damage
(directly affected, indirectly affected, or unaffected). As a result of this stratification, the
pre-treatment characteristics of the treatment and control groups are similar to each other
among the subsample of directly affected firms, as they are for the comparison groups of
unaffected firms. Appendix 1 shows firm characteristics by treatment group and describes
the critical survey questions in more detail.5
In generating a cash inflow following the disaster, the grants we provided are
similar to the checks many firms receive from insurance companies in more advanced
countries. An obvious difference is that our grants were not directly linked with losses.
Within the affected zone 120 firms were assigned to treatment (57 percent), with 90 firms
assigned to receive treatment after the baseline survey in May 2005 and a further 30 firms
assigned to receive treatment after the third survey round in November 2005. This split
frontloaded treatments so that more of the randomly allocated aid could reach tsunami
victims sooner. Among the 120 treatments, 77 were for 10,000 LKR (39 cash, 38 in-kind)
and 43 were for 20,000 LKR (21 cash, 22 in-kind).6
5
This table and Table 2 in the main paper, discussed later, show balance on baseline measures both across
areas and across treatment groups. Some indicators of pre-tsunami size, measured by recall of the enterprise
owners, indicate a lack of balance. We discuss these later in the paper.
6
Our initial plan was to survey firms for five quarterly waves only. Receipt of further funding enabled us
to continue the panel, with four additional quarterly waves collected from July 2006 through April 2007,
and a tenth and eleventh waves collected in October 2007 and April 2008. In order to compensate firms for
the additional burden of staying in the study longer than we had anticipated, we gave 2,500 LKR (~$US25)
in cash to each of the remaining untreated firms after round five of the survey. These treatments are
accounted for the analysis.
8
Attrition in the data is relatively low. Of the 205 firms in the baseline sample
reporting some damage from the tsunami, 186 report profits in round five and 173 in
round 11 (83 percent of the initial sample). However, only 197 firms report profits in the
baseline survey, and firms move in and out of the sample. One hundred thirty-five firms
report profits in all 11 rounds, and 182 report profits in eight rounds or more.
For purposes of comparison, in July 2007 we conducted an additional survey with
a sample of 424 enterprises employing between five and 50 workers, 139 of whom
suffered direct damage from the tsunami. We set out to sample these larger firms from
the same neighborhoods as the microenterprise owners.7 However, because we did not
find enough larger enterprises in these neighborhoods, the enterprise sample was drawn
from surrounding areas as well. These surveys asked enterprise owners about damage
suffered from the tsunami, the extent to which the damaged assets had been replaced or
repaired, and the sources of funds used to pay for those repairs. We also asked business
owners if they received payments from insurance claims or aid in the form of grants, or
loans. We use these data to place the recovery of our microenterprise firms in the larger
context.
2.2 Asset Damage, Aid, and Insurance
Summary data on the extent of damage, insurance coverage, and aid are shown in
Table 1, which shows data from the two samples for firms suffering some damage. The
microenterprise owners report losses of $897 in business assets and just over $2,400 in
household assets. The larger firm (SME) owners report losing an average of just over
$40,000 in business assets and $6,000 in household assets. The table demonstrates the
lack of insurance coverage in both samples. Even among the larger enterprise owners,
7
Sri Lanka is divided into 25 districts, which are divided further into 324 Divisional Secretariat (DS)
Divisions. These are further divided into just over 14,000 Grama Niladharis, or GNs, which are the smallest
administrative units. There are about 400 households in a typical GN. The microenterprise sample was
drawn from 25 GNs in the districts of Kalutara, Galle and Matara.
9
only 13% of those suffering losses reported having any insurance coverage for business
assets, and their policies generally did not cover damage from tsunamis. Even those with
insurance report that only 7.5 percent of business losses were covered. A similar pattern
holds for household assets. Among the larger enterprise owners, only 4.1 percent said
they had any insurance on household assets, and only 5.1 percent of household losses
were covered. Among microenterprise owners, none of the 176 suffering damage to
household assets reported having insurance to cover losses.8
Owners report receiving more funds from grants than from insurance; 94% of
microenterprise owners, but only a quarter of larger firm owners report receiving a grant
to cover some part of the damages they suffered.9 A quarter of the larger firm owners also
reported obtaining a loan after the tsunami, but less than 5% of the microenterprise
owners obtained a loan. Combined, however, the reported grants and loans were enough
to replace only a small part of the damaged assets—around 1/5th for microenterprise
owners and 1/6th for larger firm owners. For SME owners, where the survey asked how
the grants and loans were used, grants were more likely to cover housing losses and loans
more likely to cover business losses. For the typical SME owner, grants covered almost
one-quarter of housing losses, but less than 2 percent of business losses. Loans covered
an additional 13.7 percent of business losses for the SME owners.
Our April 2005 microenterprise survey indicates that the households of owners
received an average of $115 in aid during the first three months of the tsunami, of which
$101 came from government programs, $8 from NGOs and $6 from other sources.10
8
In contrast, 78% of households suffering damage from hurricane Andrew in Florida had insurance, and
the insured received average payouts of $32,000 (Smith and McCarty, 1996). The percentage of insured
was likely higher among businesses. Besides providing funds for the recovery of individuals’ assets, the
$14 billion dollars of insurance aid following Hurricane Andrew provided a significant boost to the local
economy that is lacking in the Sri Lankan case.
9
These percentages do not include the grants from our project.
10
Cash aid came primarily through four programs, none of which was directly tied to business losses. First,
the Sri Lankan government paid surviving family members 15,000 Sri Lankan Rupees (LKR, about $150)
for each person killed by the tsunami, to offset funeral expenses. Second, the government and the World
Bank provided four grants of 5,000 LKR (about US $50) to 220,000 households suffering direct damage
from the tsunami. Third, the government provided aid to rebuild houses destroyed by the tsunami. Finally,
10
Table 1 shows that by July 2005, 94 percent of those reporting damage had receive some
form of aid. However, the mean aid received ($332 among those receiving something)
was less than 10 percent of the reported damage.11 The grants reported by microenterprise
owners cover only a small portion of the losses they incurred. Loans were much less
common. Fewer than 4 percent of microenterprise owners reported receiving loans, and
these covered less than 1 percent of losses in aggregate.
Perhaps the most interesting aspect of the reported aid is the lack of correlation
between reported losses and reported grants and loans. For SME owners, where we are
able to separate business from household aid, the correlation between the reported loss of
household assets and the grants (loans) received is 0.70 (0.27). For business assets, on the
other hand, there is essentially no correlation between the reported losses and the (very
small level of) grants received; the correlation between losses of business assets and
loans to replace those assets is 0.16. For microenterprise owners, there is no correlation
between the level of losses on the one hand and grants or loans on the other. So while
overall aid flows into Sri Lanka following the tsunami were large, and while at least some
aid there flowed directly to households suffering losses, the data suggest that targeting is
particularly poor with regard to damage suffered by business owners.
In sum, the data paint a fairly consistent picture of the process of recovery of
business assets. None of the microenterprises and few of the larger firms were insured
against losses from the tsunami. Aid flows appear better matched to losses in housing
assets, and uncorrelated with losses in business assets. And, the level of aid flows to
small scale enterprises were small compared to the size of the losses incurred.
Section 3: Selecting a comparison sample
the government and numerous NGOs sponsored cash-for-work programs, which typically paid workers
around 300-350 LKR ($3.00-$3.50) per day to participate in cleanup and rebuilding activities.
11
The enterprises appear to have reported grants provided by our project itself, as described below, so these
amounts likely overstate the aid received by typical microenterprises. Those receiving our grants reported
cash aid averaging just over $100 more than those not receiving our grants.
11
To understand the recovery of the microenterprises affected by the tsunami, we
need to compare the trajectory of the affected firms to a counterfactual trajectory
measuring outcomes in the absence of the tsunami. The ideal, of course, would be for the
tsunami to have randomly selected enterprises for damage. In any disaster, this is
conceptually impossible. However, we believe we have two very good, if necessarily
imperfect, comparison groups. The first is a group of enterprises located in the same
neighborhoods as the enterprises damaged by the tsunami, the “indirectly affected”
enterprises. The average direct distance to the coast—calculated using GPS location
readings—is 760 meters for the indirectly affected firms, compared with 410 meters for
the directly affected firms. There is substantial overlap in the distribution of distance
from the shore in the directly and indirectly affected samples, with the median distance
among the indirectly affected firms below the 90th percentile of the directly affected
sample. Whether or not the tsunami caused direct damage to firms located very near the
coast depends in part on factors such as the shape of the ocean floor and topography on
land. There is little reason to expect that the location of the decisions of the firms—made
prior to the tsunami—were influenced by these factors.12 In that sense, the damage
among the directly and indirectly affected firms comes as close to the ideal of being
randomly assigned as we might hope. But there is a potential drawback to this group as a
comparison sample. The outcomes of the indirectly affected firms may, for at least some
period of time, deviate from the no-tsunami counterfactual because of shifts in demand
caused by either the dislocation of their customers or by the reduction in the number of
competitors. We examine these concerns as they relate to the appropriateness of these
firms as a comparison group in the next subsection.
The second potential comparison group is the “unaffected firms” which are
located further from the coast—5.2 kilometers on average. While these firms are much
12
The last major tsunami in the Indian Ocean is estimated to have occurred 500-700 years prior to the
December 2004 occurrence.
12
less likely to have experienced demand shocks after the tsunami, we might be concerned
that the locational choice of microentrepreneurs is not random with respect to damage
from the tsunami. Thus, the firms may not be comparable in other ways.
The sample selection criteria provide a high degree of comparability on measured
characteristics across all three areas. We sampled firms with no paid employees and no
motor vehicles and with an upper limit on capital stock of 100,000 Sri Lankan Rupees
(about $1000) , excluding land and buildings. We excluded firms operating in agriculture,
fishing, and professional services. Almost exactly half of the enterprises in all three
groups are engaged in retail trade in all of the three areas (51% among directly affected
firms, 46% in the indirectly affected area, and 53% among the indirectly affected firms).
Among the activities outside retail trade, in both the directly affected and indirectly
affected samples, garments, lace, food production, and coir are the most common
activities. Among the unaffected firms, garments and food production are also among the
most common non-retail activities, but production of bamboo products is also a common
activity, while lace and coir are not.
Table 2 compares the capital stock, sales and profits of enterprises in the directly
affected, indirectly affected, and unaffected sub-samples, using data for March 2005 from
the baseline survey and recalled pre-tsunami data.13 The table also shows data for the
subset of directly affected firms most severely affected by the tsunami, those losing more
than half of their assets. The first column shows data for all firms reporting any level of
damage. Among the 216 firms reporting some damage, 29% report losing all of their
assets excluding land and buildings,14 while 10% report losing less than one-quarter of
their assets and 21% report losing less than half of their assets. The second column limits
the sample to those enterprises reporting the loss of more than half of their assets.
13
Appendix Table 1 shows comparisons across treatment groups.
We exclude land and building throughout the analysis because the business activity is often carried out at
home, and separating the value of the assets used in the business from those used for living is very difficult.
14
13
Because we expect recovery to be particularly challenging for these firms, we focus most
of our attention for the remainder of the paper on these more severely affected firms. We
will show later that our main results are not sensitive to the particular threshold chosen.
Looking first at the March 2005 data, there are no significant differences across
the three areas in mean reported capital stock (less land and buildings), or reported
machinery and equipment. (See rows 1 and 2.) Directly affected firms suffering more
extensive damage (column 2) have slightly lower mean capital stock levels, though even
here the differences are not statistically significant. Rows 3 and 4 show that mean sales
and profits are also very similar for the directly affected and unaffected firms, but both
are almost 50% larger for the unaffected firms, a difference significant at the 1 percent
level.
The March 2005 data reflect the effects of the tsunami. Since recovery implies a
return to the pre-tsunami trajectory, a more relevant comparison would be based on pretsunami firm sizes. We have no data from surveys conducted before the tsunami. Instead,
we rely on reports of damage to assets and recall questions on sales and profits. These
data are shown on the bottom half of Table 2. They suggest that prior to the tsunami, the
directly affected firms were somewhat larger than the indirectly affected and unaffected
firms.
Comparing the directly- and indirectly-affected firms (columns 1 and 3), before
the tsunami the damaged firms had 27 percent more invested capital (31,648 LKR vs.
24,855 LKR,), 61% higher sales (19,133 LKR vs. 11,863 LKR) and 45% higher profits
(5,804 LKR vs. 3986 LKR). All of the differences are significant at the 1 percent level.
The pre-tsunami differences between the directly affected and unaffected firms are
somewhat less pronounced, though the former still had 15% more invested capital, 30%
higher sales and 25% higher profits, on average. The difference in capital stock is
significant at the 1 percent level and the differences in sales and profits at the 5 percent
level. There is substantial overlap in the distributions with, for example, the median pre14
tsunami invested capital among firms suffering damage being around the 58th percentile
of the distribution of either the indirectly affected or unaffected firms.
In measuring the impact of access to capital in the recovery process, we use firm
fixed effects regressions which control for any pre-tsunami differences in firms. But
because the question of how quickly the damaged firms recover from the tsunami
depends on a comparison across firms, a complete answer precludes the use of fixed
effects. In the recovery regressions, we condition on pre-tsunami size, measured by
recalled profits, profits imputed from recalled sales, recalled sales, or capital stock. We
show that the results are robust to using any of these measures of pre-tsunami size. In
robustness checks, we also use only the sample of damaged firms, and compare those
suffering damage exceeding the amounts of our grant with those suffering damage less
than our grants.
Finally, we look at changes in firm performance between 2005 and 2008 in each
of the groups. Figures 1A and 1B show the trend of profits, and invested capital by group,
normalized by a pre-tsunami measure of size as described below.15 We exclude the firms
receiving grants from us in the indirectly and unaffected areas in the graph so that the
data represent dynamics under more normal conditions.16 We also exclude the firms
suffering less extensive damage from the tsunami, using the sample from column 2 of
Table 2. The graph also includes a line for the treatment group among the affected firms,
as a visual indication of the extent to which the grants affected the recovery process – we
examine statistically the impact of the grants in the next section.
Figure 1A conditions on a measure of pre-tsunami profits imputed from recalled
sales, with the mean for each group normalized to 100.17 Focusing first on the two
15
The profits, sales, and capital stock underlying the graphs are all deflated to March 2007 values using the
Sri Lankan all-island CPI.
16
Table 2 included both treatment and control firms, but used data from pre-treatment periods. The table
would be essentially identical if the sample were limited to the control firms, though the smaller sample
size would make it less likely that differences across samples would be statistically significant.
17
The reported pre-tsunami profit data are very noisy, and have low correlations with profits reported in
subsequent survey rounds. Based on analysis of the same data used here, de Mel et al (2009) show that
15
potential comparison groups, we see that the indirectly affected firms lag behind the
unaffected firms for 2-3 quarters, and then track the unaffected firms very closely for the
remainder of the sample. This suggests that the indirectly affected and unaffected firms
are quite similar to one another after a short period of recovery for the indirectly affected
firms. Since the indirectly affected firms are located in the same neighborhoods as the
directly affected firms, this gives us some assurance that both the indirectly affected and
the unaffected firms are reasonable comparison groups for measuring the recovery
process. Also notable on the table is the fact that the directly affected firms in the control
group lag behind both the untreated indirectly affected and unaffected firms throughout
the three-year period. Directly affected firms in the treatment group, who received a
grant, have higher earnings than those not receiving grants in most quarters, giving some
suggestion that the grants helped firms recover.
Figure 1B shows the trajectory of capital stock. Here we have included a measure
of the capital stock immediately after the tsunami, based on the reported damage, as well
as the level reported in the baseline survey. Again, after 2-3 quarters, the indirectly
affected and unaffected firms track quite closely. Among the damaged firms, the treated
group lags behind until the fourth wave of the survey, and then spikes much higher after
wave 9. Recall that some grants were given after wave 3.
In sum, the indirectly affected firms are, on average, somewhat smaller in pretsunami size than the directly affected firms. But in sector of activity and location, the
two samples are very closely matched. The unaffected sample differs from the indirectly
affected sample somewhat in the detailed non-retail activities, but is very similar in pretsunami size, and in the trajectory of profits, and capital stock within 2-3 quarters
following the tsunami. We conclude that both the indirectly affected and unaffected firms
firms are able to recall sales level with a greater degree of accuracy. Therefore, we use recalled pre-tsunami
sales to impute pre-tsunami profits, using baseline (March 2005) data and a regression of profits on sales,
gender, sector, education level and the age of the business. Since the graphs use actual reported profits for
each post-tsunami period, this does not affect period-to-period changes in the graphs.
16
are good, if imperfect, comparison samples. In the interest of statistical power, we
combine them for the main regression results reported in the paper. With each set of
results, we discuss how the results would differ if we used only the unaffected firms as
the comparison sample.
Section 4: The Value of Additional Capital
Figures 1A and 1B indicate that firms affected by the tsunami lagged behind
comparable firms for 2-3 years following the tsunami, but also show some evidence that
grants sped the recovery process. The random allocation of the grants allows us to
examine the extent to which restrictions on capital limit recovery. We do this in two
ways. First, we measure whether, and by how much, the additional capital available to the
enterprise increased profits. Then we examine the role of the additional capital in
accelerating the recovery across time in both profits and capital stock.
4.1 How much did the grants increase capital?
We begin by estimating the mean impact of the grants on real profits of tsunamiaffected firms, via the following fixed effects regression for firm i in period t:
(1)
Where AMOUNTi,t is an indicator of the amount of treatment received by firm i at time t,
coded in terms of 100 LKR, DAMAGEDi indicates the firm lost more than half of its
non-land assets, and UNDAMAGEDi indicates the firm lost no assets. Firms losing half
or less of non-land assets are excluded from the sample. In robustness checks discussed
later, we show that the results are not sensitive to the specific cutoff. Firms receiving
17
2,500 LKR after round 5 will thus have AMOUNT of 25 in rounds six through 11 (and 0
before this). The δh are wave dummies, which are allowed to vary for damaged and
undamaged firms.
Treatments are coded 0, 100, or 200 in the regressions so the coefficient shows
the increase in real profits in rupees from a 100 LKR treatment. Thus, the coefficients can
be interpreted as the percentage return on the treatment. In most of the regressions we use
the amount of the treatment as the independent variable, and so are measuring the
intention to treat. As not all of the grants found their way into the enterprise, this may
differ from the return to incremental investments. The intention to treat seems the more
policy-relevant effect in the disaster recovery context.
The first column of Table 3 shows the effect of the capital grant on real profits
based on estimating equation (1). When we allow the return to the 10,000 LKR grant to
differ from the return to the 20,000 LKR grant, the coefficients suggest diminishing
returns among both directly affected and other firms, but in neither case are the returns
significantly different from linear. Similarly, the return to the in-kind grant is about
double the return to the cash grant in the directly affected area and half the return to the
cash grant in the other areas. But, again, in neither case is the difference in returns
statistically significant.18 Hence, for simplicity, we constrain the returns to be linear in
amount and identical for cash and in-kind grants. While we find that measured return on
the treatment amongst tsunami-damaged firms is almost double that in the undamaged
areas—10.1% per month vs. 5.5% per month—this difference is not statistically
significant. Note that because we use a fixed effects specification, the comparison group
of undamaged firms only has an impact on the estimated returns for damaged firms
through the estimation of the wave fixed effects. If we use only the unaffected firms
rather—that is, if we exclude the indirectly affected firms from the sample—the
18
The regression results are shown in the appendix on table 2.
18
measured return from the grant increases slightly among the affected firms to 10.3% per
month.
Column 2 reports the results of estimating equation (1) using capital stock as the
dependent variable. This measures the average amount of the grant invested in the
enterprise over all of the post-grant survey rounds. We find that about 2/3rds of the grants
remain invested in the enterprises, with this amount similar for both damaged and
undamaged firms. Finally, we report the results of an IV regression using the grant as an
instrument for changes in capital stock. We first show in column 3 that the grants had no
effect on the labor effort of the owner, measured by the number of hours worked in a
week, among either damaged or undamaged enterprises.19 The IV regression in column 4
can then be interpreted as a marginal return on the incremental capital. Consistent with
the ITT results reported above, the measured returns are 13% per month among directly
affected firms—though large standard errors imply the return is not significantly different
from zero—and 6.7% among firms not directly affected.
The intention-to-treat effects measure the private impact to an individual
microenterprise of being given our grant. This relies on the stable unit treatment value
assumption, which in practice requires that the potential outcomes for a firm are
independent of the treatment status of any other firm. A potential threat to the validity of
this assumption would be if any gain in profits to treated firms comes as a result of these
treated firms taking customers from the untreated firms. We calculate for each wave the
number of treated firms in the same industry within 500 meters of each firm, and
following de Mel et al. (2008, p. 1361) test for the significance of these spillovers. For
the directly affected firms this spillover term is insignificant (p=0.25) and the coefficient
19
We find no significant effect of the grants on hours worked in the enterprise by family members or nonfamily members, either among damaged or undamaged firms. For damaged firms, the measured effects are
negative, but highly insignificant.
19
is positive, so that there is no evidence of negative spillovers on untreated firms. These
results are shown in Appendix table 3.
The regressions in Table 3 estimate the average returns to the grants over the
entire post-tsunami period. We might expect that the effect of the grant is larger in the
period immediately after the tsunami, since that is the time when capital is likely to be
scarcest. As the directly affected firms not receiving grants recover over time, the effect
of the grants should diminish. We test this in Table 4 by allowing the impact of the grant
on profits and capital investment to vary over time. We do this by estimating regressions
of the following form:
(2)
where v indicates the period 1 year (survey waves one through four), 2 years (waves 5
through 8), or 3 or more years (waves 9 through 11) following the tsunami. Thus, returns
are allowed to vary by time period and whether or not the firm suffered damage from the
tsunami. As before, the sample includes firms suffering a loss of more than half of the
non-land assets, and those suffering no loss of assets.
Columns 1 and 2 report the time-varying effect of the grants on profits and capital
stock, respectively. Column 3 reports hours worked and column 4 an IV regression using
the grant as an instrument for capital stock. With respect to profits, we find that the
measured impact of the grant is larger for the directly affected firms for two years after
the tsunami, and smaller in the third year compared with the undamaged firms. However,
none of these differences are statistically significant. Within the directly affected area, the
20
measured effect is largest in the second year after the tsunami, but then drops in the third
year. The difference between the first two years is not statistically significant, but the
difference between the second and third years is significant at the 5 percent level. For
hours worked by the owner (column 3), we find a significant effect only among the
undamaged firms, and then only during the first year after the tsunami. In unreported
regressions, we do find that the grants are associated with a significant decrease in the
number of hours worked by members of the owner’s family in the enterprise, only during
the second year following the tsunami.
The patterns from columns 1 and 2 also hold in the IV regression in column four,
with returns first rising (insignificantly) and then falling (significantly) among the
directly affected firms. The IV regression indicates that the return on capital invested in
the directly affected enterprises is 14.6% per month during the first year following the
tsunami, and 19.9% during the second year. Given the decrease in unpaid family labor
working in the enterprise during the second year, this might understate the marginal
returns to investments during that year. With regard to capital stock, we find very little
difference across years for either the directly affected of other firms. About two-thirds of
the grant appears to have been invested in the enterprise, on average, in each of the three
time periods.
The results are very similar when we use only the unaffected firms as a
comparison group. The measured effect of the grant on profits of the directly affected
firms increases slightly to 9.75% during the first year and 15.57% during the second year,
but the statistical significance of the differences across groups and years remains
unchanged.
4.2 Did the grants enable damaged enterprises to recover?
Next we investigate whether the grants helped the damaged enterprises recover
profit levels and capital stock. Recovery here means not just getting back to their pre21
tsunami levels, but getting back to where they would have been had the tsunami not
occurred. This counterfactual is provided by using the set of untreated firms in both the
indirectly affected and unaffected areas. We estimate a regression similar to equation (2)
above. However, because the question of recovery is inherently a cross-firm comparison,
we use random effects regressions which condition on a measure of pre-tsunami firm
size. We also constrain the wave effects to be the same for the directly affected and other
firms:
(3)
where SIZEt=0 is pre-tsunami size measured by recalled profits, imputed profits, or
December 2004 capital stock excluding land, buildings and inventories. The parameter θv
then measures the extent to which untreated damaged firms have not recovered relative to
the untreated undamaged firms in year v, while βv measures the extent to which the
treatments help damaged firms to recover. Complete recovery of treated damaged firms
therefore occurs if θv + βv = 0, while complete recovery of untreated firms requires θv =0.
Table 5 reports the results of estimating equation (3) for real profits and capital
stock. For profits, we show results using both recalled November 2004 profit levels
(column 1) and the November 2004 profit levels imputed from recalled November 2004
sales (column 2), which we believe to be more accurate. In either case, the regressions
indicate that the untreated damaged firms suffered a fall in profits of between 577 and
1160 LKR per month during the year after the tsunami, and 964-1694 LKR during the
second year. However, this gap closes markedly in the third year, and although the point
estimates using imputed profits suggest untreated firms have still not fully recovered, we
cannot reject completely recovery of profit levels by this time. The size of the estimated
22
treatment effects on the grants are similar in magnitude (and of opposite sign) to these
losses during the first two years. That is, on average the grants allowed the damaged
firms to recover within the first year, in that damaged firms receiving a grant had profit
growth relative to pre-tsunami size which was almost identical to that of undamaged
firms which did not receive a grant.
Column 3 shows the effect of the grants on recovery of capital stock, conditioning
on the December 2004 pre-tsunami capital stock level. The pattern is similar as that for
profits. The untreated damaged firms fall behind for two years following the tsunami
before catching up. The grants enabled immediate recovery of capital stock, on average,
relative to the comparison group of untreated, undamaged firms.
Because the recovery regressions are based on cross-firm comparisons, the results
may be more sensitive to the choice of comparison sample. Columns 4 and 5 repeat the
regressions in columns 2 and 3 using only the unaffected untreated firms as the
comparison sample. The change in comparison sample leads to qualitatively similar
results. We see that in the case of profits, the damaged firms fell further behind, and—in
the year following the tsunami—the point estimates suggest that the average impact of
the grants was not sufficient for damaged firms to catch up. However, we still cannot
reject full recovery for the treated damaged firms within the first year. For capital stock
(column 5), the change in the comparison group has almost no effect on the magnitude of
any of the coefficients (compare column 5 with column 3).
4.3. Comparing Retail and non-Retail Firms
If capital is sufficient to accelerate enterprise recovery following disasters, then
perhaps the focus of relief efforts on rebuilding supply chains is misplaced. Alternatively,
it may be that the impact of capital on the recovery is heterogeneous, in which case we
would be mistaken to over-interpret the results on tables 3-5. Our sample is too small to
unpack all of the alternative reasons for delays in the recovery process. We will,
23
however, examine heterogeneity in one important dimension, separating enterprises in the
retail sector from those in manufacturing and services. Retailers typically sell goods
produced outside the area to a large number of customers in arms length transactions.
Those in services and manufacturing typically have fewer, more intense trading
relationships and often work with locally procured inputs, and particularly in
manufacturing, supply chains are more complex. In some cases, the tsunami disrupted
these supply chains—for example, in the coir industry, where the pits used to soak the
coconut husks were filled with debris that took many months to clear. If there are
differences between the retail and non-retail sectors in the effect of capital on recovery of
enterprises then this would suggest that access to capital may be sufficient to speed
recovery in some circumstances but not others. We caution that we did not stratify
treatment on sector of activity, and hence the analysis in this section should be treated as
exploratory and the results as suggestive.
We investigate how the impact of the grants varies across sectors, and by the
importance of individual customer and supplier relationship. We do this by estimating the
following fixed effects regression in which the treatment variable and wave dummies are
each interacted with sector or trading partner characteristics of the firm:
(4)
where S indicates the sector, retail or manufacturing and services and the other variables
are as defined above. This specification constrains the wave effects to be identical in the
retail and non-retail sectors, an assumption we relax shortly.
The first two columns of Table 6 repeat the regressions in the first two columns of
Table 3, except that we allow the effect of the grants to differ in the retail and non-retail
24
sectors.20 The results are stark. Among the directly affected firms, the grant has a large
positive effect on profits within the retail sector, but no effect outside the retail sector.
The measured effect of the grant is actually negative (but highly insignificant) for the
non-retail firms suffering damage, and just over 20% of the grant amount per month for
the retail firms. The difference in returns across sectors is significant at the 1 percent
level. Among the directly affected firms, a larger portion of the grant also makes its way
into the enterprise, 11,656 LKR for the retail firms vs. 5,708 LKR for the non-retail
firms. With respect to capital investment, the gap between the sectors is not statistically
significant, but the effect of the grant on capital stock is significant only among the retail
firms (p=.07). Among the firms not suffering damage, the measured returns are higher in
the retail sector, but not significantly so. However, we do find that the effect of the grant
on invested capital is significantly higher in the retail sector among undamaged firms.
The next four columns allow the effect of the grant to vary with time. In order to
limit the number of interaction terms, we separately estimate equation (2) for the retail
and non-retail sectors. Among the directly affected firms, we find particularly large
effects on retail sector profits during the first two years following the tsunami, and
significantly (p<.01) smaller effects more than two years after the tsunami. We find no
significant effect of the grant on profits in the non-retail sector in any period following
the tsunami, and no significant differences in effects across time. Curiously, however, the
capital stock regressions suggest that among the damaged firms, manufacturers invest as
much of the grant as retailers.21 With regards to the undamaged firms, we find no
differences in the effect of the grant on profits either across sectors or across years within
20
We combine manufacturing and services because the sample is not large enough to split them. A larger
sample might uncover differences between these sectors as well. We do not report IV regressions because
we experienced convergence issues in the smaller subsamples.
21
The differences across sectors differs in columns 5 and 6 compared with column 2. This results from not
including sector-specific wave effects in column 2. In the separate regressions in columns 5 and 6, the wave
effects do differ by sector.
25
a sector. Retailers do appear to invest more of the grant than non-retailers, though this
result appears to be driven almost entirely by the presence of a few large outliers.22
Table 7 repeats the recovery regressions (equation 3) with the sample split by
retail and non-retail. With regard to profits, the untreated directly affected firms in the
retail sector fall further and more significantly behind than those in the manufacturing
sector, and the grant almost exactly offsets the losses. Looking at capital stock (columns
3 and 4), the grants offset any effect of the tsunami in both the retail and non-retail
sectors.
These results indicate substantial and significant differences in the impact of the
grants across the two sectors. In the manufacturing and services sectors, where supply
chain and customer / supplier relations issues are likely to be more important, the grant
led to recovery of capital stock, but had no effect on profit levels following the tsunami.
In contrast, among firms in the retail sector, the grants led to rapid recovery of capital
stock and very large increases in profits.
4.4 Robustness of results
We chose the threshold of losses exceeding 50% somewhat arbitrarily. In
appendix table 3, we show that the results are not sensitive to the particular choice. We
use the regression specification from equation (3) and allow the cutoff for damage to vary
from 0% (columns 1 and 2) to 90% (columns 5 and 6). We show results for recovery of
profits conditioning on both recalled profits and profits imputed from recalled sales. The
grants always have effects which are significant at least at the 5% level one or two years
after the tsunami. The returns in the first year range from 7.1% (0% threshold using
recalled profits) to 11.9% (90% threshold using imputed profits). The range of returns in
22
There are 3 observations with very large changes in inventory investments across time, generally an
order of magnitude difference. We were unable to confirm that these are in fact data errors, and so have left
them unchanged. Given these are fixed effect regressions, and that there are no similar outliers among the
directly affected firms that are our focus here, we do not view this as a major concern.
26
the second year following the tsunami is somewhat wider, with returns ranging from
9.4% to 21.8% per month.
We also ran a regression allowing the effect of the grant to vary continuously with
the percentage of assets damaged by the tsunami, with the results shown in Appendix
table 4. In this specification, the return to firms suffering no damage is 5.9%. The
coefficient on the percentage of assets damaged is 6.04, indicating that a firm losing all of
its assets has an estimated return of 11.3% per month. Note that the coefficient on the
continuous measure of damage is not statistically significant when we use the full sample.
However, when we allow the percent damage interaction term to vary in the retail and
non-retail sector, we find a highly significant effect for retail firms, but not for
manufacturing / service firms.
Finally, in Table 8, we limit the sample to those firms suffering some damage
from the tsunami. We split this sample into two sub-samples. The first is firms for whom
our grant covered all or more than all of the assets they reported losing in the tsunami.
This is about 1/3rd of the sample of damaged firms, with the remainder of firms suffering
damage exceeding the amount of our grant. Given that even the untreated firms were able
to find resources to recover some part of their capital fairly quickly (see Figure 1B), we
should expect to find that the effect of marginal capital provided by the grant is larger
among the firms whose damage exceeded the amount of the grant. Using a specification
similar to equation 3, with the sample limited to firms suffering some damage, we find
results consistent with this expectation. Among firms suffering damage exceeding the
grant we provided, profits increased by 21.5% of the grant in the second year following
the tsunami; the grant increased profits by only 5.3% during that year among those with
damages less than the grant amount. The gap in the third year is of nearly equal size. In
spite of the small sample, the gap is statistically significant at the 5% level in both the
second and third years following the tsunami.
27
Of course, most of the untreated firms suffered damage in excess of the 2,500
LKR payment we made to them after the fifth wave. Hence, the untreated firms dominate
the group for whom damage exceeds the grant amount. This suggests the possibility that
the regression in the first column of Table 8 merely reflects the variance generated by
treatment vs. control. The regression in the second column of Table 8 shows this is not
the case. Here we exclude the untreated firms, so that the difference between the grant
exceeding or falling short of the damage amount is driven entirely by the extent of
damage and the (randomly determined) size of the grant. We find gaps between the two
groups of very similar magnitude to those reported in column 1. The third and fourth
columns break the sample (included the untreated firms) into the retail and trade sector.
Though the coefficients are uniformly larger in the retail sector, the gaps between the
treatment effects among the firms with damage greater than the treatments and those with
damage smaller than the treatments is similar in the two subsamples.
Section 5: Conclusions
Large natural disasters on the scale of the 2004 Asian tsunami leave in their wake
complete chaos. The flow of relief and recovery aid following these disasters is often
very large. In Sri Lanka, we find that the aid flow to households for the purpose of
recovering household assets damaged by the tsunami was large and relatively well
targeted, with a positively correlation between the damage suffered by the household and
the aid received directly by the household. In contrast, the aid flow to enterprises was
small and not well targeted, with no correlation between reported damage and aid
receipts. Would better targeting of aid to microenterprises speed the recovery? The
answer to this question is not obvious, because for firms, the destruction extends beyond
assets to the destruction of trading relationships, supply chains and infrastructure
necessary to carry out business normally. In this setting, it is not clear when and whether
capital alone will aid in the recovery of enterprises.
28
Using the random allocation of cash grants to enterprises, we show that providing
additional capital is sufficient to speed recovery. Incremental capital invested in the
business yields very high rates of return—around 5% per month among firms on the
roughly two-thirds of our grants which were invested in the typical recipient firms
damaged by the tsunami. These returns to capital are larger than in inland areas less
affected by the tsunami. However, we also find differences in the effect of the grants
provided to firms in different sectors. For retailers, the effects were much larger than the
sample average, while we struggle to find any significant effect of the grants among firms
in the manufacturing and services sectors. The high returns, of course, provided an
incentive to invest in the enterprises during the recovery period. The data also show that
the recovery was more rapid among those operating in the retail sector than among those
in manufacturing or services. One explanation for the difference is the greater importance
for non-retailers of specific relationships with trading partners. Higher levels of
complementarity among production assets, and more complex supply chains may also
play a role in explaining the sectoral differences.
These data come from a single location following a single disaster. But we believe
that many of the findings from the tsunami experience likely have some applicability to
the recovery of firms from other disasters, especially those arising from infrequent
natural phenomena such as tsunamis and earthquakes. Arguably, firms may take more ex
ante actions when exposed to more frequent natural disasters such as hurricanes. The
majority of microenterprises are likely to be uninsured. The general pattern of large aid
flows and poor targeting toward business recovery likewise appears generalizable, as
does the change in demand and supply chain disruption leading to faster recovery for
retail than manufacturing.
These data provide some guidance on how that aid might be more effectively
administered. We interpret the data as supporting the use of cash grants in disaster
recovery, but only in limited cases. Grants to firms in retail trade stimulate more rapid
29
recovery of these enterprises. The data also support a greater use of cash aid in household
recovery. The spending by households in local shops provides a stimulus that is lacking
when goods are delivered directly into affected areas. Finally, we believe the experiment
has demonstrated the ability of random grants to generate knowledge about how to
increase the speed with which small enterprises recover. If global warming leads to more
frequent and more severe water-related disasters, as climate change experts predict, this
knowledge will be increasingly valuable in hastening recovery from the growing
devastation.
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32
Figure 1a: Profits as a Percentage of Pre-Tsunami Imputed Profits
Months after tsunami
Figure 1B: Capital Stock as a Percentage of Pre-Tsunami Imputed Capital Stock
Months after tsunami
33
Table 2: Comparison of microenterprises affected and not affected by the tsunami
(1)
(2)
Directly
Directly
Affected (1)
Affected (2)
March 2005 Capital, without land & buildings
24,959
23,000
March 2005 Capital (machinery and equipment only)
13,962
11,484
March 2005 Sales
10,100
10,842
March 2005 profits
3,265
3,355
Pre-tsunami Capital without land and buildings (3)
Pre-tsunami Capital (machinery and equipment only)
November 2004 recalled sales
November 2004 recalled profits
31,648
18,738
19,133
5,804
31,903
17,283
20,931
5,907
(3)
Indirectly
Affected
25,139
13,773
9,189
3,117
24,855
13,482
11,863
3,986
(4)
Sig 1
vs. 3
***
***
***
***
Unaffected
27,058
13,755
15,111
4,543
27,376
14,073
14,575
4,688
Sample size
205
157
199
193
Notes:
Sig denotes significance from a t-test of comparison of means: *** p<0.01, ** p<0.05, * p<0.1
(1) Includes firms reporting any level of damage.
(2) Includes only firms reporting loss of more than 50% of their assets excluding land and buildings.
(3) For firms suffering no damage, December 2004 inventories are not reported in the survey; we use the March 2005
inventories as an estimate for the December 2005 inventories.
34
Sig 1
vs. 4
***
***
***
**
**
**
35
Table 4: Allowing the Returns to the Grants to Vary with Time Since Tsunami
(1)
(2)
(3)
Real Profit
Capital Stock Owner Hours
FE
FE
FE
Grant amount * directly affected * within 1 year of
8.73*
80.50**
1.56
tsunami
(4.92)
(38.62)
(2.09)
Grant amount * directly affected * 1 to 2 years
15.04**
96.37***
1.65
after tsunami
(7.11)
(34.26)
(2.11)
Grant amount * directly affected * more than 2
4.19
91.5
2.48
years after tsunami
(6.28)
(59.23)
(2.86)
Grant amount * not directly affected * within 1
year of tsunami
Grant amount *not directly affected * 1 to 2
years after tsunami
Grant amount * not directly affected * more than
2 years after tsunami
6.01***
70.65***
3.67***
(2.12)
(15.69)
(1.27)
4.11
103.89***
0.13
(3.03)
(25.24)
(1.47)
6.69*
78.07**
-2.13
(3.50)
(36.44)
(1.67)
Capital stock * directly affected * within 1 year of
tsunami
Capital stock * directly affected * 1 to 2 years
after tsunami
Capital stock * directly affected * more than 2
years after tsunami
0.146**
(.064)
0.199***
(.062)
0.101
(.062)
Capital stock * not directly affected * within 1
year of tsunami
Capital stock *not directly affected * 1 to 2 years
after tsunami
Capital stock * not directly affected * more than 2
years after tsunami
Constant
(4)
Real Profit
IV FE
0.0938**
(.042)
0.0484*
(.027)
0.0873**
(.034)
37.50***
261.3***
52.72***
8.59
(1.69)
(13.37)
(0.85)
(9.49)
5,399
549
5,187
549
5,560
549
5,077
548
Observations
Number of enterprises
Notes: Robust standard errors clustered at the firm level in parentheses. *, **, and *** indicate significance at the 10%,
5% and 1% levels respectively.
Within 1 year of the tsunami includes the first 4 survey waves; 1 to 2 years after the tsunami includes waves 5
through 8; more than 2 years after the tsunami includes waves 9 through 11. The regressions also include
wave effects and wave effects interacted with a dummy variable indicating the firm was directly affected by
the tsunami.
36
37
38
Table 7: Effect of Grants on Recovery of Enterprises by Sector of Activity
(1)
Real Profit
RE
Retail Trade
Directly affected * within 1 year of tsunami
Directly affected * 1 to 2 years after tsunami
(2)
Real Profit
RE
Manuf / serv
(3)
(4)
Capital Stock Capital Stock
RE
RE
Retail Trade
Manuf / serv
-15.20**
-5.25
-34.37
(7.30)
(4.16)
(45.31)
(35.60)
-24.63***
-8.04
-66.15
-86.47**
Directly affected * more than 2 years after tsunami
-90.89**
(9.20)
(5.94)
(67.98)
(41.13)
-0.1357
-4.78
71.04
-100.26**
(10.84)
(6.84)
(156.05)
(49.32)
78.31**
Grant amount * directly affected * within 1 year of
tsunami
Grant amount * directly affected * 1 to 2 years
after tsunami
Grant amount * directly affected * more than 2
years after tsunami
15.93***
1.92
52.15
(5.16)
(2.93)
(45.53)
(35.39)
24.02***
9.18
83.35**
79.57**
(8.39)
(8.31)
(40.00)
(39.48)
4.89
5.30
48.71
99.75**
(7.92)
(7.15)
(109.78)
(49.61)
Pre-tsunami size (see notes)
1.054***
(.151)
226.8
1.349***
(.142)
-360.4
0.602**
(.252)
24,339***
1.174***
(.115)
3,873**
(584.0)
(386.6)
(3,990)
(1,970)
1,443
146
1,534
156
1,414
161
1,581
165
Constant
Observations
Number of enterprises
Robust standard errors clustered at the firm level in parentheses. *, **, and *** indicate significance at the
10%, 5% and 1% levels respectively.
See the notes to Table 5. The first two columns include November 2004 profits inputted from recalled sales and
other variables, as described in the text. Columns 3 and 4 include the December 2004 capital stock excluding
land, buildings and inventories calculated from reported damage, and sales and purchases of assets between
December 2004 and the baseline survey. The regressions also include wave effects.
39
40
Appendix 1: Description of the sample and measurement of key variables
We use data from two distinct surveys of firms in the paper: a panel of microenterprises
which we refer to as the Sri Lankan Microenterprise Survey (SLMS), and a one-off
survey of larger enterprises conducted in July 2007 which we refer to as the SME sample.
Sri Lankan Microenterprise Survey
The first wave of the SLMS was gathered in March and April 2005. We set out to draw a
sample of enterprises with less than 100,000 LKR (~$1000) in capital stock, not
including land and buildings. To draw the sample, we first used the 2001 Sri Lankan
Census to select 25 Grama Niladhari divisions (GNs) in the south-western coastal
districts of Kalutara, Galle, and Matara. A GN is an administrative unit containing on
average around 400 households. We used the Census to select GNs with a high
percentage of own-account workers and modest education levels, since these were
thought to be most likely to yield enterprises with invested capital below the threshold we
had set. The GNs were stratified according to whether the area was directly affected,
indirectly affected, or unaffected by the tsunami. A door-to-door screening survey was
then carried out among households in each of the selected GNs. This survey was given to
3361 households, with less than 1 percent of households refusing to be listed. The
screening survey identified self employed workers outside of agriculture, transportation,
fishing, and professional services who were between the ages of 20 and 65 and had no
paid employees, and whose enterprises used no motorized vehicles.
The full baseline survey was given to 659 enterprises meeting these criteria. After
reviewing the baseline survey data, 50 enterprises were eliminated because either because
they exceeded the 100,000 rupee maximum size or because a follow-up visit could not
verify the existence of an enterprise. The remaining 609 firms constitute the baseline
sample. A further 9 firms atritted from the sample after the first round. Finally, using data
on damage to assets resulting from the tsunami, we eliminate 3 firms having had
machinery and equipment in excess of 100,000 LKR prior to the tsunami. The damaged
and undamaged firms then have pre-tsunami capital stock with the same support.
After the baseline, firms were surveyed ten additional times. Follow-up surveys were
administered in July and October of 2005, January, April, July and October 2006,
January, April, and October 2007, and April 2008.23
For the purposes of the analysis conducted in the paper, the most important variables
relate to enterprise profits and capital stock.
Measuring profits
Each wave of the survey asks owners about enterprise operations in the month preceding
the survey. We asked total revenues, expenses in 11 categories (expenses for raw
materials, expenses for electricity and other utilities, etc.) Beginning in round 2, we also
asked owners about inputs donated to the business by family members and goods or cash
23
All of the survey instruments are available on the SLMS project web page,
http://cpe.ucsd.edu/research/slms.htm .
41
taken from the business for use in the household. The survey asked for information on
sales, and also asked a single question about profits, worded as follows:
“What was the total income the business earning during the month of MONTH
after paying all expenses including the wages of employees, but not including any
income you paid yourself. That is, what were the profits of your business during
MONTH?”
The majority of the enterprises in the sample do not keep written business records.
Moreover, calculating profits as reported revenues minus reported expenses produces
estimates which sometime vary markedly from the response to the single question on
profits. In de Mel, McKenzie and Woodruff (2009) we report on an extensive analysis of
the data, and on additional experiments with book keeping ledgers and frequent
monitoring of sales which we conducted with the goal of obtaining a better understanding
of how to measure profits in microenterprises. We conclude that the best measure of
profits comes from the single direct question, which is the measure we use in this paper.
In the baseline survey, we asked owners what their profits were in November 2004, that
is, the month before the tsunami. In the second survey round, we asked owners what their
sales were in the month before the tsunami. Though the recall involved with the sales
data was longer than that for profits, the analysis in de Mel, McKenzie and Woodruff
(2009) suggests there is reason to believe that sales are recalled with more accuracy than
profits.
Measurement of capital stock, including damage of assets.
In the baseline survey, owners were asked about fixed assets in six categories: business
tools, machinery, furniture and equipment, vehicles, land/buildings, and other assets.
Owners were first asked:
 Do you have any business assets which are currently used in the business,
which were not damaged by the tsunami?
For each type of asset reported by the owner, we then asked:




Name of item
Condition Acquired (1=New; 2=Used; 3=Self Made
Ownership Status (1=own; 2=rented; 3=borrowed)
If you had to replace this item, how much would it cost you to purchase one in
similar condition?
We asked for up to three assets in each of the 6 categories, meaning that an individual
owner might report an answer for each of the questions as many as 18 times. In practice
owners grouped assets such than only a handful reported more than two separate
categories for any type of asset.
This part of the survey was designed to strike a balance between asking for sufficient
detail so that the owner recalled all of the assets used in the business, while at the same
42
time limiting the time required to respond. A separate question asked the market value of
current inventories. We also asked the same set of questions with regard to assets
damaged or destroyed by the tsunami, eliciting the replacement cost just before the
tsunami. For each asset damaged or destroyed owners were also asked whether they had
replaced or repaired the asset, and how much they spent doing so.
In each subsequent round of the survey, we asked owners to respond to the same
questions with regard to assets acquired, repaired, sold, or damaged since the previous
survey. These questions were asked for the same six categories of fixed assets. The fixed
capital stock in the baseline is the sum of the capital owned by the enterprise. In each
subsequent wave, the capital stock is calculated by taking the capital stock owned by the
enterprise in the previous wave and adjusting it for purchases, sales, repair and damage of
assets. For inventories, in each wave we ask the current market value of inputs, work in
progress and final goods. The fixed assets and inventories are combined to produce the
estimated capital stock in each period.
Balance by treatment group
Appendix Table 1 reports the pre-treatment means of profits, capital stock and sales, as
well as mean distance to the coast, the proportion of firms in retail and the proportion of
firms reporting valid responses for profits in the first and last wave of the survey. This is
done for the sample of damaged firms – de Mel et al. (2008) shows balance for the
sample of indirectly affected and unaffected firms.
Appendix Table 1: Balance on Treatment
November 2004 Profits
Imputed November 2004 profits
November 2004 Sales
December 2004 capital stock*
March 2005 profits
March 2005 capital stock*
Proportion retail sector
Log distance to coast
Proportion reporting profits round 1
Proportion reporting profits round 11
Untreated
4,404
3,250
14,298
15,427
3,487
13,666
46.7%
6.64
94.9%
80.2%
10,000
10,000 vs.
Treatment
Control
4,953
0.17
3,498
0.24
15,349
0.58
15,019
0.80
3,724
0.45
13,260
0.78
53.0%
0.17
6.64
0.99
94.9%
0.99
83.4%
0.24
20,000
Treatment
5,537
3,824
16,995
16,393
3,817
15,190
51.2%
6.65
97.6%
83.7%
20,000 vs.
Control
0.01
0.02
0.26
0.63
0.36
0.40
0.41
0.94
0.36
0.40
* Less land, buildings, and inventories
The SME survey
In July 2007 we surveyed 424 owners of enterprises employing between 5 and 50
workers. A part of the sample came from a household listing exercise, but the percentage
of households with an owner of a larger enterprise is so small that obtaining the full
sample in this manner was not practical. The majority of the sample came from
identifying and approaching visible businesses. We attempted to draw the sample from
exactly the same GNs from which the SLMS is drawn. However, we were unable to
locate enough SMEs in these GNs, and so extended the sample to bordering GNs as well.
Operating data were obtained with survey questions similar to those used in the SLMS
survey. We asked for capital stock at a more aggregated level—using only one row
43
instead of three for each of the six categories—because capital stock was not the focus of
the survey, as it was in the SLMS. Finally, we asked whether the SME owner had
suffered any damage to his/her home from the tsunami, and whether he/she had suffered
any loss of business assets. For those responding affirmatively in either case—and
separately for household and business losses—we asked the following questions:
 Did you have insurance to cover losses?
 If yes, how much did you receive in insurance payments?
 Did you receive any grants or other assistance from NGOs, the government, or
others to replace or repair assets?
 If yes, how much did you receive in grants or other assistance?
 Did you receive any loans from the government, banks, or others to replace or
repair?
 If yes, how much did you receive in loans?
 What percentage of your business assets damaged by the tsunami have you
replaced or repaired?
 What percentage of your household assets damaged by the tsunami have you
replaced or repaired?
44
45
46
Appendix Table 5: Returns with Continuous Percentage Damaged
(1)
(2)
(3)
Dependent variable Real Profit
Real Profit
Real Profit
FE
FE
FE
Sample All Firms
All Firms
All Firms
Grant amount
Grant Amount * Percentage of non-land
assets damaged
Grant Amount * Percentage of non-land
assets damaged*retail sector
5.29**
5.14**
(2.08)
(2.08)
6.04
-8.89
(6.09)
(5.49)
27.03***
(8.28)
Grant of 10,000 LKR
Grant of 20,000 LKR
Grant Amount 10,000 LKR * Percentage
of non-land assets damaged
Grant Amount 20,000 LKR * Percentage
of non-land assets damaged
Grant Amount 10,000 LKR * Percentage
of non-land assets damaged*retail sector
Grant Amount 20,000 LKR * Percentage
of non-land assets damaged*retail sector
Constant
Observations
R-squared
Number of enterprises
(4)
Real Profit
FE
All Firms
8.40**
7.96**
(3.46)
(3.46)
8.72**
8.49*
(4.41)
(4.41)
8.17
-15.90
(11.52)
(11.08)
8.07
-10.65
(12.40)
(10.47)
46.17**
(18.15)
34.07*
(17.35)
3,677***
3,677***
3,678***
3,676***
(160.2)
(158.2)
(159.5)
(157.4)
5,877
0.058
597
5,877
0.062
597
5,877
0.058
597
5,877
0.063
597
Robust standard errors clustered at the firm level in parentheses. *, **, and *** indicate
significance at the 10%, 5% and 1% levels respectively.
47
48